Full-life-cycle carbon footprint intelligent detection system and method

Abstract

The invention discloses a full-life-cycle carbon footprint intelligent detection system and method, belongs to the technical field of carbon emission monitoring, and aims to solve the problems of insufficient standardization of full-life-cycle carbon emission data, inaccurate abnormal link identification and delayed carbon footprint risk early warning. Carbon emission data of a production link in a full life cycle of a target product is collected and transmitted to a block chain, and standardized coding is realized through a hierarchical coding rule; detecting the collected data based on a multi-dimensional triggering rule to judge whether carbon footprint updating is triggered or not, if so, configuring an anchoring reference of a quantization range according to a production mode, and screening the data to construct a quantization data set; after the quantitative data set is corrected, the total carbon emission amount of each production link is calculated, and a carbon emission report is generated; the abnormal links are identified based on the carbon emission and proportion of the production links in the report, the abnormal source is positioned through standardized coding, historical carbon emission data are tracked to predict the emission, and carbon footprint grade change risk early warning is realized.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring technology, and more specifically to an intelligent detection system and method for carbon footprint throughout the entire life cycle. Background Technology

[0002] As a key product possessing both zero-carbon fuel and low-carbon chemical feedstock attributes, the full life-cycle carbon footprint monitoring of green methanol is a crucial foundation for its large-scale industrial application. However, the current full life-cycle carbon footprint monitoring of green methanol faces a series of industry-wide challenges. The green methanol production chain encompasses multiple stages, including raw material acquisition, synthesis and conversion, storage and transportation. The collection of dynamic data at each stage is prone to temporal and spatial breaks, resulting in missing or delayed data during transportation and usage phases. This makes it difficult to form a complete and coherent life-cycle data chain, which adversely affects the comprehensiveness of carbon footprint accounting. The lack of objective and unified standards for carbon footprint allocation methods, along with the significant impact of different quantification logics such as energy-based and mass-based methods on process variations, often leads to significant deviations in carbon footprint calculations for similar green methanol products, reducing data reliability and cross-comparison. Furthermore, the adaptability of existing detection models for emerging processes such as green hydrogen coupling and carbon capture needs improvement, making it difficult to dynamically respond to the emission characteristics of these new processes, thus affecting the accuracy of the calculation results. In special scenarios such as mixed feedstock traceability and carbon capture efficiency verification, gaps in detection technology prevent the accurate quantification of the actual emission reduction benefits from the proportion of biomass carbon sources, hindering the full realization of the low-carbon attributes of green methanol. These problems pose challenges to the standardized development and global application of the green methanol industry. Therefore, to overcome these limitations, this invention proposes a full life-cycle carbon footprint intelligent detection system and method. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide an intelligent detection system and method for carbon footprint throughout the entire life cycle. By real-time acquisition of multi-source data, intelligent coding and traceability, dynamic analysis and predictive early warning, it solves the problems of how to standardize carbon footprint data throughout the entire life cycle, improve data accuracy, accurately identify abnormal links, and achieve timely risk warning, thereby achieving intelligent detection and efficient management of carbon footprint throughout the entire life cycle.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] The intelligent carbon footprint detection system covering the entire life cycle includes:

[0006] Carbon emission data from the entire production process of the target product is collected, transmitted to the blockchain, and standardized and encoded using hierarchical coding rules.

[0007] The collected carbon emission data is detected by multi-dimensional triggering rules to determine whether a carbon footprint update is triggered; if so, the anchoring benchmark of the quantitative range is configured according to the production mode of the target product to filter carbon emission data and construct a quantitative dataset.

[0008] Set conversion coefficients for each production stage, correct carbon emission data for each production stage in the quantitative dataset, obtain the total carbon emissions for each production stage, and generate a carbon emission report.

[0009] Based on the carbon emissions and their proportion in the production process reported in the carbon emission report, we identify target production processes with anomalies, locate the source of the anomalies in the target production processes through standardized coding, track the historical carbon emission data of the target production processes to predict the carbon emissions of the target production processes, and issue early warnings of risks related to changes in carbon footprint levels.

[0010] Specifically, the hierarchical structure of the hierarchical coding rule includes: main identifier segment, link extension segment, and check segment;

[0011] The main identifier segment is used to begin with a fixed industry prefix, followed by the data collection date, production batch, and process type code, and is used to uniquely identify the basic attributes and production time of the target product.

[0012] The process extension segment is divided according to the production stages of the target product's life cycle. It adopts a four-element structure of production stage identifier, production equipment parameters, production process parameters, and production energy efficiency level to convert key information in the production process into digital identifiers.

[0013] The verification segment adopts a binary structure of fixed verification identifier and dynamic verification code, which is used to perform standardized verification on the main identifier segment and the link extension segment. The fixed verification identifier is set according to the target product coding system, and the dynamic verification code is generated by encryption based on the character sequence of the main identifier segment and the link extension segment.

[0014] Specifically, the steps for obtaining the total carbon emissions from each production stage and generating a carbon emissions report include:

[0015] Based on the carbon flow correlation graph, material balance mapping rules are configured, and conversion coefficients from intermediate products to target products in each production stage are set.

[0016] Material balance mapping rules refer to a set of rules that set the distribution ratio of intermediate products in production links relative to target products based on the physical relationships of each production link in the carbon flow correlation diagram.

[0017] The carbon emissions from the intermediate product production process are allocated to the target product according to the conversion factor, thus correcting the carbon emissions from the intermediate product production process.

[0018] Based on the characteristics of each production stage in the entire life cycle, the carbon emission data of each production stage is configured and allocated according to the material balance mapping rules. The carbon emission amount of each production stage included in the target product is calculated one by one according to the allocation rules. The carbon emission data of each production stage is aggregated according to the carbon flow path to generate the total carbon emission of each production stage in the entire life cycle.

[0019] The total carbon emissions are converted into a carbon footprint per unit product, generating a carbon emissions report. The carbon emissions report includes the quantitative range, the carbon emissions and carbon emissions percentage of each production stage, and the carbon footprint value per unit target product.

[0020] Specifically, the steps for predicting carbon emissions from target production processes and issuing early warnings about changes in carbon footprint levels include:

[0021] Based on the value range of production equipment parameters and production process parameters in the target production stage, parameter intervals are divided, and the parameter interval to which the current production equipment parameters and production process parameters of the target production stage belong is located; historical carbon emission data within the parameter interval is retrieved by using the data collection date of the main identifier segment of the standardized code and the parameter code of the extended segment of the stage.

[0022] For each parameter combination, the historical carbon emission data are sorted in ascending order by the data collection date, a time-series trend sequence is constructed, the carbon emission change trend is fitted, and a table of correspondence between parameter combinations, trend slopes and predicted values ​​is generated.

[0023] Calculate the distance values ​​between the current production equipment parameters, production process parameters, and parameter combinations, and select the target parameter combination based on the distance values;

[0024] By combining the current anchoring benchmark within the quantification range, the predicted values ​​of the target parameter group are weighted and summed to generate the predicted carbon emissions of the target production process under the current anchoring benchmark.

[0025] The system retrieves the actual carbon emissions at the time of the current carbon footprint update for the target production process, calculates the deviation between this deviation and the predicted benchmark carbon emissions, and triggers a risk warning if the deviation exceeds the preset level classification threshold. The warning level is then generated by combining the slope value of the trend sequence.

[0026] Specifically, the steps to determine whether a carbon footprint update has been triggered include:

[0027] A dynamic benchmark library is established for the carbon emission intensity of the target product production process, and a fluctuation threshold is configured for the carbon emission intensity of each production process to identify whether abnormal fluctuations in carbon emission data are statistically significant.

[0028] When the deviation between the real-time carbon emission intensity monitoring value and the carbon emission intensity benchmark value in the production process exceeds the fluctuation threshold, the monitoring period is triggered.

[0029] During the monitoring period, an independent in-depth verification process for the production process is initiated. Based on the standardized coding of carbon emission data, it is determined whether there is an abnormality in data collection. If so, the monitoring period is terminated and marked as an invalid trigger. Otherwise, carbon emission data of the production process is continuously collected to update the current carbon emission intensity of the production process.

[0030] The carbon footprint update is triggered if the percentage of time during which the deviation between the monitored carbon emission intensity value and the benchmark carbon emission intensity value exceeds the fluctuation threshold is greater than the preset time percentage threshold; otherwise, only the log is recorded and the carbon footprint update is not triggered.

[0031] Configure a standard time trigger window according to the production cycle of the target product, set the next update time node based on the timestamp of the last carbon footprint update, and trigger the carbon footprint update when the update time node is reached.

[0032] Specifically, the steps for determining whether a carbon footprint update has been triggered include:

[0033] When a carbon footprint update is triggered, an update cool-off period is configured. During the update cool-off period, carbon footprint update requests for non-urgent events are suspended. The number of carbon footprint updates triggered during the update cool-off period is counted. If the number of times the update is triggered exceeds the preset trigger update threshold, an update trigger warning is issued.

[0034] During the cooling-off period, identify the abnormal production process that triggers the carbon footprint update. If the abnormal production process is not unique, match the physical relationships between the abnormal production processes using a carbon flow correlation map.

[0035] Physical relationships include: raw material supply relationships, process dependence relationships, logistics and transportation relationships, energy flow relationships, and carbon emission correlation relationships;

[0036] The carbon flow correlation map is a topological model built based on the physical laws of material flow, energy flow and carbon flow throughout the entire life cycle of the target product, used to visualize the carbon flow path in each production stage;

[0037] If there are any mismatched abnormal production processes, an independent risk assessment will be conducted on the mismatched abnormal production processes. The duration for which the deviation between the real-time carbon emission intensity monitoring value and the carbon emission intensity benchmark value of the mismatched abnormal production processes exceeds the fluctuation threshold will be counted. If the deviation exceeds the preset duration threshold, the cooling-off period will be terminated and the full-cycle carbon footprint update will be triggered.

[0038] Specifically, the steps for constructing a quantized dataset include:

[0039] Configure the anchoring benchmark for the quantitative range according to the production mode of the target product, and define the quantitative range boundary of the anchoring benchmark.

[0040] Based on the hierarchical coding system, the standardized coding is used to parse the main identifier segment, the link extension segment and the verification segment, extract the quantitative range information of carbon emission data, screen the carbon emission data of each production link according to the quantitative range boundary of the anchor benchmark, verify the carbon emission data using the verification segment, and standardize the carbon emission data units.

[0041] Carbon emission data is categorized and aggregated according to production stages to construct a quantitative dataset containing carbon emission data for each production stage throughout the entire life cycle.

[0042] Specifically, the steps for identifying the target production process exhibiting anomalies and locating the source of the anomalies through standardized coding include:

[0043] Obtain the carbon emissions and carbon emission percentage of each production stage, configure standard thresholds, and construct an anomaly measurement model through dual-dimensional weighting to calculate the comprehensive anomaly score of each production stage. If it exceeds the standard threshold, it is determined to be a target production stage that needs to be monitored closely.

[0044] The two dimensions include the basic anomaly sub-dimension and the proportion anomaly sub-dimension. The basic anomaly sub-dimension is used to compare the carbon emissions of the current production process with the average carbon emissions of the same period in history; the proportion anomaly sub-dimension is used to quantify the degree of deviation of the carbon emission proportion of the previous production process from the anomaly of the entire production process throughout its life cycle.

[0045] Based on the updated carbon footprint quantitative dataset, the main identifier segment of the target production process is analyzed to locate the production batch and process type. The production process identifier, production equipment parameters, production process parameters and production energy efficiency level of the process extension segment are extracted to locate the abnormally associated production processes, production equipment parameters and production process parameters.

[0046] Specifically, the steps for identifying the target production process exhibiting anomalies and locating the source of the anomalies through standardized coding also include:

[0047] Based on the production equipment parameter code of the extended segment and the data collection date of the main identifier segment, historical carbon emission data of the production equipment under the same process conditions are retrieved through blockchain smart contracts to construct a historical baseline of the production equipment parameters and production process parameters of the target production segment.

[0048] The carbon emission data of the target production process is compared with the production equipment parameters and production process parameters of the target production process and the corresponding historical baseline to obtain the baseline deviation. If the absolute value of the baseline deviation is greater than the absolute threshold, the carbon emission data in which it is located is marked as abnormal carbon emission data, and the production equipment parameters and production process parameters that caused the abnormality are also marked.

[0049] Clustering algorithms are used to cluster the production equipment parameters and production process parameters of the located abnormal carbon emission data, divide the parameter abnormal groups, mark feature labels, and associate them with energy efficiency level codes to generate an anomaly analysis report.

[0050] Intelligent detection methods for carbon footprint throughout the entire life cycle include:

[0051] Step S1: Collect carbon emission data of the entire production process of the target product throughout its life cycle, transmit it to the blockchain, and standardize the encoding through hierarchical coding rules;

[0052] Step S2: Detect the collected carbon emission data through multi-dimensional triggering rules to determine whether a carbon footprint update is triggered; if so, configure the anchoring benchmark of the quantitative range according to the production mode of the target product to filter carbon emission data and construct a quantitative dataset.

[0053] Step S3: Set the conversion coefficient for the production process, correct the carbon emission data of the production process in the quantitative dataset, obtain the total carbon emissions of each production process, and generate a carbon emission report.

[0054] Step S4: Based on the carbon emissions and carbon emission ratio of the production process in the carbon emission report, identify the target production process with anomalies, locate the source of the anomaly in the target production process through standardized coding, track the trend of historical carbon emission data of the target production process, predict the carbon emission of the target production process, and issue a risk warning for changes in carbon footprint level.

[0055] The beneficial effects of this invention are:

[0056] This invention effectively solves a series of problems faced by existing technologies in full life-cycle carbon footprint management by integrating blockchain storage and hierarchical coding, multi-dimensional trigger updates, conversion coefficient correction, anomaly identification and location, and trend prediction and early warning. This results in significant benefits. On the one hand, it achieves standardized processing of carbon emission data through blockchain transmission and hierarchical coding rules. Combined with verification and unit unification during the quantitative dataset construction process, it significantly improves the standardization and accuracy of the data, providing a reliable foundation for subsequent analysis. On the other hand, it accurately determines carbon footprint update needs through multi-dimensional trigger rules, generates accurate carbon emission reports using conversion coefficient correction and carbon flow path aggregation, and locates the source of anomalies using a two-dimensional anomaly quantification model and standardized coding, achieving efficient identification of abnormal production processes and their root causes. Simultaneously, based on historical data trend tracking and parameter combination prediction, combined with deviation calculation and early warning level classification, it can detect the risk of carbon footprint level changes in advance and issue timely warnings. Ultimately, it achieves intelligent and efficient management of the entire life-cycle carbon footprint, from data collection, processing, and analysis to anomaly identification and risk warning, providing strong support for carbon emission reduction decisions in related fields. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the intelligent detection system for the entire life cycle carbon footprint of the present invention;

[0058] Figure 2 This is a flowchart illustrating the specific steps involved in triggering carbon footprint updates according to the present invention.

[0059] Figure 3 This is a flowchart of the monitoring process for triggering carbon footprint updates under cooling conditions, as described in this invention.

[0060] Figure 4 This is a flowchart illustrating how the present invention uses standardized coding to locate the source of anomalies in a target production process;

[0061] Figure 5 This is a flowchart of the intelligent detection method for carbon footprint throughout the entire life cycle of the present invention. Detailed Implementation

[0062] Please see Figure 1 This embodiment introduces a full lifecycle carbon footprint intelligent detection system, including a data acquisition module, a data encoding module, a real-time analysis module, and an anomaly tracing module:

[0063] The data acquisition module collects carbon emission data in real time from each stage of the green methanol production process through a distributed IoT sensor network. After being cleaned and standardized by edge computing nodes, the data is encrypted and transmitted to the blockchain.

[0064] In this example, at the raw material stage, RFID tags and GPS positioning are used to record the biomass transportation route and energy consumption; in the production stage, smart meters, infrared gas analyzers, and process parameter sensors are deployed to monitor power consumption, heat consumption, and exhaust emissions in real time; during transportation and use, on-board OBD systems and combustion equipment sensors dynamically capture fuel consumption and final emission data. All carbon emission data is cleaned and standardized by edge computing nodes, including unit conversion and outlier filtering, before being encrypted and transmitted to the blockchain.

[0065] The data encoding module, based on blockchain technology, standardizes and uniquely identifies carbon emission data, forming a traceable digital identity for the carbon footprint. Through hierarchical encoding rules, it integrates key information such as raw material type, production process, and transportation route, ensuring that data uploaded to the blockchain can be automatically verified via smart contracts and supporting full-chain reverse traceability. The hierarchical encoding rules employ a layered structure of main identifier segment, process extension segment, and verification segment, transforming key carbon emission information such as raw material type, production process, transportation route, and application scenario into a machine-readable encoding system. This ensures that the integrity of the data can be automatically verified via smart contracts after being uploaded to the blockchain and provides an accurate traceability index for subsequent carbon footprint quantitative analysis.

[0066] Specifically, the main identifier segment begins with a fixed industry prefix, followed by the data collection date, production batch, and process type code, which is used to uniquely identify the basic attributes and production time of the target product. For example, in the process type code, M01 represents electro-methanol, which is a process of producing hydrogen by electrolyzing water with renewable electricity and combining it with carbon dioxide capture, and M02 represents biomass methanol, which is a process of catalytic synthesis after gasification of agricultural and forestry waste.

[0067] The process extension segment is subdivided according to the production stages of the target product's life cycle. It transforms key information such as equipment parameters, process conditions, and energy efficiency performance during the production process into traceable digital identifiers. Standardized coding rules enable precise mapping and chain-like association of carbon emission data from each production stage. The process extension segment adopts a four-element structure: production stage identifier, production equipment parameters, production process parameters, and production energy efficiency level. The production stage identifier indicates that the carbon emission data originates from the green methanol production stage; production equipment parameters represent the parameters of specific production equipment, achieving unique identification of the equipment; production process parameters represent key process parameters, dynamically combining parameter codes according to different process types. For example, in the electro-methanol process, E055T250 indicates an electricity consumption of 5.5 kWh per standard cubic meter of hydrogen and a synthesis temperature of 250°C, while in the biomass methanol process, G85P25 indicates a gasification rate of 85% and a synthesis pressure of 2.5 MPa; the production energy efficiency level indicates the energy utilization efficiency of the production process and is graded and coded according to classification standards. For example, I1 represents Level 1 energy efficiency, meaning energy consumption is less than 80% of the benchmark value. I2 represents Level 2 energy efficiency, meaning it meets the benchmark requirements, while III3 represents Level 3 energy efficiency, meaning it exceeds the benchmark. Through this four-element structure, carbon emission data in the production process is transformed into standardized codes that carry equipment identity, process characteristics, and energy efficiency levels. During on-chain verification, these codes can be automatically linked to equipment operation logs, energy consumption records, and quality inspection reports, enabling precise traceability and accountability for carbon emissions throughout the entire process from raw material input to product output.

[0068] The verification segment, as the final step in the hierarchical coding rules, is used to ensure the integrity, authenticity, and tamper-proof nature of the main identifier segment and the extended segment through a standardized verification mechanism, forming a closed-loop verification of the carbon footprint digital identity. The verification segment adopts a binary structure of a fixed verification identifier and a dynamic verification code. The fixed verification identifier is set according to the functional positioning of the coding system for the full life-cycle carbon footprint management of green methanol, using fixed characters directly associated with the carbon footprint verification function, such as CFV. This forms a logically unified coding system with the industry prefix of the main identifier segment and the segment identifier of the extended segment, facilitating rapid identification of the verification attributes and application scenarios of the code. The dynamic verification code is generated based on the character sequences of the main identifier segment and the extended segment through a preset encryption algorithm. Its length dynamically adapts according to the total number of characters in the first two segments, ensuring the overall uniqueness and verifiability of the code. Specifically, the generation logic of the dynamic verification code is as follows: the characters of the main identifier segment and the extended segment are converted into a numerical sequence according to their ASCII code values. A unique verification value is calculated through an algorithm, then converted into a hexadecimal or alphanumeric string, and combined with the fixed verification identifier to form a complete verification segment. During the blockchain notarization and verification process, the smart contract will automatically extract the verification segment from the code, recalculate the verification value of the main identifier segment and the link extension segment, and compare it with the dynamic verification code in the verification segment: if they match, it is determined that the data has not been tampered with, and on-chain notarization and traceability query are allowed; if they do not match, they are marked as abnormal codes, triggering the data repair process.

[0069] The real-time analysis module detects collected carbon emission data through multi-dimensional trigger rules, enabling automated updating and quantification of the carbon footprint. By setting production thresholds, time thresholds, and event thresholds, it determines whether a carbon footprint update is triggered. If so, it dynamically calculates the full lifecycle carbon footprint based on real-time collected data and a hybrid allocation algorithm, generating a quantified carbon footprint result. Specifically, it configures the anchoring benchmark for the quantification range according to the production mode of the target product, filters carbon emission data, and constructs a quantified dataset. By setting conversion coefficients for each production stage, it corrects the carbon emission data of each production stage in the quantified dataset, obtains the total carbon emissions of each production stage, and generates a carbon emission report.

[0070] Preferably, the specific steps for determining whether a carbon footprint update has been triggered include:

[0071] Please see Figure 2 A dynamic benchmark library is established for the carbon emission intensity of each production stage of green formaldehyde. The carbon emission intensity includes carbon emission per unit product and carbon emission factor per unit transportation distance. A fluctuation threshold is configured for the carbon emission intensity of each production stage to identify whether abnormal fluctuations in carbon emission data are statistically significant. When the deviation between the real-time carbon emission intensity monitoring value and the carbon emission intensity benchmark value is greater than the fluctuation threshold, the monitoring period is triggered.

[0072] During the monitoring period, an independent in-depth verification process is initiated for this production stage. Based on the standardized coding of carbon emission data, real-time carbon emission data, edge computing preprocessing logs, and blockchain-based evidence records for this production stage are retrieved. Data consistency is cross-validated to determine if there are any data collection anomalies. If so, it indicates temporary data noise or transmission failure, ending the monitoring period and marking it as an invalid trigger. Only the anomaly log is recorded, without triggering a carbon footprint update. Otherwise, carbon emission data from the production stage continues to be collected to update the current carbon emission intensity. The percentage of time during the monitoring period where the deviation between the real-time carbon emission intensity monitoring value and the carbon emission intensity benchmark value exceeds a fluctuation threshold is calculated. If this percentage exceeds a pre-set threshold, a carbon footprint update is triggered. Otherwise, it indicates that the deviation has naturally fallen back to the normal range with the production rhythm, and only the log is recorded without triggering a carbon footprint update. The monitoring period duration is linked to the deviation level and can be customized.

[0073] In accordance with the green formaldehyde production cycle, such as the end of batch production or the switching of continuous production shifts, a standard time trigger window is configured to set the next update time node based on the timestamp of the last carbon footprint update. When the update time node is reached, the carbon footprint update is triggered.

[0074] Please see Figure 3 To avoid repeated calculations in short cycles, a cooling-off period for updates is configured. When a carbon footprint update is triggered, carbon footprint update requests for non-emergency events are suspended during the cooling-off period. Only emergency abnormal events such as equipment failure and raw material supply interruption are responded to. The number of carbon footprint updates triggered during the cooling-off period is counted. If it exceeds the preset trigger update threshold, it indicates that there is a systemic data fluctuation or configuration abnormality, and an update warning is triggered to prompt manual intervention to check sensor status, emission factor configuration, or process stability.

[0075] Otherwise, during the cooling-off period, the abnormal production links that trigger carbon footprint updates are identified. If there are multiple production links that trigger carbon footprint updates during the cooling-off period, the physical relationships between the abnormal production links are matched using a carbon flow correlation map. These physical relationships include: raw material supply relationships, process dependence relationships, logistics and transportation relationships, energy flow relationships, and carbon emission correlation relationships. Raw material supply relationships are used to establish the material input correlation between upstream raw material links and downstream production links; process dependence relationships are used to identify the coupling logic of process parameters within or across production links; logistics and transportation relationships are used to correlate the spatial transfer impact of raw material and product transportation links with upstream and downstream links; energy flow relationships are used to construct the physical mapping of energy input and output between each link, and to track the transmission effect of energy type and efficiency changes on carbon emissions throughout the entire life cycle; and carbon emission correlation relationships are used to clarify the material cycle logic between carbon source links and carbon sink links. The carbon flow correlation map is a topological model constructed based on the physical laws of material flow, energy flow, and carbon flow throughout the entire life cycle of green methanol. It is used to visualize the carbon flow path of each production link and provide a correlation verification basis for multiple abnormal triggers.

[0076] If there is an unmatched abnormal production process, an independent risk assessment will be conducted on the abnormal production process. The duration for which the deviation between the real-time carbon emission intensity monitoring value and the carbon emission intensity benchmark value of the abnormal production process exceeds the fluctuation threshold will be counted. If the deviation exceeds the preset duration threshold, the cooling-off period will be terminated and the full-cycle carbon footprint update will be triggered. Otherwise, no action will be taken.

[0077] Preferably, the specific steps for generating carbon footprint quantification results include:

[0078] The anchoring benchmark for the quantitative range is configured according to the green methanol production model. The production model includes continuous production and batch production. The production batch, preset evaluation cycle, or target production volume can be selected as the anchoring benchmark. For example, in the production batch model, the production batch code of the main identifier segment of the associated data code is automatically locked to the entire process of the production batch from raw material input to finished product warehousing, defining the quantitative range boundary of the anchoring benchmark. In the evaluation cycle model, time window parameters are configured to define the next evaluation cycle according to the timestamp of the previous quantification completion, thereby defining the quantitative range boundary of the anchoring benchmark. In the target production volume model, a target output threshold is set to define the quantitative range boundary of the anchoring benchmark, ensuring that the range of each quantification is traceable and reproducible.

[0079] Based on a hierarchical coding system, the standardized coding analyzes the main identifier segment, the link extension segment, and the verification segment to extract the quantitative range information of carbon emission data, such as production batch, link identifier, and check code. Then, according to the quantitative range of the anchor benchmark, the carbon emission data of each production link is screened. The verification segment is used to verify the integrity of carbon emission data and fill in abnormal or missing data. The carbon emission data unit is standardized to realize the conversion of energy consumption and material consumption data into carbon emission equivalent.

[0080] Carbon emission data is categorized and aggregated according to production stages. Each production stage data includes fields such as coding information, raw carbon emission data, converted carbon emission data, and data collection date. The physical relationships between each production stage are bound through a carbon flow correlation map, and a logical mapping between carbon emission data is established. A standardized quantitative dataset containing effective carbon emission data from all production stages throughout the entire life cycle is constructed to ensure that the correlation within the quantitative dataset is clear, the causal relationship between production stages is traceable, and the logic between data records is verifiable.

[0081] Based on the carbon flow correlation graph, material balance mapping rules are configured, and conversion coefficients are set for intermediate products in each production stage to target products. The conversion coefficients are determined based on the theoretical conversion rate of the process and historical production data. The material balance mapping rules refer to a set of rules that set the allocation ratio of intermediate products between target products and other uses and the corresponding carbon emission allocation logic based on the physical relationship of each production stage in the carbon flow correlation graph. This is used to solve the problem of the mismatch between the output of intermediate products and the final target product in the production stage, and to allocate the carbon emissions of the intermediate product production stage to the target product according to the conversion coefficient, so as to correct the carbon emissions of the intermediate product production stage and avoid the carbon emission quantification deviation caused by the diversion or loss of intermediate products.

[0082] Based on the characteristics of each production stage throughout the entire life cycle, allocation rules are configured. For example, energy-intensive stages adopt the energy method, allocating carbon emissions according to the proportion of energy consumption in each production stage; material conversion-dominant stages adopt the quality method, allocating according to the proportion of raw material input; and joint product stages are configured with economic value weighting coefficients, with the allocation weight dynamically adjusted according to the proportion of product output value.

[0083] In the process of carbon footprint quantification, the carbon emission data of the production process after the material balance mapping rule is first correlated, and then the carbon emission amount that should be included in the target product for each process is calculated one by one according to the configured allocation rules: for energy-intensive processes, the total carbon emission is broken down according to the energy consumption ratio of each downstream use process; for material conversion processes, it is allocated according to the proportion of raw material input of each downstream process; for combined product processes, the shared carbon emission is allocated to the target product according to the economic value weighting coefficient.

[0084] After allocating carbon emissions to each production stage, the carbon emission data of each production stage are aggregated according to the carbon flow path. The carbon emissions allocated to each production stage are added up, and duplicate calculations of shared stages are eliminated. If the energy supply stage has been allocated to each usage stage proportionally, it will not be included in the total energy consumption again. The total carbon emissions of each production stage throughout the entire life cycle from raw material acquisition to product use are generated to ensure that the total covers all relevant stages without duplicate calculations or omissions.

[0085] The total carbon emissions throughout the entire life cycle are converted into carbon footprint per unit product, eliminating the impact of differences in production scale; and a carbon emission report is generated, including the quantitative range, the carbon emissions and carbon emission ratio of each production stage, and the carbon footprint value per unit target product; the carbon footprint value per unit target product refers to the ratio of the total carbon emissions generated by the target product throughout its entire life cycle to the actual output of the product. The carbon emission report is associated with complete standardized coding, and supports reverse traceability of the original data of each stage through blockchain, ensuring that the results are verifiable and auditable.

[0086] The anomaly tracing module identifies production processes with abnormal carbon emission ratios based on carbon emission reports. These processes are designated as key monitoring targets. Through standardized coding, the module retrieves historical carbon emission data for these targets and uses a historical carbon emission baseline model to detect anomalies. The standardized coding is used to pinpoint the source of the anomalies in the target production process, such as specific equipment, process parameters, or transportation routes. The module also tracks the historical carbon emission data of the target production process and predicts the carbon emissions of the target production process based on the current carbon emission trend, such as a continuous upward or downward trend, in order to provide early warning of carbon footprint level changes.

[0087] Please see Figure 4 Preferably, the specific steps for locating the source of anomalies in the target production process through standardized coding include:

[0088] Based on carbon emission reports, the carbon emissions and their proportions at each production stage are obtained. Standard thresholds are configured, and an anomaly measurement model is constructed using a two-dimensional weighted approach to calculate the comprehensive anomaly score for each production stage. If the score exceeds the standard threshold, the stage is identified as a target production stage requiring close monitoring. The two dimensions include a basic anomaly sub-dimension and a proportion anomaly sub-dimension. The basic anomaly sub-dimension compares the current carbon emissions of a production stage with the historical average carbon emissions for the same period, calculating the deviation. This sub-dimension visually reflects the absolute fluctuation of carbon emissions data for a single production stage and clearly quantifies the degree of deviation between the current emissions and historical stable production levels. The proportion anomaly sub-dimension calculates the deviation between the current proportion and the median of the benchmark range for the carbon emission proportion of that production stage. This sub-dimension quantifies the degree of anomaly deviation of the carbon emission proportion of that production stage relative to the overall life cycle.

[0089] Based on the current updated quantitative dataset of carbon footprint, the production batch code and process type code of the main identifier segment are parsed from the standardized coding of carbon emission data associated with the target production links marked in the carbon emission report to determine the production batch and process type to which the anomaly belongs; the production link identifier, production equipment parameters, production process parameters and production energy efficiency level of the link extension segment are extracted to locate the specific production link, production equipment parameters and production process parameters associated with the anomaly, providing an accurate index for subsequent data retrieval;

[0090] Based on the production equipment parameter code of the extended segment and the data collection date of the main identifier segment, historical carbon emission data of the production equipment under the same process conditions are retrieved through a blockchain smart contract. At the same time, the dynamic verification code of the verification segment is extracted and compared with the verification segment encoded in the historical data to verify the data integrity and construct an anomaly detection dataset.

[0091] Based on the anomaly detection dataset, a historical baseline of the production equipment parameters and production process parameters of the target production process is constructed. The production equipment parameters and production process parameters of the carbon emission data of the target production process are compared with the corresponding historical baseline to obtain the baseline deviation. If the absolute value of the baseline deviation is greater than the absolute threshold, the carbon emission data in which it is located is marked as abnormal carbon emission data, and the production equipment parameters and production process parameters that caused the anomaly are also marked.

[0092] Clustering algorithms are employed to cluster the production equipment parameters and production process parameters of the located abnormal carbon emission data. Based on the correlation between the anomalies in production equipment parameters and production process parameters, parameters with similar fluctuation characteristics are divided into multiple parameter anomaly groups and labeled with feature tags, such as strong correlation fluctuations and single parameter jumps, to intuitively present the clustering patterns of abnormal parameters. The clustering results are then linked to the energy efficiency level codes of the extended stage, comparing the energy efficiency level codes corresponding to each parameter anomaly group with the historical baseline energy efficiency level to calculate the decline in energy efficiency level. An anomaly analysis report containing complete information is constructed, clearly defining the coding traceability path from the main identifier segment production batch code to the extended stage equipment and process parameter codes, providing clustering grouping results and energy efficiency level correlation data. This ensures that the anomaly analysis report can be traced back to the original data through standardized coding, providing quantitative support for precise intervention decisions.

[0093] Preferably, the specific steps for predicting carbon emissions from the target production stage include:

[0094] Based on the value ranges of production equipment and process parameters for the target production stage, parameter intervals are divided, and the current parameter interval to which the production equipment and process parameters of the target production stage belong is located. Historical carbon emission data within this parameter interval is retrieved from the blockchain storage using the standardized coded main identifier segment data collection date and the stage extension segment parameter code, ensuring data consistency with the current production model.

[0095] For each parameter combination, historical carbon emission data are sorted in ascending order by data collection date to construct a time-series trend series. An exponential smoothing method is used to calculate the moving average of the series to fit the carbon emission trend over time. Based on the trend series, the carbon emission trend for a preset prediction period under each parameter combination is predicted using an ARIMA model, generating a table corresponding to parameter combinations, trend slopes, and predicted values.

[0096] The Euclidean distance algorithm is used to calculate the distance values ​​between the current production equipment parameters, production process parameters and parameter combinations. Target parameter combinations are selected based on the distance values. For example, the top 3 parameter combinations with the smallest distances are selected as the target parameter group.

[0097] By combining the current quantification range with the anchoring benchmark, the predicted values ​​of the target parameter group are weighted and summed, with the weights allocated inversely proportional to the distance, to generate the predicted carbon emissions of the target production process under the current anchoring benchmark.

[0098] The system retrieves the actual carbon emissions at the time of the current carbon footprint update for the target production process, calculates the deviation between this deviation and the predicted benchmark carbon emissions, and triggers a risk warning if the deviation exceeds the preset level classification threshold. The system then marks the potential level as either rising or falling based on the deviation sign and generates a warning level by combining the slope value of the trend sequence.

[0099] The predicted carbon emissions, deviations, and warning levels are linked to the standardized codes of the target production process to generate an associated record consisting of a main identifier segment, a process extension segment parameter code, the predicted carbon emissions, deviations, and warning levels, which is then stored in the blockchain.

[0100] Please see Figure 5 This embodiment introduces a method for intelligent detection of carbon footprint throughout its entire life cycle, including:

[0101] Step S1: Collect carbon emission data of the entire production process of the target product throughout its life cycle, transmit it to the blockchain, and standardize the encoding through hierarchical coding rules;

[0102] Step S2: Detect the collected carbon emission data through multi-dimensional triggering rules to determine whether a carbon footprint update is triggered; if so, configure the anchoring benchmark of the quantitative range according to the production mode of the target product to filter carbon emission data and construct a quantitative dataset.

[0103] Step S3: Set the conversion coefficient for the production process, correct the carbon emission data of the production process in the quantitative dataset, obtain the total carbon emissions of each production process, and generate a carbon emission report.

[0104] Step S4: Based on the carbon emissions and carbon emission ratio of the production process in the carbon emission report, identify the target production process with anomalies, locate the source of the anomaly in the target production process through standardized coding, track the trend of historical carbon emission data of the target production process, predict the carbon emission of the target production process, and issue a risk warning for changes in carbon footprint level.

[0105] Preferably, the hierarchical structure of the hierarchical coding rule includes: a main identifier segment, a link extension segment, and a check segment;

[0106] The main identifier segment is used to begin with a fixed industry prefix, followed by the data collection date, production batch, and process type code, and is used to uniquely identify the basic attributes and production time of the target product.

[0107] The process extension segment is divided according to the production stages of the target product's life cycle. It adopts a four-element structure of production stage identifier, production equipment parameters, production process parameters, and production energy efficiency level to convert key information in the production process into digital identifiers.

[0108] The verification segment adopts a binary structure of fixed verification identifier and dynamic verification code, which is used to perform standardized verification on the main identifier segment and the link extension segment. The fixed verification identifier is set according to the target product coding system, and the dynamic verification code is generated by encryption based on the character sequence of the main identifier segment and the link extension segment.

[0109] Preferably, the specific steps for obtaining the total carbon emissions of each production stage and generating a carbon emission report include:

[0110] Based on the carbon flow correlation graph, material balance mapping rules are configured, and conversion coefficients from intermediate products to target products in each production stage are set.

[0111] Material balance mapping rules refer to a set of rules that set the distribution ratio of intermediate products in production links relative to target products based on the physical relationships of each production link in the carbon flow correlation diagram.

[0112] The carbon emissions from the intermediate product production process are allocated to the target product according to the conversion factor, thus correcting the carbon emissions from the intermediate product production process.

[0113] Based on the characteristics of each production stage in the entire life cycle, the carbon emission data of each production stage is configured and allocated according to the material balance mapping rules. The carbon emission amount of each production stage included in the target product is calculated one by one according to the allocation rules. The carbon emission data of each production stage is aggregated according to the carbon flow path to generate the total carbon emission of each production stage in the entire life cycle.

[0114] The total carbon emissions are converted into a carbon footprint per unit product, generating a carbon emissions report. The carbon emissions report includes the quantitative range, the carbon emissions and carbon emissions percentage of each production stage, and the carbon footprint value per unit target product.

[0115] Preferably, the specific steps for predicting carbon emissions from the target production stage and issuing early warnings of carbon footprint level changes include:

[0116] Based on the value range of production equipment parameters and production process parameters in the target production stage, parameter intervals are divided, and the parameter interval to which the current production equipment parameters and production process parameters of the target production stage belong is located; historical carbon emission data within the parameter interval is retrieved by using the data collection date of the main identifier segment of the standardized code and the parameter code of the extended segment of the stage.

[0117] For each parameter combination, the historical carbon emission data are sorted in ascending order by the data collection date, a time-series trend sequence is constructed, the carbon emission change trend is fitted, and a table of correspondence between parameter combinations, trend slopes and predicted values ​​is generated.

[0118] Calculate the distance values ​​between the current production equipment parameters, production process parameters, and parameter combinations, and select the target parameter combination based on the distance values;

[0119] By combining the current anchoring benchmark within the quantification range, the predicted values ​​of the target parameter group are weighted and summed to generate the predicted carbon emissions of the target production process under the current anchoring benchmark.

[0120] The system retrieves the actual carbon emissions at the time of the current carbon footprint update for the target production process, calculates the deviation between this deviation and the predicted benchmark carbon emissions, and triggers a risk warning if the deviation exceeds the preset level classification threshold. The warning level is then generated by combining the slope value of the trend sequence.

[0121] Working principle and its effects:

[0122] This invention proposes an intelligent detection system and method for carbon footprint throughout the entire lifecycle of a target product. By collecting carbon emission data from all stages of the target product's production lifecycle, transmitting it to a blockchain, and standardizing it using hierarchical coding rules, the collaborative action of the main identifier segment, the stage extension segment, and the verification segment not only achieves unique identification of the data and digitization of key information but also ensures data standardization through a verification mechanism, effectively improving the standardization and reliability of carbon emission data. Furthermore, based on the hierarchical coding parsing information and combined with the quantitative range anchoring benchmark configured for the target product's production mode, the verification and unit unification processing during the construction of the quantitative dataset further strengthens data consistency, providing a high-quality data foundation for subsequent analysis.

[0123] Based on this, the present invention uses multi-dimensional triggering rules to detect the collected data to determine the carbon footprint update requirements. The combination of dynamic benchmark library and time window ensures the accuracy of update triggering and avoids invalid updates or omissions. After the update is triggered, the conversion coefficient of the production process is set to correct the quantitative dataset. The carbon emissions of intermediate products are allocated according to the carbon flow correlation map and material balance mapping rules. The carbon emission report generated by aggregating carbon flow paths fully considers the physical correlation and conversion logic of the production process, making the calculation of the total carbon emissions of each process and the carbon footprint of each product more in line with reality, and greatly improving the accuracy and reference value of the report.

[0124] Furthermore, based on the carbon emission amount and proportion in the carbon emission report, this invention identifies abnormal production processes through a two-dimensional anomaly quantification model. With the help of standardized coding, the production equipment and process parameters of the source of the anomaly can be accurately located. At the same time, historical data is retrieved to construct a time-series trend sequence, and the carbon emission of the target process is predicted by combining parameter combinations. By calculating deviations and classifying early warning levels, risks can be perceived in advance. This series of operations not only achieves efficient identification of the root cause of the anomaly, but also ensures the timeliness of risk warning. Finally, through the synergy of various technical means, intelligent closed-loop management of the entire life cycle carbon footprint, from data processing to anomaly identification and risk warning, is realized, providing comprehensive support for efficient carbon footprint management.

[0125] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A full life-cycle carbon footprint intelligent detection system, characterized in that, include: Carbon emission data from the entire production process of the target product is collected, transmitted to the blockchain, and standardized and encoded using hierarchical coding rules. The collected carbon emission data is detected by multi-dimensional triggering rules to determine whether a carbon footprint update is triggered. If so, then the anchoring benchmark for the quantitative range is configured according to the production mode of the target product, which is used to filter the carbon emission data and construct a quantitative dataset; Set the conversion coefficient for each production stage, correct the carbon emission data of each production stage in the quantified dataset, obtain the total carbon emissions of each production stage, and generate a carbon emission report. Based on the carbon emissions and carbon emission ratio of the production process in the carbon emission report, the target production process with anomalies is identified. The source of the anomaly in the target production process is located through the standardized coding. The historical carbon emission data of the target production process is tracked to predict the carbon emission of the target production process and to provide a risk warning for changes in carbon footprint level.

2. The intelligent detection system for the entire life cycle carbon footprint as described in claim 1, characterized in that, The hierarchical structure of the hierarchical coding rule includes: a main identifier segment, a link extension segment, and a verification segment; The main identifier segment is used to start with a fixed industry prefix, followed by the data collection date, production batch and process type code, and is used to uniquely identify the basic attributes and production time of the target product. The extended segment is divided according to the production stage of the target product life cycle, and adopts a four-element structure of production stage identifier, production equipment parameters, production process parameters and production energy efficiency level to convert key information in the production process into digital identifiers. The verification segment adopts a binary structure of fixed verification identifier and dynamic verification code, which is used to perform standardized verification on the main identifier segment and the link extension segment. The fixed verification identifier is set according to the target product coding system, and the dynamic verification code is generated by encryption based on the character sequence of the main identifier segment and the link extension segment.

3. The intelligent detection system for the entire life cycle carbon footprint as described in claim 1, characterized in that, The specific steps for obtaining the total carbon emissions from each production stage and generating a carbon emission report include: Based on the carbon flow correlation graph, material balance mapping rules are configured, and conversion coefficients from intermediate products to target products in each production stage are set. The material balance mapping rule refers to a set of rules that set the distribution ratio of intermediate products in the production process relative to the target product based on the physical relationship of each production stage in the carbon flow correlation diagram. The carbon emissions from the intermediate product production process are allocated to the target product according to the conversion factor, thus correcting the carbon emissions from the intermediate product production process. Based on the characteristics of each production stage in the entire life cycle, the carbon emission data of each production stage is configured and allocated according to the material balance mapping rules. The carbon emission amount of each production stage included in the target product is calculated one by one according to the allocation rules. The carbon emission data of each production stage is aggregated according to the carbon flow path to generate the total carbon emission of each production stage in the entire life cycle. The total carbon emissions are converted into a carbon footprint per unit product, and a carbon emission report is generated. The carbon emission report includes the quantitative range, the carbon emissions and carbon emission ratio of each production stage, and the carbon footprint value per unit target product.

4. The intelligent detection system for the entire life cycle carbon footprint as described in claim 1, characterized in that, The specific steps for predicting carbon emissions from the target production process and issuing early warnings about changes in carbon footprint levels include: Based on the value range of production equipment parameters and production process parameters in the target production stage, parameter intervals are divided, and the parameter interval to which the current production equipment parameters and production process parameters of the target production stage belong is located; historical carbon emission data within the parameter interval is retrieved by using the data collection date of the main identifier segment of the standardized code and the parameter code of the extended segment of the stage. For each parameter combination, the historical carbon emission data are sorted in ascending order by the data collection date, a time-series trend sequence is constructed, the carbon emission change trend is fitted, and a table of correspondence between parameter combinations, trend slopes and predicted values ​​is generated. Calculate the distance values ​​between the current production equipment parameters, production process parameters, and parameter combinations, and select the target parameter combination based on the distance values; By combining the current anchoring benchmark within the quantification range, the predicted values ​​of the target parameter group are weighted and summed to generate the predicted carbon emissions of the target production process under the current anchoring benchmark. The system retrieves the actual carbon emissions at the time of the current carbon footprint update for the target production process, calculates the deviation between this deviation and the predicted benchmark carbon emissions, and triggers a risk warning if the deviation exceeds the preset level classification threshold. The warning level is then generated by combining the slope value of the trend sequence.

5. The intelligent detection system for the entire life cycle carbon footprint as described in claim 1, characterized in that, The specific steps for determining whether a carbon footprint update has been triggered include: A dynamic benchmark library is established for the carbon emission intensity of the target product production process, and a fluctuation threshold is configured for the carbon emission intensity of each production process to identify whether abnormal fluctuations in carbon emission data are statistically significant. When the deviation between the real-time carbon emission intensity monitoring value and the carbon emission intensity benchmark value in the production process exceeds the fluctuation threshold, the monitoring period is triggered. During the monitoring period, an independent in-depth verification process for the production process is initiated. Based on the standardized coding of carbon emission data, it is determined whether there is an abnormality in data collection. If so, the monitoring period is terminated and marked as an invalid trigger. Otherwise, carbon emission data of the production process is continuously collected to update the current carbon emission intensity of the production process. The carbon footprint update is triggered if the percentage of time during which the deviation between the monitored carbon emission intensity value and the benchmark carbon emission intensity value exceeds the fluctuation threshold is greater than the preset time percentage threshold; otherwise, only the log is recorded and the carbon footprint update is not triggered. Configure a standard time trigger window according to the production cycle of the target product, set the next update time node based on the timestamp of the last carbon footprint update, and trigger the carbon footprint update when the update time node is reached.

6. The intelligent detection system for the entire life cycle carbon footprint as described in claim 1, characterized in that, The specific steps for determining whether a carbon footprint update has been triggered also include: When a carbon footprint update is triggered, an update cool-off period is configured. During the update cool-off period, carbon footprint update requests for non-urgent events are suspended. The number of carbon footprint updates triggered during the update cool-off period is counted. If the number of times the update is triggered exceeds the preset trigger update threshold, an update trigger warning is issued. Otherwise, during the cooling-off period, identify the abnormal production process that triggered the carbon footprint update. If the abnormal production process is not unique, match the physical relationships between the abnormal production processes using a carbon flow correlation map. The physical relationships include: raw material supply relationships, process dependence relationships, logistics and transportation relationships, energy flow relationships, and carbon emission correlation relationships; The carbon flow correlation map is a topological model constructed based on the physical laws of material flow, energy flow and carbon flow throughout the entire life cycle of the target product, and is used to visualize the carbon flow path of each production stage. If there are any mismatched abnormal production processes, an independent risk assessment will be conducted on the mismatched abnormal production processes. The duration for which the deviation between the real-time carbon emission intensity monitoring value and the carbon emission intensity benchmark value of the mismatched abnormal production processes exceeds the fluctuation threshold will be counted. If the deviation exceeds the preset duration threshold, the cooling-off period will be terminated and the full-cycle carbon footprint update will be triggered.

7. The intelligent detection system for the entire life cycle carbon footprint as described in claim 1, characterized in that, The specific steps for constructing the quantized dataset include: Based on the target product production mode, configure the anchoring benchmark for the quantitative range, and define the boundary of the quantitative range of the anchoring benchmark; Based on the hierarchical coding system, the standardized coding is used to parse the main identifier segment, the link extension segment and the verification segment, extract the quantitative range information of carbon emission data, screen the carbon emission data of each production link according to the quantitative range boundary of the anchor benchmark, verify the carbon emission data using the verification segment, and standardize the carbon emission data units. Carbon emission data is categorized and aggregated according to production stages to construct a quantitative dataset containing carbon emission data for each production stage throughout the entire life cycle.

8. The intelligent detection system for the entire life cycle carbon footprint as described in claim 1, characterized in that, The specific steps for identifying the target production process with anomalies and locating the source of the anomaly in the target production process through the standardized coding include: Obtain the carbon emissions and carbon emission percentage of each production stage, configure standard thresholds, and construct an anomaly measurement model through dual-dimensional weighting to calculate the comprehensive anomaly score of each production stage. If it exceeds the standard threshold, it is determined to be a target production stage that needs to be monitored closely. The two dimensions include a basic anomaly sub-dimension and a percentage anomaly sub-dimension. The basic anomaly sub-dimension is used to compare the carbon emissions of the current production process with the average carbon emissions of the same period in history. The percentage anomaly sub-dimension is used to quantify the degree of deviation of the carbon emission percentage of the previous production process from the total production process throughout its entire life cycle. Based on the updated carbon footprint quantitative dataset, the main identifier segment of the target production process is analyzed to locate the production batch and process type. The production process identifier, production equipment parameters, production process parameters and production energy efficiency level of the process extension segment are extracted to locate the abnormally associated production processes, production equipment parameters and production process parameters.

9. The intelligent detection system for the entire life cycle carbon footprint as described in claim 8, characterized in that, The specific steps for identifying the target production process with anomalies and locating the source of the anomaly in the target production process through the standardized coding further include: Based on the production equipment parameter code of the extended segment and the data collection date of the main identifier segment, historical carbon emission data of the production equipment under the same process conditions are retrieved through blockchain smart contracts to construct a historical baseline of the production equipment parameters and production process parameters of the target production segment. The carbon emission data of the target production process is compared with the production equipment parameters and production process parameters of the target production process and the corresponding historical baseline to obtain the baseline deviation. If the absolute value of the baseline deviation is greater than the absolute threshold, the carbon emission data in which it is located is marked as abnormal carbon emission data, and the production equipment parameters and production process parameters that caused the abnormality are also marked. Clustering algorithms are used to cluster the production equipment parameters and production process parameters of the located abnormal carbon emission data, divide the parameter abnormal groups, mark feature labels, and associate them with energy efficiency level codes to generate an anomaly analysis report.

10. A method for intelligent detection of carbon footprint throughout its entire life cycle, implemented based on the intelligent detection system for carbon footprint throughout its entire life cycle as described in any one of claims 1-9, characterized in that, include: Step S1: Collect carbon emission data of the entire production process of the target product throughout its life cycle, transmit it to the blockchain, and standardize the encoding through hierarchical coding rules; Step S2: Detect the collected carbon emission data through multi-dimensional triggering rules to determine whether a carbon footprint update is triggered; If so, then the anchoring benchmark for the quantitative range is configured according to the production mode of the target product, which is used to filter the carbon emission data and construct a quantitative dataset; Step S3: Set the conversion coefficient for the production process, correct the carbon emission data of the production process in the quantified dataset, obtain the total carbon emissions of each production process, and generate a carbon emission report. Step S4: Based on the carbon emissions and carbon emission ratio of the production process in the carbon emission report, identify the target production process with anomalies, locate the source of the anomaly in the target production process through the standardized coding, track the trend of historical carbon emission data of the target production process, predict the carbon emission of the target production process, and issue a risk warning for changes in carbon footprint level.

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