Energy optimization management system based on target guidance

Abstract

The invention relates to the technical field of energy management, in particular to a target-oriented energy optimization management system, which comprises a real-time data integration module, an energy configuration dynamic optimization module, a carbon emission deviation correction module and a carbon emission optimal control module. According to the invention, through integration of latest energy usage data and carbon emission records, dynamic optimization of energy configuration is realized, more accurate prediction and adjustment of energy distribution are allowed, carbon footprint is minimized, prediction deviation is corrected by adopting a real-time feedback mechanism, data accuracy is enhanced, and overall energy management efficiency is improved. Through real-time monitoring and dynamic optimization of control parameters, energy use can be continuously adjusted, the energy consumption proportion can be optimized, the energy cost can be reduced, the operation flexibility and the response speed can be remarkably improved, more efficient energy use and lower environmental influence can be realized, and environmental protection and resource saving can be more effectively supported.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a target-oriented energy optimization management system. Background Art

[0002] The field of energy management technology is a broad field involving the design, monitoring, control and optimization of energy use, combining a variety of technologies and strategies to improve energy efficiency, reduce energy costs, and maximize the sustainable use of energy, including but not limited to data analysis of energy consumption, energy efficiency improvement measures, integration of renewable energy and implementation of automation systems. Through the application of relevant technologies, energy consumption can be managed more effectively, environmental impact can be reduced, and the safety and reliability of energy supply can be ensured.

[0003] Among them, the energy optimization management system is a specific topic in the field of energy management technology. It is mainly used to optimize energy use by real-time monitoring of energy consumption, predicting future demand and automatically adjusting energy distribution. It is widely used in industrial, commercial and residential environments to help users reduce energy waste, optimize energy costs, and improve overall energy efficiency. In addition, it can assist in decision-making and provide data support for equipment maintenance and upgrades through detailed energy usage reports and analysis.

[0004] Existing energy management technologies mostly focus on single energy efficiency improvement or cost control, lacking global dynamic optimization capabilities, which limits the application effect when real-time data feedback and market demand change rapidly. For example, when dealing with sudden changes in energy demand, existing technologies may not be able to adjust energy distribution in real time, resulting in energy waste or inability to effectively cope with peak loads. In addition, existing technologies are often not sophisticated enough in carbon emission monitoring and management, lack effective prediction and deviation correction mechanisms, and are difficult to accurately achieve carbon emission targets. They face the risk of excessive carbon emissions, which affects the market competitiveness and compliance of enterprises. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an energy optimization management system based on goal orientation.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A goal-oriented energy optimization management system includes: The real-time data integration module collects energy usage data from environmental monitoring stations and carbon emissions data from industrial activities, formats the data, aggregates the data simultaneously, and builds a real-time environment and energy database; The energy configuration dynamic optimization module calculates the carbon emission efficiency of the current energy configuration based on the real-time environment and energy database, analyzes the carbon emission impact corresponding to the energy configuration according to the calculation results, optimizes the energy configuration matching target, and obtains the adjusted energy strategy; The carbon emission deviation correction module applies the adjusted energy strategy, collects the latest energy configuration data, calculates the deviation between the real-time carbon emission data and the predicted carbon emission data, performs deviation correction according to the deviation calculation result, adjusts the correction parameters according to the real-time feedback, and generates the corrected carbon emission data; The carbon emission optimal control module adopts the corrected carbon emission data to evaluate the actual contribution of different energy configurations to carbon emissions, adjust the energy consumption ratio, monitor the adjusted carbon emission changes in real time according to the adjustment results, dynamically optimize the control parameters, perform continuous optimization processing of carbon emissions, and output a dynamically adjusted energy optimization management plan.

[0007] As a further solution of the present invention, the steps of acquiring the real-time environment and energy database are: Sensors configured at environmental monitoring stations collect data, record the energy consumption and carbon emissions of each site in real time, and generate raw energy and carbon emission data sets; Uploading the raw energy and carbon emission data set to a central processor, performing data cleaning, standardizing the data format, and obtaining processed energy use and carbon emission data; Based on the processed energy usage and carbon emission data, data aggregation is performed using the formula:

[0008] Calculate total energy consumption , analyze the overall trend of energy and carbon emissions, and obtain a real-time environment and energy database, including: Represents the power consumption value of the site, Represents the fuel consumption value, Represents carbon dioxide emissions.

[0009] As a further solution of the present invention, the steps for calculating the carbon emission efficiency are: Based on the real-time environment and energy database, extract data, including energy type, consumption and time information, to generate a preliminary data set; Processing the preliminary data set, removing outliers, unifying the data format, and classifying and arranging the energy types and consumption to obtain a cleaned data set; Using the cleaned data set, we analyze the contribution of different energy usage to carbon emissions using the formula,

[0010] Calculating total carbon emissions , generating a carbon emission efficiency index, where represents the consumption of energy type i, Represents the carbon emission coefficient of energy type i.

[0011] As a further solution of the present invention, the steps of obtaining the adjusted energy strategy are: The carbon emission efficiency index is called, the carbon emission data of the energy type is associated with its usage, the carbon emission proportion of each energy is counted, the carbon emission proportion of the energy type is sorted, and a carbon emission energy classification result is generated; Based on the carbon emission energy classification results, the emission coefficient and usage data of alternative energy are called for high-emission energy types, using the formula:

[0012] Calculate the potential carbon emissions reduction after adjusting energy configuration , generating energy configuration alternatives where, It is The amount of energy consumed, It is The original carbon emission coefficient of the energy source, It is Carbon emission coefficient of alternative energy sources; According to the energy configuration alternatives, the energy usage ratio is adjusted, and the adjusted energy strategy is formulated and output in combination with the local energy utilization rate and actual feasibility.

[0013] As a further solution of the present invention, the deviation calculation step is: Based on the adjusted energy strategy, extract energy configuration data, record energy type, usage and configuration time, and generate the latest energy configuration information; Using the latest energy configuration information, according to the carbon emission factor corresponding to each energy type, calculate the real-time carbon emissions and generate real-time carbon emission data; The real-time carbon emission data is compared with the preset carbon emission forecast data, and the formula is used.

[0014] Calculate the deviation value , generate carbon emission deviation data, where and Represent the actual and predicted carbon emissions respectively.

[0015] As a further solution of the present invention, the steps for obtaining the corrected carbon emission data are: Using the carbon emission deviation data, evaluate the current energy configuration and carbon emission situation, adjust the size and direction of the deviation value, and generate a correction strategy; Implement the correction strategy, obtain new energy usage data and corresponding carbon emissions in real time, reveal the immediate effect of the correction strategy, adjust the correction parameters based on the feedback data, and generate adjusted correction parameters; Using the adjusted correction parameters, the formula is used:

[0016] Recalculate corrected carbon emissions , generate corrected carbon emission data, where is the actual carbon emissions, is the carbon emission deviation value, is the external parameter adjustment amount.

[0017] As a further solution of the present invention, the energy consumption ratio is adjusted as follows: Based on the corrected carbon emission data, for different energy configurations, analyze the current carbon emission level of each energy source and generate an initial carbon emission contribution record of the energy source; Based on the initial carbon emission contribution record of the energy source, the formula is used:

[0018] Calculate the The proportion of energy in the new configuration , evaluate the relationship between the carbon emissions of energy configuration and the energy consumption ratio, and output the evaluation record, where, It is Corrected carbon emission data corresponding to each energy source; Based on the assessment records, the energy consumption ratio is optimized, the overall carbon emissions are minimized, and the optimized energy configuration is output.

[0019] As a further solution of the present invention, the steps for obtaining the dynamically adjusted energy optimization management solution are: Applying the above-mentioned optimized energy configuration, by deploying a distributed sensor network to capture carbon emission data of different energy types in real time, verifying and analyzing the collected data, and generating a real-time monitoring carbon emission data overview; Based on the real-time monitoring carbon emission data overview, according to the real-time carbon emission data and the preset carbon emission reduction target, the control parameters of energy consumption are adjusted, the energy consumption control strategy is updated in real time, and the adjusted control parameters are output; The adjusted control parameters and real-time carbon emission data are used to perform continuous carbon emission optimization processing. The influence of control parameter configuration on carbon emissions is simulated and analyzed, and the parameters are corrected in combination with operational feedback to output a dynamically adjusted energy optimization management plan.

[0020] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by integrating the latest energy usage data and carbon emission records, dynamic optimization of energy configuration is achieved, allowing more accurate prediction and adjustment of energy distribution to minimize carbon footprint, and a real-time feedback mechanism is used to correct prediction deviations, enhance data accuracy, and thus improve overall energy management efficiency. Through real-time monitoring and dynamic optimization of control parameters, energy usage can be continuously adjusted, energy consumption ratio can be optimized, and energy costs can be reduced, significantly improving operational flexibility and response speed, achieving more efficient energy use and lower environmental impact, and more effectively supporting environmental protection and resource conservation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a system flow chart of the present invention; Figure 2 The flowchart of obtaining the real-time environment and energy database of the present invention; Figure 3 is a flow chart for calculating the carbon emission efficiency of the present invention; Figure 4 A flowchart for obtaining the energy strategy adjusted by the present invention; Figure 5 This is a flow chart of the deviation calculation of the present invention; Figure 6 A flowchart for obtaining the corrected carbon emission data of the present invention; Figure 7 The following is a flow chart for adjusting the energy consumption ratio of the present invention; Figure 8 This is a flow chart for obtaining the dynamically adjusted energy optimization management solution of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0024] See also Figure 1 , a goal-oriented energy optimization management system includes: The real-time data integration module collects energy usage data from environmental monitoring stations and carbon emissions data from industrial activities, formats the data, aggregates the data simultaneously, and builds a real-time environment and energy database; The energy configuration dynamic optimization module calculates the carbon emission efficiency of the current energy configuration based on the real-time environment and energy database, analyzes the carbon emission impact of the energy configuration according to the calculation results, optimizes the energy configuration matching target, and obtains the adjusted energy strategy; The carbon emission deviation correction module applies the adjusted energy strategy, collects the latest energy configuration data, calculates the deviation between the real-time carbon emission data and the predicted carbon emission data, performs deviation correction based on the deviation calculation results, adjusts the correction parameters based on real-time feedback, and generates corrected carbon emission data; The carbon emission optimal control module uses the corrected carbon emission data to evaluate the actual contribution of different energy configurations to carbon emissions, adjust the energy consumption ratio, monitor the adjusted carbon emission changes in real time according to the adjustment results, dynamically optimize the control parameters, perform continuous optimization of carbon emissions, and output a dynamically adjusted energy optimization management plan.

[0025] The real-time environment and energy database includes environmental parameter data, consumption indicators and emission records. The adjusted energy strategy includes energy distribution records, carbon emission efficiency and energy configuration parameters. The corrected carbon emission data includes actual carbon emission values, predicted carbon emission values ​​and corrected differences. The dynamically adjusted energy optimization management plan specifically includes carbon emission monitoring results and control parameter optimization records.

[0026] See also Figure 2 ,The steps to obtain the real-time environment and energy database are: Sensors configured at environmental monitoring stations collect data, record the energy consumption and carbon emissions of each site in real time, and generate raw energy and carbon emission data sets; Advanced sensors are deployed at environmental monitoring stations for data collection. They are responsible for continuously monitoring various energy consumption data at each monitoring point, including but not limited to electricity, gas and other possible fuel types. In addition, they are also responsible for collecting CO2 and other greenhouse gas emission data. The monitoring equipment is equipped with real-time data transmission function to ensure that each piece of data has an exact timestamp and location identification, which is crucial for subsequent data processing. The original data is transmitted to the data collection center through encryption to ensure the security and integrity of the data during transmission. The original data set includes all energy and carbon emission data collected from each site, providing a scientific basis for environmental policy formulation and energy use evaluation.

[0027] Upload the raw energy and carbon emission data sets to the central processor, perform data cleaning, standardize the data format, and obtain processed energy use and carbon emission data; After the raw data is uploaded to the central server, a series of preprocessing operations are performed to ensure data quality, including the identification and elimination of erroneous data. Errors may come from equipment failures, operational errors or interference from external environmental factors. At the same time, the time correction program will correct time label errors caused by time zone differences or synchronization errors. Data formatting includes unifying data units and data types, such as converting various units of energy consumption into standard units, which is crucial for subsequent data analysis because only when the data format is consistent can effective data integration and analysis be carried out. After data cleaning and standardization, the energy usage data and carbon emission data of each site will be integrated into a processed data set, which will be stored in the central database to facilitate subsequent data query, analysis and report generation, ensuring the efficiency of data processing and the reliability of data quality.

[0028] Based on the processed energy usage and carbon emission data, data aggregation is performed using the formula:

[0029] Calculate total energy consumption , analyze the overall trend of energy and carbon emissions, and obtain a real-time environment and energy database, including: Represents the power consumption value of the site, Represents the fuel consumption value, represents carbon dioxide emissions; represents the power consumption, which is 6500 kWh, representing the power consumption of a specific monitoring site in one day; represents fuel consumption, which is 500 liters, representing the fuel usage of an industrial facility in one day; Represents carbon dioxide emissions, which is 10,000 tons, representing the total carbon emissions of the region for one month. The calculation process is as follows:

[0030]

[0031] The results show that the total energy consumption is very low according to the current energy and carbon emission conditions ( ), which reflects the impact of relatively high energy efficiency or large carbon emissions under the considered parameter conditions, and is used in the environment and energy database to further analyze the adjustment direction of energy utilization efficiency and carbon emission policies.

[0032] See also Figure 3 , the calculation steps of carbon emission efficiency are: Based on the real-time environment and energy database, extract data, including energy type, consumption and time information, to generate a preliminary data set; By connecting the real-time environment and energy database, the extracted data includes energy type, consumption and time information. During the execution process, it is necessary to call the energy consumption record table in the database, group and count the consumption according to the energy type classification, and extract daily, hourly or smaller granularity time data based on the timestamp field. The energy type table is further called to associate the energy ID in the consumption record table, match the corresponding energy name and carbon emission coefficient, and pair the energy consumption with the environmental factors (such as temperature and humidity) of the corresponding time period for subsequent analysis of energy consumption changes. It is also necessary to mark the entries with invalid data or abnormal values ​​in the time period for subsequent cleaning. Through these operations, a preliminary data set containing energy type, consumption, time information and environmental factors is formed, which is an important basis for subsequent analysis of carbon emission efficiency.

[0033] Process the preliminary data set, remove outliers, unify the data format, and classify and sort the energy types and consumption to obtain the cleaned data set; The preliminary data set is cleaned and processed in detail, including the steps of outlier removal, format unification and classification. For the outlier removal part, the consumption field is checked, and the records below the minimum value or above the maximum value range are marked and removed. They are adjusted to the average value or interpolation method to generate reasonable data. Secondly, the time field format is converted to ensure that all time formats are in the standard timestamp format. At the same time, the naming of the energy type field is unified to eliminate possible duplication or spelling errors. The energy consumption data is then classified and sorted according to energy type and time period to ensure that the consumption of each energy can be divided by the time dimension of hours or days. A cleaned data set is generated, which can be directly used in the subsequent carbon emission calculation process.

[0034] Using the cleaned data set, we analyze the contribution of different energy usage to carbon emissions using the formula,

[0035] Calculating total carbon emissions , generating a carbon emission efficiency index, where represents the consumption of energy type i, represents the carbon emission coefficient of energy type i; represents the total carbon emissions, It is The amount of energy consumed, It is The specific value comes from the energy consumption statistics and carbon emission coefficient table, where the consumption The unit carbon emission coefficient is obtained through the data extracted and cleaned from the real-time environment and energy database. Refer to the standard database.

[0036] The following energy consumption data were extracted from the database: coal( ton, kg CO2 / ton) natural gas( cubic meter, kg CO2 / m3) Wind energy ( kWh, kg CO2 / kWh) Calculating carbon emissions from coal:

[0037] Calculate carbon emissions from natural gas:

[0038] Calculating carbon emissions from wind power:

[0039] Total carbon emissions calculation:

[0040] Calculate total carbon emissions:

[0041] The results show that in the current energy consumption configuration, coal is the main source of carbon emissions, followed by natural gas, while wind energy carbon emissions are almost negligible. This carbon emission efficiency index can be used for subsequent analysis of the optimization direction of energy configuration, such as gradually reducing the use of coal and increasing the proportion of clean energy such as wind energy to reduce the overall carbon emissions.

[0042] See also Figure 4, the steps to obtain the adjusted energy strategy are: Call the carbon emission efficiency index, associate the carbon emission data of energy types with their usage, count the carbon emission proportion of each energy source, sort the carbon emission proportion of energy types, and generate carbon emission energy classification results; Call the carbon emission efficiency index to extract data related to energy types and their total carbon emissions. The specific operations include extracting daily or monthly energy consumption data from the energy consumption database according to the energy type field, associating and matching these data with the standard coefficient table in the carbon emission coefficient database, merging data according to the energy type field and the time field to form a preliminary carbon emission record table, and then calculating the total carbon emissions of each type according to the consumption and carbon emission coefficient of different energy types, grouping and summarizing the carbon emission data, and counting the proportion of each energy type in the total carbon emissions. By comparing the proportion values, the energy type with the highest proportion is selected, and these types are marked as high-carbon emission energy types. Finally, a high-carbon emission energy classification result table including energy type, total emissions and emission proportion is compiled for subsequent optimization analysis process.

[0043] Based on the results of carbon emission energy classification, the emission coefficient and usage data of alternative energy are called for high-emission energy types, using the formula:

[0044] Calculate the potential carbon emissions reduction after adjusting energy configuration , generating energy configuration alternatives where, It is The amount of energy consumed, It is The original carbon emission coefficient of the energy source, It is Carbon emission coefficient of alternative energy sources; Get data , , .

[0045] Calculate the carbon emissions of the original energy:

[0046] Calculate the carbon footprint of alternative energy sources:

[0047] Calculate the square root of the difference:

[0048] The results show that after optimizing the energy configuration, this alternative can reduce carbon emissions by 15,000 units, providing a reference for subsequent energy configuration optimization.

[0049] Adjust the energy usage ratio according to the energy configuration alternatives, and formulate and output the adjusted energy strategy based on the local energy utilization rate and actual feasibility; According to the energy configuration alternative plan, the energy usage ratio is adjusted. The specific implementation process includes calling the recommended usage of each energy in the optimization plan, combining the existing energy supply records in the energy database, calculating the actual usage ratio of high-carbon emission energy in the current total energy configuration, comparing the actual usage with the recommended usage of the optimization plan, marking the type of high-carbon emission energy that needs to be reduced and the corresponding reduction amount, and at the same time, for the recommended alternative low-carbon emission energy, calling its available quantity data in the local energy supply database, evaluating the feasibility of actual substitution, and then sorting the alternative plans according to energy cost and carbon emission reduction effect, generating an adjusted energy allocation plan by calling simulation tools for multiple energy configurations, and finally outputting adjustment strategies including reducing the use of high-carbon energy, increasing the proportion of alternative energy and supply conditions, which are used to form the final energy strategy report.

[0050] See also Figure 5 , the deviation calculation steps are: Based on the adjusted energy strategy, extract energy configuration data, record energy type, usage and configuration time, and generate the latest energy configuration information; The adjusted energy strategy is to extract the latest energy configuration data from the energy management system. The data includes energy type, usage, configuration time, etc., which is the key link to ensure the most accurate and latest energy usage information, thereby laying a solid foundation for subsequent carbon emission calculations. Based on detailed configuration data, real-time calculation of energy consumption will be performed. The calculation involved is not just a simple addition, but also includes the classification of various energy types and the application of corresponding carbon emission factors. The factors are obtained from the latest environmental protection standards and reflect the impact of different energy sources on the environment. What is finally generated is the latest energy configuration information, which is the basic data for accurate carbon emission calculations.

[0051] Using the latest energy configuration information, calculate the real-time carbon emissions according to the carbon emission factors corresponding to each energy type and generate real-time carbon emission data; Starting from the latest energy configuration information, the data is combined with the carbon emission factor of each energy type to calculate the real-time carbon emissions under the current actual energy use. At the same time, the calculation process is verified and accounted for to ensure the scientificity and accuracy of the calculation results. Through this series of detailed calculations, not only the specific consumption of various energy sources is obtained, but also the corresponding carbon emissions are calculated according to the environmental impact of different energy sources. Finally, real-time carbon emission data is generated, which provides the necessary data support for subsequent deviation analysis.

[0052] Compare the real-time carbon emission data with the preset carbon emission forecast data, and use the formula:

[0053] Calculate the deviation value , generate carbon emission deviation data, where and represent actual and predicted carbon emissions, respectively; Actual carbon emissions data 500 tons, predicted carbon emissions data It is 480 tons, and the calculation process is as follows: ton The results show that current carbon emissions are 20 tons higher than predicted, and this deviation requires further analysis of the causes and adjustment of strategies.

[0054] See also Figure 6 , the steps to obtain the corrected carbon emission data are: Use carbon emission deviation data to evaluate current energy configuration and carbon emissions, adjust the size and direction of the deviation value, and generate correction strategies; A detailed assessment is conducted using the deviation value of carbon emission data. This process includes comparing the actual carbon emissions with the predetermined targets, analyzing the main sources of deviation and the efficiency issues of energy use patterns, and determining the correction targets. Based mainly on the specific size and direction of the deviation, targeted correction strategies are designed, including adjusting energy use efficiency, optimizing energy allocation, and reducing unnecessary energy consumption. The strategy design takes into account the carbon emission factors of different energy types and their environmental impacts to ensure that the correction measures can effectively intervene in actual problems. The correction goal is to reduce carbon emission deviations and achieve more environmentally friendly energy use. With the support of dynamic simulation and predictive models, a set of specific and executable correction strategies are generated, aiming to achieve the predetermined emission reduction targets through precise adjustments.

[0055] Implement the correction strategy, obtain new energy usage data and corresponding carbon emissions in real time, reveal the immediate effect of the correction strategy, adjust the correction parameters based on the feedback data, and generate adjusted correction parameters; After executing the correction strategy, the real-time monitoring system begins to continuously track changes in energy usage and carbon emissions, which are collected in real time from each energy consumption node, including the consumption of various types of energy (such as natural gas, electricity, etc.) and the corresponding carbon emissions. Data collection is automated and achieved through sensors and monitoring software to ensure the accuracy and real-time nature of the data. Based on the collected data, the immediate effect of the correction strategy is analyzed to detect its actual impact on carbon emissions. Real-time feedback data provides a basis for the adjustment of correction parameters. The adjustment process takes into account the impact of changes in the external environment and policy adjustments. By adjusting the correction parameters to optimize energy configuration, it is ensured that each adjustment is accurate and goal-oriented, and finally the adjusted correction parameters are generated, which directly affect the next step of energy usage and carbon emission strategies, so that the entire system can flexibly respond to external and internal changes and achieve the goal of reducing carbon emissions.

[0056] Using the adjusted correction parameters, the formula is used:

[0057] Recalculate corrected carbon emissions , generate corrected carbon emission data, where is the actual carbon emissions, is the carbon emission deviation value, is the external parameter adjustment amount; is the actual carbon emissions, obtained from energy monitoring, which is 1,000 tons. is the carbon emission deviation value, which is 50 tons, indicating that the actual carbon emissions exceed expectations. is the external parameter adjustment, which is set according to policy or environmental factors and is 0.05. The calculation process is as follows: Calculate the square root of the deviation value:

[0058] Calculate the denominator of the formula:

[0059] Apply the formula to calculate : ton The results show that after considering actual carbon emissions and external adjustment factors, the revised carbon emissions are about Tons, the value reflects the carbon emission status after the actual implementation of corrective measures, and is a direct demonstration of the effectiveness of strategy adjustments.

[0060] See also Figure 7 , the adjustment steps of energy consumption ratio are: Based on the corrected carbon emission data, analyze the current carbon emission level of each energy source for different energy configurations and generate the initial carbon emission contribution record of energy sources; In the process of preliminary carbon emission contribution assessment, data collection is first carried out for different energy types, including direct emission data of multiple major energy categories such as coal, natural gas, and renewable energy. The raw emission data is obtained by using sensors and on-site sampling, and then the data is quality controlled to check whether there are missing or outliers, and necessary corrections are made. Data preprocessing includes standardization to eliminate the deviation between different data sources, and the application of moving average and filtering techniques to weaken the impact of accidental errors. In this stage, the data is classified, the data is aggregated according to different energy types, and an independent carbon emission database is established for each energy configuration. Then the carbon emission contribution of each energy source is statistically analyzed to provide a scientific basis for the subsequent optimization of energy consumption ratios, ensuring that when adjusting the energy structure, the specific contribution of each energy source to the total carbon emissions can be accurately grasped.

[0061] According to the energy initial carbon emission contribution record, the formula is used:

[0062] Calculate the The proportion of energy in the new configuration , evaluate the relationship between the carbon emissions of energy configuration and the energy consumption ratio, and output the evaluation record, where, It is Corrected carbon emission data corresponding to each energy source; The proportions of coal, natural gas and renewable energy collected are 0.4, 0.3 and 0.3 respectively, and the corresponding corrected carbon emission data are 500, 300 and 100 tons of carbon dioxide. The calculation process is as follows:

[0063]

[0064] The results show that based on the current energy configuration, the total expected carbon emissions are 320 tons of CO2.

[0065] According to the evaluation records, optimize the energy consumption ratio, minimize the overall carbon emissions, and output the optimized energy configuration; In the decision-making process of optimizing energy allocation, a series of complex analytical methods are used to determine the most appropriate energy combination, taking into account both environmental and economic factors. First, based on the carbon emission contribution of each energy type obtained previously, different energy consumption ratio schemes are simulated, and the environmental impact of each scheme is evaluated using the life cycle assessment (LCA) method. In addition, cost-benefit analysis is introduced to analyze the economic feasibility of each scheme taking into account different energy price fluctuations and future forecasts. Through simulation, it is possible to identify the energy configuration that reduces carbon emissions while being the most cost-effective. Furthermore, multi-criteria decision support tools such as the analytical hierarchy process (AHP) are used to comprehensively evaluate the performance of each scheme in multiple dimensions such as environmental sustainability, economic cost, and social acceptance. Finally, through iterative optimization and feedback from stakeholders, a set of energy allocation schemes that can meet long-term carbon emission reduction goals and maximize economic benefits are formulated to ensure that the implementation of the strategy is both scientific and in line with actual business needs.

[0066] See also Figure 8 ,The steps for obtaining the dynamically adjusted energy optimization management plan are: Apply optimized energy configuration, deploy distributed sensor networks to capture carbon emission data of different energy types in real time, verify and analyze the collected data, and generate a real-time monitoring carbon emission data overview; The implementation of the real-time monitoring system starts with the selection of appropriate sensors and communication technologies. It is key to select equipment that can operate stably under various climatic conditions. The equipment must have a high degree of accuracy and fast data transmission capabilities to ensure the real-time and accuracy of the monitoring data. During the installation process, the technical team deployed sensors at multiple energy consumption points, including coal-fired power plants, industrial production areas and large wind farms to ensure that raw data is obtained from the front line of energy consumption. The data is transmitted to the central processing system through encryption. The technicians monitor the incoming data stream in real time, identify and filter out abnormal data points through algorithms, and ensure the integrity and application value of the data set. In addition, the system can automatically generate carbon emission reports according to preset standards, providing decision-makers with timely environmental impact assessments. The smooth execution of this process is achieved through multiple system tests and debugging to ensure that all components can work seamlessly in actual operations, and finally form a comprehensive and efficient real-time carbon monitoring system.

[0067] Based on the real-time monitoring of carbon emission data overview, according to the real-time carbon emission data and the preset carbon emission reduction targets, adjust the control parameters of energy consumption, update the energy consumption control strategy in real time, and output the adjusted control parameters; In the process of adjusting the energy consumption control parameters, a dynamic adjustment system driven by real-time data is adopted. The system integrates a variety of analytical tools, including time series analysis, regression models, and machine learning algorithms to identify energy usage patterns and predict future trends. The decision to adjust the control parameters is based on real-time monitoring data, such as energy consumption, carbon emissions, and market energy prices. The data is processed through complex mathematical models to determine the optimal energy consumption ratio, and different algorithm settings are continuously adjusted and tested to optimize the accuracy and response speed of the algorithm. In addition, the adjustment process also takes into account external factors such as policy changes and market demand. The dynamic adjustment algorithm automatically updates the control parameters according to the changes in these factors, thereby minimizing carbon emissions while ensuring energy supply. The adjustment strategy that integrates highly technical and strategic considerations ensures the efficiency and environmental friendliness of the energy system.

[0068] Utilize the adjusted control parameters and real-time carbon emission data to continuously optimize carbon emissions. By simulating and analyzing the impact of control parameter configuration on carbon emissions, the parameters are corrected in combination with operational feedback to output a dynamically adjusted energy optimization management plan. In the process of continuous optimization of carbon emissions, historical data and real-time feedback are used to establish a complex optimization model that can simulate the specific impact of different control parameter configurations on carbon emissions. Through regular simulation runs, the environmental and economic benefits of various configurations can be foreseen. During this process, the technical team closely monitors the model output to ensure the accuracy and reliability of the simulation results. Based on the simulation results, the operation team will fine-tune the control parameters to find the best balance between energy consumption and carbon emissions. In this way, not only is the energy efficiency optimized, but carbon emissions are also effectively reduced, ultimately forming a dynamic energy management solution that is both environmentally friendly and economical.

[0069] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A goal-oriented energy optimization management system, characterized in that: The system comprises: The real-time data integration module collects energy usage data from environmental monitoring stations and carbon emissions data from industrial activities, formats the data, aggregates the data simultaneously, and builds a real-time environment and energy database; The energy configuration dynamic optimization module calculates the carbon emission efficiency of the current energy configuration based on the real-time environment and energy database, analyzes the carbon emission impact corresponding to the energy configuration according to the calculation results, optimizes the energy configuration matching target, and obtains the adjusted energy strategy; The carbon emission deviation correction module applies the adjusted energy strategy, collects the latest energy configuration data, calculates the deviation between the real-time carbon emission data and the predicted carbon emission data, performs deviation correction according to the deviation calculation result, adjusts the correction parameters according to the real-time feedback, and generates the corrected carbon emission data; The carbon emission optimal control module adopts the corrected carbon emission data to evaluate the actual contribution of different energy configurations to carbon emissions, adjust the energy consumption ratio, monitor the adjusted carbon emission changes in real time according to the adjustment results, dynamically optimize the control parameters, perform continuous optimization processing of carbon emissions, and output a dynamically adjusted energy optimization management plan.

2. The target-oriented energy optimization management system according to claim 1 is characterized in that: The steps for acquiring the real-time environment and energy database are as follows: Sensors configured at environmental monitoring stations collect data, record the energy consumption and carbon emissions of each site in real time, and generate raw energy and carbon emission data sets; Uploading the raw energy and carbon emission data set to a central processor, performing data cleaning, standardizing the data format, and obtaining processed energy use and carbon emission data; Based on the processed energy usage and carbon emission data, data aggregation is performed using the formula: ； Calculate total energy consumption , analyze the overall trend of energy and carbon emissions, and obtain a real-time environment and energy database, including: Represents the power consumption value of the site, Represents the fuel consumption value, Represents carbon dioxide emissions.

3. The target-oriented energy optimization management system according to claim 2 is characterized in that: The calculation steps of the carbon emission efficiency are as follows: Based on the real-time environment and energy database, extract data, including energy type, consumption and time information, to generate a preliminary data set; Processing the preliminary data set, removing outliers, unifying the data format, and classifying and arranging the energy types and consumption to obtain a cleaned data set; Using the cleaned data set, we analyze the contribution of different energy usage to carbon emissions using the formula, ； Calculating total carbon emissions , generating a carbon emission efficiency index, where represents the consumption of energy type i, Represents the carbon emission coefficient of energy type i.

4. The target-oriented energy optimization management system according to claim 3 is characterized in that: The steps for obtaining the adjusted energy strategy are: The carbon emission efficiency index is called, the carbon emission data of the energy type is associated with its usage, the carbon emission proportion of each energy is counted, the carbon emission proportion of the energy type is sorted, and a carbon emission energy classification result is generated; Based on the carbon emission energy classification results, the emission coefficient and usage data of alternative energy are called for high-emission energy types, using the formula: ； Calculate the potential carbon emissions reduction after adjusting energy configuration , generating energy configuration alternatives where, It is The amount of energy consumed, It is The original carbon emission coefficient of the energy source, It is Carbon emission coefficient of alternative energy sources; According to the energy configuration alternatives, the energy usage ratio is adjusted, and the adjusted energy strategy is formulated and output in combination with the local energy utilization rate and actual feasibility.

5. The target-oriented energy optimization management system according to claim 4 is characterized in that: The deviation calculation steps are: Based on the adjusted energy strategy, extract energy configuration data, record energy type, usage and configuration time, and generate the latest energy configuration information; Using the latest energy configuration information, according to the carbon emission factor corresponding to each energy type, calculate the real-time carbon emissions and generate real-time carbon emission data; The real-time carbon emission data is compared with the preset carbon emission forecast data, and the formula is used. ； Calculate the deviation value , generate carbon emission deviation data, where and Represent the actual and predicted carbon emissions respectively.

6. The target-oriented energy optimization management system according to claim 5 is characterized in that: The steps for obtaining the corrected carbon emission data are as follows: Using the carbon emission deviation data, evaluate the current energy configuration and carbon emission situation, adjust the size and direction of the deviation value, and generate a correction strategy; Implement the correction strategy, obtain new energy usage data and corresponding carbon emissions in real time, reveal the immediate effect of the correction strategy, adjust the correction parameters based on the feedback data, and generate adjusted correction parameters; Using the adjusted correction parameters, the formula is used: ； Recalculate corrected carbon emissions , generate corrected carbon emission data, where is the actual carbon emissions, is the carbon emission deviation value, is the external parameter adjustment amount.

7. The target-oriented energy optimization management system according to claim 6 is characterized in that: The energy consumption ratio is adjusted as follows: Based on the corrected carbon emission data, for different energy configurations, analyze the current carbon emission level of each energy source and generate an initial carbon emission contribution record of the energy source; Based on the initial carbon emission contribution record of the energy source, the formula is used: ； Calculate the The proportion of energy in the new configuration , evaluate the relationship between the carbon emissions of energy configuration and the energy consumption ratio, and output the evaluation record, where, It is Corrected carbon emission data corresponding to each energy source; Based on the assessment records, the energy consumption ratio is optimized, the overall carbon emissions are minimized, and the optimized energy configuration is output.

8. The target-oriented energy optimization management system according to claim 7, characterized in that: The steps for obtaining the dynamically adjusted energy optimization management solution are as follows: Applying the above-mentioned optimized energy configuration, by deploying a distributed sensor network to capture carbon emission data of different energy types in real time, verifying and analyzing the collected data, and generating a real-time monitoring carbon emission data overview; Based on the real-time monitoring carbon emission data overview, according to the real-time carbon emission data and the preset carbon emission reduction target, the control parameters of energy consumption are adjusted, the energy consumption control strategy is updated in real time, and the adjusted control parameters are output; The adjusted control parameters and real-time carbon emission data are used to perform continuous carbon emission optimization processing. The influence of control parameter configuration on carbon emissions is simulated and analyzed, and the parameters are corrected in combination with operational feedback to output a dynamically adjusted energy optimization management plan.

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