A Product Carbon Footprint Modeling Method and System

Abstract

This invention belongs to the field of carbon management technology, specifically relating to a product carbon footprint modeling method and system. Through a dynamic construction and optimization mechanism of node example networks, this invention significantly improves the modeling accuracy and computational efficiency of carbon emission data in complex production processes. Compared to traditional tree-based data models, this solution not only solves problems such as unclear data hierarchy semantics, low storage efficiency, and high complexity of recursive calculations, but also enhances the dynamic adjustment capability of the carbon emission tracking model, enabling it to better adapt to frequent changes in production processes. It achieves the verification of emission ratios of main and by-products and the integration of multi-dimensional carbon emission data, providing reliable data support for carbon management decisions. Simultaneously, through a dynamic scheduling mechanism of computational resources among heterogeneous subgraphs, it effectively improves stability and response speed in large-scale carbon footprint calculation scenarios, significantly shortening the carbon footprint assessment cycle while ensuring computational accuracy.

Description

Technical Field

[0001] This invention belongs to the field of carbon management technology, specifically relating to a product carbon footprint modeling method and system. Background Technology

[0002] With the acceleration of industrialization, carbon emissions have become increasingly serious. In order to ensure the effective supervision and control of carbon emission processes, it is obviously necessary to track the carbon footprint of a product throughout its entire production life cycle. This can not only provide enterprises with carbon emission data support to formulate effective emission reduction strategies, but also meet environmental protection requirements, so that air quality does not deteriorate due to excessive carbon emissions.

[0003] In existing technologies, software tools based on Life Cycle Analysis (LCA), such as SimaPro and GaBi, as well as various cloud services for carbon footprint, help users assess the life cycle carbon footprint of products through pre-set databases and computational models. However, these tools typically use a tree structure as the basic data structure of the model. For professionals, this presents a series of problems, such as unclear data hierarchy semantics, low storage efficiency, high complexity of recursive calculations (large cumulative error), and lack of dynamic adjustment capabilities. These issues make it difficult to adapt to the frequent changes in current enterprise production processes. Based on this, this solution provides a product carbon footprint modeling method to address the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide a product carbon footprint modeling method and system, which can achieve accurate modeling and efficient calculation of carbon emission data in complex production processes through the dynamic construction and optimization mechanism of node example networks, thereby improving the accuracy and flexibility of carbon footprint assessment.

[0005] The technical solutions adopted by the present invention are as follows:

[0006] A product carbon footprint modeling method includes:

[0007] Create a base class for carbon nodes and define a set of common attributes including carbon emission factor update functionality, energy consumption integration functionality, and a node type identifier field;

[0008] Based on the carbon node base class, node examples with multiple levels are created, including material nodes, process nodes, and transportation nodes;

[0009] Establish combination relationships between node examples, combine process nodes and material nodes to form composite nodes, and establish a connection with the update interface of transportation nodes to form dynamic updates of carbon emission data;

[0010] Traverse the node example network and perform tail recursion optimization to eliminate the risk of stack overflow, and simultaneously trigger the differential rules corresponding to the node type identifier field to complete the verification of the emission ratio of main and by-products and the conversion of measurement units.

[0011] The node example network is split into independent subtrees based on an asynchronous framework, and the carbon emission calculation of each independent subtree is performed synchronously through parallel computing. The calculation results of each independent subtree are then summarized to form the product-level carbon footprint result.

[0012] In a preferred embodiment, the step of creating the carbon node base class includes:

[0013] Obtain the benchmark carbon emission factor database and extract the benchmark carbon emission factors related to the product from the benchmark carbon emission factor database;

[0014] Product energy consumption data is acquired, which includes direct energy consumption data and indirect energy consumption data. Direct energy consumption data is read in real time from sensors used for monitoring production equipment, while indirect energy consumption data is calculated through process parameters.

[0015] Define a multi-level classification coding system that covers all stages of the product lifecycle, with each stage corresponding to a unique coding identifier field.

[0016] In a preferred embodiment, the step of creating node instances comprising multiple levels based on a carbon node base class includes:

[0017] Bind the product bill of materials and load the material data from the product bill of materials to build material nodes level by level;

[0018] Obtain the industry process rule library and define the mapping relationship between process parameters and product energy consumption data, and build process nodes step by step;

[0019] Bind transportation nodes, load transportation mode and distance data, and build transportation nodes level by level;

[0020] Among them, the material node includes the source of raw materials and the processing process, the process node includes the energy consumption of the production process, and the transportation node records the carbon emissions of logistics.

[0021] In a preferred embodiment, the step of establishing the composition relationship between node examples includes:

[0022] Obtain the product processing flow charts under the process node and material node respectively, and establish a combination mapping between nodes based on the logical relationship in the flow chart to form a composite node structure;

[0023] Connect the carbon emission data interface between the composite node and the transportation node, and collect the increase in transportation distance and weight in real time.

[0024] The distance increase is compared with a preset distance increase threshold, and when the distance increase exceeds the distance increase threshold, an update instruction is sent to the process node to recalculate the logistics carbon emissions.

[0025] The weight increase is compared with a preset weight increase threshold. When the weight increase exceeds the weight increase threshold, an adjustment command is sent to the material node to adjust the raw material consumption ratio in the material node.

[0026] In a preferred embodiment, the step of traversing the example network of nodes and performing tail recursion optimization to eliminate the risk of stack overflow includes:

[0027] Create an intermediate result storage pool and convert the recursive call hierarchy into a circular queue;

[0028] Set a depth threshold, and when the traversal depth reaches the depth threshold, trigger a batch processing mechanism to cache the current node state and intermediate results to the storage pool.

[0029] The calculation rules corresponding to the node type identifier field of each level of node examples are collected in real time, including:

[0030] At each transportation node, the transportation carbon emission factor is calculated based on the transportation distance and the transportation load, and then accumulated by combining the emission coefficients of the transportation mode.

[0031] At each process node, energy consumption is accumulated and simultaneously verified with the proportion of by-products.

[0032] Under the material node, track raw material consumption and dynamically adjust processing parameters;

[0033] Real-time collection of the remaining computational load at the current traversed node example level, and setting up dual overflow protection at the hardware and logic layers based on the remaining computational load;

[0034] When the remaining computation exceeds a preset threshold, dual overflow protection is triggered. Under hardware layer protection, the JVM heap memory is expanded, and under logical layer protection, the recursive call stack is pruned to skip redundant computation paths.

[0035] In a preferred embodiment, the steps of verifying the emission ratios of main and by-products and converting the units of measurement include:

[0036] Real-time data collection of main product output and by-product output at process nodes, and calculation of the main product and by-product output ratio;

[0037] Obtain the preset standard ratio range, compare the output ratio of main and auxiliary products with the standard ratio range, and when the output ratio of main and auxiliary products exceeds the standard ratio range, mark the corresponding process node as an abnormal node and start the ratio verification and repair mechanism.

[0038] When the ratio of main and by-product output is within the standard ratio range, a standardized conversion of measurement units is performed to convert all non-standard measurement units within the composite node into a unified carbon equivalent unit.

[0039] The execution of the proportional verification and repair mechanism includes the following steps:

[0040] Record abnormal nodes as baseline nodes, retrieve historical process parameters of the baseline nodes and compare them with current process parameters to identify the source of parameter deviation;

[0041] Obtain the carbon emission data corresponding to historical process parameters under the source of parameter deviation, and combine it with the current carbon emission data to calculate the carbon emission increment caused by parameter deviation;

[0042] Based on the carbon emission increment, the process parameters of abnormal nodes are corrected, and parameter synchronization update instructions are sent to the associated material nodes and transportation nodes.

[0043] In a preferred embodiment, the step of splitting the node example network into independent subtrees based on the asynchronous framework includes:

[0044] Based on the node type identifier field in the node example network, the node example network is divided into multiple heterogeneous subgraphs. Each heterogeneous subgraph corresponds to a different type of computation task, which includes transportation, process and material types.

[0045] Each heterogeneous subgraph is assigned an independent computation thread, and the corresponding computation rules and verification logic are preloaded in the computation thread;

[0046] The asynchronous message queue coordinates the data dependencies between computing threads, and automatically triggers the computing process of subsequent heterogeneous subgraphs after the computing thread completes the current subgraph computing task.

[0047] In a preferred embodiment, the step of simultaneously calculating the carbon emissions of each independent subtree through parallel computation, and then summarizing the calculation results of each independent subtree to form a product-level carbon footprint result, includes:

[0048] Obtain the computational complexity of the heterogeneous subgraphs under each independent subtree, and determine the execution priority of each heterogeneous subgraph based on the computational complexity;

[0049] Based on the execution priority, allocate corresponding computing resources to each heterogeneous subgraph and start the parallel computing process;

[0050] During the parallel computing process, the computational resource consumption and task progress of each heterogeneous subgraph are collected in real time. When the computational resource consumption and task progress of a heterogeneous subgraph do not match, redundant computational resources are scheduled from heterogeneous subgraphs with redundant computational resources to heterogeneous subgraphs with insufficient computational resources to balance the computational load of each subgraph.

[0051] According to the preset aggregation rules, the carbon emission calculation results of each independent subtree are weighted and summarized to form the carbon footprint result of the entire product life cycle.

[0052] The present invention also provides a product carbon footprint modeling system, which uses the above-described product carbon footprint modeling method and includes:

[0053] The initialization module is used to create the carbon node base class and define a set of general attributes, including carbon emission factor update function, energy consumption integration function, and node type identifier field;

[0054] The node example building module is used to create node examples with multiple levels based on the carbon node base class. The node examples include material nodes, process nodes, and transportation nodes.

[0055] The node combination module is used to establish combination relationships between node instances, combine process nodes and material nodes to form composite nodes, and establish a connection with the update interface of transportation nodes to form dynamic updates of carbon emission data.

[0056] The risk optimization module is used to traverse the node example network and perform tail recursive optimization to eliminate stack overflow risk. It also synchronously triggers the differential rules corresponding to the node type identifier field to complete the verification of the emission ratio of main and by-products and the conversion of measurement units.

[0057] The carbon footprint output module is used to split the node example network into independent subtrees based on an asynchronous framework, and to calculate the carbon emissions of each independent subtree synchronously through parallel computing. Then, the calculation results of each independent subtree are summarized to form the product-level carbon footprint result.

[0058] And, an electronic device, the electronic device comprising:

[0059] At least one processor;

[0060] and a memory communicatively connected to the at least one processor;

[0061] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the above-described product carbon footprint modeling method.

[0062] The technical effects achieved by this invention are as follows:

[0063] This invention significantly improves the modeling accuracy and computational efficiency of carbon emission data in complex production processes through a dynamic construction and optimization mechanism of node example networks. Compared with traditional tree-based data models, this solution not only solves problems such as unclear data hierarchy semantics, low storage efficiency, and high complexity of recursive calculations, but also enhances the dynamic adjustment capability of the carbon emission tracking model, enabling it to better adapt to frequent changes in production processes. It realizes the verification of the emission ratio of main and by-products and the integration of multi-dimensional carbon emission data, providing reliable data support for carbon management decisions. At the same time, through a dynamic scheduling mechanism of computing resources among heterogeneous subgraphs, it effectively improves the stability and response speed in large-scale carbon footprint calculation scenarios, further reducing computing energy consumption. While ensuring computational accuracy, it also significantly shortens the carbon footprint assessment cycle. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0065] Figure 2 This is a schematic diagram of the system modules of the present invention;

[0066] Figure 3 This is a schematic diagram of the electronic device structure of the present invention. Detailed Implementation

[0067] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0068] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0069] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0070] Please see Figure 1 As shown, this invention provides a product carbon footprint modeling method, including:

[0071] S1. Create a carbon node base class and define a set of general attributes including carbon emission factor update function, energy consumption integration function and node type identifier field;

[0072] In step S1, as people's environmental awareness continues to increase, the demand for carbon emission monitoring is growing. In this embodiment, firstly, a carbon node base class with carbon emission factor update function is created to achieve dynamic maintenance of basic data. At the same time, an energy consumption integration function is defined to comprehensively consider energy consumption in the product production process. Different types of carbon nodes are classified and managed through a node type identifier field, providing a foundation for subsequent data processing. The step of creating the carbon node base class includes:

[0073] Obtain the benchmark carbon emission factor database and extract the benchmark carbon emission factors related to the product from the benchmark carbon emission factor database;

[0074] Product energy consumption data is acquired, which includes direct energy consumption data and indirect energy consumption data. Direct energy consumption data is read in real time from sensors used for monitoring production equipment, while indirect energy consumption data is calculated through process parameters.

[0075] Define a multi-level classification coding system that covers all stages of the product lifecycle, with each stage corresponding to a unique coding identifier field;

[0076] Specifically, when creating the carbon node base class, it is first necessary to obtain a benchmark carbon emission factor database. This database includes carbon emission factors published by international standards organizations and historical carbon emission data accumulated within the enterprise, providing a basis for calculations of different types of carbon emissions. By extracting product-related benchmark carbon emission factors from this database, the reliability of the carbon footprint output can be ensured. Simultaneously, product energy consumption data is also acquired as the basis for carbon emission assessment. This includes both direct and indirect energy consumption data. Direct energy consumption data is obtained through real-time monitoring of production equipment, while indirect energy consumption data is calculated based on process parameters, thus comprehensively reflecting the energy consumption during the product manufacturing process. Regarding energy consumption, for example, when determining indirect energy consumption data, the operating energy consumption of auxiliary equipment can be derived based on the process parameters of a production batch, and then the corresponding carbon emissions can be calculated. For instance, assuming that a certain raw material used in a production batch requires additional heating treatment under specific process conditions, the type and amount of additional energy consumed in the heating treatment process can be quantified and analyzed, and the carbon emissions of this step can be calculated in combination with the corresponding carbon emission factors. In this way, the carbon emissions of indirect energy consumption data can be calculated, which can then be summarized into the total carbon emissions of the carbon node base class. In addition, defining a multi-level classification coding system can assist in the fine management of carbon emission data and ensure that carbon emissions at each stage can be accurately tracked.

[0077] S2. Create node instances with multiple levels based on the carbon node base class. The node instances include material nodes, process nodes, and transportation nodes.

[0078] In step S2, after the carbon node base class is determined, material nodes, process nodes, and transportation nodes are constructed level by level based on the carbon node base class. This ensures that each node contains detailed carbon emission information. Material nodes record the types, sources, and processing procedures of raw materials, providing a basis for calculating raw material carbon emissions. Process nodes integrate energy consumption data in the production process, reflecting the impact of different process steps on carbon emissions. Transportation nodes record carbon emissions during the logistics process, including transportation methods, distances, and loads. The step of creating node examples with multiple levels based on the carbon node base class includes:

[0079] Bind the product bill of materials and load the material data from the product bill of materials to build material nodes level by level;

[0080] Obtain the industry process rule library and define the mapping relationship between process parameters and product energy consumption data, and build process nodes step by step;

[0081] Bind transportation nodes, load transportation mode and distance data, and build transportation nodes level by level;

[0082] Among them, the material node includes the source of raw materials and the processing process, the process node includes the energy consumption of the production process, and the transportation node records the carbon emissions of logistics.

[0083] Specifically, when creating a node example, the product bill of materials is first bound to ensure all material data is entered into the system. By loading the material data, detailed material nodes can be built level by level. Each material node records the type, source, processing procedure, and related carbon emission information of the raw materials. Then, the industry process rule library is retrieved. This library contains a database mapping various production process parameters to energy consumption data. Based on these industry process rules, process nodes can be built level by level. Each process node integrates energy consumption data from the production process, reflecting the specific impact of different process steps on carbon emissions, facilitating the tracking of carbon emissions during production. Furthermore, it also binds... By defining transportation nodes and loading data such as transportation methods, distances, and loads, a transportation node network is constructed step by step. Each transportation node records carbon emission information during the logistics process, including emission coefficients, transportation distances, and loads under different transportation methods. Based on this, the carbon emissions during the logistics process can be calculated and incorporated into the overall carbon footprint tracking. After completing the construction of node examples, a complete carbon footprint network model can be formed. The carbon footprint model covers all stages of the product's entire life cycle, from raw material procurement and production to logistics and distribution. Each stage has corresponding nodes for carbon emission tracking, thus enabling a comprehensive understanding of the product's carbon footprint through the carbon footprint model.

[0084] S3. Establish the combination relationship between node examples, combine process nodes and material nodes to form composite nodes, and establish a connection with the update interface of transportation nodes to form dynamic updates of carbon emission data.

[0085] In step S3, after the node example network is constructed, it is necessary to establish the combination relationship between nodes to ensure that each node can work collaboratively to track carbon footprints. The combination of process nodes and material nodes realizes the correlation between energy consumption and raw material carbon emissions in the production process. Through the construction of composite nodes, the specific contribution of different production processes to carbon emissions can be reflected more intuitively. At the same time, a connection is established with the update interface of transportation nodes to ensure the real-time update of logistics carbon emission data, thereby dynamically reflecting the changes in carbon emissions throughout the product's entire life cycle. The step of establishing the combination relationship between node examples includes:

[0086] Obtain the product processing flow charts under the process node and material node respectively, and establish a combination mapping between nodes based on the logical relationship in the flow chart to form a composite node structure;

[0087] Connect the carbon emission data interface between the composite node and the transportation node, and collect the increase in transportation distance and weight in real time.

[0088] The distance increase is compared with a preset distance increase threshold, and when the distance increase exceeds the distance increase threshold, an update instruction is sent to the process node to recalculate the logistics carbon emissions.

[0089] The weight increase is compared with a preset weight increase threshold, and when the weight increase exceeds the weight increase threshold, an adjustment command is sent to the material node to adjust the raw material consumption ratio in the material node.

[0090] Specifically, when establishing the combination relationship between node examples, the product processing flow charts under the process node and material node are first obtained separately. The product processing flow chart describes the process by which raw materials are processed into the final product in different process steps. Through the logical relationship in the flow chart, the combination mapping between material node and process node can be accurately established, thus forming a composite node structure. The composite node structure combines energy consumption data with raw material carbon emission data, making carbon emission tracking more accurate. At the same time, in order to achieve real-time updates of logistics carbon emission data, the carbon emission data interface between the composite node and the transportation node is connected to collect the distance increase and weight increase during transportation in real time. The distance increase and weight increase reflect changes in the logistics links and have a direct impact on carbon emissions. Therefore, the collected distance increase is compared with a preset distance increase threshold. Once the distance increase exceeds the threshold, an update instruction is immediately sent to the process node to recalculate the logistics carbon emission to ensure data accuracy. Similarly, the weight increase is also compared with a preset weight increase threshold. If it exceeds the threshold, an adjustment instruction is sent to the material node to adjust the raw material consumption ratio to adapt to changes in the logistics process and achieve the purpose of real-time updates of carbon emission data.

[0091] S4. Traverse the node example network and perform tail recursion optimization to eliminate the risk of stack overflow, and simultaneously trigger the differential rules corresponding to the node type identifier field to complete the verification of the emission ratio of main and by-products and the conversion of measurement units.

[0092] In step S4, after the node example network is output, the entire node example network is traversed. A tail-recursive optimization algorithm is used to perform a depth-first traversal of the nodes in the network, effectively eliminating the risk of stack overflow that may occur during recursive calls. Simultaneously, the differentiation rules corresponding to the node type identifier field are triggered during the traversal to verify whether the emission ratio of primary and secondary products meets the preset standards. At the same time, the measurement units between different nodes are automatically converted to ensure the accuracy and consistency of data during transmission. The steps of traversing the node example network and performing tail-recursive optimization to eliminate the risk of stack overflow include:

[0093] Create an intermediate result storage pool and convert the recursive call hierarchy into a circular queue;

[0094] Set a depth threshold, and when the traversal depth reaches the depth threshold, trigger a batch processing mechanism to cache the current node state and intermediate results to the storage pool.

[0095] The calculation rules corresponding to the node type identifier field of each level of node examples are collected in real time, including:

[0096] At each transportation node, the transportation carbon emission factor is calculated based on the transportation distance and the transportation load, and then accumulated by combining the emission coefficients of the transportation mode.

[0097] At each process node, energy consumption is accumulated and simultaneously verified with the proportion of by-products.

[0098] Under the material node, track raw material consumption and dynamically adjust processing parameters;

[0099] Real-time collection of the remaining computational load at the current traversed node example level, and setting up dual overflow protection at the hardware and logic layers based on the remaining computational load;

[0100] When the remaining computation exceeds a preset threshold, dual overflow protection is triggered. Under the hardware layer protection, the JVM heap memory is expanded, and under the logic layer protection, the recursive call stack is pruned to skip redundant computation paths.

[0101] Specifically, during the traversal of the node example network, an intermediate result storage pool is created to store intermediate results during recursive calls. Since recursive calls may cause stack overflows, this embodiment converts the recursive call hierarchy into a circular queue to manage the state of recursive calls, effectively avoiding the risk of stack overflows. A depth threshold is also set; when the traversal depth reaches the threshold, a batch processing mechanism is triggered, caching the current node's state and intermediate results in the storage pool for subsequent processing. During traversal, the calculation rules corresponding to the node type identifier field of each level of node example are collected in real time. For transportation nodes, the transportation carbon emission factor is calculated based on transportation distance and load, and accumulated using the emission coefficient of the transportation mode to obtain the carbon emissions during the logistics process. For process nodes, energy consumption accumulation operations are performed. The system performs operations and synchronizes the calculations with the by-product ratio to ensure that the emission ratio of main and by-products meets the preset standards. For material nodes, it tracks the consumption of raw materials and dynamically adjusts the processing parameters according to the actual situation. The above calculation rules are all dynamically loaded and executed based on the node type identifier field and are well known to those skilled in the art. They can be adaptively adjusted according to actual needs. In addition, during the traversal process, the remaining computational load of the current traversed node example level is collected in real time, and a dual overflow protection mechanism of hardware layer and logic layer is set according to the remaining computational load. When the remaining computational load exceeds the preset threshold, the dual overflow protection is triggered. Under the hardware layer protection, the JVM heap memory is expanded to increase the system's memory capacity. Under the logic layer protection, the recursive call stack is pruned to skip redundant calculation paths to reduce the system's computational burden, thereby ensuring the stability of the traversal node example network process.

[0102] Secondly, the steps to complete the verification of the emission ratios of main and by-products and the conversion of measurement units include:

[0103] Real-time data collection of main product output and by-product output at process nodes, and calculation of the main product and by-product output ratio;

[0104] Obtain the preset standard ratio range, compare the output ratio of main and auxiliary products with the standard ratio range, and when the output ratio of main and auxiliary products exceeds the standard ratio range, mark the corresponding process node as an abnormal node and start the ratio verification and repair mechanism.

[0105] When the ratio of main and by-product output is within the standard ratio range, a standardized conversion of measurement units is performed to convert all non-standard measurement units within the composite node into a unified carbon equivalent unit.

[0106] The execution of the proportional verification and repair mechanism includes the following steps:

[0107] Record abnormal nodes as baseline nodes, retrieve historical process parameters of the baseline nodes and compare them with current process parameters to identify the source of parameter deviation;

[0108] Obtain the carbon emission data corresponding to historical process parameters under the source of parameter deviation, and combine it with the current carbon emission data to calculate the carbon emission increment caused by parameter deviation;

[0109] Based on the carbon emission increment, the process parameters of abnormal nodes are corrected, and parameter synchronization update instructions are sent to the associated material nodes and transportation nodes.

[0110] In the above process, when verifying the emission ratio of main and by-products, the output quantities of main products and by-products at each process node are first collected in real time. By statistically analyzing these output quantities, the output ratio of main and by-products can be calculated. Then, a preset standard ratio range is obtained. This standard ratio range is set based on industry experience and environmental requirements and is used to measure the rationality of the main and by-product output ratio. The actual main and by-product output ratio is compared with the standard ratio range. If it does not exceed the standard ratio range, a unit standardization conversion process is executed. All non-standard units of measurement within the composite node are uniformly converted to carbon equivalent units according to preset unit conversion rules. For example, if the output quantity of the main product in the process node is in tons, while the output quantity of the by-product is in kilograms, the by-product output is converted to carbon equivalent units based on the conversion relationship between tons and kilograms. The unit for the output quantity of the main product is used to unify the benchmark for carbon equivalent calculation. If it exceeds the standard ratio range, it indicates that there may be an anomaly in the production process. At this time, the corresponding process node will be automatically marked as an abnormal node, and the ratio verification and repair mechanism will be immediately activated. First, the abnormal node is recorded as the benchmark node, and the historical process parameters of the node are retrieved and compared with the current process parameters to identify the specific source of the parameter deviation. Then, the carbon emission data corresponding to the historical process parameters under the source of the parameter deviation is obtained, and combined with the current carbon emission data, the carbon emission increment caused by the parameter deviation is calculated. Then, based on the calculation result of the carbon emission increment, the process parameters of the abnormal node are corrected to ensure the environmental protection and efficiency of the production process. Parameter synchronization update instructions will also be sent to the material nodes and transportation nodes associated with the abnormal node to ensure that the carbon emission data of the entire production chain remains consistent.

[0111] S5. Based on the asynchronous framework, the node example network is split into independent subtrees, and the carbon emission calculation of each independent subtree is executed synchronously through parallel computing. Then, the calculation results of each independent subtree are summarized to form the product-level carbon footprint result.

[0112] In step S5, when outputting the product carbon footprint results, the carbon emissions over the entire product lifecycle are calculated to form a statistical summary of the total carbon emissions of the product. The step of splitting the node example network into independent subtrees based on the asynchronous framework includes:

[0113] Based on the node type identifier field in the node example network, the node example network is divided into multiple heterogeneous subgraphs. Each heterogeneous subgraph corresponds to a different type of computation task, which includes transportation, process and material types.

[0114] Each heterogeneous subgraph is assigned an independent computation thread, and the corresponding computation rules and verification logic are preloaded in the computation thread;

[0115] The asynchronous message queue coordinates the data dependencies between computing threads, and automatically triggers the computing process of subsequent heterogeneous subgraphs after the computing thread completes the current subgraph computing task.

[0116] Specifically, when calculating carbon emissions, the entire node example network is first divided into multiple heterogeneous subgraphs based on the node type identifier field. These subgraphs are categorized into transportation, process, and material types according to the type of computational task. Each type of subgraph carries a corresponding computational task. For example, the transportation subgraph is mainly responsible for calculating logistics carbon emissions, the process subgraph focuses on energy consumption and emissions in the production process, and the material subgraph tracks the consumption of raw materials and their carbon emissions. To ensure computational efficiency, each heterogeneous subgraph is assigned an independent computation thread for parallel computation. Before running, each computation thread preloads computational rules and verification logic that match its respective task to ensure the accuracy and consistency of the computation. To coordinate the data dependencies between computational threads, an asynchronous message queue mechanism is introduced. After a computational thread completes the computational task of the current subgraph, it automatically triggers the computation process of the subsequent heterogeneous subgraphs and transmits necessary data and instructions through the asynchronous message queue, thereby improving the parallelism of the computational threads and effectively avoiding the performance bottleneck caused by data synchronization.

[0117] Secondly, the steps of simultaneously calculating the carbon emissions of each independent subtree through parallel computing, and then summarizing the calculation results of each independent subtree to form the product-level carbon footprint result include:

[0118] Obtain the computational complexity of the heterogeneous subgraphs under each independent subtree, and determine the execution priority of each heterogeneous subgraph based on the computational complexity;

[0119] Based on the execution priority, allocate corresponding computing resources to each heterogeneous subgraph and start the parallel computing process;

[0120] During the parallel computing process, the computational resource consumption and task progress of each heterogeneous subgraph are collected in real time. When the computational resource consumption and task progress of a heterogeneous subgraph do not match, redundant computational resources are scheduled from heterogeneous subgraphs with redundant computational resources to heterogeneous subgraphs with insufficient computational resources to balance the computational load of each subgraph.

[0121] According to the preset aggregation rules, the carbon emission calculation results of each independent subtree are weighted and summarized to form the carbon footprint result of the entire product life cycle;

[0122] In this implementation, when outputting carbon footprint results, it is first necessary to determine the computational complexity of heterogeneous subgraphs under each independent subtree. Computational complexity reflects the time and resource consumption required to complete the computation task. Based on the evaluation results of computational complexity, reasonable computing resources are allocated to each heterogeneous subgraph, and its execution priority is determined to optimize the allocation of computing resources and improve computational efficiency. In terms of computing resource allocation, corresponding computing resources, including CPU, memory, and I / O devices, are allocated to each heterogeneous subgraph according to the execution priority, and parallel computing processes are initiated so that multiple heterogeneous subgraphs can calculate carbon emissions simultaneously. During parallel computing, the computational resource consumption and task progress of each heterogeneous subgraph are collected in real time and monitored. By comparing the actual usage of computing resources with the task progress, the problem of unbalanced computing load can be detected and addressed in a timely manner. When the computing resource consumption of a heterogeneous subgraph does not match the task progress, such as when insufficient computing resources cause the progress to lag, some resources will be scheduled from heterogeneous subgraphs with redundant computing resources to that heterogeneous subgraph, thereby balancing the computing load of each subgraph and ensuring the efficiency of parallel computing. After obtaining the carbon emission calculation results of each independent subtree, they will be weighted and summarized according to the preset aggregation rules. The aggregation rules are formulated based on the assessment of the contribution of each stage of the product's entire life cycle to carbon emissions, which can accurately reflect the impact of different stages on the total carbon emissions. Through weighted summarization, a complete and accurate product-level carbon footprint result can be formed.

[0123] Please see Figure 2 A product carbon footprint modeling system, using the above-mentioned product carbon footprint modeling method, includes:

[0124] The initialization module is used to create the carbon node base class and define a set of general attributes, including carbon emission factor update function, energy consumption integration function, and node type identifier field;

[0125] The node example building module is used to create node examples with multiple levels based on the carbon node base class. The node examples include material nodes, process nodes, and transportation nodes.

[0126] The node combination module is used to establish combination relationships between node instances, combine process nodes and material nodes to form composite nodes, and establish a connection with the update interface of transportation nodes to form dynamic updates of carbon emission data.

[0127] The risk optimization module is used to traverse the node example network and perform tail recursive optimization to eliminate stack overflow risk. It also synchronously triggers the differential rules corresponding to the node type identifier field to complete the verification of the emission ratio of main and by-products and the conversion of measurement units.

[0128] The carbon footprint output module is used to split the node example network into independent subtrees based on an asynchronous framework, and to calculate the carbon emissions of each independent subtree synchronously through parallel computing. Then, the calculation results of each independent subtree are summarized to form the product-level carbon footprint result.

[0129] The execution process of the carbon footprint modeling system has been described in detail in the above product carbon footprint modeling method, and will not be repeated here.

[0130] Please see Figure 3 An electronic device, comprising:

[0131] at least one processor;

[0132] and memory that is communicatively connected to at least one processor;

[0133] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to perform the above-described product carbon footprint modeling method.

[0134] The processor of the aforementioned electronic device can be a central processing unit (CPU), a graphics processing unit (GPU), or a digital signal processor (DSP), etc. The memory can be random access memory (RAM), read-only memory (ROM), or flash memory, etc. The electronic device also includes a communication component for data interaction with other devices or networks. The communication component supports wired or wireless communication. In addition, the electronic device may also include an arithmetic logic unit (ALU) or a floating-point unit (FPU), input devices and output devices, such as a keyboard, mouse, touch screen, monitor, or printer, etc., which can realize human-computer interaction and data input and output functions.

[0135] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0136] The foregoing is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained herein shall, unless otherwise specified or limited, be implemented in accordance with conventional means in the art.

Claims

1. A method for modeling the carbon footprint of a product, characterized in that: include: Create a base class for carbon nodes and define a set of common attributes including carbon emission factor update functionality, energy consumption integration functionality, and a node type identifier field; Node examples with multiple levels are created based on the carbon node base class, including material nodes, process nodes, and transportation nodes; Establish combination relationships between node examples, combine process nodes and material nodes to form composite nodes, and establish a connection with the update interface of transportation nodes to form dynamic updates of carbon emission data; Traverse the node example network and perform tail recursion optimization to eliminate the risk of stack overflow, and simultaneously trigger the differential rules corresponding to the node type identifier field to complete the verification of the emission ratio of main and by-products and the conversion of measurement units. The node example network is split into independent subtrees based on an asynchronous framework, and the carbon emission calculation of each independent subtree is performed synchronously through parallel computing. The calculation results of each independent subtree are then summarized to form the product-level carbon footprint result. The step of creating node instances with multiple levels based on the carbon node base class includes: Bind the product bill of materials and load the material data from the product bill of materials to build material nodes level by level; Obtain the industry process rule library and define the mapping relationship between process parameters and product energy consumption data, and build process nodes step by step; Bind transportation nodes, load transportation mode and distance data, and build transportation nodes level by level; Among them, the material node includes the source of raw materials and the processing process, the process node includes the energy consumption of the production process, and the transportation node records the carbon emissions of logistics.

2. The product carbon footprint modeling method according to claim 1, characterized in that: The steps for creating the carbon node base class include: Obtain the benchmark carbon emission factor database and extract the benchmark carbon emission factors related to the product from the benchmark carbon emission factor database; Acquire product energy consumption data, which includes direct energy consumption data and indirect energy consumption data. Direct energy consumption data is read in real time from sensors used for monitoring production equipment, while indirect energy consumption data is calculated through process parameters. Define a multi-level classification coding system that covers all stages of the product lifecycle, with each stage corresponding to a unique coding identifier field.

3. The product carbon footprint modeling method according to claim 1, characterized in that: The step of establishing the composition relationship between node examples includes: Obtain the product processing flow charts under the process node and material node respectively, and establish a combination mapping between nodes based on the logical relationship in the flow chart to form a composite node structure; Connect the carbon emission data interface between the composite node and the transportation node, and collect the increase in transportation distance and weight in real time. The distance increase is compared with a preset distance increase threshold, and when the distance increase exceeds the distance increase threshold, an update instruction is sent to the process node to recalculate the logistics carbon emissions. The weight increase is compared with a preset weight increase threshold, and when the weight increase exceeds the weight increase threshold, an adjustment command is sent to the material node to adjust the raw material consumption ratio in the material node.

4. The product carbon footprint modeling method according to claim 1, characterized in that: The steps of traversing the example network of nodes and performing tail recursion optimization to eliminate the risk of stack overflow include: Create an intermediate result storage pool and convert the recursive call hierarchy into a circular queue; Set a depth threshold, and when the traversal depth reaches the depth threshold, trigger a batch processing mechanism to cache the current node state and intermediate results to the storage pool. The calculation rules corresponding to the node type identifier field of each level of node examples are collected in real time, including: At each transportation node, the transportation carbon emission factor is calculated based on the transportation distance and the transportation load, and then accumulated by combining the emission coefficients of the transportation mode. At each process node, energy consumption is accumulated and simultaneously verified with the proportion of by-products. Under the material node, track raw material consumption and dynamically adjust processing parameters; Real-time collection of the remaining computational load at the current traversed node example level, and setting up dual overflow protection at the hardware and logic layers based on the remaining computational load; When the remaining computation exceeds a preset threshold, dual overflow protection is triggered. Under hardware layer protection, the JVM heap memory is expanded, and under logical layer protection, the recursive call stack is pruned to skip redundant computation paths.

5. The product carbon footprint modeling method according to claim 4, characterized in that: The steps for completing the verification of the emission ratios of main and by-products and the conversion of measurement units include: Real-time data collection of main product output and by-product output at process nodes, and calculation of the main product and by-product output ratio; Obtain the preset standard ratio range, compare the output ratio of main and auxiliary products with the standard ratio range, and when the output ratio of main and auxiliary products exceeds the standard ratio range, mark the corresponding process node as an abnormal node and start the ratio verification and repair mechanism. When the ratio of main and by-product output is within the standard ratio range, a standardized conversion of measurement units is performed to convert all non-standard measurement units within the composite node into a unified carbon equivalent unit. The execution of the proportional verification and repair mechanism includes the following steps: Record abnormal nodes as baseline nodes, retrieve historical process parameters of the baseline nodes and compare them with current process parameters to identify the source of parameter deviation; Obtain the carbon emission data corresponding to historical process parameters under the source of parameter deviation, and combine it with the current carbon emission data to calculate the carbon emission increment caused by parameter deviation; Based on the carbon emission increment, the process parameters of abnormal nodes are corrected, and parameter synchronization update instructions are sent to the associated material nodes and transportation nodes.

6. The product carbon footprint modeling method according to claim 1, characterized in that: The step of splitting the node example network into independent subtrees based on the asynchronous framework includes: Based on the node type identifier field in the node example network, the node example network is divided into multiple heterogeneous subgraphs. Each heterogeneous subgraph corresponds to a different type of computation task, which includes transportation, process and material types. Each heterogeneous subgraph is assigned an independent computation thread, and the corresponding computation rules and verification logic are preloaded in the computation thread; The asynchronous message queue coordinates the data dependencies between computing threads, and automatically triggers the computing process of subsequent heterogeneous subgraphs after the computing thread completes the current subgraph computing task.

7. The product carbon footprint modeling method according to claim 6, characterized in that: The steps of simultaneously calculating the carbon emissions of each independent subtree through parallel computing, and then summarizing the calculation results of each independent subtree to form the product-level carbon footprint result, include: Obtain the computational complexity of the heterogeneous subgraphs under each independent subtree, and determine the execution priority of each heterogeneous subgraph based on the computational complexity; Based on the execution priority, allocate corresponding computing resources to each heterogeneous subgraph and start the parallel computing process; During the parallel computing process, the computational resource consumption and task progress of each heterogeneous subgraph are collected in real time. When the computational resource consumption and task progress of a heterogeneous subgraph do not match, redundant computational resources are scheduled from heterogeneous subgraphs with redundant computational resources to heterogeneous subgraphs with insufficient computational resources to balance the computational load of each subgraph. According to the preset aggregation rules, the carbon emission calculation results of each independent subtree are weighted and summarized to form the carbon footprint result of the entire product life cycle.

8. A product carbon footprint modeling system, characterized in that: The product carbon footprint modeling method according to any one of claims 1 to 7 includes: The initialization module is used to create the carbon node base class and define a set of general attributes, including carbon emission factor update function, energy consumption integration function, and node type identifier field; The node example building module is used to create node examples with multiple levels based on the carbon node base class. The node examples include material nodes, process nodes, and transportation nodes. The node combination module is used to establish combination relationships between node instances, combine process nodes and material nodes to form composite nodes, and establish a connection with the update interface of transportation nodes to form dynamic updates of carbon emission data. The risk optimization module is used to traverse the node example network and perform tail recursive optimization to eliminate stack overflow risk. It also synchronously triggers the differential rules corresponding to the node type identifier field to complete the verification of the emission ratio of main and by-products and the conversion of measurement units. The carbon footprint output module is used to split the node example network into independent subtrees based on an asynchronous framework, and to calculate the carbon emissions of each independent subtree synchronously through parallel computing. Then, the calculation results of each independent subtree are summarized to form the product-level carbon footprint result.

9. An electronic device, characterized in that: The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the product carbon footprint modeling method according to any one of claims 1 to 7.

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