Product carbon footprint modeling method and system

Abstract

The invention belongs to the technical field of carbon management, and particularly relates to a product carbon footprint modeling method and system. According to the method, through the dynamic construction and optimization mechanism of the node example network, the modeling precision and calculation efficiency of carbon emission data in a complex production process are remarkably improved, and compared with a traditional data model based on a tree structure, the scheme not only solves the problems of unclear data hierarchy semantics, low storage efficiency, high recursive calculation complexity and the like, but also has the advantages of being high in practicability and the like. The dynamic adjustment capability of the carbon emission tracking model is enhanced, so that the model can better adapt to frequent change of the production process, verification of the emission proportion of main and by-products and integration of multi-dimensional carbon emission data are realized, reliable data support is provided for carbon management decision, and meanwhile, through a computing resource dynamic scheduling mechanism between heterogeneous subgraphs, the calculation efficiency is improved. The stability and the response speed in a large-scale carbon footprint calculation scene are effectively improved, and the period of carbon footprint evaluation is greatly shortened while the calculation precision is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of carbon management technology, and specifically relates to a product carbon footprint modeling method and system. Background Art

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

[0003] In existing technologies, software tools based on life cycle analysis (LCA), such as SimaPro and GaBi, and various carbon footprint cloud services, help users assess the carbon footprint of products throughout their life cycle through preset databases and calculation 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 recursive calculation complexity (large cumulative errors), and a lack of dynamic adjustment capabilities. These problems make it difficult to adapt to the frequent changes in current corporate production processes. Based on this, this solution provides a product carbon footprint modeling method to address the above issues. Summary of the Invention

[0004] The purpose of the present 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: A product carbon footprint modeling method, comprising: Create a carbon node base class and define a common property set including carbon emission factor update function, energy consumption integration function and node type identification field; Creating node instances including multiple levels based on the carbon node base class, wherein the node instances include material nodes, process nodes, and transportation nodes; Establish a combination relationship between node instances, combine process nodes and material nodes to form a composite node, and establish a connection with the update interface of the transportation node to form a dynamic update of carbon emission data; Traverse the node example network and perform tail recursion optimization to eliminate stack overflow risks. Simultaneously trigger the differentiation rules corresponding to the node type identification field to complete the main and by-product emission ratio verification and measurement unit conversion; Based on the asynchronous framework, the node example network is split into independent subtrees, and the carbon emissions calculation of each independent subtree is synchronously executed through parallel computing. The calculation results of each independent subtree are then aggregated to form a product-level carbon footprint result.

[0006] In a preferred embodiment, the step of creating a carbon node base class includes: Obtain a benchmark carbon emission factor database and extract product-related benchmark carbon emission factors from the benchmark carbon emission factor database; Obtain 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 to monitor production equipment, while indirect energy consumption data is calculated based on process parameters. Define a multi-level classification and coding system that covers all stages of the product life cycle, and each stage corresponds to a unique coding identification field.

[0007] In a preferred embodiment, the step of creating a node instance including multiple levels based on the carbon node base class includes: Bind the product bill of materials, load the material data in the product bill of materials, and build material nodes step by step; Obtain the industry process rule library, define the mapping relationship between process parameters and product energy consumption data, and build process nodes step by step; Bind the transport node, load the transport mode and distance data, and build the transport node step by step; 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 link, and the transportation node records the logistics carbon emissions.

[0008] In a preferred solution, the step of establishing a combination relationship between node instances includes: Obtain product processing flow charts under process nodes and material nodes 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 composite node to the carbon emission data interface of the transport node, and collect the distance increase and weight increase of the transport distance in real time; Compare the distance increase with a preset distance increase threshold, and when the distance increase exceeds the distance increase threshold, send an update instruction 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 instruction is sent to the material node to adjust the raw material consumption ratio in the material node.

[0009] In a preferred embodiment, the step of traversing the example node network and performing tail recursion optimization to eliminate the risk of stack overflow includes: 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 the batch processing mechanism to cache the current node status and intermediate results to the storage pool; The calculation rules corresponding to the node type identification fields of node examples at each level are collected in real time, including: At the transport node, the transport carbon emission factor is calculated based on the transport distance and transport load, and is cumulatively calculated in combination with the emission coefficient of the transport mode; At each process node, perform energy consumption accumulation and synchronize verification with the by-product ratio; Under the material node, track raw material consumption and dynamically adjust processing parameters; Collect the remaining computational load of the current traversal node instance level in real time, and set dual overflow protection at the hardware and logic layers based on the remaining computational load; When the remaining computing power exceeds the preset threshold, double overflow protection is triggered. Under the hardware layer protection, the JVM heap memory is expanded. Under the logic layer protection, the recursive call stack is pruned to skip redundant computing paths.

[0010] In a preferred embodiment, the steps of verifying the emission ratio of main products and by-products and converting the measurement units include: Collect the output quantity of main products and by-products at process nodes in real time, and calculate the output ratio of main products to by-products; Obtain the preset standard ratio range, and compare the output ratio of the main and by-products with the standard ratio range. When the output ratio of the main and by-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 output ratio of main products and by-products is within the standard ratio range, perform measurement unit standardization conversion and convert all non-standard measurement units in the composite node into unified carbon equivalent units; The following steps are included when executing the ratio check and repair mechanism: Record the abnormal node as the reference node, retrieve the historical process parameters of the reference node and compare them with the current process parameters to identify the source of the parameter deviation; Obtain the carbon emission data corresponding to the 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 the parameter deviation; Based on the carbon emission increment, the process parameters of the abnormal nodes are corrected, and parameter synchronization update instructions are sent to the associated material nodes and transportation nodes.

[0011] In a preferred solution, the step of splitting the node example network into independent subtrees based on the asynchronous framework includes: According to the node type identification field in the node example network, the node example network is divided into multiple heterogeneous subgraphs, each of which corresponds to a different computing task type, where the computing task types include transportation, process, and material types; Assign an independent computing thread to each heterogeneous subgraph and preload the corresponding computing rules and verification logic into the computing thread; The data dependencies between computing threads are coordinated through asynchronous message queues. After a computing thread completes the current subgraph computing task, the computing process of subsequent heterogeneous subgraphs is automatically triggered.

[0012] In a preferred embodiment, the step of synchronously executing carbon emission calculations for each independent subtree through parallel computing and then aggregating the calculation results of each independent subtree to form a product-level carbon footprint result includes: 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; According to the execution priority, corresponding computing resources are allocated to each heterogeneous subgraph and parallel computing processes are started; During the parallel computing process, the computing resource consumption and task progress of each heterogeneous subgraph are collected in real time. When the computing resource consumption and task progress of a heterogeneous subgraph do not match, redundant computing resources are dispatched from the heterogeneous subgraph with redundant computing resources to the heterogeneous subgraph with insufficient computing resources to balance the computing 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 product's entire life cycle.

[0013] The present invention also provides a product carbon footprint modeling system, which uses the above-mentioned product carbon footprint modeling method, including: Initialization module, used to create the carbon node base class and define a common property set including carbon emission factor update function, energy consumption integration function and node type identification field; A node example building module, used to create node examples including multiple levels based on the carbon node base class, wherein the node examples include material nodes, process nodes, and transportation nodes; The node combination module is used to establish the combination relationship between node examples, combine the process node and the material node to form a composite node, and establish a connection with the update interface of the transportation node to form a dynamic update of carbon emission data; The risk optimization module is used to traverse the node example network and perform tail recursion optimization to eliminate stack overflow risks. It also triggers the differentiation rules corresponding to the node type identification field to complete the main and by-product emission ratio verification and measurement unit conversion; The carbon footprint output module is used to split the node example network into independent subtrees based on an asynchronous framework, and synchronously perform carbon emission calculations for each independent subtree through parallel computing. The calculation results of each independent subtree are then aggregated to form a product-level carbon footprint result.

[0014] And, an electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the above-mentioned product carbon footprint modeling method.

[0015] The technical effects achieved by the present invention are: The present invention significantly improves the modeling accuracy and computing efficiency of carbon emission data in complex production processes through the dynamic construction and optimization mechanism of the node example network. Compared with the traditional tree-structured data model, this solution not only solves the problems of 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, and realizes the verification of the main and by-product emission ratios and the integration of multi-dimensional carbon emission data, providing reliable data support for carbon management decisions. At the same time, through the dynamic scheduling mechanism of computing resources between heterogeneous subgraphs, it effectively improves the stability and response speed in large-scale carbon footprint calculation scenarios, further reduces computing energy consumption, and greatly shortens the carbon footprint assessment cycle while ensuring calculation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flow chart of the method of the present invention; Figure 2 It is a schematic diagram of the system modules of the present invention; Figure 3 It is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "one embodiment" or "embodiment" 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 various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive of other embodiments.

[0020] See also Figure 1 As shown, the present invention provides a product carbon footprint modeling method, comprising: S1. Create a carbon node base class and define a common attribute set including carbon emission factor update function, energy consumption integration function and node type identification field; In step S1, as people's environmental awareness continues to improve, the demand for monitoring carbon emissions is increasing. In this embodiment, first, by creating a carbon node base class with a carbon emission factor update function, dynamic maintenance of basic data is achieved. 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 the node type identification field, providing a basis for subsequent data processing. The steps of creating the carbon node base class include: Obtain a benchmark carbon emission factor database and extract product-related benchmark carbon emission factors from the benchmark carbon emission factor database; Obtain 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 to monitor production equipment, while indirect energy consumption data is calculated based on process parameters. Define a multi-level classification and coding system that covers all stages of the product life cycle, with each stage corresponding to a unique coding identification field; Specifically, when creating a carbon node base class, you first need to obtain the benchmark carbon emission factor database. The benchmark carbon emission factor database includes carbon emission factors published by the International Organization for Standardization and historical carbon emission data accumulated within the enterprise, so as to provide corresponding calculation basis for different types of carbon emissions. By extracting the benchmark carbon emission factors related to the product from the benchmark carbon emission factor database, the reliability of the carbon footprint output can be ensured. At the same time, product energy consumption data will be obtained as the basis for carbon emission assessment, including direct energy consumption data and indirect energy consumption data. Direct energy consumption data is obtained through real-time monitoring of production equipment, and indirect energy consumption data is calculated based on process parameters, thereby comprehensively reflecting the energy consumption in the product production process. Consumption situation, for example, when determining indirect energy consumption data, the operating energy consumption of auxiliary equipment can be derived based on the process parameters of the production batch, and then the corresponding carbon emissions can be calculated. For example, assuming that in a certain production batch, a certain raw material used requires additional heating treatment under specific process conditions, the type and amount of additional energy consumed in the heating process are quantified and analyzed, and the carbon emissions of this link are calculated in combination with the corresponding carbon emission factor. In this way, the carbon emissions of the indirect energy consumption data can be calculated and used for subsequent aggregation into the total carbon emissions of the carbon node base class. In addition, defining a multi-level classification and coding system can assist in the fine management of carbon emission data to ensure that carbon emissions at each stage can be accurately tracked.

[0021] S2. Create node examples including multiple levels based on the carbon node base class, including material nodes, process nodes, and transportation nodes; In step S2, after the carbon node base class is determined, material nodes, process nodes, and transportation nodes are constructed step by step based on the carbon node base class to ensure that each node contains detailed carbon emission-related information. The material node records the type, source, and processing of raw materials, providing a basis for calculating the carbon emissions of raw materials. The process node integrates energy consumption data in the production process to reflect the impact of different process steps on carbon emissions. The transportation node records carbon emissions in the logistics process, including transportation methods, distances, and loads. The steps of creating node examples including multiple levels based on the carbon node base class include: Bind the product bill of materials, load the material data in the product bill of materials, and build material nodes step by step; Obtain the industry process rule library, define the mapping relationship between process parameters and product energy consumption data, and build process nodes step by step; Bind the transport node, load the transport mode and distance data, and build the transport node step by step; 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; Specifically, when creating a node example, the product bill of materials will be bound first to ensure that all material data is entered into the system. By loading the material data, detailed material nodes can be constructed step by step. Each material node records the type, source, processing process and related carbon emission information of the raw materials. Then the industry process rule library will be obtained. The industry process rule library contains a database of mapping relationships between various production process parameters and energy consumption data. Based on the industry process rules, process nodes can be constructed step by step. Each process node integrates the energy consumption data in the production link, reflecting the specific impact of different process steps on carbon emissions, so as to track carbon emissions in the production process. In addition, it will be bound Define the transportation nodes, load the data such as transportation mode, distance and load, and build the transportation node network step by step. The transportation nodes record the carbon emission information in the logistics process, including the emission coefficient, transportation distance and transportation load under different transportation modes. Based on this, the carbon emissions in the logistics process can be calculated and included in the overall carbon footprint tracking. After completing the construction of the node example, a complete carbon footprint network model can be formed. The carbon footprint model covers all stages of the product life cycle, from raw material procurement, production and processing to logistics distribution. Each stage has a corresponding node to track carbon emissions, so that the carbon footprint of the product can be fully understood through the carbon footprint model.

[0022] S3. Establish a combination relationship between node instances, combine process nodes and material nodes to form a composite node, and establish a connection with the update interface of the transportation node to achieve dynamic update of carbon emission data; In step S3, after the node example network is constructed, it is necessary to establish a combination relationship between the nodes to ensure that each node can work together to complete the carbon footprint tracking. 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 more intuitively reflected. At the same time, a connection is established with the update interface of the transportation node to ensure the real-time update of logistics carbon emission data, thereby dynamically reflecting the carbon emission changes throughout the product life cycle. The steps of establishing a combination relationship between node examples include: Obtain product processing flow charts under process nodes and material nodes 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 composite node to the carbon emission data interface of the transport node, and collect the distance increase and weight increase of the transport distance in real time; Compare the distance increase with a preset distance increase threshold, and when the distance increase exceeds the distance increase threshold, send an update instruction 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 instruction is sent to the material node to adjust the raw material consumption ratio in the material node; Specifically, when establishing a combination relationship between node examples, the product processing flow charts under the process node and material node are first obtained respectively. The product processing flow chart describes the process of raw materials being processed into final products in different process steps. Through the logical relationship in the flow chart, the combination mapping between the material node and the process node can be accurately established, thereby 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 update of logistics carbon emission data, the composite node will be connected to the carbon emission data interface of the transportation node to collect the distance increase and weight increase during the transportation process in real time. The distance increase and weight increase reflect the changes in the logistics link and have a direct impact on carbon emissions. Therefore, the collected distance increase will be compared with the preset distance increase threshold. Once the distance increase exceeds the threshold, an update instruction will be immediately sent to the process node to recalculate the logistics carbon emissions to ensure data accuracy. Similarly, the weight increase will also be compared with the preset weight increase threshold. If it exceeds the threshold, an adjustment instruction will be sent to the material node to adjust the raw material consumption ratio to adapt to changes in the logistics process, thereby achieving the purpose of real-time update of carbon emission data.

[0023] S4. Traverse the node example network and perform tail recursion optimization to eliminate the risk of stack overflow. Simultaneously trigger the differentiation rules corresponding to the node type identification field to complete the main and by-product emission ratio verification and measurement unit conversion. In step S4, after the node example network is output, the entire node example network is traversed, and the nodes in the network are depth-first traversed using a tail recursive optimization algorithm to effectively eliminate the stack overflow risk that may occur during the recursive call process. In addition, the differentiation rule corresponding to the node type identification field is synchronously triggered during the traversal process to verify whether the emission ratio of the main and by-products meets the preset standard. At the same time, the measurement units between different nodes are automatically converted to ensure the accuracy and consistency of the data during the transmission process. The steps of traversing the node example network and performing tail recursive optimization to eliminate the stack overflow risk 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 the batch processing mechanism to cache the current node status and intermediate results to the storage pool; The calculation rules corresponding to the node type identification fields of node examples at each level are collected in real time, including: At the transport node, the transport carbon emission factor is calculated based on the transport distance and transport load, and is cumulatively calculated in combination with the emission coefficient of the transport mode; At each process node, perform energy consumption accumulation and synchronize verification with the by-product ratio; Under the material node, track raw material consumption and dynamically adjust processing parameters; Collect the remaining computational load of the current traversal node instance level in real time, and set dual overflow protection at the hardware and logic layers based on the remaining computational load; When the remaining computational load exceeds the preset threshold, double overflow protection is triggered. At the hardware level, the JVM heap memory is expanded. At the logic level, the recursive call stack is pruned to skip redundant computation paths. Specifically, in the process of traversing the node example network, an intermediate result storage pool will be created to store the intermediate results in the recursive call process. Since recursive calls may cause stack overflow, in this embodiment, the level of recursive calls is converted into a circular queue, and the state of recursive calls is managed by the circular queue, which effectively avoids the risk of stack overflow. At the same time, a depth threshold is set. When the traversal depth reaches the depth threshold, the batch processing mechanism will be triggered to cache the state of the current node and the intermediate results in the storage pool for subsequent processing. During the traversal process, the calculation rules corresponding to the node type identification field of the node examples at each level will be collected in real time. For transportation nodes, the transportation carbon emission factor will be calculated based on the transportation distance and transportation load, and the emission coefficient of the transportation mode will be accumulated and calculated to obtain the carbon emissions in the logistics process. For process nodes, energy consumption accumulation operations will be performed The operation is carried out and synchronized with the by-product ratio to ensure that the emission ratio of the main and by-products meets the preset standards. For material nodes, the consumption of raw materials will be tracked, and the processing parameters will be dynamically adjusted according to actual conditions. The above calculation rules are dynamically loaded and executed based on the node type identification 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 computing amount of the current traversal node example level will be collected in real time, and a dual overflow protection mechanism of the hardware layer and the logic layer will be set according to the remaining computing amount. When the remaining computing amount exceeds the preset threshold, double overflow protection will be triggered. Under the hardware layer protection, the JVM heap memory will be expanded to increase the memory capacity of the system. Under the logic layer protection, the recursive call stack will be pruned and redundant computing paths will be skipped to reduce the computing burden of the system, thereby ensuring the stability of the node example network process.

[0024] Secondly, the steps to verify the emission ratio of main and by-products and convert the measurement units are completed, including: Collect the output quantity of main products and by-products at process nodes in real time, and calculate the output ratio of main products to by-products; Obtain the preset standard ratio range, and compare the output ratio of the main and by-products with the standard ratio range. When the output ratio of the main and by-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 output ratio of main products and by-products is within the standard ratio range, the unit standardization conversion is performed to convert all non-standard units of measurement in the composite node into unified carbon equivalent units; The following steps are included when executing the ratio check and repair mechanism: Record the abnormal node as the benchmark node, retrieve the historical process parameters of the benchmark node and compare them with the current process parameters to identify the source of the parameter deviation; Obtain the carbon emission data corresponding to the 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 the parameter deviation; Based on the carbon emission increment, the process parameters of the abnormal nodes are corrected, and parameter synchronization update instructions are sent to the associated material nodes and transportation nodes; In the above, when verifying the emission ratio of main and by-products, the main product output quantity and by-product output quantity under the process node will be collected in real time first. By counting the main product output quantity and by-product output quantity, the output ratio of the main and by-products can be calculated, and then the preset standard ratio range will be obtained. The standard ratio range is set based on industry experience and environmental protection 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, the measurement unit standardization conversion process will be executed, and all non-standard measurement units in the composite node will be uniformly converted into carbon equivalent units through the preset unit conversion rules. For example, the main product output quantity involved in the process node is in tons, and the by-product output is in kilograms. At this time, the by-product output will be converted into carbon equivalent units based on the conversion relationship between tons and kilograms. The unit of the main product output quantity is used to unify the benchmark for carbon equivalent calculation. If it exceeds the standard proportion range, it means that there may be an abnormality in the production process. At this time, the corresponding process node will be automatically marked as an abnormal node, and the proportion verification and repair mechanism will be started immediately. First, the abnormal node will be recorded as the benchmark node, and the historical process parameters of the node will be retrieved for difference comparison 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 will be obtained, and combined with the current carbon emission data, the carbon emission increment caused by the parameter deviation will be calculated. After that, based on the calculation result of the carbon emission increment, the process parameters of the abnormal node will be 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.

[0025] S5. Split the node example network into independent subtrees based on the asynchronous framework, and synchronously perform carbon emission calculations for each independent subtree through parallel computing. Then, aggregate the calculation results of each independent subtree to form a product-level carbon footprint result. In step S5, when outputting the product carbon footprint result, the carbon emissions over the entire product life cycle are calculated to form statistics on the total carbon emissions of the product. The step of splitting the node example network into independent subtrees based on the asynchronous framework includes: According to the node type identification field in the node example network, the node example network is divided into multiple heterogeneous subgraphs, each of which corresponds to a different computing task type, where the computing task types include transportation, process, and material types; Assign an independent computing thread to each heterogeneous subgraph and preload the corresponding computing rules and verification logic into the computing thread; The data dependencies between computing threads are coordinated through asynchronous message queues. After a computing thread completes the current subgraph computing task, the computing process of subsequent heterogeneous subgraphs is automatically triggered. Specifically, when calculating carbon emissions, the entire node example network is first divided into multiple heterogeneous subgraphs based on the node type identification field in the node example network. The heterogeneous subgraphs are divided into transportation, process, and material categories according to the different types of computing tasks. Each type of heterogeneous subgraph carries corresponding computing tasks. 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 the efficiency of the calculation, an independent computing thread is assigned to each heterogeneous subgraph for parallel computing. Before running, the computing threads will pre-load the computing rules and verification logic that match their respective tasks to ensure the accuracy and consistency of the calculation. To coordinate the data dependencies between the computing threads, an asynchronous message queue mechanism is introduced. After the computing thread completes the computing task of the current subgraph, it automatically triggers the computing process of the subsequent heterogeneous subgraph and transmits the necessary data and instructions through the asynchronous message queue, thereby improving the parallelism of the computing threads and effectively avoiding the performance bottleneck caused by data synchronization.

[0026] Secondly, the carbon emission calculation of each independent subtree is synchronously performed through parallel computing, and the calculation results of each independent subtree are aggregated to form the product-level carbon footprint result, including the following steps: 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; According to the execution priority, corresponding computing resources are allocated to each heterogeneous subgraph and parallel computing processes are started; During the parallel computing process, the computing resource consumption and task progress of each heterogeneous subgraph are collected in real time. When the computing resource consumption and task progress of a heterogeneous subgraph do not match, redundant computing resources are dispatched from the heterogeneous subgraph with redundant computing resources to the heterogeneous subgraph with insufficient computing resources to balance the computing 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 product's entire life cycle; In this implementation, when outputting the carbon footprint result, it is first necessary to clarify the computational complexity of the heterogeneous subgraphs under each independent subtree. The computational complexity reflects the time and resource consumption required to complete the computational task. According to the evaluation results of the 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 computing efficiency. In terms of computing resource allocation, corresponding computing resources, including CPU, memory, and I / O devices, will be allocated to each heterogeneous subgraph based on the execution priority, and a parallel computing process will be started so that multiple heterogeneous subgraphs can calculate carbon emissions at the same time. During the parallel computing process, the computing resource consumption and task progress of each heterogeneous subgraph will be collected in real time. By monitoring The comparison between the actual usage of computing resources and the progress of tasks can timely discover and deal with the problem of unbalanced computing load. When the computing resource consumption of a heterogeneous subgraph does not match the progress of the task, for example, insufficient computing resources lead to a delay in progress, some resources will be dispatched from the heterogeneous subgraph with redundant computing resources to the heterogeneous subgraph to balance the computing load of each subgraph and ensure 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 evaluation of the contribution of each link to carbon emissions in the entire life cycle of the product. They can accurately reflect the impact of different links on the total carbon emissions. Through weighted aggregation, a complete and accurate product-level carbon footprint result can be formed.

[0027] See also Figure 2 A product carbon footprint modeling system, using the above-mentioned product carbon footprint modeling method, comprises: Initialization module, used to create the carbon node base class and define a common property set including carbon emission factor update function, energy consumption integration function and node type identification field; A node instance building module is used to create node instances with multiple levels based on the carbon node base class. The node instances include material nodes, process nodes, and transportation nodes. The node combination module is used to establish the combination relationship between node examples, combine the process node and the material node to form a composite node, and establish a connection with the update interface of the transportation node to form a dynamic update of carbon emission data; The risk optimization module is used to traverse the node example network and perform tail recursion optimization to eliminate stack overflow risks. It also triggers the differentiation rules corresponding to the node type identification field to complete the main and by-product emission ratio verification and measurement unit conversion; The carbon footprint output module is used to split the node example network into independent subtrees based on an asynchronous framework, and synchronously perform carbon emission calculations for each independent subtree through parallel computing. The calculation results of each independent subtree are then aggregated to form a product-level carbon footprint result.

[0028] The execution process of the above carbon footprint modeling system has been described in detail in the above product carbon footprint modeling method, and will not be repeated here.

[0029] See also Figure 3 , an electronic device, the electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned product carbon footprint modeling method.

[0030] The processor of the above-mentioned electronic device can be a central processing unit (CPU), a graphics processing unit (GPU) or a digital signal processor (DSP), etc., and the memory can be a random access memory (RAM), a read-only memory (ROM) or a flash memory, etc. The electronic device also includes a communication component for exchanging data with other devices or networks. The communication component supports wired communication or wireless communication. In addition, the electronic device can also include an operator, such as an arithmetic logic unit (ALU) or a floating-point unit (FPU), input devices and output devices, such as a keyboard, a mouse, a touch screen, a display or a printer, etc., which can realize human-computer interaction and data input and output functions.

[0031] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0032] 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 product carbon footprint modeling method, characterized by: include: Create a carbon node base class and define a common property set including carbon emission factor update function, energy consumption integration function and node type identification field; Creating node instances including multiple levels based on the carbon node base class, wherein the node instances include material nodes, process nodes, and transportation nodes; Establish a combination relationship between node instances, combine process nodes and material nodes to form a composite node, and establish a connection with the update interface of the transportation node to form a dynamic update of carbon emission data; Traverse the node example network and perform tail recursion optimization to eliminate stack overflow risks. Simultaneously trigger the differentiation rules corresponding to the node type identification field to complete the main and by-product emission ratio verification and measurement unit conversion; Based on the asynchronous framework, the node example network is split into independent subtrees, and the carbon emissions calculation of each independent subtree is synchronously executed through parallel computing. The calculation results of each independent subtree are then aggregated to form a product-level carbon footprint result.

2. A product carbon footprint modeling method according to claim 1, characterized in that: The steps of creating a carbon node base class include: Obtain a benchmark carbon emission factor database and extract product-related benchmark carbon emission factors from the benchmark carbon emission factor database; Obtain 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 to monitor production equipment, while indirect energy consumption data is calculated based on process parameters. Define a multi-level classification and coding system that covers all stages of the product life cycle, and each stage corresponds to a unique coding identification field.

3. The product carbon footprint modeling method according to claim 1, characterized in that: The step of creating a node instance including multiple levels based on the carbon node base class includes: Bind the product bill of materials, load the material data in the product bill of materials, and build material nodes step by step; Obtain the industry process rule library, define the mapping relationship between process parameters and product energy consumption data, and build process nodes step by step; Bind the transport node, load the transport mode and distance data, and build the transport node step by step; 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 link, and the transportation node records the logistics carbon emissions.

4. The product carbon footprint modeling method according to claim 1, characterized in that: The step of establishing a combination relationship between node instances includes: Obtain product processing flow charts under process nodes and material nodes 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 composite node to the carbon emission data interface of the transport node, and collect the distance increase and weight increase of the transport distance in real time; Compare the distance increase with a preset distance increase threshold, and when the distance increase exceeds the distance increase threshold, send an update instruction 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 instruction is sent to the material node to adjust the raw material consumption ratio in the material node.

5. The product carbon footprint modeling method according to claim 1, characterized in that: The steps of traversing the node example network 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 the batch processing mechanism to cache the current node status and intermediate results to the storage pool; The calculation rules corresponding to the node type identification fields of node examples at each level are collected in real time, including: At the transport node, the transport carbon emission factor is calculated based on the transport distance and transport load, and is cumulatively calculated in combination with the emission coefficient of the transport mode; At each process node, perform energy consumption accumulation and synchronize verification with the by-product ratio; Under the material node, track raw material consumption and dynamically adjust processing parameters; Collect the remaining computational load of the current traversal node instance level in real time, and set dual overflow protection at the hardware and logic layers based on the remaining computational load; When the remaining computing power exceeds the preset threshold, double overflow protection is triggered. Under the hardware layer protection, the JVM heap memory is expanded. Under the logic layer protection, the recursive call stack is pruned to skip redundant computing paths.

6. A product carbon footprint modeling method according to claim 5, characterized in that: The steps of verifying the emission ratio of main and by-products and converting the measurement units include: Collect the output quantity of main products and by-products at process nodes in real time, and calculate the output ratio of main products to by-products; Obtain the preset standard ratio range, and compare the output ratio of the main and by-products with the standard ratio range. When the output ratio of the main and by-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 output ratio of main products and by-products is within the standard ratio range, the unit standardization conversion is performed to convert all non-standard units of measurement in the composite node into unified carbon equivalent units; The following steps are included when executing the ratio check and repair mechanism: Record the abnormal node as the benchmark node, retrieve the historical process parameters of the benchmark node and compare them with the current process parameters to identify the source of the parameter deviation; Obtain the carbon emission data corresponding to the 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 the parameter deviation; Based on the carbon emission increment, the process parameters of the abnormal nodes are corrected, and parameter synchronization update instructions are sent to the associated material nodes and transportation nodes.

7. 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: According to the node type identification field in the node example network, the node example network is divided into multiple heterogeneous subgraphs, each of which corresponds to a different computing task type, where the computing task types include transportation, process, and material types; Assign an independent computing thread to each heterogeneous subgraph and preload the corresponding computing rules and verification logic into the computing thread; The data dependencies between computing threads are coordinated through asynchronous message queues. After a computing thread completes the current subgraph computing task, the computing process of subsequent heterogeneous subgraphs is automatically triggered.

8. A product carbon footprint modeling method according to claim 7, characterized in that: The step of synchronously executing carbon emission calculations for each independent subtree through parallel computing and then aggregating the calculation results of each independent subtree to form a product-level carbon footprint result includes: 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; According to the execution priority, corresponding computing resources are allocated to each heterogeneous subgraph and parallel computing processes are started; During the parallel computing process, the computing resource consumption and task progress of each heterogeneous subgraph are collected in real time. When the computing resource consumption and task progress of a heterogeneous subgraph do not match, redundant computing resources are dispatched from the heterogeneous subgraph with redundant computing resources to the heterogeneous subgraph with insufficient computing resources to balance the computing 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 product's entire life cycle.

9. A product carbon footprint modeling system, characterized by: The product carbon footprint modeling method according to any one of claims 1 to 8 comprises: Initialization module, used to create the carbon node base class and define a common property set including carbon emission factor update function, energy consumption integration function and node type identification field; A node example building module, used to create node examples including multiple levels based on the carbon node base class, wherein the node examples include material nodes, process nodes, and transportation nodes; The node combination module is used to establish the combination relationship between node examples, combine the process node and the material node to form a composite node, and establish a connection with the update interface of the transportation node to form a dynamic update of carbon emission data; The risk optimization module is used to traverse the node example network and perform tail recursion optimization to eliminate stack overflow risks. It also triggers the differentiation rules corresponding to the node type identification field to complete the main and by-product emission ratio verification and measurement unit conversion; The carbon footprint output module is used to split the node example network into independent subtrees based on an asynchronous framework, and synchronously perform carbon emission calculations for each independent subtree through parallel computing. The calculation results of each independent subtree are then aggregated to form a product-level carbon footprint result.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the product carbon footprint modeling method according to any one of claims 1 to 8.

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