Industrial and commercial energy efficiency carbon emission online monitoring system and method based on Internet of Things

Abstract

The invention discloses an industrial and commercial energy efficiency carbon emission online monitoring system and method based on the Internet of Things, and relates to the technical field of carbon emission monitoring. According to a carbon emission calculation boundary, a real-time carbon emission index of each element of an enterprise is obtained, and after the real-time carbon emission index is input into a carbon emission calculation model, a carbon emission calculation model is obtained; the carbon emission measuring and calculating model outputs the total carbon emission estimator of the enterprise, the monitoring system obtains carbon emission influence parameters of all elements, a correction coefficient is generated after the influence parameters are comprehensively analyzed through a regression analysis algorithm, the correction coefficient is used for correcting the total carbon emission estimator of the enterprise, and the total carbon emission predictor of the enterprise is obtained. And obtaining the energy efficiency score of the enterprise, and generating a corresponding reward and punishment strategy for the enterprise by using a fuzzy rule in combination with the energy efficiency score and the total carbon emission prediction amount. And the monitoring system corrects the total estimated amount of the carbon emission, so that the prediction accuracy is improved, and finally, energy efficiency management and carbon emission control are combined and considered, so that the overall resource utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring, and in particular to an industrial and commercial energy efficiency carbon emission online monitoring system and method based on the Internet of Things. Background Art

[0002] Against the backdrop of global warming and the increasing constraints of resources and environment on the sustainable development of the economy and society, research related to carbon emissions has attracted more and more attention from the academic community. Strengthening energy-saving management in key units (especially industrial manufacturing enterprises, high-energy-consuming enterprises, and commercial complexes), improving energy utilization efficiency, controlling total energy consumption, and promoting ecological civilization have become important tasks in energy consumption management.

[0003] The prior art has the following defects:

[0004] 1. The existing monitoring system usually only considers some factors in the carbon emission monitoring process, and fails to fully cover all energy types and energy consumption equipment types of the enterprise, resulting in inaccurate estimation of total carbon emissions. In addition, the estimation of total carbon emissions may be biased due to the influence of various factors. The monitoring system does not effectively correct such deviations, which is easy to increase the error of total carbon emission estimation;

[0005] 2. The monitoring system often considers energy efficiency management and carbon emission control separately, which makes it difficult for enterprises to systematically optimize resource use. Enterprises may improve efficiency in one aspect (such as reducing energy consumption), but may not be able to reduce carbon emissions. Vice versa, enterprises may focus on reducing carbon emissions but ignore improving energy efficiency, resulting in inefficient overall resource use.

[0006] Based on this, the present invention proposes an online monitoring system and method for industrial and commercial energy efficiency carbon emissions based on the Internet of Things. By comprehensively analyzing the carbon emission influencing parameters of each factor, it can generate more accurate carbon emission measurement boundaries and total estimates, and correct the total carbon emission estimates, thereby improving the accuracy of the prediction. Finally, combining energy efficiency management with carbon emission control is conducive to improving the overall resource utilization efficiency. Summary of the invention

[0007] The purpose of the present invention is to provide an industrial and commercial energy efficiency carbon emission online monitoring system and method based on the Internet of Things to solve the shortcomings of the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: an online monitoring method for industrial and commercial energy efficiency carbon emissions based on the Internet of Things, the monitoring method comprising the following steps:

[0009] The monitoring system obtains the energy type and energy-consuming equipment used by the enterprise through the API interface of the enterprise management platform, generates the enterprise carbon emission calculation boundary based on the energy type and energy-consuming equipment, obtains the real-time carbon emission index of each element of the enterprise according to the carbon emission calculation boundary, inputs the real-time carbon emission index into the carbon emission calculation model, and the carbon emission calculation model outputs the total estimated carbon emission of the enterprise;

[0010] Obtain the carbon emission influencing parameters of each factor, generate correction coefficients after comprehensive analysis of the influencing parameters through regression analysis algorithms, and use the correction coefficients to correct the total estimated carbon emissions of the enterprise to obtain the total predicted carbon emissions of the enterprise;

[0011] Obtain the energy efficiency score of the enterprise, use fuzzy rules combined with the energy efficiency score and the total predicted carbon emissions to generate corresponding reward and punishment strategies for the enterprise, and send the reward and punishment strategies to the enterprise management platform.

[0012] In a preferred embodiment, after the real-time carbon emission index is input into the carbon emission calculation model, the carbon emission calculation model outputs the total estimated carbon emission of the enterprise, including the following steps:

[0013] The real-time carbon emission index is input into the carbon emission calculation model, and the model function expression is: Where T_EM is the total estimated carbon emissions, C i is the real-time carbon emission index of the i-th factor.

[0014] In a preferred embodiment, the monitoring system obtains the carbon emission influencing parameters of each factor, and generates a correction coefficient after comprehensively analyzing the influencing parameters through a regression analysis algorithm, including the following steps:

[0015] The correction coefficient is calculated by linear regression analysis algorithm using the fossil fuel calorific value decline rate, power grid distribution loss, material recovery rate, equipment aging rate, and transportation mode load decline rate. The expression is: In the formula, correcting z is the correction coefficient, μ is the environmental impact factor (i.e., the impact of the environment on the total estimated amount of corporate carbon emissions), μ takes a value of ±0.132, {J1, J2, J3, J4, J5} are variables (J1 is the calorific value decline rate of fossil fuels, J2 is the power distribution loss of the power grid, J3 is the equipment aging rate, J4 is the material recycling rate, and J5 is the load decline rate of the transportation mode), {β1, β2, β3, β4, β5} are the regression coefficients of each variable, and the regression coefficients are all greater than 0.

[0016] In a preferred embodiment, using the correction coefficient to correct the total estimated carbon emissions of the enterprise to obtain the total predicted carbon emissions of the enterprise includes the following steps:

[0017] The correction coefficient is used to correct the total estimated carbon emissions of the enterprise to obtain the total predicted carbon emissions of the enterprise. The expression is: In the formula, T_EY is the total predicted carbon emissions, T_EM is the total estimated carbon emissions, and correcting z is the correction factor.

[0018] In a preferred embodiment, the energy efficiency score of the enterprise is obtained, and a corresponding reward and punishment strategy is generated for the enterprise by combining the energy efficiency score and the total predicted carbon emissions using fuzzy rules, including the following steps:

[0019] The energy efficiency score and the total predicted carbon emissions are used as the input variables of the fuzzy rules, and the reward and punishment strategy is used as the output variable of the fuzzy rules. The input variables are reasoned through the fuzzy rules to output different output variables.

[0020] If the energy efficiency score is greater than or equal to the score threshold, and the total predicted carbon emissions are less than or equal to the carbon emissions threshold, the output reward and punishment strategy is: reward, because the enterprise controls carbon emissions while maintaining high energy efficiency, so it is rewarded;

[0021] If the energy efficiency score is greater than or equal to the score threshold, and the total predicted carbon emissions are greater than the carbon emissions threshold, the output reward and punishment strategy is: warning, although the enterprise has high energy efficiency, its carbon emissions exceed the standard;

[0022] If the energy efficiency score is less than the score threshold, and the total predicted carbon emissions are less than or equal to the carbon emissions threshold, the output reward and punishment strategy is: partial reward or warning, the enterprise has low carbon emissions but low energy efficiency;

[0023] If the energy efficiency score is less than the score threshold and the total predicted carbon emissions are greater than the carbon emissions threshold, the output reward and punishment strategy is: punishment. If the enterprise fails to meet the standards in both energy efficiency and carbon emissions, compulsory measures will be taken.

[0024] In a preferred embodiment, the calculation logic of the energy efficiency score is: obtain the enterprise's energy efficiency ratio, energy utilization rate and energy-saving contribution rate, normalize the energy efficiency ratio, energy utilization rate and energy-saving contribution rate, map the value range of the energy efficiency ratio, energy utilization rate and energy-saving contribution rate to [0,1], obtain the normalized value of the energy efficiency ratio, the normalized value of the energy utilization rate and the normalized value of the energy-saving contribution rate, and sum the normalized value of the energy efficiency ratio, the normalized value of the energy utilization rate and the normalized value of the energy-saving contribution rate to obtain the energy efficiency score.

[0025] In a preferred embodiment, obtaining the real-time carbon emission index of each element of the enterprise according to the carbon emission calculation boundary includes the following steps:

[0026] Obtain each element of the enterprise based on the carbon emission measurement boundary, including fossil fuels, power grids, materials, equipment depreciation, and transportation methods;

[0027] And based on various standards, obtain the carbon emission coefficient of fossil fuels, the carbon emission coefficient of power grids, the carbon emission coefficient of materials, the carbon emission coefficient of equipment depreciation, and the carbon emission coefficient of transportation methods;

[0028] The calculation expression of the real-time carbon emission index of fossil fuels is: Where C1 is the real-time carbon emission index of fossil fuels, CffH i is the carbon emission coefficient of the i-th type of fossil fuel, consume i is the consumption of the i-th type of fossil fuel, n is the amount of fossil fuel;

[0029] The calculation expression of the real-time carbon emission index of the power grid is: C2 = CffD*ecr, where C2 is the real-time carbon emission index of the power grid, CffD is the carbon emission coefficient of the power grid, and ecr is the power consumption of the power grid;

[0030] The calculation expression of the real-time carbon emission index of the material is: Where C3 is the real-time carbon emission index of the material, CffL i is the carbon emission coefficient of the i-th type of material, used i is the usage of the i-th type of material, and m is the quantity of the material;

[0031] The calculation expression of the real-time carbon emission index of equipment depreciation is: C4 = CffS*device, where C4 is the real-time carbon emission index of equipment depreciation, CffS is the carbon emission coefficient of equipment depreciation, and device is the steel consumption;

[0032] The calculation expression of the real-time carbon emission index of the transportation mode is: Where C5 is the real-time carbon emission index of transportation mode, CffYi i is the carbon emission coefficient of the ith mode of transportation, lpad i is the load capacity of the i-th mode of transportation, distance i is the transportation distance of the i-th mode of transportation, and p is the type of transportation mode.

[0033] An online monitoring system for industrial and commercial energy efficiency carbon emissions based on the Internet of Things, including a carbon emission total estimation calculation module, a correction module, and a reward and punishment strategy module;

[0034] Total carbon emission estimation calculation module: obtain the energy type and energy consumption equipment type used by the enterprise through the API interface of the enterprise management platform, generate the enterprise carbon emission estimation boundary based on the energy type and energy consumption equipment type, obtain the real-time carbon emission index of each element of the enterprise according to the carbon emission estimation boundary, input the real-time carbon emission index into the carbon emission estimation model, and the carbon emission estimation model outputs the total carbon emission estimation of the enterprise;

[0035] Correction module: obtain the carbon emission influencing parameters of each factor, generate correction coefficients after comprehensive analysis of the influencing parameters through regression analysis algorithm, and use the correction coefficients to correct the total estimated carbon emissions of the enterprise to obtain the total predicted carbon emissions of the enterprise;

[0036] Reward and punishment strategy module: obtain the energy efficiency score of the enterprise, use fuzzy rules combined with the energy efficiency score and the total predicted carbon emissions to generate corresponding reward and punishment strategies for the enterprise, and send the reward and punishment strategies to the enterprise management platform.

[0037] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0038] The present invention obtains the real-time carbon emission index of each element of the enterprise based on the carbon emission measurement boundary. After the real-time carbon emission index is input into the carbon emission measurement model, the carbon emission measurement model outputs the total estimated carbon emission of the enterprise. The monitoring system obtains the carbon emission influencing parameters of each element, generates a correction coefficient after comprehensively analyzing the influencing parameters through a regression analysis algorithm, uses the correction coefficient to correct the total estimated carbon emission of the enterprise to obtain the total predicted carbon emission of the enterprise, and obtains the energy efficiency score of the enterprise, and uses fuzzy rules combined with the energy efficiency score and the total predicted carbon emission to generate a corresponding reward and punishment strategy for the enterprise. The monitoring system can generate more accurate carbon emission measurement boundaries and total estimates by comprehensively analyzing the carbon emission influencing parameters of each element, and correct the total estimated carbon emission, thereby improving the accuracy of the prediction. Finally, energy efficiency management and carbon emission control are considered in combination, which is conducive to improving the overall resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0040] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not 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.

[0042] Example 1: Please refer to Figure 1 As shown, the online monitoring method of industrial and commercial energy efficiency carbon emissions based on the Internet of Things described in this embodiment includes the following steps:

[0043] The monitoring system obtains the energy type and energy consumption equipment used by the enterprise through the API interface of the enterprise management platform, generates the enterprise carbon emission calculation boundary based on the energy type and energy consumption equipment, obtains the real-time carbon emission index of each element of the enterprise according to the carbon emission calculation boundary, inputs the real-time carbon emission index into the carbon emission calculation model, and the carbon emission calculation model outputs the total carbon emission estimate of the enterprise. The monitoring system obtains the carbon emission influencing parameters of each element, generates a correction coefficient after comprehensively analyzing the influencing parameters through the regression analysis algorithm, uses the correction coefficient to correct the total carbon emission estimate of the enterprise to obtain the total predicted carbon emission of the enterprise, and obtains the energy efficiency score of the enterprise, and uses fuzzy rules combined with the energy efficiency score and the total predicted carbon emission to generate corresponding reward and punishment strategies for the enterprise, and the reward and punishment strategies are sent to the enterprise management platform.

[0044] The present invention obtains the real-time carbon emission index of each element of the enterprise based on the carbon emission measurement boundary. After the real-time carbon emission index is input into the carbon emission measurement model, the carbon emission measurement model outputs the total estimated carbon emission of the enterprise. The monitoring system obtains the carbon emission influencing parameters of each element, generates a correction coefficient after comprehensively analyzing the influencing parameters through a regression analysis algorithm, uses the correction coefficient to correct the total estimated carbon emission of the enterprise to obtain the total predicted carbon emission of the enterprise, and obtains the energy efficiency score of the enterprise, and uses fuzzy rules combined with the energy efficiency score and the total predicted carbon emission to generate a corresponding reward and punishment strategy for the enterprise. The monitoring system can generate more accurate carbon emission measurement boundaries and total estimates by comprehensively analyzing the carbon emission influencing parameters of each element, and correct the total estimated carbon emission, thereby improving the accuracy of the prediction. Finally, energy efficiency management and carbon emission control are considered in combination, which is conducive to improving the overall resource utilization efficiency.

[0045] Embodiment 2: The monitoring system obtains the energy type and energy consumption equipment type used by the enterprise through the API interface of the enterprise management platform, and generates the enterprise carbon emission calculation boundary based on the energy type and energy consumption equipment type, including the following steps:

[0046] Energy types mainly include fossil energy and purchased electricity and heat, etc. The boundaries of carbon emissions and energy consumption calculation are different. Energy consumption calculation includes renewable energy, such as solar energy, hydropower, wind energy and biomass energy, while carbon emissions calculation does not include clean renewable energy because they produce little or no carbon emissions. Among them, fossil energy includes three categories: coal, petroleum and natural gas. Coal includes raw coal, washed coal, other washed coal, coal products, coke, and coke oven gas; petroleum includes crude oil, gasoline, kerosene, diesel, fuel oil, and liquefied petroleum gas; natural gas energy is natural gas.

[0047] Energy-consuming equipment used by enterprises refers to equipment that realizes its own functions by converting, transmitting and utilizing energy. Energy-consuming equipment is widely present in the field of industrial production, including various types of equipment, such as power equipment, lighting equipment, heating equipment, ventilation equipment and cooling equipment, including cranes, trailers, generators, steam boilers, excavators, pumps, air conditioners, lighting, etc. In addition, the main energy-consuming equipment of commercial users includes cooling equipment such as air conditioners and freezers, lighting equipment and auxiliary energy-consuming equipment such as elevators and fire protection systems. While paying attention to the carbon emissions generated by the energy consumption of enterprise equipment, the carbon emissions generated by equipment depreciation should also be considered.

[0048] The method for defining the boundaries of carbon emissions measurement is as follows:

[0049] To do a good job in calculating corporate carbon emissions, we must first understand the three measurement boundaries of corporate-level carbon emissions, namely scope 1, scope 2, and scope 3. Scope 1 is direct emissions, and scopes 2 and 3 are indirect emissions.

[0050] 1) Scope 1 emissions

[0051] Scope 1 emissions refer to direct emissions from emission sources directly controlled or owned by the corporate entity, including activities such as stationary combustion, mobile combustion, chemical or production processes or fugitive emissions.

[0052] The main source of direct emissions is the combustion of fossil energy, which is divided into stationary emission sources and mobile emission sources, such as emissions from combustion of boilers, furnaces, vehicles, etc. It also includes greenhouse gases released by material consumption and equipment depreciation during industrial processes and on-site manufacturing. Material consumption refers to the carbon emissions generated by the consumption of carbon-containing raw materials in the production process of an enterprise;

[0053] Including raw materials and machine materials, mainly cement, sand, fly ash, steel, catalysts, chemicals, etc., these materials have carbon emissions during use. As a large part of the solid resources of the enterprise, the equipment still maintains its original physical form during the long-term use, but its value is gradually reduced due to continuous wear and tear. Therefore, when measuring the carbon emissions of the enterprise, in addition to considering the carbon emissions from energy consumption, the carbon emissions from equipment depreciation should also be considered.

[0054] 2) Scope 2 emissions

[0055] Scope 2 emissions refer to indirect emissions from purchased electricity and heat for the company's own use, including electricity, heat, steam and cooling, etc. This part of emissions is not directly generated by the company's production and operation. There are two main sources of indirect emissions, one is electricity, and the other is heat and steam. For many companies, purchased electricity is one of their largest sources of greenhouse gas emissions.

[0056] 3) Scope 3 emissions

[0057] Scope 3 emissions refer to indirect emissions of enterprises outside of Scope 2, including all emissions that may be generated in the enterprise's supply chain and upstream and downstream of the enterprise, such as the mining, production and transportation of raw materials and the transportation, use and recycling of products. These are more specific and difficult to track emissions.

[0058] Carbon emission accounting should at least establish a carbon footprint inventory for the first two scopes. Since scope 3 data is larger, more difficult to obtain, and more difficult to calculate, it is necessary to obtain carbon emission data for material production and transportation, product transportation and use, etc. from suppliers, customers, consumers and other partners related to the company's production and operation activities, or make estimates based on the industry average level to support companies in more comprehensive and accurate carbon emissions calculations, thereby providing a reference for formulating practical and effective energy-saving and emission reduction measures.

[0059] Based on the full life cycle theory, the carbon emission accounting boundary covers the entire value chain of the enterprise's upstream, production and downstream links, including the production of purchased raw materials and goods, the transportation of purchased raw materials and goods, product production or enterprise operation process, the transportation of sold products and goods, the use of sold products and goods, and waste recycling and treatment.

[0060] Among them, upstream emissions refer to indirect carbon emissions related to purchased or acquired goods and services, and downstream emissions refer to indirect carbon emissions related to sold goods and services. Emissions from both upstream and downstream links of an enterprise belong to Scope 3 indirect emissions.

[0061] Obtaining the real-time carbon emission index of each element of the enterprise based on the carbon emission measurement boundary includes the following steps:

[0062] Obtain each element of the enterprise based on the carbon emission measurement boundary, including fossil fuels, power grids, materials, equipment depreciation, and transportation methods;

[0063] And based on various standards, obtain the carbon emission coefficient of fossil fuels, the carbon emission coefficient of power grids, the carbon emission coefficient of materials, the carbon emission coefficient of equipment depreciation, and the carbon emission coefficient of transportation methods;

[0064] Obtain the real-time fossil fuel consumption of the enterprise, and calculate the real-time carbon emission index of fossil fuels based on the fossil fuel consumption and the fossil fuel carbon emission coefficient. The expression is:

[0065] Where C1 is the real-time carbon emission index of fossil fuels, CffH i is the carbon emission coefficient of the i-th type of fossil fuel, consume i is the consumption of the i-th type of fossil fuel, and n is the amount of fossil fuel.

[0066] Obtain the real-time power grid energy consumption of the enterprise, and calculate the real-time carbon emission index of the power grid based on the power grid energy consumption and the carbon emission coefficient of the power grid. The expression is:

[0067] C2=CffD*ecr, where C2 is the real-time carbon emission index of the power grid, CffD is the carbon emission coefficient of the power grid, and ecr is the electricity consumption of the power grid.

[0068] The carbon emission coefficient of the electricity currently used is mainly taken from the national power grid carbon emission coefficient and the regional power grid carbon emission coefficient, which is suitable for different goals and management needs. Among them, the national power grid average carbon emission coefficient is applicable and timely.

[0069] Obtain the real-time material usage of the enterprise, and calculate the real-time carbon emission index of the material based on the material usage and the material carbon emission coefficient. The expression is:

[0070] Where C3 is the real-time carbon emission index of the material, CffL i is the carbon emission coefficient of the i-th type of material, used i is the usage of the i-th type of material, and m is the quantity of the material.

[0071] In the process of calculating the depreciation of mechanical equipment, only the material consumption of mechanical equipment is considered, and the material consumption of equipment only considers steel. The consumption is calculated according to the staircase depreciation method. The carbon emissions in the equipment depreciation link are converted into the product of steel consumption and the carbon emission coefficient of equipment depreciation.

[0072] Obtain the real-time steel consumption of the enterprise, and calculate the real-time carbon emission index of equipment depreciation based on the steel consumption and the equipment depreciation carbon emission coefficient. The expression is:

[0073] C4=CffS*device, where C4 is the real-time carbon emission index of equipment depreciation, CffS is the carbon emission coefficient of equipment depreciation, and device is the steel consumption.

[0074] Obtain the real-time load and distance of the enterprise's transportation mode, and calculate the real-time carbon emission index of the transportation mode based on the load, distance and carbon emission coefficient of the transportation mode. The expression is:

[0075] Where C5 is the real-time carbon emission index of transportation mode, CffYi i is the carbon emission coefficient of the i-th mode of transportation, load i is the load capacity of the i-th mode of transportation, distance i is the transportation distance of the i-th mode of transportation, and p is the type of transportation mode.

[0076] Obtain fossil fuel carbon emission coefficients, power grid carbon emission coefficients, material carbon emission coefficients, equipment depreciation carbon emission coefficients, and transportation mode carbon emission coefficients based on various standards;

[0077] The carbon emission coefficients of fossil fuels are shown in Table 1:

[0078] Table 1 Fossil fuel combustion parameters and carbon emission coefficients

[0079]

[0080] The carbon emission coefficient of the power grid is shown in Table 2:

[0081] Table 2 Carbon emission coefficient of power grid

[0082]

[0083] The material carbon emission coefficient is shown in Table 3:

[0084] Table 3 Material carbon emission coefficient

[0085]

[0086]

[0087] The carbon emission coefficients of transportation modes are shown in Table 4:

[0088] Table 4 Carbon emission coefficient of power grid

[0089] Transport Type Carbon emission coefficient [kgCO2e / (t·km)] Light gasoline truck transportation (load 2t) 0.334 Medium-sized gasoline truck transportation (load capacity 8t) 0.115 Heavy gasoline truck transportation (load 10t) 0.104 Heavy-duty gasoline truck transportation (load capacity 18t) 0.104 Light diesel truck transportation (load 2t) 0.286 Medium diesel truck transportation (load capacity 8t) 0.179 Heavy diesel truck transportation (load 10t) 0.162 Heavy diesel truck transportation (load 18t) 0.129 Heavy diesel truck transportation (load capacity 30t) 0.078 Heavy diesel truck transportation (load capacity 46t) 0.057 Electric locomotive transport 0.010 Diesel locomotive transport 0.011 Railway transportation (China market average) 0.010 Tanker transport (load 2000t) 0.019 Dry bulk carrier transport (load 2500t) 0.015 Container ship transportation (load 200TEU) 0.012

[0090] Equipment depreciation carbon emission coefficient: In the process of calculating mechanical equipment depreciation, only the material consumption of mechanical equipment is considered, and the equipment material consumption only considers steel. The consumption is calculated according to the staircase depreciation method. The carbon emission of equipment depreciation is converted into the product of steel consumption and steel emission factor. According to previous research and expert knowledge, the carbon emission coefficient of steel production is 2.079tCO2 / t.

[0091] After the real-time carbon emission index is input into the carbon emission calculation model, the carbon emission calculation model outputs the total estimated carbon emission of the enterprise, including the following steps:

[0092] The real-time carbon emission index is input into the carbon emission calculation model, and the model function expression is:

[0093] Where T_EM is the total estimated carbon emissions, C i is the real-time carbon emission index of the i-th factor.

[0094] The monitoring system obtains the carbon emission influencing parameters of each factor, and generates a correction coefficient after comprehensively analyzing the influencing parameters through a regression analysis algorithm, including the following steps:

[0095] Obtain each element of the enterprise based on the carbon emission measurement boundary, including fossil fuels, power grids, materials, equipment depreciation, and transportation methods;

[0096] The monitoring system obtains the influencing parameters of carbon emissions of each factor, including the calorific value decline rate of fossil fuels, power grid distribution losses, material recovery rate, equipment aging rate, and transportation mode load decline rate;

[0097] The calculation logic of the fossil fuel calorific value decline rate is: subtract the current calorific value from the initial calorific value of the fossil fuel to obtain the calorific value difference, and then divide the calorific value difference by the initial calorific value to obtain the fossil fuel calorific value decline rate;

[0098] When the calorific value of fossil fuels decreases, it means that less energy is produced per unit of fuel, so more fuel is needed to produce the same energy output. This results in increased fuel usage, which in turn increases carbon emissions.

[0099] The calculation logic of the power distribution loss of the power grid is as follows: subtract the power received by the user end from the power generation of the power grid to obtain the power difference, and then divide the power difference by the power generation to obtain the power distribution loss of the power grid;

[0100] Increased distribution losses in the power grid mean more electricity is wasted during transmission, so to meet the same electricity demand, power plants need to produce more electricity, which increases the burning of fossil fuels and, in turn, increases carbon emissions.

[0101] The calculation logic of material recovery rate is: divide the amount of recovered materials by the total material consumption to obtain the material recovery rate;

[0102] A higher material recycling rate means more materials are reused, thus reducing the need for new materials. The production of new materials usually requires a lot of energy and generates corresponding carbon emissions. Therefore, an increase in material recycling rates helps to reduce total carbon emissions, while a low material recycling rate will lead to an increase in total carbon emissions.

[0103] The calculation logic of the equipment aging rate is as follows: subtract the current production efficiency from the initial production efficiency of the equipment to obtain the production efficiency difference, and then divide the production efficiency difference by the initial production efficiency to obtain the equipment aging rate;

[0104] Equipment aging often results in a decrease in the efficiency of the equipment, such as increased fuel consumption or less efficient use of electricity. Decreased equipment performance means more energy is required to maintain the same level of production, leading to increased carbon emissions.

[0105] The calculation logic of the load reduction rate of the transport mode is as follows: subtract the actual load from the maximum load of the transport vehicle to obtain the load difference, and then divide the load difference by the maximum load to obtain the load reduction rate of the transport mode;

[0106] When the load factor of a transport tool decreases, the same transport task requires more transport tools or more transports, which increases fuel consumption. Low-load-factor transport methods are less efficient, leading to higher carbon emissions per unit of cargo transport, which in turn increases total carbon emissions.

[0107] In this application, a larger correction coefficient indicates that under the influence of multiple types of influencing parameters, it will lead to an increase in the actual total estimated carbon emissions of the enterprise;

[0108] The correction coefficient is calculated by linear regression analysis algorithm using the fossil fuel calorific value decline rate, power grid distribution loss, material recovery rate, equipment aging rate, and transportation mode load decline rate. The expression is: In the formula, correcting z is the correction coefficient, μ is the environmental impact factor (i.e., the impact of the environment on the total estimated amount of corporate carbon emissions), μ takes a value of ±0.132, {J1, J2, J3, J4, J5} are variables (J1 is the calorific value decline rate of fossil fuels, J2 is the power distribution loss of the power grid, J3 is the equipment aging rate, J4 is the material recycling rate, and J5 is the load decline rate of the transportation mode), {β1, β2, β3, β4, β5} are the regression coefficients of each variable, and the regression coefficients are all greater than 0;

[0109] The logical factors of the correction coefficient when used in the present invention are as follows: taking the influence of variables on the total estimated amount of carbon emissions as an example, the first is the indicator, that is, the factor that causes the change in the total estimated amount of carbon emissions (the present invention refers to the influence of variables on the total estimated amount of carbon emissions); the second is the weight of these indicators, that is, the proportion of each major influencing data when it is generated; the third is the operation equation, that is, what kind of mathematical operation process is used to obtain the result, and the correction coefficient is obtained by calculating the indicators with their respective weights through the operation equation.

[0110] The main influencing data obtained from the sample were transformed and processed into a data language recognized by computer software; secondly, these evaluation factors were analyzed by Logistic regression using SPSS software to screen out factors and their weights that are significantly correlated with the results; thirdly, the evaluation factors and weights were brought into the Logistic regression equation for calculation to obtain the results, which are as follows:

[0111] First, ensure the integrity of the main effect data, handle missing values ​​and outliers, and convert the data into a format that SPSS software can recognize. Usually, the data is stored in .csv, .xlsx and other formats, and then imported into SPSS. Open SPSS software, import the processed data file, and transform the variables as needed. For example, for continuous variables, standardize or normalize them. Select the "Analyze" menu, then select the "Binary Logistic" option under "Regression". In the dialog box, add the dependent variable (outcome) and the independent variable (main effect data) to the corresponding boxes. SPSS will fit the Logistic regression based on the selected variables. Return to the model. In the output results, you will see the coefficients, standard errors, p-values ​​and other information of the model. Check the coefficients and p-values ​​in the output results to determine which variables are significantly correlated with the results. Usually, a p-value less than 0.05 is considered significant. While fitting the model, use variable selection methods, such as stepwise regression, to help screen the most relevant factors. According to the coefficients of the Logistic regression model, the size of the coefficient reflects the degree of influence of each factor on the result, and the positive and negative signs of the coefficients indicate the direction of the influence. After obtaining the significant factors and their coefficients, the Logistic regression equation is obtained, which is used to calculate the probability of each sample and then predict the results.

[0112] Using the correction coefficient to correct the total estimated carbon emissions of the enterprise to obtain the total predicted carbon emissions of the enterprise includes the following steps:

[0113] The larger the correction coefficient, the more the total estimated carbon emissions need to be increased. The correction coefficient is used to correct the total estimated carbon emissions of the enterprise to obtain the total predicted carbon emissions of the enterprise. The expression is:

[0114] In the formula, T_EY is the total predicted carbon emissions, T_EM is the total estimated carbon emissions, and correcting z is the correction factor.

[0115] Obtain the energy efficiency score of the enterprise, use fuzzy rules to combine the energy efficiency score and the total carbon emission forecast to generate corresponding reward and punishment strategies for the enterprise, and send the reward and punishment strategies to the enterprise management platform, including the following steps:

[0116] Obtain the energy efficiency ratio, energy utilization rate and energy conservation contribution rate of the enterprise, normalize the energy efficiency ratio, energy utilization rate and energy conservation contribution rate, map the value range of the energy efficiency ratio, energy utilization rate and energy conservation contribution rate to [0,1], obtain the normalized value of the energy efficiency ratio, the normalized value of the energy utilization rate and the normalized value of the energy conservation contribution rate, sum the normalized value of the energy efficiency ratio, the normalized value of the energy utilization rate and the normalized value of the energy conservation contribution rate to obtain the energy efficiency score. The larger the energy efficiency score, the higher the comprehensive energy efficiency level of the enterprise;

[0117] The energy efficiency score and the total predicted carbon emissions are used as the input variables of the fuzzy rules, and the reward and punishment strategy is used as the output variable of the fuzzy rules. The input variables are reasoned through the fuzzy rules to output different output variables.

[0118] If the energy efficiency score is greater than or equal to the score threshold, and the total predicted carbon emissions are less than or equal to the carbon emissions threshold, the output reward and punishment strategy is: reward, because the enterprise has controlled carbon emissions while maintaining high energy efficiency, it is given a reward, which may be a tax incentive, financial subsidy or other forms of incentive measures;

[0119] If the energy efficiency score is greater than or equal to the score threshold, and the total predicted carbon emissions are greater than the carbon emissions threshold, the output reward and punishment strategy is: warning, although the enterprise's energy efficiency is high, but the carbon emissions exceed the standard, it is necessary to guide the enterprise to further reduce carbon emissions, and may provide technical support or require the formulation of a rectification plan;

[0120] If the energy efficiency score is less than the score threshold, and the total predicted carbon emissions are less than or equal to the carbon emissions threshold, the output reward and punishment strategy is: partial reward or warning. Although the company's carbon emissions are within a reasonable range, its energy efficiency is low and there may be a waste of resources. The company is advised to improve its production process and improve energy efficiency.

[0121] If the energy efficiency score is less than the score threshold and the total predicted carbon emissions are greater than the carbon emissions threshold, the output reward and punishment strategy is: punishment. The company fails to meet the standards in both energy efficiency and carbon emissions, and compulsory measures need to be taken, such as fines, production restrictions, or requiring the company to make rectifications to comply with regulations.

[0122] Embodiment 3: The industrial and commercial energy efficiency carbon emission online monitoring system based on the Internet of Things described in this embodiment includes a carbon emission total estimation amount calculation module, a correction module, and a reward and punishment strategy module;

[0123] Total carbon emission estimation calculation module: obtains the energy type and energy consumption equipment type used by the enterprise through the API interface of the enterprise management platform, generates the enterprise carbon emission estimation boundary based on the energy type and energy consumption equipment type, obtains the real-time carbon emission index of each element of the enterprise according to the carbon emission estimation boundary, inputs the real-time carbon emission index into the carbon emission estimation model, and the carbon emission estimation model outputs the total carbon emission estimation of the enterprise, which is sent to the correction module;

[0124] Correction module: obtains the carbon emission influencing parameters of each factor, generates correction coefficients after comprehensive analysis of the influencing parameters through regression analysis algorithm, uses the correction coefficients to correct the total estimated carbon emissions of the enterprise to obtain the total predicted carbon emissions of the enterprise, and sends the total predicted carbon emissions to the reward and punishment strategy module;

[0125] Reward and punishment strategy module: obtain the energy efficiency score of the enterprise, use fuzzy rules combined with the energy efficiency score and the total predicted carbon emissions to generate corresponding reward and punishment strategies for the enterprise, and send the reward and punishment strategies to the enterprise management platform.

[0126] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0127] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0128] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An online monitoring method for industrial and commercial energy efficiency and carbon emissions based on the Internet of Things, characterized by: The monitoring method comprises the following steps: The monitoring system obtains the energy type and energy-consuming equipment used by the enterprise through the API interface of the enterprise management platform, generates the enterprise carbon emission calculation boundary based on the energy type and energy-consuming equipment, obtains the real-time carbon emission index of each element of the enterprise according to the carbon emission calculation boundary, inputs the real-time carbon emission index into the carbon emission calculation model, and the carbon emission calculation model outputs the total estimated carbon emission of the enterprise; Obtain the carbon emission influencing parameters of each factor, generate correction coefficients after comprehensive analysis of the influencing parameters through regression analysis algorithms, and use the correction coefficients to correct the total estimated carbon emissions of the enterprise to obtain the total predicted carbon emissions of the enterprise; Obtain the energy efficiency score of the enterprise, use fuzzy rules combined with the energy efficiency score and the total predicted carbon emissions to generate corresponding reward and punishment strategies for the enterprise, and send the reward and punishment strategies to the enterprise management platform.

2. According to claim 1, a method for online monitoring of industrial and commercial energy efficiency and carbon emissions based on the Internet of Things is characterized by: After the real-time carbon emission index is input into the carbon emission calculation model, the carbon emission calculation model outputs the total estimated carbon emission of the enterprise, including the following steps: The real-time carbon emission index is input into the carbon emission calculation model, and the model function expression is: Where T_EM is the total estimated carbon emissions, C i is the real-time carbon emission index of the i-th factor.

3. The method for online monitoring of industrial and commercial energy efficiency and carbon emissions based on the Internet of Things according to claim 2 is characterized by: The monitoring system obtains the carbon emission influencing parameters of each factor, and generates a correction coefficient after comprehensively analyzing the influencing parameters through a regression analysis algorithm, including the following steps: The correction coefficient is calculated by linear regression analysis algorithm using the fossil fuel calorific value decline rate, power grid distribution loss, material recovery rate, equipment aging rate, and transportation mode load decline rate. The expression is: In the formula, correcting z is the correction coefficient, μ is the environmental impact factor (i.e., the impact of the environment on the total estimated amount of corporate carbon emissions), μ takes a value of ±0.132, {J1, J2, J3, J4, J5} are variables (J1 is the calorific value decline rate of fossil fuels, J2 is the power distribution loss of the power grid, J3 is the equipment aging rate, J4 is the material recycling rate, and J5 is the load decline rate of the transportation mode), {β1, β2, β3, β4, β5} are the regression coefficients of each variable, and the regression coefficients are all greater than 0.

4. The method for online monitoring of industrial and commercial energy efficiency and carbon emissions based on the Internet of Things according to claim 3 is characterized by: Using the correction coefficient to correct the total estimated carbon emissions of the enterprise to obtain the total predicted carbon emissions of the enterprise includes the following steps: The correction coefficient is used to correct the total estimated carbon emissions of the enterprise to obtain the total predicted carbon emissions of the enterprise. The expression is: In the formula, T_EY is the total predicted carbon emissions, T_EM is the total estimated carbon emissions, and correcting z is the correction factor.

5. The method for online monitoring of industrial and commercial energy efficiency and carbon emissions based on the Internet of Things according to claim 4 is characterized by: Obtain the energy efficiency score of the enterprise, and use fuzzy rules to combine the energy efficiency score and the total carbon emission forecast to generate corresponding reward and punishment strategies for the enterprise, including the following steps: The energy efficiency score and the total predicted carbon emissions are used as the input variables of the fuzzy rules, and the reward and punishment strategy is used as the output variable of the fuzzy rules. The input variables are reasoned through the fuzzy rules to output different output variables. If the energy efficiency score is greater than or equal to the score threshold, and the total predicted carbon emissions are less than or equal to the carbon emissions threshold, the output reward and punishment strategy is: reward, because the enterprise controls carbon emissions while maintaining high energy efficiency, so it is rewarded; If the energy efficiency score is greater than or equal to the score threshold, and the total predicted carbon emissions are greater than the carbon emissions threshold, the output reward and punishment strategy is: warning, although the enterprise has high energy efficiency, its carbon emissions exceed the standard; If the energy efficiency score is less than the score threshold, and the total predicted carbon emissions are less than or equal to the carbon emissions threshold, the output reward and punishment strategy is: partial reward or warning, the enterprise has low carbon emissions but low energy efficiency; If the energy efficiency score is less than the score threshold and the total predicted carbon emissions are greater than the carbon emissions threshold, the output reward and punishment strategy is: punishment. If the enterprise fails to meet the standards in both energy efficiency and carbon emissions, compulsory measures will be taken.

6. The method for online monitoring of industrial and commercial energy efficiency and carbon emissions based on the Internet of Things according to claim 5 is characterized by: The calculation logic of the energy efficiency score is as follows: obtain the enterprise's energy efficiency ratio, energy utilization rate and energy-saving contribution rate, normalize the energy efficiency ratio, energy utilization rate and energy-saving contribution rate, map the value range of the energy efficiency ratio, energy utilization rate and energy-saving contribution rate to [0,1], obtain the normalized value of the energy efficiency ratio, the normalized value of the energy utilization rate and the normalized value of the energy-saving contribution rate, and sum the normalized value of the energy efficiency ratio, the normalized value of the energy utilization rate and the normalized value of the energy-saving contribution rate to obtain the energy efficiency score.

7. The method for online monitoring of industrial and commercial energy efficiency and carbon emissions based on the Internet of Things according to claim 6 is characterized by: Obtaining the real-time carbon emission index of each element of the enterprise based on the carbon emission measurement boundary includes the following steps: Obtain each element of the enterprise based on the carbon emission measurement boundary, including fossil fuels, power grids, materials, equipment depreciation, and transportation methods; And based on various standards, obtain the carbon emission coefficient of fossil fuels, the carbon emission coefficient of power grids, the carbon emission coefficient of materials, the carbon emission coefficient of equipment depreciation, and the carbon emission coefficient of transportation methods; The calculation expression of the real-time carbon emission index of fossil fuels is: Where C1 is the real-time carbon emission index of fossil fuels, CffH i is the carbon emission coefficient of the i-th type of fossil fuel, consume i is the consumption of the i-th type of fossil fuel, n is the amount of fossil fuel; The calculation expression of the real-time carbon emission index of the power grid is: C2 = CffD*ecr, where C2 is the real-time carbon emission index of the power grid, CffD is the carbon emission coefficient of the power grid, and ecr is the power consumption of the power grid; The calculation expression of the real-time carbon emission index of the material is: Where C3 is the real-time carbon emission index of the material, CffL i is the carbon emission coefficient of the i-th type of material, used i is the usage of the i-th type of material, and m is the quantity of the material; The calculation expression of the real-time carbon emission index of equipment depreciation is: C4 = CffS*device, where C4 is the real-time carbon emission index of equipment depreciation, CffS is the carbon emission coefficient of equipment depreciation, and device is the steel consumption; The calculation expression of the real-time carbon emission index of the transportation mode is: Where C5 is the real-time carbon emission index of transportation mode, CffYi i is the carbon emission coefficient of the ith mode of transportation, lpad i is the load capacity of the i-th mode of transportation, distance i is the transportation distance of the i-th mode of transportation, and p is the type of transportation mode.

8. An online monitoring system for industrial and commercial energy efficiency and carbon emissions based on the Internet of Things, used to implement the monitoring method according to any one of claims 1 to 7, characterized in that: It includes the total carbon emission estimation module, the correction module, and the reward and punishment strategy module; Total carbon emission estimation calculation module: obtain the energy type and energy consumption equipment type used by the enterprise through the API interface of the enterprise management platform, generate the enterprise carbon emission estimation boundary based on the energy type and energy consumption equipment type, obtain the real-time carbon emission index of each element of the enterprise according to the carbon emission estimation boundary, input the real-time carbon emission index into the carbon emission estimation model, and the carbon emission estimation model outputs the total carbon emission estimation of the enterprise; Correction module: obtain the carbon emission influencing parameters of each factor, generate correction coefficients after comprehensive analysis of the influencing parameters through regression analysis algorithm, and use the correction coefficients to correct the total estimated carbon emissions of the enterprise to obtain the total predicted carbon emissions of the enterprise; Reward and punishment strategy module: obtain the energy efficiency score of the enterprise, use fuzzy rules combined with the energy efficiency score and the total predicted carbon emissions to generate corresponding reward and punishment strategies for the enterprise, and send the reward and punishment strategies to the enterprise management platform.

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