Full-life-cycle carbon footprint accounting and optimizing method for zero-carbon park equipment
By constructing a dynamic carbon footprint accounting model, integrating multi-source information and conducting stability analysis, the problems of model deviation and data silos in park carbon management were solved, achieving accurate carbon emission accounting and optimization throughout the entire life cycle, and reducing carbon emissions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-03
AI Technical Summary
Existing carbon management methods in industrial parks suffer from problems such as model deviation, data silos, lagging regulation, and limited accounting scope, resulting in insufficient accounting accuracy and lagging regulation, making it impossible to achieve accurate carbon footprint tracking and optimization throughout the entire life cycle.
By constructing a dynamic carbon footprint accounting model, integrating multi-source information, conducting stability analysis and gradient analysis, identifying risk points and generating optimization instructions, carbon emissions can be reduced throughout the entire life cycle.
It has enabled accurate carbon emission accounting and optimization throughout the entire life cycle, reducing carbon emissions and improving the accuracy of accounting and the foresight of regulation.
Smart Images
Figure CN121787743A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of carbon emission technology, and in particular to a method for calculating and optimizing the carbon footprint of equipment throughout its entire life cycle in a zero-carbon industrial park. Background Technology
[0002] Currently, the mainstream approach to carbon management in industrial parks is static accounting, with the core technical pathways and application scenarios as follows: Input-output method: The core is used to calculate the direct and indirect carbon emissions of different functional areas within the park. By integrating the carbon source and carbon sink data of each area, a total carbon emission statistic at the park level is formed.
[0003] Life cycle assessment (LCA): Currently, it is mostly limited to specific fields such as photovoltaic module recycling and new energy project construction. It focuses on determining the carbon footprint of key stages from equipment manufacturing, transportation and installation to scrapping and recycling, but lacks systematic integration of all aspects of the entire life cycle.
[0004] Carbon footprint factor database-dependent method: This method uses fixed emission factors published by a country or region as the basis for calculation. For example, the carbon accounting method promoted by Yinchuan City in characteristic industrial parks such as photovoltaic and wine industries completes the emission calculation by matching the industry type and selecting the corresponding factor.
[0005] Existing static accounting and management methods have revealed significant shortcomings in the actual operation of zero-carbon industrial parks, specifically manifested in the following four core issues: Model deviation problem: Static carbon emission models are based on fixed parameters and cannot respond in real time to dynamic changes in equipment operating status (such as load fluctuations and failure losses) and environmental parameters (such as temperature and light intensity). This leads to a significant deviation between the calculation results and the actual carbon emission status of the physical system, with an error rate often exceeding 25%.
[0006] Data silo problem: Data from systems such as energy supply, transportation, building operation and maintenance, and industrial production within the park are independent of each other and lack a unified multi-source information fusion mechanism. Carbon flow tracking has gaps and cannot form a complete carbon flow map.
[0007] The problem of lagging regulation: Optimization strategies are mostly based on historical statistical data and have not established quantitative analysis models for the cascading effects of carbon emissions. It is difficult to predict the chain reaction of changes in equipment carbon emissions on the system, resulting in regulatory measures lagging behind actual carbon emission fluctuations and failing to achieve forward-looking management.
[0008] Limited scope of accounting: Most methods only cover the equipment operation stage and do not fully include the entire life cycle of "equipment manufacturing - construction and installation - operation and maintenance - scrapping and recycling". In particular, there is a lack of accurate accounting for the recycling and disposal stage of retired equipment, resulting in incomplete carbon footprint statistics. Summary of the Invention
[0009] The purpose of this invention is to overcome the shortcomings of existing technologies, such as static and fixed carbon footprint accounting models, lack of data fusion mechanisms across multiple systems, unquantified carbon emission cascade effects, and incomplete coverage of the entire equipment lifecycle, leading to insufficient accounting accuracy and lagging regulation. This invention provides a method for calculating and optimizing the carbon footprint of equipment throughout its entire lifecycle in zero-carbon industrial parks. By determining the scope of the carbon footprint calculation for the entire lifecycle of zero-carbon industrial park equipment, collecting multi-dimensional carbon-related data, constructing a dynamic carbon footprint accounting model, integrating multi-source information, performing stability analysis and gradient analysis, constructing an equipment energy topology network to locate risk points, quantifying the interlocking effects of risk points to identify vulnerable nodes, and generating equipment optimization instructions, the invention achieves the goal of reducing carbon emissions throughout the entire lifecycle of zero-carbon industrial park equipment.
[0010] The objective of this invention is achieved through the following technical solution: A method for calculating and optimizing the carbon footprint of equipment throughout its entire life cycle in a zero-carbon industrial park includes the following steps: Step 1: Determine the scope of carbon footprint accounting and collect multi-dimensional carbon-related data within the scope of carbon footprint accounting; Step 2: Based on multi-dimensional carbon-related data, construct a dynamic carbon footprint accounting model and output a standardized carbon flow dataset; Step 3: The standardized carbon flow dataset is fused with park equipment operation data and environmental monitoring data to perform multi-source information fusion, and the fused multi-source information is subjected to stability analysis and gradient analysis. Step 4: Construct an equipment energy topology network based on the equipment energy connection relationship, and locate the key equipment or nodes in the topology network as risk points by combining the stability analysis results and gradient analysis data. Step 5: Quantify the intensity of the interlocking impact of the risk point anomaly on upstream and downstream equipment and the system as a whole through the carbon emission impact transmission model, and identify the vulnerable nodes; Step 6: Generate device optimization instructions for vulnerable nodes to reduce carbon emissions.
[0011] Preferably, in step 1, the carbon footprint accounting scope includes the entire process of raw material mining, equipment manufacturing, transportation and storage, on-site installation, operation and maintenance, and decommissioning and recycling.
[0012] Preferably, in step 1, the multi-dimensional carbon-related data includes carbon source data, carbon sink data, equipment data, and energy data. The carbon source data includes energy consumption and fuel usage data, the carbon sink data includes data on carbon sequestration through greening and carbon capture, the equipment data includes operating parameters and maintenance records, and the energy data includes electricity prices and energy consumption metering data.
[0013] Preferably, in step 2, the dynamic carbon footprint accounting model is as follows: Using life cycle assessment as the model framework, energy efficiency benchmark parameters are introduced as initial values for the model, and a dynamic correction interface is reserved.
[0014] Preferably, in step 3, when the standardized carbon flow dataset is fused with park equipment operation data and environmental monitoring data, a unified data standard and time-series alignment algorithm are established to perform heterogeneous fusion and standardization processing on the multi-source information.
[0015] Preferably, in step 3, stability analysis and gradient parsing are performed on the fused multi-source information, specifically as follows: The Lyapunov index stability analysis method is used to determine the dynamic stability of the carbon emission system of the equipment and the whole park by calculating the convergence of the motion trajectory of the carbon emission system. The optical flow method is applied to analyze the thermal gradient, and based on infrared thermal imaging data and spatial coordinate information, the spatiotemporal hot spots and diffusion paths of carbon emissions are identified.
[0016] As a preferred option, the zero-carbon park equipment life-cycle carbon footprint accounting and optimization method also dynamically updates the dynamic carbon footprint accounting model. Specifically, the energy efficiency benchmark parameters in the dynamic carbon footprint accounting model are dynamically updated based on the stability analysis results, gradient analysis data, and the quantitative value of the interlocking influence intensity.
[0017] Preferably, in step 6, the equipment optimization instructions include energy efficiency parameter adjustment, load balancing allocation, operation plan change, and faulty equipment replacement.
[0018] The beneficial effects of this invention are: This invention breaks through the traditional fixed boundary accounting model. The accounting scope can be dynamically adjusted according to the equipment operation stage and park policy objectives. It not only fully covers the entire process, but also extends to the recycling and reuse stage of retired equipment, ensuring the matching of accounting boundaries with management needs.
[0019] To address the challenges of heterogeneous data formats and inconsistent time series across multiple systems within the park, including energy, operations and maintenance, transportation, and production, a unified data standard and time series alignment algorithm are established to achieve efficient fusion and standardized processing of multi-source data, providing a data foundation for accurate accounting.
[0020] This innovative approach introduces dynamic system stability analysis and spatial gradient analysis into the field of carbon emission source tracing. It assesses carbon emission stability from a dynamic system perspective and locates the sources and diffusion paths of carbon emission anomalies from a spatial dimension, thus overcoming the shortcomings of traditional source tracing methods that can only locate static hotspots and cannot identify dynamic anomalies.
[0021] Based on the energy supply and consumption relationships between devices, an equipment correlation graph is constructed. The impact of carbon emission changes of a single node on the correlated nodes and the global system is simulated and quantified through a network propagation model, providing technical support for predicting carbon emission risks and formulating forward-looking control strategies. Attached Figure Description
[0022] Figure 1 This is a flowchart of the present invention. Detailed Implementation
[0023] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.
[0024] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.
[0025] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.
[0026] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0027] Example: A method for calculating and optimizing the carbon footprint of equipment throughout its entire lifecycle in a zero-carbon industrial park, such as... Figure 1 As shown, it includes the following steps: Step 1: Determine the scope of carbon footprint accounting and collect multi-dimensional carbon-related data within the scope of carbon footprint accounting; Step 2: Based on multi-dimensional carbon-related data, construct a dynamic carbon footprint accounting model and output a standardized carbon flow dataset; Step 3: The standardized carbon flow dataset is fused with park equipment operation data and environmental monitoring data to perform multi-source information fusion, and the fused multi-source information is subjected to stability analysis and gradient analysis. Step 4: Construct an equipment energy topology network based on the equipment energy connection relationship, and locate the key equipment or nodes in the topology network as risk points by combining the stability analysis results and gradient analysis data. Step 5: Quantify the intensity of the interlocking impact of the risk point anomaly on upstream and downstream equipment and the system as a whole through the carbon emission impact transmission model, and identify the vulnerable nodes; Step 6: Generate device optimization instructions for vulnerable nodes to reduce carbon emissions.
[0028] In step 1, the carbon footprint accounting scope includes the entire process of raw material mining, equipment manufacturing, transportation and warehousing, on-site installation, operation and maintenance, and decommissioning and recycling.
[0029] In step 1, the multi-dimensional carbon-related data includes carbon source data, carbon sink data, equipment data, and energy data. The carbon source data includes energy consumption and fuel usage data; the carbon sink data includes data on carbon sequestration through greening and carbon capture; the equipment data includes operating parameters and maintenance records; and the energy data includes electricity prices and energy consumption metering data. This multi-dimensional carbon-related data is processed through data cleaning to remove outliers, normalization to unify data formats and units, and machine learning algorithms to predict and fill in missing values, thus forming a standardized carbon flow dataset.
[0030] In step 2, the dynamic carbon footprint accounting model is specifically as follows: Using life cycle assessment as the model framework, energy efficiency benchmark parameters are introduced as initial values for the model, and a dynamic correction interface is reserved to provide technical support for subsequent model updates that incorporate real-time data.
[0031] In step 3, when fusing standardized carbon flow datasets with park equipment operation data and environmental monitoring data, a unified data standard and time-series alignment algorithm are established to perform heterogeneous fusion and standardization processing on the multi-source information. Real-time operation data includes equipment energy efficiency, load rate, and operating time; environmental monitoring data includes temperature, solar radiation, and wind speed. In this embodiment, park economic activity data, including output value, production capacity, and number of employees, is also included, obtained through an input-output method to achieve correlation analysis between physical operating status and economic activities.
[0032] In step 3, stability analysis and gradient parsing are performed on the fused multi-source information, specifically as follows: The Lyapunov index stability analysis method is used to determine the dynamic stability of the carbon emission system of the equipment and the whole park by calculating the convergence of the motion trajectory of the carbon emission system. The optical flow method is applied to analyze the thermal gradient, and based on infrared thermal imaging data and spatial coordinate information, the spatiotemporal hot spots and diffusion paths of carbon emissions are identified.
[0033] The core physical meaning of the Lyapunov exponent is the average divergence / convergence rate of the system trajectory between two adjacent points in phase space, where the "maximum Lyapunov exponent" directly determines the system stability. The specific calculation process is as follows: Step a: Based on the full life cycle carbon footprint accounting model (refer to the document "Dynamic Carbon Footprint Accounting Model"), establish the dynamic evolution equations of the state variables.
[0034] Step b: Phase space reconstruction Since the carbon emission system is a high-dimensional nonlinear system, it is necessary to convert the one-dimensional time series data into a high-dimensional phase space trajectory through "phase space reconstruction".
[0035] Step c: Calculate the maximum Lyapunov exponent The "small data volume method" is used to calculate and track the distance changes between adjacent trajectories in phase space. Specific operations are as follows: For the reconstructed set of points in phase space, find the nearest neighbor for each point; Track the distance to neighboring points as it evolves over time; Take the logarithm of the distances between all neighboring point pairs and calculate the slope of the linear fit; The slope is the Lyapunov exponent. The average of the calculation results for all point pairs is taken as the "maximum Lyapunov exponent," which serves as a quantitative indicator of system stability.
[0036] Step d: Setting the stability assessment threshold Based on the actual engineering practice of equipment operation in the zero-carbon park, a critical value for stability judgment is set.
[0037] The specific process of applying the optical flow method to analyze thermal gradients is as follows: The core of optical flow is to calculate the motion vector of temperature pixels between consecutive frames. The direction of this vector represents the direction of heat diffusion, and its magnitude represents the rate of heat diffusion. The specific operation is as follows: 1. Optical Flow Algorithm Selection and Parameter Settings To address the "high frame rate, low noise" characteristics of thermal imaging data from park equipment, the Lucas-Kanade sparse optical flow algorithm was selected. Core parameter settings are as follows: Feature point extraction: The Shi-Tomasi corner detection algorithm is used to select pixels with significant temperature changes in the thermal image (such as the edges of high-temperature areas and device interfaces) as tracking feature points (to avoid invalid calculations for uniform temperature areas); Window size: A tracking window of 15×15 pixels is set to balance local temperature consistency and motion capture accuracy; Number of iterations: 5~8 iterations to ensure convergence of the optical flow vector (error ≤ 0.5 pixels).
[0038] 2. Calculation of optical flow vector For the preprocessed consecutive thermal imaging frames, perform the following operations: Extract the feature point set for frame t; in frame t+1, find the matching point corresponding to each feature point by pyramid hierarchical tracing using the Lucas-Kanade algorithm; calculate the optical flow vector of each feature point.
[0039] 3. Optimization of optical flow field Abnormal optical flow vectors are eliminated, while effective thermal motion trajectories are retained to form a complete thermal flow field for the equipment.
[0040] The thermal gradient is the rate of temperature change per unit space, which is positively correlated with the carbon emission intensity of equipment. The magnitude and direction of the thermal gradient are calculated using optical flow vectors. 1. Spatial gradient operator calculation Based on the spatial displacement and temperature change of the optical flow vector, the Sobel gradient operator is used to calculate the thermal gradient vector of each feature point.
[0041] 2. Determination of thermal gradient amplitude and direction The gradient magnitude represents the intensity of the thermal gradient (the larger the magnitude, the more drastic the temperature change and the more concentrated the carbon emissions). The gradient direction represents the dominant direction of thermal diffusion (which is consistent with the direction of carbon emission diffusion).
[0042] 3. High gradient region labeling A threshold for the thermal gradient amplitude is set, and regions with amplitudes exceeding the threshold are marked as "high gradient candidate regions". These regions are the core candidates for spatiotemporal hotspots of carbon emissions.
[0043] The method for calculating and optimizing the carbon footprint of equipment throughout its entire life cycle in zero-carbon industrial parks also dynamically updates the dynamic carbon footprint calculation model. Specifically, it uses stability analysis results, gradient analysis data, and the quantified value of the intensity of interlocking effects to dynamically update the energy efficiency benchmark parameters in the dynamic carbon footprint calculation model.
[0044] In step 6, the equipment optimization instructions include energy efficiency parameter adjustment, load balancing allocation, operation plan change, and faulty equipment replacement.
[0045] The following is a detailed implementation of applying the technical solution of this embodiment to the carbon footprint accounting and control of a photovoltaic system in a park based on dynamic calibration. Taking a 10MW distributed photovoltaic system deployed in a zero-carbon park in a desert area as the application scenario, the implementation process and effects of this method are explained in detail: S1: Scope Delineation and Initial Modeling The full lifecycle accounting scope of this photovoltaic system is clearly defined as follows: silicon material smelting → silicon wafer production → photovoltaic module manufacturing → road transportation to the industrial park → on-site installation → 25 years of operation and power generation → dismantling and recycling of decommissioned modules. During the system design phase, based on standard test conditions (STC: irradiance 1000W / m²),... 2 The initial energy efficiency benchmark parameters for unit power generation were determined using the following methods: temperature 25℃, AM1.5 spectrum. The carbon emission factor was 0.05kgCO2e / kWh. Based on this, an initial carbon footprint accounting model was constructed.
[0046] S2: Dynamic calibration under multiple working conditions during the construction phase During the installation and construction of photovoltaic systems, IoT sensors (including irradiance sensors, temperature sensors, and inverter status monitors) and infrared thermal imaging equipment are deployed simultaneously to simulate three typical operating conditions (high temperature and strong light, low temperature and weak light, and cloudy fluctuations). Real-time collection of operating boundary characteristic spectra (such as inverter efficiency curves and module temperature loss coefficients) and thermodynamic traces (temperature distribution data of modules and combiner boxes) is conducted.
[0047] Lyapunov index analysis showed that when the ambient temperature exceeded 45℃, the carbon emission system stability index of a certain batch of modules dropped to 0.3 (the critical value is 0.5), indicating that the operational stability was in a critical state. Combined with the results of thermal gradient analysis using the optical flow method, it was further determined that the combiner box numbered H3-12 in subarray 3 of this batch of modules was the source of the temperature hotspot (the temperature was 12℃ higher than that of the surrounding equipment), and was identified as a topologically vulnerable node.
[0048] S3: Dynamic Correction and Adjustment during Operation and Maintenance Phase The carbon emission cascading effect of this vulnerable node is quantified based on a topological network model: a 10% decrease in the efficiency of the combiner box will lead to a 5% decrease in the overall power generation efficiency of the 20 photovoltaic modules associated with it. Based on the full life cycle calculation, it is expected to increase carbon emissions by an additional 12tCO2e.
[0049] Based on the above analysis, the system generates two core instructions: the first is a dynamic correction instruction, which updates the energy efficiency benchmark parameter of subarray 3 to 0.07 kgCO2e / kWh, so that the accounting model matches the actual operating status; the second is a dual coordinated control instruction, the direct control instruction is "the operation and maintenance personnel shall complete the cleaning of subarray 3 components and the replacement of H3-12 combiner box within 4 hours", and the indirect induction instruction is "the 12tCO2e successfully reduced by this subarray shall be credited to the park's carbon asset account, which shall be used to offset the carbon quota of related enterprises in the park or to exchange for photovoltaic operation and maintenance subsidies".
[0050] Effect verification After the implementation of the control measures, the operational stability index of subarray No. 3 rebounded to 0.8, and the carbon emission system returned to stability. Monitoring data for one month showed that the carbon accounting data of this subarray was 96.2% consistent with the actual electricity meter readings and the results of third-party carbon verification, verifying the accuracy and effectiveness of this method.
[0051] The following is a specific implementation method for applying the technical solution of this embodiment to the carbon footprint accounting and emission reduction task allocation of manufacturing equipment in an industrial park. The injection molding production line (including 5 injection molding machines and supporting auxiliary equipment) in an industrial park aims to achieve real-time accounting of the carbon footprint of the production process and accurate allocation of emission reduction tasks.
[0052] Implementation process A carbon footprint calculation model for this type of injection molding machine is constructed based on the whole life cycle method. The calculation scope covers the equipment manufacturing, installation and commissioning, production operation (8 hours per shift), maintenance and decommissioning stages, with a focus on the carbon footprint calculation of power consumption, raw material consumption (plastic particles) and auxiliary gas (compressed air) during the production operation stage.
[0053] During a single production shift, real-time operating data for five injection molding machines was collected using smart meters, raw material meters, and gas flow meters: total power consumption was 1200 kWh, raw material consumption was 800 kg, and auxiliary gas consumption was 200 m³ / h. 3 Combined with dynamically updated emission factors (electricity emission factor 0.6 kg CO2e / kWh, plastic particle emission factor 3.2 kg CO2e / kg, compressed air emission factor 0.1 kg CO2e / m³), 3 The total carbon footprint of the production line for this shift is calculated to be 800×3.2+1200×0.6+200×0.1=3220kgCO2e.
[0054] Compared with the rated carbon footprint threshold of the production line (3000kgCO2e / shift), the system determined that the total carbon footprint exceeded the threshold by 7.3%, and through carbon emission traceability analysis, the injection molding machine numbered M3 was identified as the main source of the excess (its carbon footprint accounted for 35%, which is 18% higher than similar equipment).
[0055] The system automatically generates an emission reduction task allocation plan: the M3 injection molding machine is responsible for 120 kg of the total emission reduction of 220 kg CO2e, and the remaining 100 kg is shared by the remaining 4 machines and the raw material procurement process. At the same time, dual control instructions are issued: the direct control instruction is to "reduce the holding pressure of the M3 injection molding machine from 120 bar to 100 bar and adjust the cooling time from 15 seconds to 12 seconds"; the indirect induction instruction is "if the emission reduction target for this shift is achieved, the production line operation and maintenance team will receive a carbon credit bonus of 2,000 yuan for the month".
[0056] Implementation effect Monitoring data from the next production shift after the adjustment showed that the total carbon footprint of the production line dropped to 2980 kg CO2e, achieving the emission reduction target; among them, the carbon footprint of the M3 injection molding machine decreased by 32%, verifying the accuracy of the emission reduction task allocation and the effectiveness of the adjustment measures, and realizing the refined management of the carbon footprint of manufacturing equipment.
[0057] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.
[0058] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.
Claims
1. A method for calculating and optimizing the carbon footprint of equipment throughout its entire life cycle in a zero-carbon industrial park, characterized in that, Includes the following steps: Step 1: Determine the scope of carbon footprint accounting and collect multi-dimensional carbon-related data within the scope of carbon footprint accounting; Step 2: Based on multi-dimensional carbon-related data, construct a dynamic carbon footprint accounting model and output a standardized carbon flow dataset; Step 3: The standardized carbon flow dataset is fused with park equipment operation data and environmental monitoring data to perform multi-source information fusion, and the fused multi-source information is subjected to stability analysis and gradient analysis. Step 4: Construct an equipment energy topology network based on the equipment energy connection relationship, and locate the key equipment or nodes in the topology network as risk points by combining the stability analysis results and gradient analysis data. Step 5: Quantify the intensity of the interlocking impact of the risk point anomaly on upstream and downstream equipment and the system as a whole through the carbon emission impact transmission model, and identify the vulnerable nodes; Step 6: Generate device optimization instructions for vulnerable nodes.
2. The method for calculating and optimizing the carbon footprint of equipment throughout its entire life cycle in a zero-carbon industrial park, as described in claim 1, is characterized in that... In step 1, the carbon footprint accounting scope includes the entire process of raw material mining, equipment manufacturing, transportation and warehousing, on-site installation, operation and maintenance, and decommissioning and recycling.
3. The method for calculating and optimizing the carbon footprint of equipment throughout its entire life cycle in a zero-carbon industrial park, as described in claim 1, is characterized in that... In step 1, the multi-dimensional carbon-related data includes carbon source data, carbon sink data, equipment data, and energy data. The carbon source data includes energy consumption and fuel usage data. The carbon sink data includes data on carbon sequestration through greening and carbon capture. The equipment data includes operating parameters and maintenance records. The energy data includes electricity prices and energy consumption metering data.
4. The method for calculating and optimizing the carbon footprint of equipment throughout its entire life cycle in a zero-carbon industrial park, as described in claim 1, is characterized in that... In step 2, the dynamic carbon footprint accounting model is specifically as follows: Using life cycle assessment as the model framework, energy efficiency benchmark parameters are introduced as initial values for the model, and a dynamic correction interface is reserved.
5. The method for calculating and optimizing the carbon footprint of equipment throughout its entire life cycle in a zero-carbon industrial park according to claim 1, characterized in that, In step 3, when the standardized carbon flow dataset is fused with park equipment operation data and environmental monitoring data, a unified data standard and time-series alignment algorithm are established to perform heterogeneous fusion and standardization processing on the multi-source information.
6. The method for calculating and optimizing the carbon footprint of equipment throughout its entire life cycle in a zero-carbon industrial park, as described in claim 5, is characterized in that... In step 3, stability analysis and gradient parsing are performed on the fused multi-source information, specifically as follows: The Lyapunov index stability analysis method is used to determine the dynamic stability of the carbon emission system of the equipment and the whole park by calculating the convergence of the motion trajectory of the carbon emission system. The optical flow method is applied to analyze the thermal gradient, and based on infrared thermal imaging data and spatial coordinate information, the spatiotemporal hot spots and diffusion paths of carbon emissions are identified.
7. The method for calculating and optimizing the carbon footprint of equipment throughout its entire life cycle in a zero-carbon industrial park according to claim 1, characterized in that, The dynamic carbon footprint accounting model is also dynamically updated. Specifically, the energy efficiency benchmark parameters in the dynamic carbon footprint accounting model are dynamically updated based on the stability analysis results, gradient analysis data, and the quantified values of the intensity of interlocking effects.
8. The method for calculating and optimizing the carbon footprint of equipment throughout its entire life cycle in a zero-carbon industrial park according to claim 1, characterized in that, In step 6, the equipment optimization instructions include energy efficiency parameter adjustment, load balancing allocation, operation plan change, and faulty equipment replacement.