Carbon emission dynamic optimization management system and method based on big data analysis

Abstract

The invention relates to the field of carbon emission management, and particularly discloses a carbon emission dynamic optimization management system and method based on big data analysis, and the system comprises a region division module, a data collection module, a data analysis module, a data processing module, a data early warning module, a data accounting module, a data storage module, and a data evaluation module. The invention further discloses a carbon emission dynamic optimization management method based on big data analysis. According to the method disclosed by the invention, the abnormal emission source is accurately positioned through sub-region division and real-time monitoring; intelligent early warning is carried out based on the abnormal level, a report and an evaluation coefficient are automatically generated, the carbon emission management efficiency and the scientificity of an emission reduction decision are remarkably improved, and accurate management and control and dynamic optimization of carbon emission are achieved. According to the invention, through modular collaborative design, the monitoring precision is improved, the real-time monitoring capability is enhanced, and closed-loop carbon accounting and management are realized.

Description

Technical Field

[0001] This invention relates to the field of carbon emission management technology, and more specifically, to a dynamic optimization management system and method for carbon emissions based on big data analysis. Background Technology

[0002] Carbon emissions primarily refer to the release of carbon dioxide and other greenhouse gases into the atmosphere by human activities. These gases accumulate in the atmosphere, creating the "greenhouse effect," a core driver of environmental crises such as global warming, frequent extreme weather events, and rising sea levels. Therefore, effectively managing carbon emissions has become an urgent priority for global sustainable development. "Carbon emission management" has emerged as a response to this need; it is a comprehensive system of strategies, technologies, and actions, with the core objective of monitoring, accounting for, reducing, and ultimately neutralizing anthropogenic greenhouse gas emissions.

[0003] Currently, most enterprises have introduced carbon emission management systems for carbon emission management. These systems primarily collect carbon emission data. However, current carbon emission management systems have some technical shortcomings and deficiencies in their use. The main technical shortcomings are as follows: 1. Coarse monitoring granularity: Traditional systems use the enterprise as a whole as the accounting unit, which cannot locate specific emission equipment, resulting in a lag in the identification of carbon leakage points.

[0004] 2. Lack of dynamic response capability: The existing solution relies on monthly manual data reporting, and it takes an average of 6.5 hours to issue an early warning for abnormal emissions, thus missing the best control window.

[0005] 3. Disconnect between accounting and assessment: The accounting report is separated from real-time monitoring data, resulting in the lack of credibility of carbon emission data. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, this invention provides a dynamic optimization management system and method for carbon emissions based on big data analysis, which can utilize big data analysis methods to achieve dynamic management of carbon emission data.

[0007] To achieve the above objectives, this invention provides a carbon emission dynamic optimization management system based on big data analysis, comprising: The area division module is used to divide the carbon emission target area into monitoring sub-areas according to the equipment function, and to number each monitoring sub-area sequentially as 1,2,...,i,...,n; The data acquisition module is used to collect carbon emission data of each monitoring sub-region within the carbon emission target area in real time, and obtain the carbon emission data of each monitoring sub-region. The data analysis module is used to analyze the carbon emission data status of each monitoring sub-region within the carbon emission target area. The data processing module is used to filter carbon emission data of each monitoring sub-region in an abnormal state of the carbon emission target area and to count the numbers of each monitoring sub-region in an abnormal state. The data early warning module is used to display the numbers of each monitoring sub-region in the carbon emission target area that are in an abnormal state. Based on the abnormality level of carbon emission data in each monitoring sub-region of the carbon emission target area, after comparative analysis, corresponding handling measures are taken. The data calculation module is used to automatically generate carbon emission reports by inputting carbon emission data from each monitoring sub-region within the carbon emission target area; The data storage module is used to store carbon emission data and carbon emission reports for each monitoring sub-region within the carbon emission target area. The data assessment module is used to calculate the carbon emission assessment coefficient for each monitoring sub-region within the carbon emission target area, compare and analyze the carbon emission assessment coefficient with the standard carbon emission assessment coefficient, and then take corresponding processing measures.

[0008] Preferably, when the area division module divides the monitoring sub-areas according to the equipment function, it specifically includes: dividing the equipment cluster with continuous production process into the same sub-area, aggregating and grouping equipment with the same energy consumption type, and configuring an independent Internet of Things identification code for each monitoring sub-area to associate with carbon emission data sources.

[0009] Preferably, the carbon emission data includes carbon emission types, carbon emission amounts, total energy consumption, carbon emission reductions, and carbon intensity per unit of energy consumption.

[0010] Preferably, the method by which the data analysis module determines the data status is as follows: When the carbon emissions of a monitored sub-region exceed the historical average for multiple consecutive time periods, or the carbon intensity per unit of energy consumption exceeds the industry benchmark, the data is marked as abnormal.

[0011] Preferably, the data early warning module determines the anomaly level of carbon emission data based on predefined anomaly level judgment criteria and executes corresponding processing measures, including: When the carbon emission data anomaly level is Level 1, an automatic parameter optimization command is sent to the equipment control system, and the adjusted emission data is verified. When the carbon emission data anomaly level is level 2, an audible and visual alarm is triggered, a parameter adjustment operation interface is pushed to the operation and maintenance terminal, manual adjustment instructions are received and sent to the equipment; When the carbon emission data anomaly level is level three, a pre-instruction for load reduction / shutdown is immediately sent to the equipment control system. After sending a safety confirmation request to the operation and maintenance terminal, the pre-instruction is executed upon receiving a manual confirmation signal.

[0012] Preferably, the calculation method of the data calculation module is as follows: Input carbon emission data into the data accounting module to calculate the total direct emissions for the carbon emission target area. The formula is as follows: ,in This represents the total direct emissions of the carbon emission target area, of which and These represent the activity data and emission factors of the i-th monitoring sub-region within the carbon emission target area, respectively.

[0013] Preferably, the carbon data report includes actual carbon intensity per unit of energy consumption, target carbon intensity per unit of energy consumption, emission reduction compliance rate, and energy efficiency.

[0014] Preferably, the step of the data assessment module calculating the carbon emission assessment coefficient includes: Extract historical data of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session, and calculate the carbon intensity deviation rate. Combined with emission reduction compliance rate and energy efficiency Generate evaluation coefficients ,in This represents the actual carbon intensity per unit energy consumption of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session. This represents the target carbon intensity per unit of energy consumption within the carbon emission target area during the t-th monitoring. and Let represent the emission reduction compliance rate and energy efficiency of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session, respectively. , and These are the weighting factors corresponding to the carbon intensity deviation rate, emission reduction compliance rate, and energy efficiency, respectively. .

[0015] To achieve the above objectives, the present invention also provides a method for dynamic optimization management of carbon emissions based on big data analysis, comprising: S1: Target Area Division and Numbering: The carbon emission target area is divided into monitoring sub-areas according to the equipment function, and each monitoring sub-area is sequentially numbered as 1, 2, ..., i, ..., n; S2: Real-time acquisition of multi-source data: Real-time acquisition of carbon emission data for each monitoring sub-region; S3: Dynamic Analysis of Data Status: Based on the carbon emission data of each monitoring sub-region, analyze its carbon emission data status and determine whether it is normal or abnormal; S4: Data anomaly classification and processing: Filter the carbon emission data of each monitoring sub-region in an abnormal state, count their numbers and send them to the data early warning module; at the same time, send the carbon emission data of each monitoring sub-region in a normal state to the data accounting module; S5: Abnormal Monitoring Early Warning Tiered Response Execution: Displays the monitoring sub-area number that is in an abnormal state, matches the corresponding handling measures according to the abnormal level of carbon emission data, and executes them; S6: Automated report generation: Using data accounting methods, carbon emission reports are generated by inputting monitoring sub-area data that is in a normal state; S7: Full-cycle data storage: Stores real-time carbon emission data and carbon emission reports for each monitoring sub-region; S8: Periodic verification and process diversion of assessment coefficients: Extract historical carbon emission data, calculate the carbon emission assessment coefficients for each monitoring sub-region, and then compare the carbon emission assessment coefficients with the standard carbon emission assessment coefficients. If the carbon emission assessment coefficients are less than or equal to the standard carbon emission assessment coefficients, return to the data acquisition module to continue carbon emission data acquisition. If the carbon emission assessment coefficients are greater than the standard carbon emission assessment coefficients, send the data to the data early warning module to take optimization measures.

[0016] The technical effects and advantages of this invention are as follows: 1. This invention establishes independent monitoring units by finely dividing and numbering the areas according to the functions of the equipment through a regional division module; it acquires exclusive data for each sub-region in real time through a data acquisition module, and analyzes the status through a data analysis module; it dynamically calculates the carbon emission assessment coefficient for each monitoring sub-region using a data evaluation module; and it accurately locates abnormal emission sources, greatly reducing monitoring errors and improving monitoring accuracy.

[0017] 2. This invention relies on a data acquisition module to acquire data streams from monitored sub-areas, and a data analysis module to diagnose the status in real time; the data processing module automatically filters abnormal sub-area numbers, and the data early warning module displays the abnormal numbers in real time and matches corresponding handling measures according to the level of data abnormality; thus realizing dynamic monitoring throughout the entire process from data acquisition to early warning response, greatly improving accident response efficiency and strengthening real-time monitoring capabilities.

[0018] 3. This invention is based on the input of the data acquisition module, the automatic generation of standardized carbon emission reports by the data accounting module, the archiving of all historical data and reports by the data storage module, supporting traceability and auditing, the quantification of carbon emission assessment coefficients for sub-regions by the data evaluation module, and the triggering of processing measures by the data early warning module; thus, a closed-loop process is constructed to improve annual emission reduction efficiency and realize closed-loop carbon accounting and management. Attached Figure Description

[0019] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0020] Figure 1 This is a flowchart of the module implementation steps of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 As shown, the present invention provides a dynamic optimization management system for carbon emissions based on big data analysis, including a regional division module, a data acquisition module, a data analysis module, a data processing module, a data early warning module, a data accounting center, a data storage module, and a data evaluation module.

[0023] The region division module is connected to the data acquisition module, the data analysis module is connected to both the data acquisition module and the data processing module, the data processing module is connected to both the data early warning module and the data accounting module, and the data accounting module, data storage module, and data evaluation module are connected.

[0024] It should be noted that the connection methods between the modules include, but are not limited to, physical connection, electrical connection, wired connection and wireless connection.

[0025] The region division module divides the carbon emission target area into monitoring sub-regions according to the equipment functions, and numbers each monitoring sub-region sequentially as 1, 2, ..., i, ..., n.

[0026] When dividing monitoring sub-regions according to equipment function in the region division module, the specific steps include: dividing equipment clusters with continuous production processes into the same sub-region, aggregating and grouping equipment with the same energy consumption type, and configuring an independent Internet of Things (IoT) identifier for each monitoring sub-region to associate with carbon emission data sources.

[0027] The area division module divides target areas according to equipment functions and sequentially numbers monitoring sub-areas, thereby achieving precise location of emission sources and clear binding with equipment entities, which greatly improves regulatory efficiency, lays a data foundation for refined carbon management, and supports differentiated emission reduction decisions.

[0028] The data acquisition module collects carbon emission data from each monitoring sub-region within the carbon emission target area in real time, thus obtaining the carbon emission data for each monitoring sub-region.

[0029] The carbon emission data includes the types of carbon emissions, the amount of carbon emissions, the total energy consumption, the amount of carbon emission reduction, and the carbon intensity per unit of energy consumption.

[0030] The data acquisition module includes sensors, a data acquisition unit, and a communication device. The sensors are used to measure the gas concentration, flow rate, energy consumption, and environmental parameters of carbon emissions. The sensors include a concentration sensor, a flow sensor, an energy consumption monitor, and an environmental parameter sensor. The data acquisition unit uses the sensors to collect carbon emission data, and the communication device sends the collected carbon emission data to the data analysis module.

[0031] The specific process for the data acquisition module to collect carbon emission data is as follows: S1: Real-time monitoring of sensor layer: Concentration sensor measures the concentration of emitted gas in each monitoring sub-area in real time, flow sensor collects the flow rate of emitted gas synchronously, energy consumption monitor records the energy consumption data of equipment, and environmental parameter sensor acquires environmental data such as temperature, humidity and air pressure. S2: Data Acquisition Unit Integration and Processing: The data acquisition unit polls and reads all sensor data at a preset frequency, and then performs data preprocessing: 1. Unify unit conversion, 2. Add data tags, 3. Encapsulate into structured data packets; S3: Secure transmission of communication devices: An encrypted communication tunnel is established through the industrial IoT gateway, and a breakpoint resume mechanism is adopted: When the network is interrupted, the data is cached locally and automatically retransmitted after the network is restored. The data packets are sent to the data analysis module in real time using the MQTT protocol. S4: Data analysis module receives: The data analysis module parses the data packet, classifies it according to the sub-region number and stores it in the cache queue, triggering the subsequent data status analysis process.

[0032] It should be noted that the unit for emission gas concentration is ppm, and the unit for emission gas flow rate is m³ / s. 3 / h, the equipment energy consumption data includes power consumption and fuel consumption, in units of kW·h and kg respectively. The preset frequency for the data acquisition unit to read sensor data is per minute or per hour. The data tag includes the number of each monitoring sub-area and the precise timestamp.

[0033] The carbon emissions include carbon dioxide, methane, nitrous oxide, hydrofluorocarbons, perfluorinated carbon, sulfur hexafluoride, and nitrogen trifluoride. The carbon emission amounts include carbon emissions per minute, per hour, per day, per month, per year, and total carbon emissions. The comprehensive energy consumption includes comprehensive energy consumption per minute, per hour, per day, per month, per year, and total comprehensive energy consumption. The carbon emission reductions include actual carbon emission reductions, planned carbon emission reductions, and baseline carbon emission reductions. The formula for calculating the carbon intensity per unit of energy consumption is: Carbon intensity per unit of energy consumption = Total carbon emissions / Total comprehensive energy consumption.

[0034] The data acquisition module uses multiple types of sensors to acquire carbon emission concentrations, flow rates, energy consumption, and environmental parameters in each monitoring sub-area in real time. After standardization processing by the data acquisition unit and the addition of spatiotemporal tags, the data is reliably uploaded in real time through an encrypted communication channel. This achieves accurate acquisition of all elements of emission data, dynamic traceability, and secure transmission, providing highly complete data support for real-time monitoring and accurate carbon accounting.

[0035] The data analysis module is used to analyze the carbon emission data status of each monitoring sub-region within the carbon emission target area.

[0036] In a preferred embodiment of the present invention, the method by which the data analysis module determines the data status is as follows: When the carbon emissions of a monitored sub-region exceed the historical average for multiple consecutive time periods or the carbon intensity per unit of energy consumption exceeds the industry benchmark, it is marked as data anomaly.

[0037] It should be noted that the specific method used by the data analysis module to determine the data status is as follows: If the carbon emissions of a sub-region exceed the historical average by ±2 standard deviations for 3 consecutive hours, or if the carbon intensity per unit of energy consumption exceeds the industry benchmark by 20%, it is marked as data anomaly.

[0038] The above numerical settings are based on statistical principles and industry regulatory practices: ±2 standard deviations cover 95.4% of normally distributed data, while 3 standard deviations cover 99.7% of normally distributed data. Using 2 standard deviations balances sensitivity and false alarm rate, ensuring that outliers are statistically significant. For three consecutive hours, transient interference, such as sensor jitter, must be excluded, and alarms are only triggered when there is a continuous anomaly. The 20% threshold is based on the upper limit of energy efficiency fluctuations in industrial equipment. The industry typically allows fluctuations of ±15% to 20%. Exceeding 20% ​​indicates energy efficiency degradation. The industry benchmark value serves as an objective reference to avoid potential systematic biases in the company's own historical data.

[0039] The data analysis module monitors carbon emissions in each monitoring sub-area in real time. When emissions exceed a preset safety threshold for multiple consecutive time periods, it automatically marks the abnormal state, enabling rapid identification and accurate location of emission risks, and providing a basis for timely intervention and refined management.

[0040] The data processing module is used to filter carbon emission data of each monitoring sub-region in an abnormal state of the carbon emission target area, count the numbers of each monitoring sub-region in an abnormal state of the carbon emission target area, and send them to the data early warning module. At the same time, it sends the carbon emission data of each monitoring sub-region in a normal state of the carbon emission target area to the data accounting module.

[0041] The data processing module filters carbon emission data from abnormal monitoring sub-regions, assigns them numbers, and sends them to the data early warning module. At the same time, it synchronizes data from normal monitoring sub-regions to the data accounting module, enabling precise identification of abnormal sources and efficient distribution of all data, providing categorized data support for risk early warning and carbon accounting.

[0042] The data early warning module is used to display the numbers of each monitoring sub-region in the carbon emission target area that are in an abnormal state. Based on the abnormality level of carbon emission data in each monitoring sub-region of the carbon emission target area, after comparative analysis, corresponding handling measures are taken.

[0043] The device includes a device control system, the data early warning module includes an operation and maintenance terminal, the carbon emission dynamic optimization management system based on big data analysis is electrically connected to the device control system and the operation and maintenance terminal, and the data early warning module can send instructions to the device control system and the operation and maintenance terminal.

[0044] In a preferred embodiment of the present invention, the data early warning module determines the anomaly level of carbon emission data based on a predefined anomaly level judgment standard and executes corresponding processing measures, including: When the carbon emission data anomaly level is Level 1, an automatic parameter optimization command is sent to the equipment control system, and the adjusted emission data is verified. When the carbon emission data anomaly level is level 2, an audible and visual alarm is triggered, a parameter adjustment operation interface is pushed to the operation and maintenance terminal, manual adjustment instructions are received and sent to the equipment; When the carbon emission data anomaly level is level three, a pre-instruction for load reduction / shutdown is immediately sent to the equipment control system. After sending a safety confirmation request to the operation and maintenance terminal, the pre-instruction is executed upon receiving a manual confirmation signal.

[0045] It should be noted that the carbon emission data refers to hourly carbon emissions, hourly comprehensive energy consumption, and hourly unit energy intensity. The specific criteria for determining the abnormal level of the carbon emission data and the corresponding measures are as follows: If the hourly unit energy consumption intensity is less than or equal to 105% of the preset safety threshold for unit energy consumption intensity and the duration is greater than or equal to 3 hours, it is judged as a level one anomaly, and an automatic parameter optimization command is sent to the equipment control system, and the emission data after adjustment is verified. If the hourly unit energy consumption intensity is greater than 105% but less than 115% of the preset safety threshold for unit energy consumption intensity and the duration is greater than or equal to 2 hours, it is judged as a level 2 anomaly, triggering an audible and visual alarm, pushing the parameter adjustment operation interface to the operation and maintenance terminal, receiving manual adjustment instructions and sending them to the equipment. If the unit energy consumption intensity per hour is greater than or equal to the preset safety threshold of 115% of the unit energy consumption intensity, it is judged as a level three anomaly. The pre-instruction of load reduction / shutdown is immediately sent to the equipment control system. After sending a safety confirmation request to the operation and maintenance terminal, the pre-instruction is executed after receiving the manual confirmation signal.

[0046] The data early warning module employs a three-tiered carbon emission anomaly detection mechanism. If the unit energy consumption intensity is less than or equal to a threshold of 105% and remains below that threshold for 3 consecutive hours, the module automatically optimizes equipment parameters and performs closed-loop verification, reducing reliance on manual intervention. If the unit energy consumption intensity is greater than 105% but less than 115% and remains below that threshold for 2 consecutive hours, an audible and visual alarm is triggered, and a notification is sent to the user interface for manual adjustment, ensuring production continuity. If the unit energy consumption intensity is greater than or equal to a threshold of 115%, a shutdown pre-command is immediately sent and executed after safety confirmation, implementing a risk mitigation mechanism. This data early warning module enables early identification, early intervention, and early handling of carbon emission anomalies, effectively reducing unplanned equipment downtime losses, improving energy efficiency, and ensuring production safety.

[0047] The data calculation module is used to automatically generate carbon emission reports by inputting carbon emission data from each monitoring sub-region within the carbon emission target area.

[0048] In a preferred embodiment of the present invention, the calculation method of the data calculation module is as follows: Input carbon emission data into the data accounting module to calculate the total direct emissions for the carbon emission target area. The formula is as follows: ,in This represents the total direct emissions of the carbon emission target area, of which and These represent the activity data and emission factors of the i-th monitoring sub-region within the carbon emission target area, respectively.

[0049] The carbon data report includes actual carbon intensity per unit of energy consumption, target carbon intensity per unit of energy consumption, emission reduction compliance rate, and energy efficiency. The activity data... Including but not limited to energy activity data and process activity data, the emission factors need to be selected according to the emission source type: the energy emission factors adopt the default value of the "Guidelines for the Compilation of Provincial Greenhouse Gas Inventories" and the process emission factors are based on measured data or industry standards.

[0050] The data accounting module collects the comprehensive energy consumption and carbon emissions per unit of energy in each monitoring sub-region in real time, and generates a comprehensive report with one click, including the actual carbon intensity per unit of energy consumption, the target carbon intensity per unit of energy consumption, the emission reduction compliance rate, and the energy efficiency, so as to realize the panoramic visualization of regional carbon emissions and support for precise carbon reduction decision-making.

[0051] It should be noted that energy activity data includes electricity, gas, and diesel consumption, while process activity data includes feedstock decomposition and chemical reactant mass, and emission reduction compliance rate. =(1−(Planned carbon emission reductions - Actual carbon emission reductions)) The baseline carbon emissions are calculated as follows: (Baseline carbon emissions) × 100%. Actual carbon emissions are the total carbon emissions measured within the statistical period, which can be 1 minute, 1 hour, 1 day, 1 month, or 1 year. The baseline carbon emissions are historical emissions before the emission reduction plan begins, while the planned carbon emission reductions are the emissions to be reduced within a predetermined period. Energy efficiency is also considered. =Comprehensive energy consumption Output or =Comprehensive energy consumption Output value, output volume converted to tons (t), output value converted to yuan, carbon emissions and comprehensive energy consumption uniformly converted to standard coal (tce) or joules (GJ), using the conversion factor of the "China Energy Statistical Yearbook".

[0052] The data storage module is used to store carbon emission data and carbon emission reports for each monitoring sub-region within the carbon emission target area.

[0053] In a preferred embodiment of the present invention, the data storage module stores real-time carbon emission data and carbon emission reports for each monitoring sub-region.

[0054] It should be noted that the data storage module stores carbon emission data and reports according to the monitoring sub-regions, and historical carbon emission data and reports can be traced and compared at any time.

[0055] The data storage module stores real-time / historical carbon emission data and reports for each monitoring sub-region in a partitioned and categorized manner, enabling full life-cycle traceability of carbon emissions, multi-dimensional comparative analysis, and compliance audit support, thus providing a data foundation for optimizing emission reduction strategies.

[0056] The data evaluation module is used to calculate the carbon emission evaluation coefficient for each monitoring sub-region within the carbon emission target area, compare and analyze the carbon emission evaluation coefficient with the standard carbon emission evaluation coefficient, and then take corresponding processing measures.

[0057] In a preferred embodiment of the present invention, the step of the data assessment module calculating the carbon emission assessment coefficient includes: Extract historical data of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session, and calculate the carbon intensity deviation rate. Combined with emission reduction compliance rate and energy efficiency Generate evaluation coefficients ,in This represents the actual carbon intensity per unit energy consumption of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session. This represents the target carbon intensity per unit of energy consumption within the carbon emission target area during the t-th monitoring. and Let represent the emission reduction compliance rate and energy efficiency of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session, respectively. , and These are the weighting factors corresponding to the carbon intensity deviation rate, emission reduction compliance rate, and energy efficiency, respectively. .

[0058] It should be noted that, The values ​​can be 0.6, 0.3, and 0.1.

[0059] The above numerical settings are based on the following technical logic and actual engineering needs: Carbon intensity per unit of energy consumption is the most core indicator for carbon emission control, directly reflecting energy cleanliness and equipment status. Industrial data has shown that carbon intensity deviation contributes more than 60% to the total emission exceedance. The emission reduction compliance rate reflects the effectiveness of short-term carbon reduction actions in a sub-region and is a key compliance indicator for assessing management efficiency. Its weight is lower than carbon intensity but higher than energy efficiency. Improved energy efficiency can indirectly reduce carbon emissions, but it needs to be fully converted into emission reduction effects through energy structure optimization. It is mainly used to identify contradictory scenarios of "high energy efficiency - high emissions" and assist in optimizing energy substitution strategies.

[0060] Please see Figure 1 As shown, this invention provides a method for dynamic optimization management of carbon emissions based on big data analysis, including: S1: Target area division and numbering; S2: Real-time acquisition of multi-source data; S3: Dynamic analysis of data status; S4: Data anomaly classification and handling; S5: Implementation of graded response to abnormal monitoring and early warning; S6: Automated report generation; S7: Full-cycle data storage; S8: Periodic verification of evaluation coefficients and process diversion.

[0061] Steps S1-S8 are executed according to a specific procedure. For details, please refer to the following documentation. Figure 1 .

[0062] S1 specifically includes the following steps: the area division module divides the carbon emission target area into monitoring sub-areas according to the equipment function, and sequentially numbers each monitoring sub-area as 1, 2, ..., i, ..., n.

[0063] The region division module divides a cluster of equipment with a continuous production process into the same sub-region. Equipment with the same energy consumption type is aggregated and grouped. Each monitoring sub-region is configured with an independent Internet of Things (IoT) identifier code to associate with carbon emission data sources.

[0064] The S2 specifically includes the following steps: using the data acquisition module to collect carbon emission data of each monitoring sub-region in real time, and obtaining carbon emission data of each monitoring sub-region within the carbon emission target area.

[0065] S3 specifically includes the following steps: based on the carbon emission data of each monitoring sub-region, the data analysis module analyzes the status of its carbon emission data and determines whether it is normal or abnormal.

[0066] S4 specifically includes the following steps: the data processing module filters the carbon emission data of each monitoring sub-region in an abnormal state, counts their numbers and sends them to the data early warning module; at the same time, the carbon emission data of each monitoring sub-region in a normal state is sent to the data accounting module.

[0067] S5 specifically includes the following steps: the data early warning module displays the monitoring sub-area number that is in an abnormal state, matches the corresponding handling measures according to the abnormal level of carbon emission data, and executes them.

[0068] The data early warning module takes the following measures based on the level of carbon emission data anomaly: If the hourly unit energy consumption intensity is less than or equal to 105% of the preset safety threshold for unit energy consumption intensity and the duration is greater than or equal to 3 hours, it is judged as a level one anomaly, and an automatic parameter optimization command is sent to the equipment control system, and the emission data after adjustment is verified. If the hourly unit energy consumption intensity is greater than 105% but less than 115% of the preset safety threshold for unit energy consumption intensity and the duration is greater than or equal to 2 hours, it is judged as a level 2 anomaly, triggering an audible and visual alarm, pushing the parameter adjustment operation interface to the operation and maintenance terminal, receiving manual adjustment instructions and sending them to the equipment. If the unit energy consumption intensity per hour is greater than or equal to the preset safety threshold of 115% of the unit energy consumption intensity, it is judged as a level three anomaly. A pre-instruction for load reduction / shutdown is sent to the equipment control system. After sending a safety confirmation request to the operation and maintenance terminal, the pre-instruction is executed after receiving a manual confirmation signal.

[0069] S6 specifically includes the following steps: The data accounting module uses a data accounting method to input monitoring sub-area data in a normal state to generate a carbon emission report.

[0070] The data calculation method is as follows: Input carbon emission data into the data accounting module to calculate the total direct emissions for the carbon emission target area. The formula is as follows: ,in This represents the total direct emissions of the carbon emission target area, of which and These represent the activity data and emission factors of the i-th monitoring sub-region within the carbon emission target area, respectively.

[0071] S7 specifically includes the following steps: The data storage module stores real-time carbon emission data and carbon emission reports for each monitoring sub-area, and can trace and compare historical carbon emission data and reports in real time.

[0072] S8 specifically includes the following steps: The data evaluation module extracts historical carbon emission data, calculates the carbon emission evaluation coefficient for each monitoring sub-region, and then compares the carbon emission evaluation coefficient with the standard carbon emission evaluation coefficient. If the carbon emission evaluation coefficient is less than or equal to the standard carbon emission evaluation coefficient, the data acquisition module continues to collect carbon emission data. If the carbon emission evaluation coefficient is greater than the standard carbon emission evaluation coefficient, the data is sent to the data early warning module to take optimization measures.

[0073] Returning to the data acquisition module to continue carbon emission data acquisition means that the data acquisition module maintains the normal acquisition frequency to collect carbon emission data.

[0074] The optimization measures taken by the data early warning module include the following steps: (1): The data early warning module issues a data early warning, sends an automatic parameter optimization instruction to the equipment control system, increases the acquisition frequency to the minute level, and continues to execute the above process steps; (2): Recalculate the carbon emission assessment coefficient and compare it with the standard carbon emission assessment coefficient; (3): When the carbon emission assessment coefficient is less than or equal to the standard carbon emission assessment coefficient, the data warning status is lifted, and the data acquisition module returns to maintain the normal acquisition frequency to collect carbon emission data.

[0075] The carbon emission assessment coefficient is calculated as follows: Extract historical data of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session, and calculate the carbon intensity deviation rate. Combined with emission reduction compliance rate and energy efficiency Generate evaluation coefficients ,in This represents the actual carbon intensity per unit energy consumption of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session. This represents the target carbon intensity per unit of energy consumption within the carbon emission target area during the t-th monitoring. and Let represent the emission reduction compliance rate and energy efficiency of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session, respectively. , and These represent the weighting factors corresponding to the carbon intensity deviation rate, emission reduction compliance rate, and energy efficiency, respectively. .

[0076] It should be noted that, The values ​​can be 0.6, 0.3, and 0.1.

[0077] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A carbon emission dynamic optimization management system based on big data analysis, characterized in that, include: The area division module is used to divide the carbon emission target area into monitoring sub-areas according to the equipment function, and to number each monitoring sub-area sequentially as 1,2,...,i,...,n; The data acquisition module is used to collect carbon emission data of each monitoring sub-region within the carbon emission target area in real time, and obtain the carbon emission data of each monitoring sub-region. The data analysis module is used to analyze the carbon emission data status of each monitoring sub-region within the carbon emission target area. The data processing module is used to filter carbon emission data of each monitoring sub-region in an abnormal state of the carbon emission target area and to count the numbers of each monitoring sub-region in an abnormal state. The data early warning module is used to display the numbers of each monitoring sub-region in the carbon emission target area that are in an abnormal state. Based on the abnormality level of carbon emission data in each monitoring sub-region of the carbon emission target area, after comparative analysis, corresponding handling measures are taken. The data calculation module is used to automatically generate carbon emission reports by inputting carbon emission data from each monitoring sub-region within the carbon emission target area; The data storage module is used to store carbon emission data and carbon emission reports for each monitoring sub-region within the carbon emission target area. The data assessment module is used to calculate the carbon emission assessment coefficient for each monitoring sub-region within the carbon emission target area, compare and analyze the carbon emission assessment coefficient with the standard carbon emission assessment coefficient, and then take corresponding processing measures.

2. The carbon emission dynamic optimization management system based on big data analysis according to claim 1, characterized in that, When dividing monitoring sub-regions according to equipment function in the region division module, the specific steps include: dividing equipment clusters with continuous production processes into the same sub-region, aggregating and grouping equipment with the same energy consumption type, and configuring an independent Internet of Things (IoT) identifier for each monitoring sub-region to associate with carbon emission data sources.

3. The carbon emission dynamic optimization management system based on big data analysis according to claim 1, characterized in that, The carbon emission data includes the types of carbon emissions, the amount of carbon emissions, the total energy consumption, the amount of carbon emission reduction, and the carbon intensity per unit of energy consumption.

4. The carbon emission dynamic optimization management system based on big data analysis according to claim 1, characterized in that, The method by which the data analysis module determines the data status is as follows: When the carbon emissions of a monitored sub-region exceed the historical average for multiple consecutive time periods or the carbon intensity per unit of energy consumption exceeds the industry benchmark, it is marked as data anomaly.

5. The carbon emission dynamic optimization management system based on big data analysis according to claim 1, characterized in that, The data early warning module, based on predefined anomaly level judgment criteria, determines the anomaly level of carbon emission data and executes corresponding processing measures, including: When the carbon emission data anomaly level is Level 1, an automatic parameter optimization command is sent to the equipment control system, and the adjusted emission data is verified. When the carbon emission data anomaly level is level 2, an audible and visual alarm is triggered, a parameter adjustment operation interface is pushed to the operation and maintenance terminal, manual adjustment instructions are received and sent to the equipment; When the carbon emission data anomaly level is level three, a pre-instruction for load reduction / shutdown is immediately sent to the equipment control system. After sending a safety confirmation request to the operation and maintenance terminal, the pre-instruction is executed upon receiving a manual confirmation signal.

6. The carbon emission dynamic optimization management system based on big data analysis according to claim 1, characterized in that, The calculation method of the data calculation module is as follows: Input carbon emission data into the data accounting module to calculate the total direct emissions for the carbon emission target area. The formula is as follows: ,in This represents the total direct emissions of the carbon emission target area, of which and These represent the activity data and emission factors of the i-th monitoring sub-region within the carbon emission target area, respectively.

7. The carbon emission dynamic optimization management system based on big data analysis according to claim 1, characterized in that, The carbon data report includes actual carbon intensity per unit of energy consumption, target carbon intensity per unit of energy consumption, emission reduction compliance rate, and energy efficiency.

8. The carbon emission dynamic optimization management system based on big data analysis according to claim 7, characterized in that, The steps for calculating the carbon emission assessment coefficient by the data assessment module include: Extract historical data of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session, and calculate the carbon intensity deviation rate. Combined with emission reduction compliance rate and energy efficiency Generate evaluation coefficients ,in This represents the actual carbon intensity per unit energy consumption of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session. This represents the target carbon intensity per unit of energy consumption within the carbon emission target area during the t-th monitoring. and Let represent the emission reduction compliance rate and energy efficiency of the i-th monitoring sub-region within the carbon emission target area during the t-th monitoring session, respectively. , and These are the weighting factors corresponding to the carbon intensity deviation rate, emission reduction compliance rate, and energy efficiency, respectively. .

9. The carbon emission dynamic optimization management system method based on big data analysis according to any one of claims 1-8, characterized in that, include: S1: Target Area Division and Numbering: The carbon emission target area is divided into monitoring sub-areas according to the equipment function, and each monitoring sub-area is sequentially numbered as 1, 2, ..., i, ..., n; S2: Real-time acquisition of multi-source data: Real-time acquisition of carbon emission data for each monitoring sub-region; S3: Dynamic Analysis of Data Status: Based on the carbon emission data of each monitoring sub-region, analyze its carbon emission data status and determine whether it is normal or abnormal; S4: Data anomaly classification and processing: Filter the carbon emission data of each monitoring sub-region in an abnormal state, count their numbers and send them to the data early warning module; at the same time, send the carbon emission data of each monitoring sub-region in a normal state to the data accounting module; S5: Abnormal Monitoring Early Warning Tiered Response Execution: Displays the monitoring sub-area number that is in an abnormal state, matches the corresponding handling measures according to the abnormal level of carbon emission data, and executes them; S6: Automated report generation: Using data accounting methods, carbon emission reports are generated by inputting monitoring sub-area data that is in a normal state; S7: Full-cycle data storage: Stores real-time carbon emission data and carbon emission reports for each monitoring sub-region; S8: Periodic verification and process diversion of assessment coefficients: Extract historical carbon emission data, calculate the carbon emission assessment coefficients for each monitoring sub-region, and then compare the carbon emission assessment coefficients with the standard carbon emission assessment coefficients. If the carbon emission assessment coefficients are less than or equal to the standard carbon emission assessment coefficients, return to the data acquisition module to continue carbon emission data acquisition. If the carbon emission assessment coefficients are greater than the standard carbon emission assessment coefficients, send the data to the data early warning module to take optimization measures.

Images