Park carbon footprint accounting and zero-carbon path planning method, system, device and medium

Abstract

The application provides a park carbon footprint accounting and zero-carbon path planning method, system, equipment and medium, and belongs to the technical field of carbon emission management. The method automatically collects multi-source original activity data of the park through the deployment of edge side data collection agents and gateways; the data is cleaned, standardized, quality evaluated and blockchain trusted stored, forming traceable available data; unified space-time indexes are used for data alignment and fusion to generate fused data; based on the fused data, dynamic emission factor matching, equipment energy efficiency correction and implicit carbon accounting are performed, and a full-coverage carbon emission list is output in combination with uncertainty analysis; a multi-objective optimization model is constructed based on the list, taking cost and carbon emission minimization as the goal, to solve and generate a Pareto optimal zero-carbon path scheme; finally, visual display, simulation deduction and report generation are realized through an interactive platform. The application realizes high-precision dynamic accounting of park carbon emissions and scientific and intelligent zero-carbon path planning.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission management technology, and more specifically relates to a method, system, equipment and medium for calculating carbon footprint and planning zero-carbon pathways in industrial parks. Background Technology

[0002] With the deepening of the "dual carbon" goals, industrial parks, as key units of industrial agglomeration and energy consumption, have made accurate carbon accounting and scientific zero-carbon transformation crucial for achieving regional carbon neutrality. However, the traditional carbon management systems currently widely used in industrial parks have significant limitations in terms of technical architecture and functionality, making it difficult to meet the dynamic, refined, and intelligent needs of modern zero-carbon management.

[0003] Existing technical solutions generally rely on manually submitted periodic reports of single energy consumption data (such as electricity and gas). This data source is narrow and cannot effectively connect to and integrate multi-dimensional real-time data sources such as IoT sensors, equipment operating systems, environmental monitoring networks, and enterprise resource planning (ERP) systems. This results in incomplete accounting boundaries, omission of important indirect emission sources, and lagging data collection processes with significant subjective errors. Furthermore, the system lacks an effective mechanism for automated verification and integrated management of data consistency and authenticity, making it impossible to construct a real-time, comprehensive carbon profile of the industrial park.

[0004] A more fundamental flaw lies in its static accounting model and weak forward-looking planning capabilities. Such systems typically employ a simplified multiplication formula of "active data × fixed emission factor," where the emission factor remains constant over the long term. This fails to reflect real-time changes in the grid's carbon intensity and completely ignores key dynamic factors such as actual equipment operating efficiency, the coupling effect of multi-energy complementary systems, and implicit carbon emissions from the supply chain. This results in insufficient accuracy in the accounting results, failing to depict the spatiotemporal dynamics of carbon emissions. Furthermore, the system's functionality is limited to historical data statistics and periodic report generation, completely lacking the ability to simulate future emission reduction paths based on multi-source data-driven optimization algorithms, conduct multi-objective optimization decisions, and perform interactive scenario analysis. This makes it difficult for park managers to develop technically feasible, economically optimal, and dynamically adaptable zero-carbon transition roadmaps.

[0005] Therefore, breaking through data silos, achieving real-time fusion and high-quality governance of multi-source heterogeneous data, constructing a dynamic carbon accounting model that can reflect the actual operating status of the system, and developing intelligent zero-carbon path planning and simulation capabilities on this basis have become key technical issues that urgently need to be addressed to improve the carbon management efficiency of industrial parks and help them scientifically achieve their carbon neutrality goals. Summary of the Invention

[0006] To address the above problems, the present invention aims to provide a method, system, equipment, and medium for carbon footprint accounting and zero-carbon pathway planning in industrial parks. By integrating multi-source heterogeneous data to construct a dynamic carbon accounting model, and employing multi-objective optimization algorithms and an interactive simulation platform for intelligent planning and scenario simulation, the invention achieves comprehensive, high-precision dynamic accounting of carbon emissions in industrial parks and low-cost, high-efficiency scientific emission reduction pathway planning.

[0007] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, embodiments of this application provide a method for calculating the carbon footprint of a park and planning a zero-carbon pathway, including: By deploying data acquisition agents and a unified data access gateway at the edge, the system automatically collects multi-source raw activity data from the park for carbon emission accounting. The collected raw activity data is cleaned, standardized, quality assessed, and reliably stored to form standardized and traceable usable data. By using a unified timeline resampling method and an association method based on asset, space, and time indexes, the available data is spatiotemporally aligned and fused to generate fused data. Based on the fused data, dynamic emission factor matching and calculation, equipment-level energy efficiency dynamic correction and implicit carbon emission accounting are performed sequentially, and the results are integrated with uncertainty analysis to output the full-caliber carbon emission inventory of the park. Based on the carbon emission inventory, a set of Pareto-optimal zero-carbon implementation path schemes are generated by constructing and solving a multi-objective optimization model with the present value of total cost and cumulative carbon emissions as the objectives. An interactive platform integrating data dashboards, geographic information systems, and scenario simulators is used to visualize and interactively simulate the carbon emission inventory and zero-carbon implementation path schemes, and generate decision reports.

[0008] In an optional implementation, the automatic collection of multi-source raw activity data from the park for carbon emission accounting includes: Data on purchased electricity that indirectly contributes to carbon emissions is collected through smart meters, and data on gas and water consumption is collected through gas and water meters to form energy consumption data. The operating parameters of air conditioning, lighting, and elevator equipment obtained from the building equipment management system are used to form equipment operating status data. Environmental and energy production data are generated by measuring temperature, humidity, and CO2 concentration from environmental sensors, and by measuring photovoltaic power generation and energy storage charging and discharging from a distributed energy monitoring platform.

[0009] In an optional implementation, the step of cleaning, standardizing, quality assessing, and reliably storing the collected raw activity data to form standardized and traceable usable data includes: The raw activity data collected is sequentially subjected to syntax checks and threshold-based business rule checks. Abnormal data is marked and converted into standard data through unit conversion and format parsing. The standard data is periodically evaluated for quality, a data quality report is generated, and the key data for pre-calculation are hashed to generate digital fingerprints and sent to the blockchain network for storage, forming standardized and traceable usable data.

[0010] In an optional implementation, the available data is spatiotemporally aligned and fused using a unified timeline resampling and association method based on asset, spatial, and temporal indexes to generate fused data, including: Establish a unified main timeline, and perform time resampling and missing value imputation on all available data at a uniform frequency; Based on the main timeline, spatial location labels are assigned to all data points on the main timeline to construct a three-dimensional index of assets, space, and time. This three-dimensional index is then used to associate available data describing the same asset, the same spatial location, and the same time period into a single data record, generating fused data.

[0011] In an optional implementation, based on the fused data, dynamic emission factor matching and calculation, equipment-level energy efficiency dynamic correction, and implicit carbon emission accounting are performed sequentially. The results are then integrated with uncertainty analysis to output a comprehensive carbon emission inventory for the park, including: For purchased electricity data in the fused data, dynamic marginal emission factors matching the electricity consumption period are obtained by accessing the real-time emission factor API of the power grid. and according to the formula Calculate indirect emissions from electricity; among which, This represents the total carbon emissions generated from purchased electricity during the accounting period. The total number of time intervals divided within the accounting period. This is the time interval number. For the first Electricity consumption at each time interval; For the pre-defined key energy-consuming equipment, the actual operating energy efficiency is obtained by interpolating its built-in partial load performance curve based on the equipment operating status data and energy consumption data in the fused data. The initial carbon emissions of the equipment were calculated based on its energy consumption and baseline emission factor. According to the formula Its carbon emissions were revised; among them, The revised equipment carbon emissions. The rated energy efficiency of the equipment; For the preset key material consumption, the corresponding emission factors are matched by calling the life cycle assessment database. and according to the formula Calculate the corresponding total implicit carbon emissions; among which, This represents the total implicit carbon emissions generated by material consumption during the accounting period. This represents the total number of material types. For material type serial number, For the first The consumption of various materials; Integrating the above , and Uncertainty analysis was performed using Monte Carlo simulation, and a full-caliber carbon emission inventory with confidence intervals was output. Using data on employee commuting, logistics transportation, waste disposal, and procurement activities as input features, a heterogeneous graph neural network model was constructed and trained. Using a trained heterogeneous graph neural network model, an estimate of indirect emissions from the value chain is output to supplement the full-caliber carbon emissions inventory.

[0012] In an optional implementation, based on the carbon emission inventory, a set of Pareto-optimal zero-carbon implementation pathways is generated by constructing and solving a multi-objective optimization model with the present value of total cost and cumulative carbon emissions as objectives, including: A database of economic parameters for emission reduction technologies is constructed based on a pre-set set of applicable emission reduction technology parameters for the park. The pre-set set of applicable emission reduction technology parameters for the park includes parameters such as unit investment cost, operation and maintenance cost, expected life, annual emission reduction capacity and physical deployment constraints of one or more emission reduction technologies in photovoltaic, energy storage, high-efficiency heat pump, energy-saving renovation and carbon sink projects. Load the database of emission reduction technical and economic parameters, user-defined planning targets, and resource constraints; Based on the loaded data, the following multi-objective optimization mathematical model is constructed: Decision variables are , indicating the first Year The scale of deployment of these emission reduction technologies; The objective function is:

[0013]

[0014] The constraints are:

[0015]

[0016] in, The total number of years in the planning period. The year number is used as the serial number. The total number of emission reduction technologies, For technology serial numbers, The discount rate; and The first The technology in the Annual capital expenditures and operating expenditures are provided by the aforementioned database of emission reduction techno-economic parameters; For the first The technology in the The annual emission reductions are provided by the emission reduction techno-economic parameter database; For the first The baseline carbon emissions for the year are obtained based on the carbon emissions inventory through a pre-set prediction model, and the prediction requirements are defined by the planning targets set by the user. For the first The largest investment budget of the year For the first The technology in the The annual deployable physical scale is defined by the aforementioned resource constraints; The multi-objective optimization mathematical model is solved using a non-dominated sorting genetic algorithm with an elitist strategy. Through iterative optimization via selection, crossover, and mutation operations, a set of Pareto optimal solutions is obtained as a zero-carbon implementation path scheme.

[0017] In an optional implementation, the interactive platform integrating data dashboards, geographic information systems, and scenario simulators is used to visualize, interactively simulate, and generate decision reports on the carbon emission inventory and zero-carbon implementation path schemes, including: The data dashboard displays the real-time total carbon emissions, emission composition by category, ranking of key emission sources, and historical trends in the full-caliber carbon emission inventory in the form of dynamic charts. Using a geographic information system, the spatial distribution of carbon emission intensity of different buildings or areas can be displayed on the electronic map of the park using heat maps or hierarchical coloring. The scenario simulator provides an interactive interface for users to select or adjust the technology deployment timing and investment scale parameters in the zero-carbon implementation path scheme, and calls the multi-objective optimization model for re-evaluation, refreshing and displaying the adjusted cumulative carbon emission trajectory and cost-benefit analysis charts in real time on the same interface; Based on the user's final confirmed path and parameters, a structured decision report is automatically generated, which includes carbon emission status analysis, recommended implementation path, investment plan, emission reduction benefits, and sensitivity analysis.

[0018] Secondly, embodiments of this application also provide a system for calculating the carbon footprint of a park and planning a zero-carbon pathway, including: The data acquisition module is used to automatically collect multi-source raw activity data of the park for carbon emission accounting through data acquisition agents deployed on the edge and a unified data access gateway; The data processing module is used to clean, standardize, assess the quality, and reliably store the collected raw activity data to form standardized and traceable usable data. The data fusion module is used to perform spatiotemporal alignment and fusion processing on the available data through a unified timeline resampling and an association method based on asset, space, and time indexes to generate fused data. The dynamic carbon accounting module is used to perform dynamic emission factor matching and calculation, equipment-level energy efficiency dynamic correction and implicit carbon emission accounting in sequence based on the fused data, and integrate the results with uncertainty analysis to output the full-caliber carbon emission inventory of the park. The zero-carbon pathway planning module is used to generate a set of Pareto-optimal zero-carbon implementation pathway schemes based on the carbon emission inventory by constructing and solving a multi-objective optimization model with the present value of total cost and cumulative carbon emissions as the objectives. The interactive decision-making module is used to visualize, interactively simulate, and generate decision reports on the carbon emission inventory and zero-carbon implementation path scheme through an interactive platform that integrates data dashboards, geographic information systems, and scenario simulators.

[0019] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the park carbon footprint accounting and zero-carbon path planning method as described in any of the above.

[0020] Fourthly, embodiments of this application also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the park carbon footprint accounting and zero-carbon pathway planning method as described in any of the above.

[0021] As can be seen from the above technical solutions, the present invention has the following advantages: The carbon footprint accounting and zero-carbon pathway planning method for industrial parks provided in this application firstly achieves full coverage of carbon emission accounting boundaries and high data credibility by integrating heterogeneous data from multiple sources such as the Internet of Things, environmental sensors, and business systems, and implementing strict quality control, thus overcoming the shortcomings of traditional methods such as single and lagging data sources. Secondly, by introducing dynamic emission factors, real-time correction of equipment-level energy efficiency, and implicit carbon accounting models, the accounting results can accurately reflect the progress of grid clean transformation, the actual operating status of equipment, and the carbon footprint of the supply chain, solving the problem of insufficient accuracy of static accounting models. Furthermore, based on a multi-objective optimization algorithm, the technically and economically optimal Pareto path scheme is automatically generated, and dynamic extrapolation and adjustment are supported by an interactive scenario simulator, transforming carbon management from passive historical statistics to forward-looking and iterative planning decisions, which can effectively support the implementation of customized, low-cost, and high-efficiency zero-carbon transformation in industrial parks.

[0022] This application automatically integrates heterogeneous data from multiple sources, including smart meters, equipment management systems, environmental sensors, and enterprise resource planning systems, by deploying edge acquisition agents and a unified gateway. This covers activity data across all dimensions of energy, equipment, environment, and business, completely overcoming the limitations of traditional methods that rely on manual reporting and single data sources. Simultaneously, it introduces rule-based data cleaning, automated quality assessment, and blockchain-based trusted storage processes, ensuring the quality, timeliness, and immutability of data throughout the entire chain from data collection to accounting applications, laying a solid data foundation for subsequent advanced analysis.

[0023] This application constructs a multi-level dynamic accounting framework: it introduces a real-time marginal emission factor from the power grid to accurately characterize the carbon intensity changes of purchased electricity; it dynamically corrects additional emissions caused by equipment aging and deviations from optimal operation by interpolating performance curves under real-time equipment operating conditions; and it integrates a life cycle assessment database to account for implicit carbon in the supply chain. Uncertainty quantification is achieved through Monte Carlo simulation, ultimately outputting a list with confidence intervals, enabling the accounting results to accurately and sensitively reflect the spatiotemporal dynamics and system complexity of carbon emissions in the industrial park.

[0024] This application constructs a multi-objective optimization model based on a high-precision carbon emission inventory, aiming to minimize the present value of total cost during the planning period and the cumulative carbon emissions. The model integrates a library of emission reduction technologies including cost and performance parameters, and is subject to multiple constraints such as investment budget and physical resources. It employs an advanced evolutionary algorithm to automatically solve the problem, generating a set of Pareto path schemes that achieve the optimal balance between economic benefits and emission reduction effects. This represents a leap from manual experience-based decision-making to data-driven, model-optimized approaches, ensuring the technical feasibility and economic rationality of the path schemes.

[0025] This application utilizes a visualization platform integrating interactive dashboards, geographic information systems, and scenario simulators, enabling managers to intuitively monitor carbon emission heat maps, trace carbon flows, and analyze trends. More importantly, it allows for direct "hypothesis analysis" of recommended pathways within the simulator, interactively adjusting technical parameters or constraints. The system recalculates and displays the adjusted emission reduction trajectory and cost-benefit changes in near real-time, achieving immersive exploration and dynamic evaluation of complex planning schemes, greatly enhancing the agility and confidence in decision-making. Attached Figure Description

[0026] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating the carbon footprint accounting and zero-carbon pathway planning method for the industrial park provided in this application.

[0028] Figure 2 A schematic diagram of the structure of the park carbon footprint accounting and zero-carbon pathway planning system provided in this application.

[0029] Figure 3 A schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0030] The various embodiments of this disclosure will be described more fully in the detailed steps of the park carbon footprint accounting and zero-carbon pathway planning method described below. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0031] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or the possibility of adding one or more combinations of the foregoing.

[0032] 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.

[0033] Please see Figure 1 The diagram shows a flowchart of a method for carbon footprint accounting and zero-carbon pathway planning in a specific embodiment. The method includes: S1: Automatically collects multi-source raw activity data of the park for carbon emission accounting through data collection agents deployed on the edge and a unified data access gateway.

[0034] In this specific implementation, the aim is to build a comprehensive, real-time data foundation. During implementation, data acquisition agents are deployed on-site within the park and configured with a unified data access gateway. Based on predetermined protocols and acquisition frequencies, raw activity data is automatically and concurrently acquired from various sources. For energy consumption data, smart meters, gas meters, and water meters are directly read to obtain data on purchased electricity, gas, and water consumption for indirect carbon emissions. For equipment operation status data, parameters such as operating power, frequency, and start / stop status of key equipment like air conditioners, lighting, and elevators are periodically acquired from the building equipment management system. Simultaneously, environmental and energy production data are integrated, including temperature, humidity, and CO2 concentration measurements read from an environmental sensor network, as well as photovoltaic power generation and energy storage charging / discharging data synchronized from a distributed energy monitoring platform. Furthermore, by interfacing with the enterprise resource planning system, purchase and consumption records of key materials are periodically acquired to form business activity data. All collected raw data is appended with precise timestamps, equipment identifiers, and spatial location codes, and pushed to the internal message bus, providing a source for subsequent streaming processing.

[0035] S2: The collected raw activity data is cleaned, standardized, quality assessed, and reliably stored to form standardized and traceable usable data.

[0036] In this specific implementation, this step governs the raw activity data to ensure its quality and reliability. First, rigorous data cleaning is performed: each incoming piece of raw activity data undergoes sequential syntax parsing and threshold checks based on physical rules or business logic. For example, it checks whether meter readings show negative values ​​or abnormal jumps, and verifies the logical consistency between equipment status and energy consumption records. Abnormal data is marked and logged, while normal data enters a standardization process. The standardization process includes converting all values ​​to the International System of Units (SI) and mapping heterogeneous data structures to an internally defined unified data model.

[0037] To establish a long-term, reliable data audit chain and implement trusted evidence storage, the following measures are taken: periodically conduct quality assessments on standardized data (covering completeness, timeliness, and consistency) and generate reports; at the same time, calculate the SHA-256 hash value of the key input data and the final accounting results on which the accounting depends as digital fingerprints, and send the fingerprints, along with timestamps and data digests, to blockchain nodes for evidence storage, thereby forming standardized and traceable usable data.

[0038] S3: By using a unified timeline resampling method and an association method based on asset, space, and time indexes, the available data is spatiotemporally aligned and fused to generate fused data.

[0039] In a specific implementation, this step aims to address the inconsistency of multi-source data in the spatiotemporal dimensions and construct a fused data view that can be used for unified analysis.

[0040] First, a unified main timeline is established (e.g., with 15-minute intervals as the basic interval). Then, spatiotemporal alignment is performed, resampling all available data onto the main timeline according to their timestamps, and filling in missing time points using linear interpolation or forward imputation algorithms. Simultaneously, each data point is assigned a precise location label within the park's spatial model. Based on this, a three-dimensional "asset-space-time" index is constructed. The association and fusion process utilizes this index to efficiently and automatically associate and integrate multi-dimensional data describing the same physical asset, located in the same spatial position, and within the same time period, forming fused data records with complete contextual information. This provides a unique and consistent data foundation for subsequent refined accounting.

[0041] S4: Based on the fused data, perform dynamic emission factor matching and calculation, equipment-level energy efficiency dynamic correction and implicit carbon emission accounting in sequence, and integrate the results with uncertainty analysis to output the full-caliber carbon emission inventory of the park.

[0042] In a specific implementation, this step is the core of the calculation, which involves performing three types of calculations in sequence and integrating the analysis.

[0043] First, dynamic emission factor matching and calculation are performed: For indirect emissions from purchased electricity, dynamic marginal emission factors are obtained by accessing real-time carbon intensity data services from the power grid and precisely matching the park's electricity consumption periods every 15 minutes. And strictly follow the formula Calculate the total carbon emissions from electricity If real-time factors cannot be obtained, a self-built model will be used to dynamically estimate the regional power generation structure.

[0044] Secondly, dynamic energy efficiency correction at the equipment level is performed: For key energy-consuming equipment such as chillers, their real-time operating parameters (such as load rate) are extracted from the fused data, and the actual operating energy efficiency under the current conditions is obtained by interpolating their built-in partial load performance curves. Then, the initial emissions are calculated based on its energy consumption and baseline emission factor. Finally, according to the formula Calculate the corrected carbon emissions ,in Rated energy efficiency.

[0045] Next is the implicit carbon emission accounting: for key materials such as steel and concrete, their consumption is obtained from the fused data. By calling an external LCA database to match the corresponding emission factors and according to the formula Calculate the total amount of hidden carbon emissions .

[0046] To address the uncertainties in data and models, in the integration , and Subsequently, Monte Carlo simulations were used for uncertainty analysis, ultimately outputting a comprehensive carbon emissions inventory with confidence intervals. Furthermore, for indirect emissions from the value chain that are difficult to measure directly (such as employee commutes), a heterogeneous graph neural network model was trained using relevant activity data to intelligently extrapolate and supplement the final inventory.

[0047] For example, based on multi-dimensional activity data such as employee commuting distance and mode, logistics transportation routes and vehicles, waste collection frequency and type, and raw material procurement categories and amounts, a heterogeneous graph is constructed with nodes of park-employee-supplier-logistics provider and business relationships such as commuting, transportation, and procurement as edges. The feature information of multi-hop neighbor nodes in the graph is aggregated through graph neural networks, and the emission data actually filled in by some enterprises is used as a supervision signal to train the model parameters in a semi-supervised learning manner. Finally, the trained model is used to infer and output the value chain indirect emission estimates covering upstream and downstream activities to supplement and improve the park's full-caliber carbon emission inventory.

[0048] S5: Based on the carbon emission inventory, a set of Pareto-optimal zero-carbon implementation path schemes are generated by constructing and solving a multi-objective optimization model with the present value of total cost and cumulative carbon emissions as objectives.

[0049] In this specific implementation, this step generates a scientific path based on an accurate carbon emission inventory through mathematical optimization. First, a database containing economic parameters of emission reduction technologies such as photovoltaics, energy storage, and heat pumps is loaded, along with user-defined carbon neutrality targets, investment budgets, and other planning goals and resource constraints. The database is constructed based on a preset set of emission reduction technology parameters applicable to the industrial park. This parameter set includes parameters such as unit investment cost, operation and maintenance cost, expected lifespan, annual emission reduction capacity, and physical deployment constraints for one or more emission reduction technologies in photovoltaic, energy storage, high-efficiency heat pump, energy-saving retrofit, and carbon sink projects.

[0050] Subsequently, a multi-objective optimization mathematical model was constructed. This model uses decision variables... (indicating the first) Year With the deployment scale of the technology as the core, two objective functions are established: one is to minimize the total net present value cost. Second, minimize cumulative carbon emissions. The model constraints include: the total investment in each year shall not exceed the budget. And the scale of each technology deployment does not exceed the physical limit. .

[0051] in, The total number of years in the planning period. For year serial number, The total number of emission reduction technologies, For technology serial numbers, The discount rate; and The first The technology in the Annual capital expenditures and operating expenditures are provided by the aforementioned database of emission reduction techno-economic parameters; For the first The technology in the The annual emission reductions are provided by the emission reduction techno-economic parameter database; For the first The baseline carbon emissions for the year are obtained from the historical full-caliber carbon emissions inventory through a pre-set prediction model, and the prediction requirements are defined by the planning targets set by the user. For the first The largest investment budget of the year For the first The technology in the The annual deployable physical scale is defined by the aforementioned resource constraints. To solve this multi-objective optimization mathematical model, a non-dominated sorting genetic algorithm with an elitist strategy is employed. By initializing the population and iteratively performing selection, crossover, and mutation operations, the solution set continuously approaches the Pareto optimal front. Finally, a set of Pareto optimal path solutions that achieve the best balance between cost and emission reduction targets is output. Simultaneously, sensitivity analysis is performed on representative paths to generate detailed year-on-year technology deployment roadmaps and investment plans.

[0052] It is important to note that in this step, the pre-built prediction model uses the time series data constructed from historical carbon emission inventories as training data. Its core architecture is a neural network capable of capturing temporal dependencies, specifically a long short-term memory network model. By inputting the preprocessed and normalized historical carbon emission data series into this network, the model learns and captures the long-term trends, potential cyclical patterns, and complex nonlinear characteristics of carbon emission changes. After training, it is used for forward inference to generate baseline carbon emission predictions for each year in the future planning period under a no-intervention scenario, thereby providing a quantitative future benchmark input for the multi-objective optimization model of the zero-carbon path.

[0053] S6: Through an interactive platform that integrates data dashboards, geographic information systems, and scenario simulators, the carbon emission inventory and zero-carbon implementation path scheme are visualized, interactively simulated, and decision reports are generated.

[0054] In a specific implementation, this step transforms all the aforementioned results into an intuitive and actionable decision report. Specifically, this information is presented to managers through an interactive platform integrating multiple components.

[0055] The system utilizes a data dashboard to display real-time data in the form of dynamic charts, showing total carbon emissions, their breakdown by category, ranking of key emission sources, and historical trends. A geographic information system (GIS) integrates the data with a park map, presenting a heat map that visually illustrates the spatial distribution of carbon emission intensity across different buildings.

[0056] Deep interaction with managers is possible through scenario simulators. For example, users can select a baseline path generated in step S5 and then directly adjust parameters such as "PV installed capacity" or "carbon neutrality target year" via sliders or input boxes. The backend planning engine's rapid assessment module then recalculates almost in real time and immediately refreshes and displays new charts such as cumulative carbon emission trajectories, cost curves, and return on investment. This "what-if analysis" function allows managers to dynamically assess the long-term impact of different strategies.

[0057] Finally, it supports one-click generation of structured decision reports, covering current situation analysis, details of recommended paths, investment and emission reduction benefit tables, and sensitivity analysis conclusions, providing comprehensive and intuitive written evidence for the final scientific decision-making.

[0058] In this embodiment, firstly, IoT sensing and blockchain-based evidence storage ensure the comprehensiveness, real-time nature, and reliability of data sources, overcoming the shortcomings of traditional manual data reporting, which is often delayed and lacks diversity. Secondly, the introduction of dynamic emission factors, equipment operating condition energy efficiency corrections, and supply chain implicit carbon accounting models significantly improves the accuracy and timeliness of the comprehensive carbon emission inventory. Furthermore, a multi-objective optimization algorithm is used to automatically generate Pareto-optimal zero-carbon pathways, and an interactive scenario simulator is combined to enable dynamic simulation and real-time evaluation of the solutions. This transforms carbon management from a static reporting model into a data-driven, continuously optimized, and scientific decision-making process.

[0059] like Figure 2 As shown, the following are embodiments of the park carbon footprint accounting and zero-carbon path planning system provided in this disclosure. This system and the park carbon footprint accounting and zero-carbon path planning methods in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the park carbon footprint accounting and zero-carbon path planning system, please refer to the embodiments of the park carbon footprint accounting and zero-carbon path planning methods described above.

[0060] A system for calculating the carbon footprint of an industrial park and planning a zero-carbon pathway includes: The data acquisition module is used to automatically collect multi-source raw activity data of the park for carbon emission accounting through data acquisition agents deployed on the edge and a unified data access gateway.

[0061] The data processing module is used to clean, standardize, assess the quality of, and reliably store the collected raw activity data to form standardized and traceable usable data.

[0062] The data fusion module is used to perform spatiotemporal alignment and fusion processing on the available data through a unified timeline resampling and association method based on asset, space, and time indexes to generate fused data.

[0063] The dynamic carbon accounting module is used to perform dynamic emission factor matching and calculation, equipment-level energy efficiency dynamic correction and implicit carbon emission accounting in sequence based on the fused data, and integrate the results with uncertainty analysis to output the full-caliber carbon emission inventory of the park.

[0064] The zero-carbon pathway planning module is used to generate a set of Pareto-optimal zero-carbon implementation pathway schemes based on the carbon emission inventory by constructing and solving a multi-objective optimization model with the present value of total cost and cumulative carbon emissions as the objectives.

[0065] The interactive decision-making module is used to visualize, interactively simulate, and generate decision reports on the carbon emission inventory and zero-carbon implementation path scheme through an interactive platform that integrates data dashboards, geographic information systems, and scenario simulators.

[0066] The park carbon footprint accounting and zero-carbon path planning system provided in this embodiment automatically acquires raw activity data covering all dimensions of energy, equipment, environment, and business by deploying edge acquisition agents and a unified data gateway. After cleaning, standardization, and reliable evidence storage, high-quality fused data is constructed. On this basis, the system uses dynamic emission factor matching, real-time equipment energy efficiency correction, and implicit carbon accounting models to achieve high-precision and dynamic accounting of the park's total carbon emissions. Furthermore, based on a multi-objective optimization algorithm, it automatically generates Pareto optimal path schemes that balance cost and emission reduction targets. It also uses an interactive platform with an integrated scenario simulator for visualization and dynamic simulation, thereby upgrading the park's carbon management from the traditional static statistical reporting mode to a smart zero-carbon management closed loop of data-driven, model-optimized, real-time feedback, and scientific decision-making. This significantly improves the accuracy of carbon emission accounting, the economy of path planning, and the scientific and agile nature of management decisions.

[0067] Figure 3 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0068] The carbon footprint accounting and zero-carbon pathway planning method for industrial parks provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0069] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0070] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0071] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0072] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0073] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0074] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0075] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0076] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0077] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0078] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0079] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0080] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0081] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0082] The aforementioned electronic equipment realizes the proposed method for carbon footprint accounting and zero-carbon path planning in the park by constructing a dynamic carbon accounting model driven by multi-source data fusion, and using multi-objective optimization algorithms and interactive scenario simulation for intelligent planning and deduction. This achieves the beneficial effect of realizing high-precision dynamic accounting of carbon emissions in the park and low-cost scientific emission reduction path planning, thereby upgrading carbon management from static and lagging reporting to a smart decision-making closed loop.

[0083] The storage medium provided in this application stores a program product capable of implementing carbon footprint accounting and zero-carbon pathway planning methods for industrial parks.

[0084] The methods for carbon footprint accounting and zero-carbon pathway planning in the industrial park include: By deploying data acquisition agents and a unified data access gateway at the edge, the system automatically collects multi-source raw activity data from the park for carbon emission accounting. The collected raw activity data is cleaned, standardized, quality assessed, and reliably stored to form standardized and traceable usable data. By using a unified timeline resampling method and an association method based on asset, space, and time indexes, the available data is spatiotemporally aligned and fused to generate fused data. Based on the fused data, dynamic emission factor matching and calculation, equipment-level energy efficiency dynamic correction and implicit carbon emission accounting are performed sequentially, and the results are integrated with uncertainty analysis to output the full-caliber carbon emission inventory of the park. Based on the carbon emission inventory, a set of Pareto-optimal zero-carbon implementation path schemes are generated by constructing and solving a multi-objective optimization model with the present value of total cost and cumulative carbon emissions as the objectives. An interactive platform integrating data dashboards, geographic information systems, and scenario simulators is used to visualize and interactively simulate the carbon emission inventory and zero-carbon implementation path schemes, and generate decision reports.

[0085] In some possible implementations, the park carbon footprint accounting and zero-carbon pathway planning method of this disclosure can be implemented as a program product, which includes program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0086] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0087] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for carbon footprint accounting and zero-carbon pathway planning in industrial parks, characterized in that, include: By deploying data acquisition agents and a unified data access gateway at the edge, the system automatically collects multi-source raw activity data from the park for carbon emission accounting. The collected raw activity data is cleaned, standardized, quality assessed, and reliably stored to form standardized and traceable usable data. By using a unified timeline resampling method and an association method based on asset, space, and time indexes, the available data is spatiotemporally aligned and fused to generate fused data. Based on the fused data, dynamic emission factor matching and calculation, equipment-level energy efficiency dynamic correction and implicit carbon emission accounting are performed sequentially, and the results are integrated with uncertainty analysis to output the full-caliber carbon emission inventory of the park. Based on the carbon emission inventory, a set of Pareto-optimal zero-carbon implementation path schemes are generated by constructing and solving a multi-objective optimization model with the present value of total cost and cumulative carbon emissions as the objectives. An interactive platform integrating data dashboards, geographic information systems, and scenario simulators is used to visualize and interactively simulate the carbon emission inventory and zero-carbon implementation path schemes, and generate decision reports.

2. The method for calculating the carbon footprint of a park and planning a zero-carbon pathway according to claim 1, characterized in that, The automatically collected raw activity data from multiple sources within the park used for carbon emission calculation includes: Data on purchased electricity that indirectly contributes to carbon emissions is collected through smart meters, and data on gas and water consumption is collected through gas and water meters to form energy consumption data. The operating parameters of air conditioning, lighting, and elevator equipment obtained from the building equipment management system are used to form equipment operating status data. Environmental and energy production data are generated by measuring temperature, humidity, and CO2 concentration from environmental sensors, and by measuring photovoltaic power generation and energy storage charging and discharging from a distributed energy monitoring platform.

3. The method for calculating the carbon footprint and planning a zero-carbon pathway in a park according to claim 2, characterized in that, The process of cleaning, standardizing, quality assessing, and reliably storing the collected raw activity data to form standardized and traceable usable data includes: The raw activity data collected is sequentially subjected to syntax checks and threshold-based business rule checks. Abnormal data is marked and converted into standard data through unit conversion and format parsing. The standard data is periodically evaluated for quality, a data quality report is generated, and the key data for pre-calculation are hashed to generate digital fingerprints and sent to the blockchain network for storage, forming standardized and traceable usable data.

4. The method for calculating the carbon footprint of a park and planning a zero-carbon pathway according to claim 3, characterized in that, The method of spatiotemporal alignment and fusion processing of the available data through unified timeline resampling and association based on asset, spatial, and temporal indexes generates fused data, including: Establish a unified main timeline, and perform time resampling and missing value imputation on all available data at a uniform frequency; Based on the main timeline, spatial location labels are assigned to all data points on the main timeline to construct a three-dimensional index of assets, space, and time. This three-dimensional index is then used to associate available data describing the same asset, the same spatial location, and the same time period into a single data record, generating fused data.

5. The method for calculating the carbon footprint of a park and planning a zero-carbon pathway according to claim 4, characterized in that, Based on the fused data, dynamic emission factor matching and calculation, equipment-level energy efficiency dynamic correction, and implicit carbon emission accounting are performed sequentially. The results are then integrated with uncertainty analysis to output a comprehensive carbon emission inventory for the park, including: For purchased electricity data in the fused data, dynamic marginal emission factors matching the electricity consumption period are obtained by accessing the real-time emission factor API of the power grid. and according to the formula Calculate indirect emissions from electricity; among which, This represents the total carbon emissions generated from purchased electricity during the accounting period. The total number of time intervals divided within the accounting period. This is the time interval number. For the first Electricity consumption at each time interval; For the pre-defined key energy-consuming equipment, the actual operating energy efficiency is obtained by interpolating its built-in partial load performance curve based on the equipment operating status data and energy consumption data in the fused data. The initial carbon emissions of the equipment were calculated based on its energy consumption and baseline emission factor. According to the formula Its carbon emissions were revised; among them, The revised equipment carbon emissions, The rated energy efficiency of the equipment; For the preset key material consumption, the corresponding emission factors are matched by calling the life cycle assessment database. and according to the formula Calculate the corresponding total implicit carbon emissions; among which, This represents the total implicit carbon emissions generated by material consumption during the accounting period. This represents the total number of material types. For material type serial number, For the first The consumption of various materials; Integrating the above , and Uncertainty analysis was performed using Monte Carlo simulation, and a full-caliber carbon emission inventory with confidence intervals was output. Using data on employee commuting, logistics transportation, waste disposal, and procurement activities as input features, a heterogeneous graph neural network model was constructed and trained. Using a trained heterogeneous graph neural network model, an estimate of indirect emissions from the value chain is output to supplement the full-caliber carbon emissions inventory.

6. The method for calculating the carbon footprint of a park and planning a zero-carbon pathway according to claim 5, characterized in that, Based on the carbon emission inventory, a set of Pareto-optimal zero-carbon implementation pathways is generated by constructing and solving a multi-objective optimization model with the present value of total cost and cumulative carbon emissions as objectives, including: A database of economic parameters for emission reduction technologies is constructed based on a pre-set set of applicable emission reduction technology parameters for the park. The pre-set set of applicable emission reduction technology parameters for the park includes parameters such as unit investment cost, operation and maintenance cost, expected life, annual emission reduction capacity and physical deployment constraints of one or more emission reduction technologies in photovoltaic, energy storage, high-efficiency heat pump, energy-saving renovation and carbon sink projects. Load the database of emission reduction technical and economic parameters, user-defined planning targets, and resource constraints; Based on the loaded data, the following multi-objective optimization mathematical model is constructed: Decision variables are , indicating the first Year The scale of deployment of these emission reduction technologies; The objective function is: The constraints are: in, The total number of years in the planning period. For year serial number, The total number of emission reduction technologies, For technology serial numbers, The discount rate; and The first The technology in the Annual capital expenditures and operating expenditures are provided by the aforementioned database of emission reduction techno-economic parameters; For the first The technology in the The annual emission reductions are provided by the emission reduction techno-economic parameter database; For the first The baseline carbon emissions for the year are obtained from the historical full-caliber carbon emissions inventory through a pre-set prediction model, and the prediction requirements are defined by the planning targets set by the user. For the first The largest investment budget of the year For the first The technology in the The annual deployable physical scale is defined by the aforementioned resource constraints; The multi-objective optimization mathematical model is solved using a non-dominated sorting genetic algorithm with an elitist strategy. Through iterative optimization via selection, crossover, and mutation operations, a set of Pareto optimal solutions is obtained as a zero-carbon implementation path scheme.

7. The method for calculating the carbon footprint of a park and planning a zero-carbon pathway according to claim 6, characterized in that, The interactive platform, integrating data dashboards, geographic information systems, and scenario simulators, visualizes and interactively simulates the carbon emission inventory and zero-carbon implementation pathways, and generates decision reports, including: The data dashboard displays the real-time total carbon emissions, emission composition by category, ranking of key emission sources, and historical trends in the full-caliber carbon emission inventory in the form of dynamic charts. Using a geographic information system, the spatial distribution of carbon emission intensity of different buildings or areas can be displayed on the electronic map of the park using heat maps or hierarchical coloring. The scenario simulator provides an interactive interface for users to select or adjust the technology deployment timing and investment scale parameters in the zero-carbon implementation path scheme, and calls the multi-objective optimization model for re-evaluation, refreshing and displaying the adjusted cumulative carbon emission trajectory and cost-benefit analysis charts in real time on the same interface; Based on the user's final confirmed path and parameters, a structured decision report is automatically generated, which includes carbon emission status analysis, recommended implementation path, investment plan, emission reduction benefits, and sensitivity analysis.

8. A system for calculating the carbon footprint and planning a zero-carbon pathway in a park, characterized in that, The system employs the park carbon footprint accounting and zero-carbon pathway planning method as described in any one of claims 1 to 7; The system includes: The data acquisition module is used to automatically collect multi-source raw activity data of the park for carbon emission accounting through data acquisition agents deployed on the edge and a unified data access gateway; The data processing module is used to clean, standardize, assess the quality, and reliably store the collected raw activity data to form standardized and traceable usable data. The data fusion module is used to perform spatiotemporal alignment and fusion processing on the available data through a unified timeline resampling and an association method based on asset, space, and time indexes to generate fused data. The dynamic carbon accounting module is used to perform dynamic emission factor matching and calculation, equipment-level energy efficiency dynamic correction and implicit carbon emission accounting in sequence based on the fused data, and integrate the results with uncertainty analysis to output the full-caliber carbon emission inventory of the park. The zero-carbon pathway planning module is used to generate a set of Pareto-optimal zero-carbon implementation pathway schemes based on the carbon emission inventory by constructing and solving a multi-objective optimization model with the present value of total cost and cumulative carbon emissions as the objectives. The interactive decision-making module is used to visualize, interactively simulate, and generate decision reports on the carbon emission inventory and zero-carbon implementation path scheme through an interactive platform that integrates data dashboards, geographic information systems, and scenario simulators.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the park carbon footprint accounting and zero-carbon path planning method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the park carbon footprint accounting and zero-carbon path planning method as described in any one of claims 1 to 7.

Images