A carbon footprint accounting method considering the whole life cycle

Abstract

The present application relates to carbon footprint management technical field, specifically to a kind of carbon footprint accounting method considering life cycle, comprising the following steps, analysis life cycle path evaluation influence and compresses generation path numbering sequence, according to numbering reconstruction evaluation transmission identifies abnormality and reconstruction, identifies turning point matching factor and adapts set, positioning heat loss inversion critical contrast monitoring and accounting verification information, according to verification conversion collection stratified summary carbon content.The present application, by the comprehensive utilization of process path information, realizes the measurement of emission intensity and activity and path screening, the analysis of node carbon transmission characteristics improves the accuracy of carbon flow judgment, combined with the matching of operating section and factor condition, using the identification and truncation of energy link heat loss characteristics, enhance the rationality of inversion range, the comparison of emission monitoring result and energy input, strengthen the verification of accounting data, combine cross-level conversion and collection, ensure the consistency and integrity of carbon emission mapping.

Description

Technical Field

[0001] This invention relates to the field of carbon footprint management technology, and in particular to a carbon footprint accounting method that considers the entire life cycle. Background Technology

[0002] Carbon footprint management technology encompasses the entire process of quantifying, assessing, recording, and managing energy resource consumption and greenhouse gas emissions. Its core content is the systematic accounting and tracking of carbon emissions generated by products, services, or activities throughout their entire life cycle based on life cycle assessment methods. It integrates interdisciplinary fields such as environmental science, energy systems engineering, information systems, and management science. Through the establishment of emission factor databases, life cycle inventory data collection mechanisms, carbon emission accounting models, and supporting software platforms, it forms a technical system covering carbon data collection, accounting modeling, statistical analysis, and report generation. This system is applied in multiple industries, including industrial manufacturing, transportation, construction engineering, power systems, and consumer goods, supporting scenarios such as enterprise carbon management, policy formulation, carbon trading, and low-carbon product certification. In China, the carbon footprint accounting method that considers the entire life cycle refers to constructing a carbon emission inventory based on life cycle inventory data for carbon emissions generated by a product or system at each stage of its life cycle, including raw material acquisition, production and manufacturing, transportation and distribution, use and maintenance, and disposal and recycling. Through carbon emission factor matching and phased accounting methods, the carbon emission amount is calculated in stages according to unified boundaries and functional units. In the accounting process, life cycle boundary determination rules, system boundary expansion mechanisms, and emission intensity aggregation methods are adopted to achieve standardized organization and aggregation of multi-stage carbon emission data. Combined with rule templates and emission accounting tables, carbon emission accounting documents for projects or products are generated. Specifically, it involves life cycle database management, emission factor table maintenance, and phased accounting.

[0003] Traditional carbon footprint accounting techniques, based on lifecycle inventory, often suffer from unclear boundary ranges when multiple paths run in parallel under complex processes. This leads to the double counting or omission of some low-contribution paths. The carbon transfer relationship between nodes fails to effectively identify input-output differences, resulting in biases. The factor-dependent average selection of the operating segment fails to reflect stage-specific changes. Abrupt changes in heat loss during energy transfer are not identified, causing fuzzy inversion boundaries. Cross-level accounting is aggregated using a simple summation method, leading to inconsistent functional unit mappings. In industrial manufacturing or energy systems, this can cause accounting result biases and reduce the reference value of carbon emission data in policy-making and trading scenarios. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a carbon footprint accounting method that considers the entire life cycle.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a carbon footprint accounting method considering the entire life cycle, comprising the following steps:

[0006] S1: Call the lifecycle path list, analyze the process path structure, mark the energy consumption source, emission type and path frequency of the node, evaluate the emission intensity and activity of the path based on the relationship between energy consumption and frequency, calculate the path impact index, screen path compression objects and adjust the nesting relationship, and generate the path number sequence.

[0007] S2: Based on the path number sequence, analyze the difference between the input and output of emissions at each node, evaluate the carbon transfer efficiency, and identify abnormal node numbers by combining the differences in energy consumption sources and emission ratios, remove the corresponding paths and reconstruct the connections between adjacent nodes to generate a path reconstruction connection number set.

[0008] S3: Reconstruct the connection number set according to the path, analyze the node load change rate and emission quantity trend, identify the starting point of the turning section, extract the load control type, emission type and energy consumption structure, match the carbon factor library conditions, and generate a carbon factor adaptation set.

[0009] S4: Call the carbon factor adaptation set, analyze the energy input and output structure between path segments, construct a list of conversion segments, identify the heat loss distribution characteristics and determine the inversion critical position, summarize the energy input before the critical position and compare it with the emission monitoring data, and generate carbon footprint accounting verification information.

[0010] S5: Based on the carbon footprint accounting verification information, analyze the carbon factor level and unit type of each path node, evaluate the matching relationship between emissions and functional units, convert carbon factors based on the unit conversion ratio, collect the conversion results, summarize carbon values ​​by level, and generate multi-level carbon total data.

[0011] As a further embodiment of the present invention, the path numbering sequence includes path identifier encoding, compressed node number, and nested hierarchical order; the path reconstruction connection number set includes node connection pair information, pruned path topology, and connection path number mapping; the carbon factor adaptation set includes load segment number, factor label matching result, and factor associated segment number; the carbon footprint accounting verification information includes pre-truncation energy input structure, emission monitoring matching items, and inverted path segment intensity comparison index; and the multi-layer carbon aggregation total data includes converted path carbon emission value, structural hierarchical aggregation code, and functional unit affiliation relationship.

[0012] As a further aspect of the present invention, the step of obtaining the path number sequence specifically includes:

[0013] S111: Call the lifecycle path list to obtain the process path structure formed by the accounting object in each lifecycle stage, collect the energy source category and corresponding emission type and quantity of each node in each path, and count the frequency of the path in the process network to generate path node feature mapping values.

[0014] S112: Based on the path node feature mapping value, extract the number of emission types and the frequency of path occurrence in each path node, assess emission intensity and process activity, determine the difference in the impact of process path on carbon footprint, calculate the path carbon offset difference value, and obtain the path impact index value.

[0015] S113: Call the path influence index value, filter and compare the carbon offset degree of each path, filter the process path compression objects, and perform structural adjustment and hierarchical reconstruction of the process nesting structure relationship, establish the mapping relationship between the path set number and the compression structure, and generate the path number sequence.

[0016] As a further aspect of the present invention, the step of obtaining the path reconstruction connection number set specifically includes:

[0017] S211: Based on the path numbering sequence, collect the node number in each path, detect the number of emission inputs and emission outputs corresponding to the node, normalize and subtract them, and combine them with the node energy consumption source type data to generate the node carbon transfer deviation value.

[0018] S212: Call the node carbon transfer deviation value, extract the emission input and output, energy consumption source ratio and emission ratio between node pairs, calculate the carbon transfer efficiency between nodes, calculate the carbon transfer efficiency difference index value of node pairs, identify the node number with abnormal transfer efficiency, and establish a set of abnormal carbon transfer node numbers.

[0019] S213: Call the set of abnormal carbon transfer node numbers, remove the path corresponding to the abnormal node number from the process network, and re-establish the sequential connection of the remaining nodes after removal. Based on the node connection relationship of the adjusted path, establish a path reconstruction connection number set.

[0020] As a further aspect of the present invention, the step of obtaining the carbon factor adaptation set specifically includes:

[0021] S311: Reconstruct the connection number set according to the path, detect the load change rate and unit emission quantity of each running node in the path network, calculate the trend curve of the two data, identify the intersection of the trend curves, extract the path segment number corresponding to the intersection, and generate a set of turning segment numbers.

[0022] S312: Call the set of transition section numbers, extract the load control type, emission item combination and energy consumption structure distribution in the transition section, normalize each feature data in the section, and generate a set of transition section feature values.

[0023] S313: Based on the feature value set of the transition segment, a similarity value set is formed by matching it with the corresponding condition fields in the carbon factor library, the carbon emission factor of the current transition segment is determined, and a carbon factor fitting set is established.

[0024] As a further aspect of the present invention, the steps for obtaining the carbon footprint verification information are specifically as follows:

[0025] S411: Call the carbon factor adaptation set, collect the energy input types and energy output composition of the path segments according to the path segment order in the connection number set, determine the difference combination type and transmission characteristics of input and output between path segments, perform structural marking on the conversion relationship, and establish an energy conversion segment number list.

[0026] S412: Call the energy conversion segment number list, identify the energy consumption distribution type and heat loss conversion ratio characteristics of each segment in the list, assess the concentration of heat loss phenomenon during the transfer process, and determine the inversion critical position based on the heat loss conversion degree to establish a heat loss inversion critical position set;

[0027] S413: Based on the set of critical positions for heat loss inversion, accumulate the energy input data before the critical position and compare it with the recorded emission monitoring data to generate carbon footprint accounting verification information.

[0028] As a further aspect of the present invention, the process of determining the critical position of inversion based on the degree of heat loss conversion specifically involves: obtaining the heat loss conversion ratio characteristic value of each segment in the energy conversion segment number list, arranging all heat loss conversion ratio characteristic values ​​in the path segment order, and using the rate of change of the difference in heat loss conversion ratio between adjacent path segments as the judgment criterion to identify the path segment intervals where the heat loss conversion ratio continuously increases and exceeds the judgment benchmark, wherein the judgment benchmark is the critical judgment threshold for heat loss inversion calculated based on the weighted average rate of change of the heat loss conversion ratio of the entire path segment;

[0029] The process of obtaining the critical threshold for heat loss inversion is as follows: the heat loss conversion ratio characteristic value of each segment in the energy conversion segment number list is numerically weighted with the unit energy consumption value of its corresponding path segment, and the weighted result is compared with the average heat loss value. The node position with the largest offset value and the rate of change of change is extracted as the reference point for the sudden change of heat loss trend, and the rate of change of the heat loss conversion ratio of the reference point is used as the critical threshold for heat loss inversion.

[0030] As a further aspect of the present invention, the steps for obtaining the multi-layer carbon total data are as follows:

[0031] S511: Based on the carbon footprint accounting verification information, detect the carbon factor level type and unit type corresponding to each path node, calculate the matching value between node emissions and functional units, pair and map the emissions and functional unit information of the level nodes, and generate level node mapping data.

[0032] S512: Call the hierarchical node mapping data, convert the carbon factor across levels based on the unit conversion ratio, calculate the carbon factor conversion value corresponding to each path segment, and obtain the path segment conversion result set;

[0033] S513: Based on the path segment conversion result set, summarize the carbon value of each path segment according to the structural hierarchy, calculate the total carbon collection within the hierarchical structure, collect and summarize the data and classify and number it according to the hierarchy to generate multi-level carbon collection total data.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0035] In this invention, by comprehensively utilizing process path information, the emission intensity and activity can be measured and path can be screened. The analysis of node carbon transfer characteristics improves the accuracy of carbon flow judgment. By combining the matching of operating sections and factor conditions, the identification and truncation of heat loss characteristics in the energy link enhances the rationality of the inversion range. The comparison between emission monitoring results and energy input strengthens the verifiability of accounting data. By combining cross-level conversion and aggregation, the consistency and integrity of carbon emission mapping are ensured. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the main steps of the present invention;

[0037] Figure 2 This is a flowchart of the path number sequence acquisition process of the present invention;

[0038] Figure 3 This is a flowchart of the path reconstruction connection number set acquisition process of the present invention;

[0039] Figure 4 This is a flowchart of the process for obtaining the carbon factor adaptation set in this invention;

[0040] Figure 5 This is a flowchart of the carbon footprint accounting verification information acquisition process of the present invention;

[0041] Figure 6 This is a flowchart of the multi-layer carbon total data acquisition process of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0044] Please see Figure 1 This invention provides a technical solution: a carbon footprint accounting method considering the entire life cycle, comprising the following steps:

[0045] S1: Call the lifecycle path list, analyze the process path structure, mark the energy consumption source, emission type and path frequency of the node, evaluate the emission intensity and activity of the path based on the relationship between energy consumption and frequency, calculate the path impact index, screen path compression objects and adjust the nesting relationship, and generate the path number sequence.

[0046] S2: Based on the path number sequence, analyze the difference between the input and output of emissions at each node, assess carbon transfer efficiency, identify abnormal node numbers by combining the differences in energy consumption sources and emission ratios, remove the corresponding paths and reconstruct the connections between adjacent nodes, and generate a path reconstruction connection number set.

[0047] S3: Reconstruct the connection number set based on the path, analyze the node load change rate and emission quantity trend, identify the starting point of the turning section, extract the load control type, emission type and energy consumption structure, match the carbon factor library conditions, and generate a carbon factor adaptation set.

[0048] S4: Call the carbon factor adaptation set, analyze the energy input and output structure between path segments, construct a list of conversion segments, identify the heat loss distribution characteristics and determine the inversion critical position, summarize the energy input before the critical position and compare it with the emission monitoring data, and generate carbon footprint accounting verification information.

[0049] S5: Based on the carbon footprint accounting verification information, analyze the carbon factor level and unit type of each path node, assess the matching relationship between emissions and functional units, convert carbon factors based on the unit conversion ratio, collect the conversion results, summarize carbon values ​​by level, and generate multi-level carbon total data.

[0050] The path numbering sequence includes path identifier codes, compressed node numbers, and nested hierarchical order. The path reconstruction connection number set includes node connection pair information, the pruned path topology, and the mapping of connection path numbers. The carbon factor adaptation set includes load segment numbers, factor label matching results, and factor-related segment numbers. The carbon footprint accounting verification information includes the energy input structure before truncation, emission monitoring matching items, and inverted path segment intensity comparison indicators. The multi-layer carbon aggregation total data includes the converted path carbon emission value, structural hierarchical aggregation codes, and functional unit affiliation relationships.

[0051] Please see Figure 2 The specific steps for obtaining the path number sequence are as follows:

[0052] S111: Call the lifecycle path list to obtain the process path structure formed by the accounting object in each lifecycle stage, collect the energy source category and corresponding emission type and quantity of each node in each path, and count the frequency of the path in the process network to generate path node feature mapping values.

[0053] The accounting object is set as the carbon footprint of a factory producing lithium-ion battery cathode material (lithium iron phosphate) throughout its entire life cycle. An existing life cycle path list is used, which includes the entire process from raw material acquisition, material production, battery assembly, use to waste disposal. The process path structure of "lithium iron phosphate synthesis" in the "material production" stage is obtained. This process path contains multiple serial nodes, specifically including nodes such as "precursor mixing," "solid-state reaction," "washing and drying," "sintering," and "product packaging." Each node has clearly defined input and output materials and energy consumption. For example, the "sintering" node is the core energy-consuming link in lithium iron phosphate synthesis, using electricity and natural gas as energy sources, which correspondingly generate carbon dioxide (CO2). ), nitrogen oxides ( Emissions such as these were collected from sources of energy consumption at the sintering node, categorized as "electricity" and "natural gas," with the corresponding number of emission types being [number missing]. species, that is and Meanwhile, statistical analysis of the frequency of the "sintering" path within the entire "lithium iron phosphate synthesis" process network revealed that this sintering step is a continuous production process, occurring in each batch of material production. This path occurs weekly, considering multiple production batches throughout the entire process cycle. Each node's energy source categories, corresponding emission types and frequencies are systematically recorded. For example, the "precursor mixing" node's energy source category is "electricity," and the emission types and frequencies are... kind( The path appears weekly. Next, the energy source category for the "washing and drying" node is "steam," and the number of emission types is [number missing]. kind( The path appears weekly. Then, all these collected data are organized and correlated to generate path node feature mapping values ​​that describe the unique attributes of each path node.

[0054] S112: Based on the feature mapping values ​​of path nodes, extract the quantity of emission types and the frequency of path occurrence in each path node, assess emission intensity and process activity, and determine the differences in the impact of process paths on carbon footprint, using the following formula:

[0055] ;

[0056] Calculate the path carbon offset difference value to obtain the path impact index value;

[0057] in, The path carbon offset difference value represents the first... The difference in carbon footprint impact between the two paths and the reference path. To normalize the number of emission types, representing the number of the first emission type. The ratio of the number of emission types corresponding to energy consumption sources in a given path to the number of the largest emission type in all paths is obtained by dividing the number of emission types in that path by the number of the largest emission type in all paths. Let be the normalized path frequency, representing the th path. The ratio of the frequency of a path in the process network to the maximum frequency is obtained by dividing the number of times the path appears by the maximum frequency of paths in the process. Normalized emission intensity, representing the first The ratio of the carbon emission intensity of a path in a unit process to the maximum emission intensity of all paths in the life cycle network is obtained by dividing the original emission intensity by the maximum emission intensity. The activity level of the process indicates the activity level of the first... The activity level of a path in the process network can be calculated based on the operation frequency, activation frequency, or energy consumption event frequency of the nodes in the path. The normalized reference carbon footprint value represents the first... The carbon footprint value of a reference path is the proportion of the baseline carbon footprint value of all paths. It is calculated by dividing the reference carbon footprint value of that path by the maximum value among all reference carbon footprint values ​​of all paths. The path number index indicates the sequential numbering of the path within the lifecycle process network. Each independent path is used to distinguish the position and characteristics of different process paths within the path set;

[0058] Based on the feature mapping values ​​of path nodes, the number of emission types and the frequency of path occurrence are extracted for each path node. Emission intensity and process activity are assessed to determine the differences in the impact of process paths on carbon footprint, using the following formula: ; Calculate the path carbon offset difference value, where, The path carbon offset difference value represents the first... The difference in carbon footprint impact of a path relative to a reference path measures the path's contribution to the overall carbon footprint across multiple dimensions. A higher value indicates a greater carbon emission shift and more volatile fluctuations along the path, meaning a more concentrated carbon risk within the network. To normalize the number of emission types, representing the number of the first emission type. The ratio of the number of emission types corresponding to energy consumption sources in a given path to the number of the largest emission type in all paths is obtained by dividing the number of emission types in that path by the number of the largest emission type in all paths. Let be the normalized path frequency, representing the th path. The ratio of the frequency of a path in the process network to the maximum frequency is obtained by dividing the number of times the path appears by the maximum frequency of paths in the process. Normalized emission intensity, representing the first The ratio of the carbon emission intensity of a path in a unit process to the maximum emission intensity of all paths in the life cycle network is obtained by dividing the original emission intensity by the maximum emission intensity. The activity level of the process indicates the activity level of the first... The activity level of a path in the process network is calculated based on the operation frequency, activation frequency, or energy consumption event frequency of the nodes in the path. The normalized reference carbon footprint value represents the first... The carbon footprint value of a reference path is the proportion of the baseline carbon footprint value of all paths. It is calculated by dividing the reference carbon footprint value of that path by the maximum value among all reference carbon footprint values ​​of all paths. The path number index indicates the sequential numbering of the path within the lifecycle process network. A single independent path is used to distinguish the position and characteristics of different process paths within the path set. In the formula, the numerator term... This represents the combined impact of emission diversity and process usage intensity on a pathway. The greater the variety of emissions and the higher the frequency of use, the greater the pathway's potential impact on its carbon footprint. The denominator term... The combined effect of emission intensity and system activity of a pathway is measured. Higher emission intensity and stronger process activity indicate a higher potential carbon emission risk. Dividing the two yields the relative weight of the pathway between emission potential and risk. Finally, the normalized reference carbon footprint value is subtracted. The absolute value is then used to calculate the path carbon offset difference. The calculation logic lies in accurately identifying highly sensitive pathways with abnormal carbon emission performance or significant contributions to the system's carbon footprint by comparing the deviation of actual emission potential and risk performance from a preset reference benchmark. This provides a quantitative basis for subsequent pathway optimization. For example, in the lithium iron phosphate production process, for pathway optimization... ("Sintering" path), path (Washing and drying path), path (For the "product packaging" path) calculation, firstly, the number of emission types for each path is obtained from the path node feature mapping values. (Sintering) is species, path (Washing and drying) is species, path (Product packaging) is The number of species with the largest emissions across all pathways is Calculation path Normalized emission types and quantities ,path of ,path of Secondly, the frequency of path occurrence, path ,path ,path The frequency of occurrence is weekly. The maximum frequency is Therefore, the path Normalized path frequency ,path of ,path of Next, the emission intensity and process activity are assessed. For the sintering pathway, the original carbon emission intensity per unit product (e.g., per kilogram of lithium iron phosphate) is... kilogram The maximum emission intensity across all pathways is [equivalent per kilogram of product]. kilogram Equivalent per kilogram of product, therefore, normalized emission intensity The original carbon intensity of the washing and drying path is kilogram Equivalent per kilogram of product, normalized emission intensity The original carbon intensity of the product packaging path is kilogram Equivalent per kilogram of product, normalized emission intensity Regarding process activity Based on the operation frequency, activation frequency, and energy consumption event frequency of nodes in the path, the process activity level of the "sintering" path is set as follows: The "washing and drying" path is The "product packaging" path is Finally, a normalized reference carbon footprint value is obtained. This reference carbon footprint value is set based on industry benchmarks or historical best data. For the "sintering" path, the reference carbon footprint value is [value missing]. kilogram The maximum value among all reference carbon values ​​for equivalent / kilogram product is [value]. kilogram Equivalent per kilogram of product, then normalized reference carbon footprint value For the "washing and drying" path, its reference carbon footprint value is kilogram Equivalent per kilogram of product, For the "product packaging" path, its reference carbon footprint value is kilogram Equivalent per kilogram of product, Substitute the above parameter values ​​into the formula to calculate;

[0059] path (Sintering) Path carbon offset difference value for:

[0060] ;

[0061] path (Washing and drying) path carbon offset difference value for:

[0062] ;

[0063] path (Product packaging) Path carbon offset difference value for:

[0064] ;

[0065] Among them, the path carbon offset difference value is used to assess the magnitude and intensity of the offset of a process path relative to the carbon emission reference value in the system. It comprehensively measures the path's contribution to the overall carbon footprint across multiple dimensions, including the complexity of the path structure (emission types), the intensity of path use (frequency of occurrence), emission density (intensity), and the degree of system influence (activity). The higher the value, the greater the carbon emission offset and the more drastic the fluctuation, indicating a more concentrated carbon risk in the network. This indicator can serve as an important criterion for path selection, helping to identify highly sensitive path segments with abnormal carbon emission performance or significant contributions to the system's carbon footprint throughout the entire process lifecycle, facilitating subsequent path compression, structural adjustment, and node optimization. The results show that the path The carbon offset difference value is the highest, and the path Secondly, the path The lowest value indicates that the carbon emissions of the "product packaging" route fluctuate the most and the carbon risk is the most concentrated, while the carbon emissions of the "sintering" route are relatively stable. The carbon offset difference values ​​of all routes together constitute the route impact index value.

[0066] S113: Call the path influence index value, filter and compare the carbon offset degree of each path, filter the process path compression objects, and perform structural adjustment and hierarchical reconstruction of the process nesting structure relationship, establish the mapping relationship between the path set number and the compression structure, and generate the path number sequence.

[0067] The call path affects the indicator value, which includes the carbon offset difference value for each path. For example, the carbon offset difference value for the sintering path is... The carbon offset difference value of the washing and drying path is The carbon offset difference value of the product packaging path is The system sets a threshold for determining carbon offset differences. This threshold is used to identify high-carbon-risk pathways. Its setting references industry best practices and historical optimization experience, and is adjusted based on the company's own emission reduction targets. For example, if a company's emission reduction target is to reach an internationally advanced level, the threshold can be set at the top level within the industry. The average deviation value of high carbon emission pathways. If a company's emission reduction target is to achieve a significant reduction based on its current level, the threshold can be set as the average of all pathway deviation values ​​plus a standard deviation. In this embodiment, the carbon deviation value judgment threshold is set as follows: By comparing the degree of carbon shift for each path, those with a carbon shift difference value higher than or equal to... The path was identified as a high-carbon risk path, requiring process path compression and structural adjustments, including changes to the product packaging path. ) and washing and drying path ( ) was identified as a process path compression object, sintering path ( If a process is identified as a high-carbon risk path, the system will not compress it. Instead, it will restructure and reconstruct the nested process relationships. For example, in the product packaging stage, the original process may contain multiple nested subprocesses, such as "outer film production - wrapping", "box production - packing", and "pallet strapping - warehousing". Since the product packaging path is identified as a high-carbon risk path, the system will disassemble and reconstruct the subprocesses such as outer film production and box production, explore the use of recyclable materials or lightweight packaging solutions, and adjust the original nested structure to a flat structure. For example, the two nodes of "outer film production" and "box production" can be optimized into a single "environmentally friendly packaging integration" node, thereby reducing the risk by simplifying the dependencies between nodes. Regarding its carbon footprint impact, for the washing and drying path, if its efficiency is low and emissions are high, it can be considered to replace it with a more efficient membrane separation technology, or integrate it with the waste heat recovery system after sintering to reduce steam consumption. This reconstruction will change the series or parallel relationship between nodes in the original process and establish a mapping relationship between path set numbers and compression structures. For example, the original "product packaging path - number P003" is mapped to the new "environmentally friendly packaging integrated path - number P_NEW003", and the original "washing and drying path - number P002" is mapped to the new "high-efficiency washing and drying path - number P_NEW002". All the paths that have been screened and adjusted together form a path number sequence.

[0068] Please see Figure 3 The specific steps for obtaining the path reconstruction connection number set are as follows:

[0069] S211: Based on the path number sequence, collect the node number in each path, detect the number of emission inputs and emission outputs corresponding to the node, normalize and subtract them, and combine them with the node energy consumption source type data to generate the node carbon transfer deviation value.

[0070] Based on the path numbering sequence, for example, the "sintering" node (numbered N001) and the "high-efficiency washing and drying" node (numbered N_NEW002) in the reconstructed "lithium iron phosphate synthesis" process are processed. The data for the "sintering" node number N001 is collected, and its emission input and emission output quantities are detected. The emission input quantity refers to the total amount of effluent transferred from the previous node to the current node or the total amount of effluent to be treated generated by the current node itself. For example, the sintering furnace produces a large amount of flue gas (containing...) during combustion. , , These flue gases are direct emissions from the sintering process itself; if we assume that the input is... unit In equivalent quantities, after passing through the furnace or subsequent processing, some of these inputs will be emitted, while others may be captured or converted into other substances. For example, the amount of emission input detected corresponding to the "sintering" node is... kilogram Equivalent, emission output quantity is kilogram Equivalent, while the emission input quantity corresponding to the "high-efficiency washing and drying" node is

[0071] kilogram Equivalent, emission output quantity is kilogram For comparison, these input and output quantities are normalized using a maximum normalization method, taking the largest output input quantity among all nodes. kilogram (equivalent) and maximum emission output ( kilogram Based on the equivalent value, the emission input quantity of the "sintering" node is normalized to... Emission output quantities normalized to The emission input quantities of the "high-efficiency washing and drying" node are normalized to Emission output quantities normalized to Then, by subtracting the normalized input and output quantities, the carbon transfer deviation of the "sintering" node is calculated as follows: The carbon transfer bias at the "high-efficiency washing and drying" node is At the same time, combined with the node energy source type data, for example, the main energy source types of the "sintering" node are "natural gas" and "electricity", while the main energy source type of the "high-efficiency washing and drying" node is "electricity", these energy source type data will be combined with the above carbon transfer deviation value to generate the node carbon transfer deviation value.

[0072] S212: Call the node carbon transfer deviation value, extract the inter-node emission input and output, energy source ratio and emission ratio, and calculate the inter-node carbon transfer efficiency using the following formula:

[0073] ;

[0074] Calculate the carbon transfer efficiency difference index value of node pairs, identify the node numbers with abnormal transfer efficiency, and establish a set of abnormal carbon transfer node numbers;

[0075] in, For nodes With nodes The carbon transfer efficiency difference index between the two is calculated by combining the input-output difference with the energy consumption structure difference. For nodes The normalized value of the emission input quantity, through the node The received emission inflows are normalized based on the maximum value within the path node set. For nodes The normalized value of the emission output quantity, through the node The amount of emissions released downstream is obtained by normalizing the output based on the maximum value within the set of path nodes. For nodes The normalized value of the proportion of energy consumption sources, obtained through statistical nodes. The proportion of each energy consumption source in the data was obtained by normalizing the data according to the energy consumption dimension set. For nodes The normalized value of the emission ratio, obtained through statistical nodes. The corresponding proportions of emission types are obtained by normalizing the emission type set. For nodes Energy consumption source dimension Normalized structure values ​​on the nodes In the The energy consumption ratio under each energy consumption type is obtained by normalizing it relative to the maximum value of the dimension. For nodes In terms of energy sources Normalized structure values ​​on the nodes In the The energy consumption ratio under each energy consumption type is obtained by normalizing it relative to the maximum value of the dimension. This is the index of the forward nodes in the path, used to identify the starting node in the direction of carbon transport. This is the index of the backward nodes in the path, used to identify the target node in the direction of carbon transport. The index number is used to represent the specific node in the energy source dimension. Energy consumption categories The upper limit of the index for the total number of energy source dimensions, representing the total number of all energy categories contained in the node's energy consumption structure, and the upper limit of the average operation used to normalize the structure difference;

[0076] By calling the node carbon transfer deviation value, extracting the emission input and output, energy source ratio and emission ratio between node pairs, and calculating the carbon transfer efficiency between nodes using the following formula: ; Calculate the carbon transfer efficiency difference index value of node pairs, where, For nodes With nodes The carbon transfer efficiency difference index between nodes is calculated by combining the input-output difference and the energy consumption structure difference. It represents the normalized offset strength of the carbon flow input and output between the two nodes and the comprehensive deviation of the distribution consistency under the multi-energy consumption structure. When this value is large, it means that there is a significant imbalance or structural distribution difference in the carbon flow transfer process between the corresponding nodes. Such nodes are more likely to become anomalies, risk links or optimization targets in the carbon footprint network. For nodes The normalized value of the emission input quantity, through the node The received emission inflows are normalized based on the maximum value within the path node set. For nodes The normalized value of the emission output quantity, through the node The amount of emissions released downstream is obtained by normalizing the output based on the maximum value within the set of path nodes. For nodes The normalized value of the proportion of energy consumption sources, obtained through statistical nodes. The proportion of each energy consumption source in the data was obtained by normalizing the data according to the energy consumption dimension set. For nodes The normalized value of the emission ratio, obtained through statistical nodes. The corresponding proportions of emission types are obtained by normalizing the emission type set. For nodes In terms of energy sources Normalized structure values ​​on the nodes In the The energy consumption ratio under each energy consumption type is obtained by normalizing it relative to the maximum value of the dimension. For nodes In terms of energy sources Normalized structure values ​​on the nodes In the The energy consumption ratio under each energy consumption type is obtained by normalizing it relative to the maximum value of the dimension. This is the index of the forward nodes in the path, used to identify the starting node in the direction of carbon transport. This is the index of the backward nodes in the path, used to identify the target node in the direction of carbon transport. The index number is used to represent the specific node in the energy source dimension. Energy consumption categories The upper limit of the index for the total number of energy source dimensions represents the total number of all energy categories contained in the node's energy consumption structure. It is used to normalize the average operational upper limit of the structure difference. The advantage of this formula is that it not only considers the input-output balance of carbon emissions between nodes, but also... (the sentence is incomplete and requires further context). The term reflects the net shift in carbon flow, also through the denominator. The project incorporates the density impact of nodal energy consumption structure and emission type, making the assessment more comprehensive. Furthermore, through the second part... This further quantifies the differences between two nodes in terms of various energy sources (such as electricity, natural gas, and steam). The greater the difference, the more significant the potential differences in energy utilization patterns or emission reduction potential between the two nodes. By considering these factors, the formula can more accurately identify node pairs with abnormal carbon transfer efficiency in the process, thereby focusing on risky links and guiding optimization. For example, in the lithium iron phosphate production process, considering the sintering node (node...) ) and subsequent high-efficiency washing and drying nodes (nodes) Carbon transfer efficiency between nodes Normalized values ​​of emission inputs ,node Normalized value of emissions output Regarding the proportion of energy consumption sources, nodes The energy sources for (sintering) are natural gas and electricity, assuming natural gas accounts for a certain percentage. Electricity share The largest proportion of energy consumption sources for all nodes is Therefore, node Normalized value of the proportion of energy consumption sources ,node (High-efficiency washing and drying) Energy consumption comes from electricity, accounting for [percentage missing]. After normalization, it becomes Normalized values ​​of the proportion of energy consumption sources for all nodes The maximum value is ,therefore, Normalization Regarding emission ratios, nodes The main emissions are and Assuming percentage , percentage ,node The main emissions are , percentage The maximum proportion of each emission type at all nodes is Therefore, node Normalized value of emission ratio ,node Emission ratio normalized value Normalized values ​​of the proportions of all emission types at each node The maximum value is ,therefore,

[0077] Normalization Regarding the source of energy consumption Suppose there is One dimension of energy consumption sources, namely natural gas and electricity, nodes In the natural gas dimension Normalized structure value on In the electricity dimension Normalized structure value on ,node In the natural gas dimension Normalized structure value on In the electricity dimension Normalized structure value on Substitute the above parameters into the formula to calculate the carbon transfer efficiency difference index value of the node pair. :

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] ;

[0083] The carbon transfer efficiency difference index is a crucial dimensionless value used to measure the balance and consistency of node pairs in a path network during carbon emission transfer. It represents the normalized offset strength of carbon flow input and output between two nodes, as well as the comprehensive deviation in distribution consistency under a multi-energy-consuming structure. A large value indicates significant imbalance or structural distribution differences in the corresponding node pair during carbon flow transfer; such nodes are more likely to become anomalies, risk points, or optimization targets in the carbon footprint network. When the value is less than a preset threshold, the transfer efficiency is considered normal, and the node connection can be retained. Therefore, the carbon transfer efficiency difference index directly determines the screening criteria for abnormal nodes and paths in the network, serving as the core basis for subsequent path removal and structural restructuring operations, and contributing to the health management and risk focus of the carbon emission process network. The system sets a threshold for the carbon transfer efficiency difference index, which is used to determine whether the carbon transfer efficiency of a node pair is abnormal. Its setting references historical data analysis, expert experience, and industry standards. For example, by statistically analyzing the carbon transfer efficiency difference index values ​​of all node pairs over the past year, calculating their mean and standard deviation, the threshold is set as the mean plus... The standard deviation is set to a threshold of [number] times. In this embodiment, the threshold is set to [number]. When the carbon transfer efficiency difference index value Greater than or equal to the threshold If the carbon transfer efficiency is abnormal, the result indicates that the carbon transfer efficiency difference index between the sintering node and the high-efficiency washing and drying node is [value missing]. Higher than the preset threshold Therefore, this node pair is identified as having an abnormal carbon transport efficiency, and a set of anomalous carbon transport node numbers is established, which includes nodes... (Sintered nodes) and nodes The number of the (high-efficiency washing and drying node).

[0084] S213: Call the abnormal node number set of carbon transfer, remove the path corresponding to the abnormal node number from the process network, and re-establish the sequential connection of the remaining nodes after removal. Based on the node connection relationship of the adjusted path, establish the path reconstruction connection number set.

[0085] The system retrieves a set of abnormal carbon transfer node numbers, for example, numbers for the sintering node (N001) and the high-efficiency washing and drying node (N_NEW002). The paths corresponding to these abnormal node numbers are removed from the lithium iron phosphate production process network. Specifically, if the carbon transfer efficiency between the sintering node and the high-efficiency washing and drying node is abnormal, it indicates a high carbon risk due to the direct connection or interaction between these two nodes, requiring intervention. The original path segment directly from the sintering node to the high-efficiency washing and drying node is removed. After removal, the remaining nodes in the process network, for example, after the precursor mixing node (N000), have their original direct connection to the sintering node removed. The high-efficiency washing and drying node is then connected to the product packaging node (P_NEW003). After removing the nodes, the sequential connections of the remaining nodes are re-established. For example, if the sintering node... If a node is identified as abnormal and removed, the connection from its upstream node (e.g., the "precursor mixing" node) to its downstream node (e.g., the "washing and drying" node or the "waste heat recovery" system) needs to be reassessed. If the sintering process is optimized to a cleaner alternative process, the upstream node will connect to the new alternative process node. Alternatively, if two adjacent abnormal nodes are removed, their upstream nodes will directly connect to the upstream neighbor of the downstream node, forming a new path. For example, the precursor mixing node no longer connects to the sintering node but directly connects to a new "low-carbon solid-state reaction" node, while the high-efficiency washing and drying node now directly connects to the product packaging node. Based on the node connection relationships of the adjusted path, a path reconstruction connection number set is established. This set contains all adjusted and reconnected path segments and their corresponding numbers, ensuring the logical integrity and traceability of the entire production process.

[0086] Please see Figure 4 The specific steps for obtaining the carbon factor fit set are as follows:

[0087] S311: Based on the path reconstruction connection number set, detect the load change rate and unit emission quantity of each running node in the path network, calculate the trend curve of the two data, identify the intersection of the trend curves, extract the path segment number corresponding to the intersection, and generate the turning segment number set.

[0088] Based on the path reconstruction connection number set, for example, a reconstructed path in the sintering section of the lithium iron phosphate production process, which includes nodes such as "pretreatment," "low-carbon sintering furnace operation," and "waste heat recovery," the load change rate and unit emission quantity of the "low-carbon sintering furnace operation" node in the path network are detected. The load change rate refers to the magnitude of change in furnace temperature, pressure, or feed rate per unit time, such as the temperature increase per minute. Unit emissions refer to the amount of emissions produced per unit of output under specific load conditions. kilograms of lithium iron phosphate produced Equivalent, for example, per kilogram of product kilogram Equivalent data are obtained through real-time sensor monitoring and historical data analysis, for example, in Within the capacity range of tons / day, the load change rate of the low-carbon sintering furnace is from arrive (Dimensionless, indicating relative change), the quantity of emissions per unit ranges from... arrive kilogram For equivalent / kilogram products, curve fitting is performed on these two data points to calculate their trend curves. For example, there may be a non-linear relationship between the rate of change of load and the quantity of emissions per unit, initially decreasing and then increasing. The intersection of the trend curves is identified; this intersection represents a critical point where the trends of the rate of change of load and the quantity of emissions per unit change significantly reverse. For instance, when the rate of change of load reaches a certain value, the growth rate of emissions per unit may change from a slow increase to a rapid increase, or from a decrease to an increase. This intersection may occur when the rate of change of load is... At that time, the corresponding unit emission quantity was kilogram For equivalent per kilogram of product, extract the path segment number corresponding to the intersection point. For example, in the "Low-carbon sintering furnace operation" node, the load change rate is... arrive The interval between operations is marked as a transition segment, and a set of transition segment numbers is generated, which contains the unique identifiers of all identified transition segments.

[0089] S312: Call the transition section number set, extract the load control type, emission item type combination and energy consumption structure distribution within the transition section, normalize each feature data within the section, and generate the transition section feature value set.

[0090] Retrieve the set of transition segment numbers, for example, from this set to obtain a specific transition segment (number TS001) for the "Low-carbon Sintering Furnace Operation" node, which corresponds to the load change rate at... arrive Between, the amount of emissions per unit is arrive kilogram The operating range of the equivalent / kilogram product is used to extract the load control type, emission item combination, and energy consumption structure distribution within this transition zone. The load control type refers to the control strategy employed within this zone, such as "constant temperature and pressure control" or "segmented variable temperature control." The emission item combination refers to the specific types and proportions of emissions generated within this zone, for example... occupy , occupy Energy consumption structure distribution refers to the proportion of consumption from different energy sources (such as electricity and natural gas) within a given area. For example, electricity accounts for... Natural gas accounts for Each feature data point within the segment is normalized. For example, for load control types, it can be quantized into discrete values, such as "constant temperature and pressure control" being quantized as... "Segmented temperature control" is quantified as And normalize based on the maximum value, for combinations of emission item categories, percentage Normalization , percentage Normalization The proportion of electricity in the energy consumption structure distribution Normalization Natural gas percentage Normalization All the feature data after quantization and normalization together form the feature value set of the transition section.

[0091] S313: Based on the feature value set of the transition segment, a similarity value set is formed by matching it with the corresponding condition fields in the carbon factor library, the carbon emission factor of the current transition segment is determined, and a carbon factor fitting set is established.

[0092] Based on the characteristic value set of the transition zone, which includes the normalized value of the load control type for a specific transition zone of the "low-carbon sintering furnace operation" node (e.g., ), and the normalized value of the combination of emission item categories (e.g. ) and normalized values ​​of energy consumption structure distribution (e.g., electricity: ,natural gas: By matching the data with corresponding condition fields in the carbon factor library, a pre-built database containing a large amount of carbon emission factor data under different process conditions, energy consumption types, emission types, and load modes, each carbon factor record has its corresponding condition field description. For example, a carbon factor might correspond to "electrolytic aluminum production, constant current control," Main emissions The condition for "electricity consumption" involves performing multi-dimensional similarity calculations between the fields of the feature value set of the transition zone and the condition fields in the carbon factor library. For example, variations of cosine similarity or Euclidean distance are used to evaluate the degree of matching in three dimensions: load control type, emission item category combination, and energy consumption structure distribution. For instance, if a record in the carbon factor library has an energy consumption structure distribution of electricity... ,natural gas The energy consumption structure distribution in the current transition zone (electricity) ,natural gas Similarity calculations are performed to obtain a high similarity value. All matching results together form a similarity value set, which contains the similarity score between each carbon factor database record and the current transition segment. Based on the similarity value set, the carbon emission factor of the current transition segment is determined, and the matching with the highest similarity or higher than a preset matching threshold (e.g., ...) is selected.

[0093] The threshold is set based on historical data fit, expert evaluation, and industry benchmarks to ensure that the selected carbon factors accurately represent actual emissions. If multiple carbon factors have similarity scores higher than the threshold, a weighted average or expert evaluation can be used to select the final suitable factor. For example, the carbon emission factor in the carbon factor library that best matches the characteristics of this transitional period can be selected. kilogram Based on the equivalent / kilowatt-hour of electricity, a carbon factor adaptation set is established, which includes all transition segments and their corresponding adaptation carbon emission factors.

[0094] Please see Figure 5 The specific steps for obtaining carbon footprint verification information are as follows:

[0095] S411: Call the carbon factor adaptation set, collect the energy input types and energy output composition of the path segments according to the path segment order in the connection number set, determine the difference combination type and transmission characteristics of input and output between path segments, perform structural marking on the conversion relationship, and establish a list of energy conversion segment numbers.

[0096] The carbon factor adaptation set is invoked, which includes the carbon factor for the transition section of the "Low-carbon Sintering Furnace Operation" node. kilogram Electricity quantity / kWh, based on the path segment order within the path reconstruction connection number set, for example, "preprocessing" (path segment P01) - "Low-carbon sintering furnace operation" (path segment P02) - "Waste heat recovery" (path segment P03) - "High-efficiency washing and drying" (path segment P04) collects the energy input types and energy output composition for each path segment. For path segment P02 "Low-carbon sintering furnace operation", its energy input types include electricity (e.g., ... kilowatt-hours) and natural gas (e.g.) cubic meters), energy output components include thermal energy (e.g. Megajoules), waste heat steam (e.g.) tons) and a small amount of electricity (e.g. (UKWh, through combined heat and power), determine the difference in input and output combination types and transmission characteristics between path segments. For example, the energy input of path segment P02 is multiple energy types, and the output is multiple types of energy (heat, steam, electricity). This belongs to the complex energy conversion type of "multiple inputs-multiple outputs". Its transmission characteristics include energy efficiency, energy quality changes and energy carrier conversion. The conversion relationship is structurally marked. For example, path segment P02 is marked as an "energy comprehensive utilization type" conversion segment, and its specific parameters such as electricity-heat energy conversion efficiency and natural gas-heat energy conversion efficiency are recorded. All energy conversion segments that have been structurally marked jointly establish an energy conversion segment number list, which contains the unique number of each energy conversion segment and its detailed energy conversion characteristic description.

[0097] S412: Call the list of energy conversion segment numbers, identify the energy consumption distribution type and heat loss conversion ratio characteristics of each segment in the list, assess the degree of heat loss concentration during the transfer process, and determine the inversion critical position based on the degree of heat loss conversion, and establish a set of heat loss inversion critical positions.

[0098] The energy conversion segment number list is retrieved, and the energy consumption distribution type and heat loss conversion ratio characteristics of each segment in the list are identified. For example, in the lithium iron phosphate production process, the energy conversion segment list contains four main segments: P01 "Pretreatment", P02 "Low-carbon sintering furnace operation", P03 "Waste heat recovery", and P04 "High-efficiency washing and drying". For the P02 "Low-carbon sintering furnace operation" segment, its energy consumption distribution type is "gas-electric composite", and its heat loss conversion ratio characteristic value is... (Right now (The input energy is lost in the form of heat loss). For the P03 "waste heat recovery" section, its energy consumption distribution type is "waste heat utilization type", and the characteristic value of the heat loss conversion ratio is... Assess the concentration of heat loss during the transfer process and arrange all heat loss conversion ratio characteristic values ​​in the order of path segments. For example, P01 is... P02 is P03 is P04 is The critical inversion position is determined based on the degree of heat loss conversion. Specifically, the characteristic value of the heat loss conversion ratio for each segment is obtained. For example, P01 to P04 are respectively... , , , Based on the rate of change of the difference in heat loss conversion ratio between adjacent path segments, the system identifies path segment intervals where the heat loss conversion ratio continuously increases and exceeds the judgment benchmark. First, it calculates the critical judgment threshold for heat loss inversion, and then numerically weights the characteristic value of the heat loss conversion ratio of each segment with its corresponding unit energy consumption value. For example, the unit energy consumption of P01 is... Unit, P02 unit energy consumption is Unit, P03 unit energy consumption is Unit, P04 unit energy consumption is Units, weighted result: P01: P02: P03: P04: The average heat loss of all path segments is calculated as follows: ,Right now The weighted results are compared with the mean heat loss, and the node with the largest offset and a continuously increasing rate of change is extracted as the reference point for abrupt changes in the heat loss trend. For example, the heat loss conversion ratio from P01 to P02 is... Increase to Things have changed. From P02 to P03 Reduce to From P03 to P04 Reduce to The offset value is the largest ( And the rate of change increases continuously (in this example, only P01 to P02 shows an increasing trend). Therefore, the P02 node position is extracted as the reference point for the abrupt change in heat loss trend, and the rate of change of the heat loss conversion ratio at the reference point P02 is used. As the critical threshold for heat loss inversion, we return to the step of determining the critical inversion position, identifying the path segment interval where the heat loss conversion ratio continuously increases and exceeds the judgment benchmark, and comparing the rate of change of the difference in heat loss conversion ratio between adjacent path segments: the rate of change of the difference between P01 and P02 is... The rate of change of the difference between P02 and P03 is The rate of change of the difference between P03 and P04 is Due to the rate of change of the heat loss conversion ratio difference between P01 and P02 Exceeding the judgment threshold (The rate of change of the reference point for the sudden change in heat loss trend), and P01 and P02 are continuously increasing. Therefore, node P02 is identified as the critical position for inversion, and a set of critical positions for heat loss inversion is established, which includes the number of node P02.

[0099] Table 1: Heat loss and energy consumption data of the energy conversion section in the lithium iron phosphate production process

[0100] ;

[0101] Table 1 lists the characteristic values ​​of heat loss conversion ratio and unit energy consumption value of each energy conversion stage in the lithium iron phosphate production process.

[0102] S413: Based on the set of critical positions retrieved by heat loss, accumulate the energy input data before the critical positions and compare it with the recorded emission monitoring data to generate carbon footprint accounting verification information;

[0103] Based on the heat loss inversion critical position set, which includes the P02 "Low-carbon sintering furnace operation" node, the energy input data before the inversion critical position is accumulated, that is, the energy input data of the P01 "preprocessing" path segment is accumulated. For example, the total energy input of P01 is... The electricity generated in kilowatt-hours corresponds to a certain amount of carbon emissions during its use, and this is compared with recorded emissions monitoring data. For example, in the P01 "pretreatment" section, the actual carbon emissions monitored by real-time sensors are... kilogram Equivalent, and based on the energy input of that segment ( (kilowatt-hours of electricity) and a suitable carbon factor (e.g., the amount of electricity generated per kilowatt-hour of electricity) and the appropriate carbon factor (e.g., the amount of electricity generated per kilowatt-hour of electricity). kilogram (equivalent), the theoretical carbon emissions calculated are kilogram Equivalents are used to generate carbon footprint accounting verification information by comparing theoretical calculations with actual monitoring values. For example, in this case, the theoretical calculations and actual monitoring values ​​are in perfect agreement, indicating that the carbon footprint accounting results before the inversion critical position are highly accurate, providing strong support for subsequent accounting results.

[0104] Please see Figure 6 The specific steps for obtaining total carbon aggregation data at multiple levels are as follows:

[0105] S511: Based on the carbon footprint accounting verification information, detect the carbon factor hierarchical type and unit type corresponding to each path node, calculate the matching value between node emissions and functional units, pair and map the emissions and functional unit information of hierarchical nodes, and generate hierarchical node mapping data.

[0106] Based on carbon footprint accounting verification information, the accuracy of the accounting results was confirmed. The carbon factor level type and unit type corresponding to each path node were examined. For example, in the lithium iron phosphate production process, the carbon factor level type corresponding to the "precursor mixing" node (number N000) is "raw material processing level," and its functional unit is "per ton of precursor." Meanwhile, the carbon factor level type corresponding to the "low-carbon sintering furnace operation" node (number P02) is "core production process level," and its functional unit is "per kilogram of lithium iron phosphate product." The matching value between node emissions and functional units was calculated. For example, for the "precursor mixing" node, its emissions are... kilogram Equivalents per ton of precursor, its functional unit is "per ton of precursor", and the matching value is... For the "low-carbon sintering furnace operation" node, the emissions are: kilogram The equivalent per kilogram of lithium iron phosphate product, whose functional unit is "per kilogram of lithium iron phosphate product", has a matching value of The emission levels and functional unit information of the hierarchical nodes are paired and mapped. For example, the "precursor mixing" node is mapped to "raw material processing level". kilogram (equivalent / ton precursor), mapping the "low-carbon sintering furnace operation" node to ("core production process level"). kilogram (Equivalent / kg lithium iron phosphate product), all these pairing mapping information together generate hierarchical node mapping data.

[0107] S512: Call the hierarchical node mapping data, convert the carbon factor across levels based on the unit conversion ratio, calculate the carbon factor conversion value corresponding to each path segment, and obtain the path segment conversion result set;

[0108] Call the hierarchical node mapping data, which includes the mapping of the "precursor mixing" node ("raw material processing hierarchy"). kilogram The mapping between the equivalent amount / ton of precursor and the "low-carbon sintering furnace operation" node ("core production process level") kilogram (Equivalent per kilogram of lithium iron phosphate product), based on a unit conversion ratio, converts carbon factors across levels. For example, if the functional unit of the final product is "per kilogram of lithium iron phosphate product," the carbon emissions of the "raw material processing level" (in "per ton of precursor") need to be converted into equivalent emissions of "per kilogram of lithium iron phosphate product." Assuming production... Kilograms of lithium iron phosphate products require For kilograms of precursor, the unit conversion ratio is: For kilograms of precursor / kilograms of lithium iron phosphate product, the emissions equivalent to the "precursor mixing" node per kilogram of lithium iron phosphate product functional unit are: kilogram Equivalent / ton precursor ( (kg precursor / kg lithium iron phosphate product) ( ton / kilograms) = kilogram The functional unit of the "low-carbon sintering furnace operation" node is itself "per kilogram of lithium iron phosphate product," therefore its carbon factor conversion value is directly [value missing]. kilogram The carbon factor conversion value is calculated for each path segment based on the equivalent of one kilogram of lithium iron phosphate product. All calculated conversion values ​​are used to obtain the path segment conversion result set. This result set ensures that all carbon emission data are compared and accumulated under a unified functional unit.

[0109] S513: Based on the path segment conversion result set, summarize the carbon value of each path segment according to the structural level, calculate the total carbon collection within the hierarchical structure, collect and summarize the data and classify and number it according to the level to generate multi-level carbon collection total data.

[0110] Based on the path segment conversion result set, which includes the converted value of the "precursor mixing" path segment, kilogram The equivalent value per kilogram of lithium iron phosphate product, converted to the "low-carbon sintering furnace operation" path segment is: kilogram For lithium iron phosphate products in equivalent weight per kilogram, the carbon value of each pathway segment is summarized according to structural level. For example, the "precursor mixing" pathway segment is classified into the "raw material processing level," and the "low-carbon sintering furnace operation" pathway segment is classified into the "core production process level." If the "raw material processing level" includes other pathway segments, such as the "lithium source extraction" pathway segment, the converted value is... kilogram For equivalent / kg lithium iron phosphate products, the total carbon collection within the "raw material processing level" is calculated as follows: kilogram For lithium iron phosphate products in equivalent weight per kilogram, if the "core production process level" also includes the "washing and drying" path segment, the converted value is... kilogram For equivalent / kg lithium iron phosphate products, the total carbon collection within the "core production process level" is calculated as follows: kilogram For lithium iron phosphate products in equivalent quantities per kilogram, these aggregated data are collected and categorized by level, for example, the "raw material processing level" is numbered CL01, and its total quantity is... kilogram For product equivalents per kilogram, the "core production process level" is numbered CL02, and its total quantity is... kilogram Equivalent per kilogram of product, the aggregated data from all levels together generate multi-level total carbon collection data, which clearly shows the carbon footprint contribution of the product at different life cycle levels.

[0111] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for accounting carbon footprint considering life cycle, characterized in that, Comprise the following steps: S1: call life cycle path list, analyze process path structure, mark node energy consumption source, emission species and path frequency, evaluate path emission intensity and activity according to energy consumption and frequency relationship, calculate path impact index, screen path compression object and adjust nesting relationship, generate path number sequence; S2: according to the path number sequence, analyze the difference between the input and output of each node, evaluate the carbon transfer efficiency, combine the energy consumption source and the difference of emission proportion, identify the abnormal node number, remove the corresponding path and rebuild the connection of adjacent nodes, generate path reconstruction connection number set; S3: according to the path reconstruction connection number set, analyze the load change rate and emission quantity trend of the node, identify the turning section starting point, extract the load control type, emission species and energy consumption structure, match the carbon factor library condition, generate carbon factor adaptation set; S4: call the carbon factor adaptation set, analyze the energy input and output structure between path segments, build the conversion segment list, identify the heat loss distribution characteristics and judge the inversion critical position, summarize the energy input before the critical position and compare it with the emission monitoring data, generate carbon footprint accounting verification information. 2.The method of claim 1, wherein, The path number sequence includes path identification code, compressed node number and nesting level sequence, the path reconstruction connection number set includes node connection pair information, pruned path topology structure and connection path number mapping, the carbon factor adaptation set includes load section number, factor label matching result and factor associated section number, and the carbon footprint accounting verification information includes energy input structure before truncation, emission monitoring matching item and inversion path segment intensity comparison index. 3.The method of claim 1, wherein, The acquisition step of the path number sequence is specifically: S111: call life cycle path list, get the process path structure formed by the accounting object in each life cycle stage, collect the energy consumption source category and corresponding emission species quantity of each node in each path, and count the frequency type of the path in the process network, generate path node feature mapping value; S112: according to the path node feature mapping value, extract the emission species quantity and path frequency of each path node, evaluate the emission intensity and process activity, judge the influence difference of process path on carbon footprint, calculate the path carbon offset difference value, and get the path impact index value; S113: call the path impact index value, compare the carbon offset degree of each path, screen the process path compression object, adjust the structure relationship of process nesting structure, establish the mapping relationship between path set number and compression structure, and generate path number sequence. 4.The method of claim 3, wherein, The acquisition step of the path reconstruction connection number set is specifically: S211: according to the path number sequence, collect the node number in each path, detect the emission input quantity and emission output quantity corresponding to the node, normalize and difference, combine the node energy consumption source type data, and generate node carbon transfer deviation value; S212: Call the node carbon transfer bias value, extract the emission input and output between node pairs, energy source proportion and emission proportion, calculate the carbon transfer efficiency between nodes, calculate the carbon transfer efficiency difference index value of the node pair, identify the node number of the transfer efficiency anomaly, and establish the carbon transfer abnormal node number set; S213: Call the carbon transfer abnormal node number set, remove the abnormal node number corresponding path from the process network, and reestablish the sequential connection of the remaining nodes after removal, and establish the path reconstruction connection number set according to the node connection relationship of the adjusted path. 5.The method of accounting for carbon footprint considering life cycle according to claim 4, characterized in that, The acquisition step of the carbon factor adaptation set is specifically: S311: According to the path reconstruction connection number set, detect the load change rate and unit emission quantity of each running node in the path network, calculate the trend curve of the two data, identify the intersection point of the trend curve, extract the path section number corresponding to the intersection point, and generate the turning section number set; S312: Call the turning section number set, extract the load control type, emission category combination and energy consumption structure distribution in the turning section, and normalize each feature data in the section to generate the turning section feature value set; S313: According to the turning section feature value set, match with the corresponding condition field in the carbon factor library to form a similarity value set, judge the carbon emission factor of the current turning section, and establish the carbon factor adaptation set. 6.The method of accounting for carbon footprint considering the whole life cycle according to claim 5, wherein, The acquisition step of the carbon footprint accounting verification information is specifically: S411: Call the carbon factor adaptation set, collect the energy input type and energy output composition of the path section according to the path section order in the connection number set, judge the difference combination type and transfer characteristics of the input and output between the path sections, mark the structure of the conversion relationship, and establish the energy conversion section number list; S412: Call the energy conversion section number list, identify the energy consumption distribution type and heat loss conversion proportion characteristic of each section in the list, evaluate the concentration degree of heat loss phenomenon in the transfer process, and judge the inversion critical position according to the heat loss conversion degree, and establish the heat loss inversion critical position set; S413: According to the heat loss inversion critical position set, accumulate the energy input data before the inversion critical position and compare with the recorded emission monitoring data to generate the carbon footprint accounting verification information. 7.The method of claim 6, wherein, The process of judging the inversion critical position according to the heat loss conversion degree is specifically: obtain the heat loss conversion proportion characteristic value of each section in the energy conversion section number list, arrange all the heat loss conversion proportion characteristic values in the order of path section, and take the heat loss conversion proportion difference change rate of adjacent path sections as the judgment basis, identify the path section interval where the heat loss conversion proportion continuously increases and exceeds the judgment reference, and the judgment reference is the heat loss inversion critical judgment threshold calculated based on the weighted average change rate of the heat loss conversion proportion of all path sections. The acquisition process of the heat loss inversion critical judgment threshold is specifically that the heat loss conversion ratio characteristic value of each section in the energy conversion section number list is numerically weighted with the unit energy consumption value of the corresponding path section, and the weighted result is compared with the heat loss average value, the node position with the maximum deviation value and the continuously increasing change rate is extracted as the heat loss trend mutation reference point, and the heat loss conversion ratio change rate of the reference point is used as the heat loss inversion critical judgment threshold. 8.The method of claim 1, wherein, The method further comprises: S5: According to the carbon footprint accounting verification information, analyze the carbon factor level and unit type of each path node, evaluate the matching relationship between the emission and the functional unit, convert the carbon factor based on the unit conversion ratio, collect the conversion result, aggregate the carbon value according to the level, and generate multi-level carbon aggregation data; The multi-level carbon aggregation data comprises the converted path carbon emission value, the structure level aggregation code and the functional unit attribution relationship. 9.The method of claim 8, wherein, The acquisition step of the multi-level carbon aggregation data is specifically: S511: According to the carbon footprint accounting verification information, detect the carbon factor level type and unit type corresponding to each path node, calculate the matching value of the node emission and the functional unit, pair and map the emission and the functional unit information of the level node, and generate level node mapping data; S512: Call the level node mapping data, convert the carbon factor across the level based on the unit conversion ratio, calculate the carbon factor conversion value corresponding to each path section, and obtain the path section conversion result set; S513: According to the path section conversion result set, aggregate the carbon value of each path section according to the structure level, calculate the carbon aggregation total amount in the level structure, collect the aggregation data and classify according to the level, and generate multi-level carbon aggregation data.

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