Energy management system based on carbon emission

Abstract

The invention provides an energy management system based on carbon emission, and relates to the technical field of carbon emission. Comprising the steps that a data acquisition module acquires energy consumption, carbon emission related and historical management data in real time, and extracts data such as actual consumption and time distribution; the carbon emission extraction module calculates a carbon emission coefficient, a carbon emission amount and trend data according to the related data. The carbon emission change prediction module constructs a seasonal time sequence model, predicts energy consumption time distribution data, and combines the carbon emission trend to obtain carbon emission change data. The strategy adjusting module extracts strategies from historical data through association rule mining and then adjusts according to carbon emission changes to form an optimized energy management scheme. According to the method, the carbon emission trend data is obtained through calculation, so that enterprises and related departments can accurately master the carbon emission condition, the energy consumption time distribution data is sorted to obtain the energy consumption change data, a more reliable basis is provided for carbon emission change prediction, and accurate prediction of the energy consumption is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emissions, and particularly to an energy management system based on carbon emissions. Background Art

[0002] Under the background of the world actively addressing climate change and vigorously promoting sustainable development, carbon emissions management has become a key link for various industries to achieve green transformation. As the foundation for the operation of the economic society, energy consumption is closely linked to carbon emissions. How to efficiently manage energy to reduce carbon emissions has become the focus of attention from all walks of life.

[0003] Traditional energy management methods have deficiencies. In the carbon emissions calculation link, there is a lack of systematicness and accuracy. Past methods often cannot comprehensively and systematically integrate data on actual energy consumption, carbon emission intensity data, and carbon emission factor data, resulting in large errors in the calculated carbon emission coefficients, carbon emissions, and carbon emission trend data, and it is difficult to accurately reflect the true carbon emissions situation. This makes it difficult for enterprises and relevant departments to have a reliable basis when formulating emission reduction targets and strategies, and it is easy to have problems such as unreasonable target setting and poor implementation effects of strategies, and it is impossible to effectively promote the precise control and reduction of carbon emissions.

[0004] The accuracy of energy consumption prediction is crucial for reasonably planning energy use and ensuring the stability of energy supply. However, existing energy management systems have obvious deficiencies in this regard. Most systems fail to fully consider the time distribution characteristics of energy consumption and lack scientific and effective prediction models. This results in large deviations in energy consumption prediction results, and it is impossible to accurately grasp the changing trend of energy consumption in advance, leading to a lack of foresight in energy planning and difficulty in achieving the optimal resource allocation. This not only causes energy waste but also increases unnecessary energy procurement costs, affecting the economic benefits and sustainable development capabilities of enterprises.

[0005] The scientificity and effectiveness of energy management strategies are directly related to energy utilization efficiency and carbon emission control effects. However, existing energy management strategies generally have problems of insufficient optimization. On the one hand, the formulation of strategies lacks in-depth mining and analysis of historical data, and it is difficult to extract truly effective strategies from past energy management practices; on the other hand, during the implementation of strategies, there is a lack of a mechanism for dynamic evaluation and adjustment based on real-time carbon emission data. This leads to the situation that some strategies cannot adapt to the changing energy use scenarios and carbon emission requirements in actual applications, and cannot fully play the role of reducing carbon emissions and improving energy utilization efficiency, restricting the effectiveness of enterprises in energy conservation and emission reduction and cost control.

[0006] In summary, it is extremely urgent to develop a new type of energy management system that can accurately calculate carbon emissions, accurately predict energy consumption, and effectively optimize energy management strategies. Summary of the Invention

[0007] The present invention provides an energy management system based on carbon emissions, which is used to solve the defects in the prior art that the calculation of carbon emissions is inaccurate, the energy consumption cannot be accurately predicted, and the management strategy is not perfect enough.

[0008] The present invention provides an energy management system based on carbon emissions, including: A data acquisition module, which is used to collect energy consumption data and energy consumption time distribution data in real time; A carbon emission extraction module, which is used to extract carbon emission trend data from the energy consumption data; A carbon emission change prediction module, which is used to construct a seasonal time series model, input the energy consumption time distribution data, output the energy consumption change data, when the energy structure changes, analyze the proportion change of different energies in the total energy consumption, obtain the proportion impact data on the carbon emission, and calculate the energy consumption per unit economic output and the carbon emission per unit economic output to measure the impact of energy efficiency on carbon emission to obtain the efficiency change data, and combine the carbon emission trend data to obtain the carbon emission change data; An adjustment strategy module, which is used to adjust the energy management strategy according to the carbon emission change data to generate an optimized energy management plan.

[0009] According to the energy management system based on carbon emissions provided by the present invention, the data acquisition module is further used for: Collecting the carbon emission factor data and the carbon emission intensity data in real time, and collecting historical management data and actual consumption data.

[0010] According to the energy management system based on carbon emissions provided by the present invention, the extraction of the carbon emission extraction module includes: Obtaining a carbon emission coefficient according to the actual consumption data in combination with the carbon emission intensity data, obtaining carbon emission amount data according to the carbon emission factor data and the actual consumption data, and obtaining carbon emission trend data according to the carbon emission coefficient and the energy consumption data.

[0011] According to the energy management system based on carbon emissions provided by the present invention, the steps of obtaining the carbon emission coefficient include: Determining the energy type of the actual consumption data from the energy statistical report and the enterprise production record, and determining the energy type corresponding to the carbon emission intensity data according to the carbon emission database; Sorting out and corresponding matching the energy types and the energy types of different units and multiple sources, so that the actual consumption data of each energy type and the energy type correspond to the corresponding carbon emission intensity data; For each energy type and energy type, the carbon emission intensity and the actual consumption are weighted to obtain a carbon emission coefficient.

[0012] According to an energy management system based on carbon emissions provided by the present invention, the steps of obtaining the carbon emission data include: Dividing the life cycle stages of different energy types to obtain multiple energy stages, finding the corresponding carbon emission factors for different energy stages through the carbon emission factor data, and counting the actual activity data of each energy stage; Multiplying the carbon emission factors of each energy stage by the actual activity data to calculate the carbon emissions of each energy stage, and adding up the carbon emissions of each energy stage to obtain the carbon emission data.

[0013] According to an energy management system based on carbon emissions provided by the present invention, the steps of obtaining the carbon emission trend data include: Based on the carbon emission coefficient data, calculating the consumption of various energies within each preset statistical period to obtain the emissions of each energy, and summarizing the emissions of each energy to obtain the carbon emissions for the period; Arranging the carbon emissions for each preset statistical period in chronological order to form carbon emission time series data, and using chart tools to draw a trend chart with time as the abscissa and carbon emissions as the ordinate based on the carbon emission time series data; By using the moving average method to smooth the carbon emission time series data, making the trend chart show a long-term trend, and then analyzing the long-term trend to obtain the carbon emission trend data.

[0014] According to an energy management system based on carbon emissions provided by the present invention, the steps of obtaining the energy consumption change data include: Sorting out the energy consumption time distribution data to obtain an energy time series, and performing a differencing operation on the energy time series using the unit root test method to transform it into a stationary series; By observing the seasonal characteristics of the stationary series, determining the seasonal period, and performing seasonal differencing to obtain a seasonal series; By plotting the autocorrelation function and partial autocorrelation function graphs, and combining different parameter combinations, determining the parameters of the seasonal time series model to obtain model parameters, and using the model parameters to fit the seasonal series to obtain a seasonal series model; Diagnosing the seasonal series model to check whether the residuals are white noise. If so, continue to use the seasonal series model; otherwise, readjust the model parameters until the check shows white noise; Using the seasonal sequence model, input the information of the new time point, predict the energy consumption to obtain the predicted consumption value, compare the predicted consumption value with the actual consumption data, and calculate the energy consumption change data.

[0015] The steps of obtaining the carbon emission change data according to an energy management system based on carbon emissions provided by the present invention include: Calculate the change in carbon emissions caused by the change in energy consumption according to the energy consumption change data in combination with the efficiency change data to obtain the energy change carbon emission change amount; According to the proportion influence data, calculate the influence degree of the energy structure change on carbon emissions to obtain the proportion influence carbon emission change amount; Statistical data before and after the change in energy intensity caused by the improvement of energy efficiency to obtain the original energy intensity and the subsequent energy intensity, and calculate the intensity change data through the economic output of the original energy intensity and the subsequent energy intensity; Then calculate the intensity carbon emission change amount according to the intensity change data and the corresponding carbon emission factor of the intensity change data; Add the proportion influence carbon emission change amount, the energy change carbon emission change amount and the intensity carbon emission change amount to obtain the carbon emission change data.

[0016] The steps of extracting the energy management strategy from the historical management data according to an energy management system based on carbon emissions provided by the present invention by using the association rule mining method include: Collect the energy usage data, equipment operation status, production data and environmental data related to energy management from the historical management data, convert and integrate them, and establish a unified data table; Use the Apriori algorithm to find the frequent item sets from the unified data table by means of layer-by-layer search, then generate association rules according to the frequent item sets, and determine the parameters of the association rules by using the minimum support degree and the minimum confidence degree; Find out the mining item sets that meet the minimum support degree from the association rules, generate confidence rules that meet the minimum confidence degree based on the mining item sets, analyze the causal relationship and influence mechanism of the confidence rules to obtain the analysis results, and extract the energy management strategy from the analysis results.

[0017] The steps of obtaining the optimized energy management plan according to an energy management system based on carbon emissions provided by the present invention include: Analyze the change trend, change range and time distribution of carbon emissions in the carbon emission change data to obtain the key change data, and disassemble the key change data into factor influence data; Evaluate the energy management strategy based on the key change data and the factor influence data, classify the energy management strategy according to the effect of reducing carbon emissions, and obtain effective strategies and inefficient strategies; Strengthen and expand the implementation scope for the effective strategies to obtain enhanced strategies. Analyze the reasons for the failure of the inefficient strategies from aspects such as implementation plans, technical solutions, and external environmental changes, and make improvements to obtain improved strategies; Based on the carbon emission data, specify corresponding new strategies from aspects such as equipment aging, climate change, and process optimization. Integrate the enhanced strategies, the improved strategies, and the new strategies to obtain an optimized energy management plan.

[0018] An energy management system based on carbon emissions provided by the present invention obtains a carbon emission coefficient by combining actual consumption data with carbon emission intensity data, obtains carbon emission amount data according to carbon emission factor data and actual consumption data, and further calculates to obtain carbon emission trend data, solving the defects of lack of systematicness and accuracy in carbon emission calculation, helping enterprises and relevant departments accurately grasp the carbon emission situation, and formulating reasonable emission reduction targets and strategies. And a seasonal time series model is constructed. By sorting, differencing, determining the seasonal period, and fitting the model steps for the energy consumption time distribution data, accurate prediction of energy consumption is realized, and energy consumption change data is obtained, thereby providing a more reliable basis for carbon emission change prediction, realizing accurate prediction of energy consumption, being able to understand the change trend of energy consumption in advance, and providing strong support for energy planning and resource allocation. It also evaluates and classifies the energy management strategy, takes strengthening, improvement and other measures for effective strategies and inefficient strategies respectively, and formulates new strategies based on carbon emission data, and finally integrates to obtain an optimized energy management plan, realizing the optimization and improvement of the energy management strategy, and at the same time helping enterprises reduce energy costs and improve economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic structural diagram of an energy management system based on carbon emissions provided by an embodiment of the present invention; Figure 2 is Figure 1 The flowchart for the carbon emission extraction module to obtain the carbon emission coefficient in Figure 3 isFigure 1 Flowchart of the carbon emission extraction module to obtain carbon emission trend data; Figure 4 yes Figure 1 Flowchart of the carbon emission change prediction module obtaining carbon emission change data. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0022] The following combination Figures 1-4 The present invention describes an energy management system based on carbon emissions.

[0023] like Figure 1 As shown, an embodiment of the present invention provides an energy management system based on carbon emissions, including: The data acquisition module is used to collect energy consumption data and carbon emission-related data in real time, collect historical management data, extract actual consumption data and energy consumption time distribution data from energy consumption data, and carbon emission-related data includes carbon emission factor data and carbon emission intensity data.

[0024] The carbon emission extraction module is used to obtain the carbon emission coefficient based on the actual consumption data combined with the carbon emission intensity data, to obtain the carbon emission data based on the carbon emission factor data and the actual consumption data, and to obtain the carbon emission trend data based on the carbon emission coefficient and energy consumption data.

[0025] The steps to obtain the carbon emission factor include: Determine the energy type of actual consumption data from energy statistical reports and enterprise production records, and determine the energy type corresponding to the carbon emission intensity data based on the carbon emission database.

[0026] For each energy type and category, the carbon emission intensity and actual consumption are weighted to obtain the carbon emission coefficient.

[0027] For each energy type and energy type, the carbon emission intensity is divided by the actual consumption to obtain the corresponding emission coefficient, and multiple emission coefficients are summarized to obtain the carbon emission coefficient.

[0028] The steps to obtain carbon emissions data include: Multiple energy stages are obtained by dividing the life cycle stages of different energy types. The carbon emission factors corresponding to different energy stages are found through carbon emission factor data, and the actual activity data for each energy stage is counted.

[0029] By multiplying the carbon emission factors of each energy stage by the actual activity data, the carbon emissions of each energy stage are calculated, and the carbon emission data is obtained by summing up the carbon emissions of each energy stage.

[0030] The steps to obtain carbon emission trend data include: Based on the carbon emission coefficient data, the emissions of various energy sources within each preset statistical period are calculated to obtain the emissions of each energy source, and the emissions of each energy source are summarized to obtain the carbon emissions for the period.

[0031] The carbon emissions for each preset statistical period are arranged in chronological order to form carbon emission time series data. Using charting tools, a trend chart is drawn with time as the abscissa and carbon emissions as the ordinate based on the carbon emission time series data.

[0032] By using the moving average method to smooth the carbon emission time series data, the trend chart shows the long-term trend, and then the long-term trend is analyzed to obtain the carbon emission trend data.

[0033] The carbon emission change prediction module is used to construct a seasonal time series model, input the time distribution data of energy consumption for prediction, output the energy consumption change data, and combine the carbon emission trend data with the energy consumption change data to obtain the carbon emission change data.

[0034] The steps to obtain energy consumption change data include: The time distribution data of energy consumption is sorted to obtain an energy time series, and the unit root test method is used to perform differencing operations on the energy time series to transform it into a stationary series. The formula is expressed as:

[0035] In the formula, is the stationary series, is the energy time series, is the order differencing operator.

[0036] By observing the seasonal characteristics of the stationary series, the seasonal period is determined, and seasonal differencing is performed to obtain the seasonal series. The formula is expressed as:

[0037] In the formula, is the seasonal series, is the order seasonal differencing, Seasonal difference operator.

[0038] By plotting the autocorrelation function and the partial autocorrelation function graphs and combining different parameter combinations, the parameters of the seasonal time series model are determined to obtain the model parameters. Using the model parameters, the seasonal sequence is model-fitted to obtain the seasonal sequence model, which is expressed by the formula:

[0039] In the formula, is the ordinary autoregressive polynomial of the autoregressive part, is the seasonal autoregressive polynomial of the autoregressive part, is a white noise sequence with a mean of 0 and a variance of is the ordinary autoregressive polynomial of the moving average part, is the seasonal autoregressive polynomial of the moving average part.

[0040] Diagnose the seasonal sequence model to check whether the residuals are white noise. If so, continue to use the seasonal sequence model; otherwise, readjust the model parameters until the check shows white noise.

[0041] Using the seasonal sequence model, input the information of the new time point, predict the energy consumption to obtain the predicted consumption value, and compare the predicted consumption value with the actual consumption data to calculate the energy consumption change data.

[0042] The steps to obtain the carbon emission change data include: Make the carbon emission trend data and the energy consumption change data consistent in the time scale, and process the missing values and outliers.

[0043] When the energy structure changes, analyze the proportion changes of different energies in the total energy consumption, obtain the proportion impact data according to the impact of the proportion changes on carbon emissions, and measure the impact of energy efficiency on carbon emissions by calculating the energy consumption per unit economic output and the carbon emissions per unit economic output to obtain the efficiency change data.

[0044] According to the energy consumption change data combined with the efficiency change data, calculate the carbon emission change caused by the energy consumption change to obtain the energy change carbon emission change amount, which is expressed by the formula:

[0045] In the formula, is the energy change carbon emission change amount, is the efficiency change data, is the carbon emission factor corresponding to the efficiency change data.

[0046] According to the impact data of proportion, the impact of energy structure changes on carbon emissions is calculated, and the change in carbon emissions due to proportion is obtained. The formula is expressed as follows:

[0047] Where, is the proportion of a certain energy in the energy structure, It is the proportion of a certain energy in the energy structure after it decreases. is the total energy consumption, is the carbon emission factor corresponding to a certain energy source, It is the proportion that affects the change in carbon emissions.

[0048] The data before and after the energy intensity changes due to energy efficiency improvement are collected to obtain the original energy intensity and the post-energy intensity. The intensity change data is calculated through the economic output of the original energy intensity and the post-energy intensity. The formula is expressed as:

[0049] Where, is the intensity change data, is the original energy intensity, is the post-energy intensity, It is economic output.

[0050] Then, the intensity carbon emission change is calculated based on the intensity change data and the carbon emission factor corresponding to the intensity change data. The formula is:

[0051] Where, is the change in carbon emission intensity, is the carbon emission factor corresponding to the intensity change data.

[0052] The carbon emission change data is obtained by adding the carbon emission change affected by the proportion, the carbon emission change affected by energy, and the carbon emission change affected by intensity. The formula is:

[0053] Where, is the carbon emission change data, is the change in carbon emissions from energy changes, Is the proportion that affects the change in carbon emissions, is the change in intensity carbon emissions.

[0054] The adjustment strategy module is used to extract energy management strategies from historical management data using association rule mining methods, and adjust the energy management strategies according to carbon emission change data to obtain an optimized energy management plan.

[0055] The steps for obtaining an energy management strategy include: Collect energy consumption data, equipment operation status, production data, and environmental data related to energy management from historical management data, convert and integrate them, and establish a unified data table.

[0056] Use the Apriori algorithm to find frequent item sets from the unified data table by layer-by-layer search, then generate association rules based on the frequent item sets, and determine the parameters of the association rules using the minimum support and minimum confidence.

[0057] Find the itemsets that meet the minimum support from the association rules, generate confidence rules that meet the minimum confidence based on the mined item sets, analyze the causal relationship and influence mechanism of the confidence rules to obtain the analysis results, and extract the energy management strategy from the analysis results.

[0058] The steps for obtaining an optimized energy management plan include: Analyze the change trend, change amplitude, and time distribution of carbon emissions in the carbon emission change data to obtain key change data, and disassemble the key change data to obtain factor influence data.

[0059] Evaluate the energy management strategy based on the key change data and factor influence data, classify the energy management strategy according to the effect of reducing carbon emissions, and obtain effective strategies and inefficient strategies.

[0060] Strengthen and expand the implementation scope for effective strategies to obtain strengthened strategies. For inefficient strategies, analyze the reasons for strategy failure from the implementation plan, technical plan, and external environment changes, and make improvements to obtain improved strategies.

[0061] Based on the carbon emission data, specify corresponding new strategies from aspects such as equipment aging, climate change, and process optimization, and integrate the strengthened strategies, improved strategies, and new strategies to obtain an optimized energy management plan.

[0062] An energy management system based on carbon emissions provided by this embodiment obtains a carbon emission coefficient by combining actual consumption data with carbon emission intensity data, obtains carbon emission data based on carbon emission factor data and actual consumption data, and further calculates carbon emission trend data, solving the defects of lack of systematicness and accuracy in carbon emission calculation, and helping enterprises and relevant departments accurately grasp the carbon emission situation and formulate reasonable emission reduction targets and strategies. And a seasonal time series model is constructed. By sorting, differencing, determining the seasonal period, and fitting the model steps for the energy consumption time distribution data, accurate prediction of energy consumption is realized, and energy consumption change data is obtained, thus providing a more reliable basis for carbon emission change prediction, realizing accurate prediction of energy consumption, being able to understand the change trend of energy consumption in advance, and providing strong support for energy planning and resource allocation. It also evaluates and classifies energy management strategies, takes measures such as strengthening and improving for effective strategies and inefficient strategies respectively, and formulates new strategies based on carbon emission data, and finally integrates to obtain an optimized energy management plan, realizing the optimization and improvement of energy management strategies, and at the same time helping enterprises reduce energy costs and improve economic benefits.

[0063] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0064] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course also by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An energy management system based on carbon emissions, characterized in that, Including: A data acquisition module for real-time acquisition of energy consumption data and energy consumption time distribution data; A carbon emission extraction module for extracting carbon emission trend data from the energy consumption data; A carbon emission change prediction module for constructing a seasonal time series model, inputting the energy consumption time distribution data, outputting energy consumption change data, analyzing the proportion change of different energies in the total energy consumption when the energy structure changes, obtaining proportion impact data on the impact of carbon emissions, and calculating the energy consumption per unit economic output and the carbon emissions per unit economic output to measure the impact of energy efficiency on carbon emissions to obtain efficiency change data, and combining the carbon emission trend data to obtain carbon emission change data; An adjustment strategy module for adjusting the energy management strategy according to the carbon emission change data to generate an optimized energy management plan.

2. The energy management system based on carbon emissions according to claim 1, wherein, The data acquisition module is further used for: Real-time acquisition of the carbon emission factor data and the carbon emission intensity data, and collection of historical management data and actual consumption data.

3. The energy management system based on carbon emissions according to claim 2, wherein, The extraction of the carbon emission extraction module includes: Obtaining a carbon emission coefficient according to the actual consumption data in combination with the carbon emission intensity data, obtaining carbon emission amount data according to the carbon emission factor data and the actual consumption data, and obtaining carbon emission trend data according to the carbon emission coefficient and the energy consumption data.

4. An energy management system based on carbon emissions according to claim 3, characterized in that, The steps of obtaining the carbon emission coefficient include: Determining the energy types of the actual consumption data from the energy statistical statements and enterprise production records, and determining the energy types corresponding to the carbon emission intensity data according to the carbon emission database; Sorting and corresponding matching of the energy types and the energy types from different units and multiple sources, so that the actual consumption data of each energy type and the energy type correspond to the corresponding carbon emission intensity data; For each energy type and energy type, the carbon emission intensity and the actual consumption are weighted to obtain a carbon emission coefficient.

5. An energy management system based on carbon emissions according to claim 3, characterized in that, The steps of obtaining the carbon emission amount data include: Dividing the life cycle stages of different energy types to obtain multiple energy stages, finding the carbon emission factors corresponding to different energy stages through the carbon emission factor data, and counting the actual activity data of each energy stage; Multiplying the carbon emission factors of each energy stage by the actual activity data to calculate the carbon emissions of each energy stage, and adding the carbon emissions of each energy stage to obtain the carbon emission amount data.

6. An energy management system based on carbon emissions according to claim 3, characterized in that, The steps of obtaining the carbon emission trend data include: Calculating the energy consumption of various energies in each preset statistical period according to the carbon emission coefficient data to obtain the emissions of each energy, and summarizing the emissions of each energy to obtain the carbon emissions in the period; Arranging the carbon emissions in each preset statistical period in chronological order to form carbon emission time series data, and using chart tools to draw a change trend chart with time as the abscissa and carbon emissions as the ordinate according to the carbon emission time series data; By using the moving average method to smooth the carbon emission time series data, the long-term trend is shown in the change trend graph, and then the carbon emission trend data is obtained by analyzing the long-term trend.

7. An energy management system based on carbon emissions according to claim 2, characterized in that, The steps of obtaining the energy consumption change data include: Sort the energy consumption time distribution data to obtain an energy time series, and use the unit root test method to perform a differencing operation on the energy time series to transform it into a stationary series; By observing the seasonal characteristics of the stationary series, determine the seasonal period and perform seasonal differencing to obtain a seasonal series; By plotting the autocorrelation function and partial autocorrelation function graphs, and combining different parameter combinations, determine the parameters of the seasonal time series model to obtain model parameters, and use the model parameters to perform model fitting on the seasonal series to obtain a seasonal series model; Diagnose the seasonal series model to check whether the residuals are white noise. If so, continue to use the seasonal series model; otherwise, readjust the model parameters until the check shows white noise; Using the seasonal series model, input new time point information to predict energy consumption to obtain a predicted consumption value, compare the predicted consumption value with the actual consumption data, and calculate to obtain the energy consumption change data.

8. An energy management system based on carbon emissions according to claim 1, characterized in that, The steps of obtaining the carbon emission change data include: Calculate the change in carbon emissions caused by changes in energy consumption by combining the energy consumption change data with the efficiency change data to obtain the energy change carbon emission change; According to the proportion impact data, calculate the impact degree of the energy structure change on carbon emissions to obtain the proportion impact carbon emission change; Statistically obtain the original energy intensity and the post-energy intensity before and after the change in energy intensity caused by the improvement of energy efficiency, and calculate the intensity change data through the economic output of the original energy intensity and the post-energy intensity; Then calculate the intensity carbon emission change based on the intensity change data and the corresponding carbon emission factor of the intensity change data; Add the proportion impact carbon emission change, the energy change carbon emission change, and the intensity carbon emission change to obtain the carbon emission change data.

9. The energy management system based on carbon emissions according to claim 2, characterized in that, The steps of extracting the energy management strategy from the historical management data using the association rule mining method include: Collect energy usage data, equipment operation status, production data, and environmental data related to energy management from the historical management data, perform conversion and integration, and establish a unified data table; Use the Apriori algorithm to find frequent item sets from the unified data table by layer-by-layer search, then generate association rules based on the frequent item sets, and use the minimum support and minimum confidence to determine the parameters of the association rules; Find the items that meet the minimum support from the association rules as the mining item sets, based on the mining item sets, generate confidence rules that meet the minimum confidence, analyze the causal relationship and influence mechanism of the confidence rules to obtain the analysis results, and extract the energy management strategy from the analysis results.

10. An energy management system based on carbon emissions according to claim 1, characterized in that The steps of obtaining the optimized energy management plan include: Analyze the change trend, change range, and time distribution of carbon emissions in the carbon emission change data to obtain key change data, and decompose the key change data into factor impact data; Evaluate the energy management strategy based on the key change data and the factor impact data, and classify the energy management strategy according to the effect of reducing carbon emissions to obtain effective strategies and inefficient strategies; Strengthen and expand the implementation scope for the effective strategies to obtain enhanced strategies. For the inefficient strategies, analyze the reasons for strategy failure from aspects such as implementation plans, technical solutions, and external environmental changes, and make improvements to obtain improved strategies; Based on the carbon emission data, specify corresponding new strategies from aspects such as equipment aging, climate change, and process optimization, and integrate the enhanced strategies, the improved strategies, and the new strategies to obtain an optimized energy management plan.

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