Carbon footprint big data warehouse table design method for copper product full life cycle

Abstract

The invention relates to the technical field of data processing, and discloses a copper product full life cycle-oriented carbon footprint big data warehouse table design method, which comprises the following steps of: carrying out abnormal data filtering on an original carbon footprint data set of a copper product full life cycle to obtain standardized carbon footprint data; based on the standardized carbon footprint data, performing time series data storage optimization on the table structure of the big data warehouse to obtain a dynamic partition table structure; according to the standardized carbon footprint data, performing data mapping on the dynamic partition table structure to obtain an initial data table; traceability information embedding is carried out on the initial data table, and a carbon footprint big data warehouse table is obtained; performing storage structure reconstruction on the carbon footprint big data warehouse table to obtain an efficient storage data warehouse; carrying out life cycle carbon emission analysis on the efficient storage data warehouse to obtain a structured carbon footprint analysis report; according to the invention, the efficiency of carbon footprint data processing can be improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for designing a big data warehouse table for the carbon footprint of copper products throughout their entire lifecycle. Background Technology

[0002] In the process of managing the carbon footprint of copper products throughout their entire lifecycle, the original carbon footprint data often comes from complex sources and is of diverse data types. Existing technologies lack a systematic solution for filtering outomas in the original carbon footprint datasets, making it difficult to comprehensively identify and remove invalid, redundant, or abnormal data. This results in low standardization of the subsequent carbon footprint data, failing to provide a high-quality data foundation for the design of big data warehouse tables. Furthermore, existing technologies do not fully consider the temporal characteristics of carbon footprint data when designing the table structure for big data warehouses. The table structure is fixed and lacks dynamic partitioning optimization capabilities, making it unable to adapt to the storage needs of carbon footprint data at different stages. This easily leads to problems such as scattered data storage and low query efficiency, hindering the subsequent processing and application of carbon footprint data.

[0003] Existing technologies do not adequately emphasize the embedding of data traceability information during the construction of carbon footprint data warehouses. This results in a lack of clear records of the source, flow path, and processing of carbon footprint data, making it difficult to meet the core requirement of traceability of the carbon footprint throughout the entire life cycle of copper products. Furthermore, in the storage structure reconstruction stage, a scientific storage optimization strategy was not developed based on the access patterns and popularity characteristics of carbon footprint data. The unreasonable division of data storage hierarchy not only wastes storage resources but also makes data extraction time-consuming during subsequent life cycle carbon emission analysis, hindering the efficient generation of structured carbon footprint analysis reports and making it difficult to support accurate decision-making in copper product carbon footprint management. Therefore, how to improve the efficiency of constructing a carbon footprint data warehouse for the entire life cycle of copper products and generating structured analysis reports has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method for designing a big data warehouse table for the carbon footprint of copper products throughout their entire life cycle to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for designing a big data warehouse table for the carbon footprint of copper products throughout their entire lifecycle, comprising: S1. Filter out abnormal data from the original carbon footprint dataset of the entire life cycle of copper products to obtain the standardized carbon footprint data of the entire life cycle of copper products. S2. Based on the standardized carbon footprint data, optimize the time-series data storage of the big data warehouse table structure to obtain the dynamic partitioned table structure of the big data warehouse table structure. S3. Based on the standardized carbon footprint data, perform data mapping on the dynamic partitioned table structure to obtain the initial data table for the entire life cycle of the copper product; S4. Embed traceability information into the initial data table to obtain a big data warehouse table of the carbon footprint of the copper product throughout its entire life cycle. S5. Reconstruct the storage structure of the carbon footprint big data warehouse table to obtain a high-efficiency storage data warehouse for the entire life cycle of the copper product; S6. Perform lifecycle carbon emission analysis on the high-efficiency storage data warehouse to obtain a structured carbon footprint analysis report of the copper product throughout its entire lifecycle.

[0006] In a preferred embodiment, the step of filtering outlier data from the original carbon footprint dataset of the entire lifecycle of copper products to obtain standardized carbon footprint data of the entire lifecycle of copper products includes: The original carbon footprint dataset of the entire life cycle of copper products is validated to obtain an abnormal data identifier set of the original carbon footprint dataset; Based on the abnormal data identifier set, the original carbon footprint dataset is cleaned to obtain the cleaned carbon footprint data of the original carbon footprint dataset. The cleaned carbon footprint data is normalized to obtain standardized carbon footprint data for the entire life cycle of the copper product.

[0007] In a preferred embodiment, the step of optimizing the time-series data storage of the big data warehouse table structure based on the standardized carbon footprint data to obtain a dynamic partitioned table structure for the big data warehouse table structure includes: The standardized carbon footprint data is parsed using time-series pattern analysis to obtain a time-series feature description set of the standardized carbon footprint data. Based on the time-series feature description set, the standardized carbon footprint data is classified by activity level to obtain a data lifecycle partitioning scheme for the standardized carbon footprint data. The data lifecycle partitioning scheme is mapped to a partitioning strategy to obtain the dynamic partitioning configuration parameters of the data lifecycle partitioning scheme; Based on the dynamic partitioning configuration parameters, the partitioning granularity of the big data warehouse table structure is optimized to obtain the optimized storage partition of the big data warehouse table structure. A partition metadata index is established for the optimized storage partition to obtain the dynamic partition table structure of the big data warehouse table structure.

[0008] In a preferred embodiment, the step of performing time-series pattern parsing on the standardized carbon footprint data to obtain a time-series feature description set of the standardized carbon footprint data includes: Identify the timestamp field in the standardized carbon footprint data to obtain the timestamp sequence of the standardized carbon footprint data; By analyzing the distribution density and periodic fluctuation patterns of data points in the timestamp sequence, the data acquisition frequency characteristics of the timestamp sequence are obtained. Based on the timestamp sequence and the data collection frequency characteristics, the generation mode of the standardized carbon footprint data is defined to obtain the time-series feature description set of the standardized carbon footprint data.

[0009] In a preferred embodiment, the step of mapping the dynamic partitioned table structure based on the standardized carbon footprint data to obtain an initial data table for the entire lifecycle of the copper product includes: The mapping relationship between the fields of the standardized carbon footprint data and the target fields of the dynamic partitioned table structure is configured to obtain the field mapping rule set of the mapping relationship; Based on the field mapping rule set, the standardized carbon footprint data is structurally transformed to obtain the partitioned data mapping result of the standardized carbon footprint data; Perform a data integrity audit on the partition data mapping result to obtain the verified mapped data of the partition data mapping result; Based on the verified mapping data, a logical view is synthesized on the dynamic partitioned table structure to obtain the initial data table for the entire life cycle of the copper product.

[0010] In a preferred embodiment, embedding traceability information into the initial data table to obtain a big data warehouse table of the carbon footprint of the copper product throughout its entire life cycle includes: The initial data table is processed to generate traceability metadata, resulting in a traceability identifier set for the initial data table; Based on the traceability identifier set, metadata association and anchoring are performed on the initial data table to obtain the anchored traceability information data table of the initial data table; By deriving the data lineage relationship from the anchored traceability information data table, a big data warehouse table of the carbon footprint of the copper product throughout its entire life cycle is obtained.

[0011] In a preferred embodiment, the process of reconstructing the storage structure of the carbon footprint big data warehouse tables to obtain a high-efficiency storage data warehouse for the entire lifecycle of copper products includes: Storage efficiency analysis is performed on the carbon footprint big data warehouse tables to obtain a set of storage optimization strategies for the carbon footprint big data warehouse tables; Based on the storage optimization strategy set, the carbon footprint big data warehouse table is divided into storage levels to obtain a multi-level storage architecture scheme for the carbon footprint big data warehouse table. Based on the multi-level storage architecture scheme, the carbon footprint big data warehouse table is redistributed to obtain the reconstructed storage structure of the carbon footprint big data warehouse table; The reconstructed storage structure is subjected to storage performance verification to obtain a high-efficiency storage data warehouse for the entire lifecycle of the copper product.

[0012] In a preferred embodiment, the step of performing storage efficiency analysis on the carbon footprint big data warehouse table to obtain a set of storage optimization strategies for the carbon footprint big data warehouse table includes: Access behavior features are extracted from the carbon footprint big data warehouse tables to obtain the access pattern feature set of the carbon footprint big data warehouse tables; Based on the access pattern feature set, the access popularity of the carbon footprint big data warehouse table is classified to obtain the data popularity level set of the carbon footprint big data warehouse table. Based on the data heat level set, a temperature distribution topology is constructed on the carbon footprint big data warehouse table to obtain the data temperature distribution map of the carbon footprint big data warehouse table. Based on the temperature distribution map of the data, a hierarchical storage strategy is synthesized for the carbon footprint big data warehouse table to obtain a set of storage optimization strategies for the carbon footprint big data warehouse table.

[0013] In a preferred embodiment, the step of performing lifecycle carbon emission analysis on the high-efficiency storage data warehouse to obtain a structured carbon footprint analysis report of the copper product's entire lifecycle includes: Data from each stage of the lifecycle of the high-efficiency storage data warehouse is extracted to obtain a carbon footprint list for each stage of the high-efficiency storage data warehouse. Based on the carbon footprint inventory for each stage, the carbon emissions of the copper product throughout its entire life cycle are calculated to obtain the stage carbon emission indicators for the entire life cycle of the copper product. Based on the carbon emission indicators of the aforementioned stages, the carbon emission contribution of the copper product throughout its entire life cycle is deconstructed to obtain the carbon emission structure feature set of the copper product throughout its entire life cycle. The carbon emission structure feature set is standardized and transformed into a report to obtain a structured carbon footprint analysis report of the copper product's entire life cycle.

[0014] In a preferred embodiment, the formula for calculating the stage carbon emission index is as follows: ; In the formula, The carbon emission indicators for the aforementioned stage. For the carbon footprint list of each stage, the first... The consumption of this type of energy, For the preset first The baseline emission factor for this energy source For the preset first The corresponding process energy efficiency coefficient for each type of energy source For the carbon footprint list of each stage, the first... The turnover rate of various materials. For the preset first The carbon content coefficient of the material. For the preset first The recycling rate of this material This is the summation symbol.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention filters out abnormal data from the original carbon footprint dataset of copper products throughout their entire life cycle, and obtains standardized carbon footprint data through validity verification, cleaning, and normalization. Based on this data, the time-series storage of the big data warehouse table structure is optimized to form a dynamic partitioned table structure. Subsequently, the carbon footprint big data warehouse table is constructed by combining data mapping and traceability information embedding, which effectively improves the standardization and traceability of carbon footprint data and provides a high-quality foundation for data storage and analysis.

[0016] 2. This invention reconstructs the storage structure of the carbon footprint big data warehouse tables, formulates optimization strategies based on access characteristics and popularity classification, and builds an efficient data warehouse. Then, based on this warehouse, it extracts the carbon footprint list of each stage, calculates carbon emission indicators, and generates structured analysis reports, which significantly improves the storage and analysis efficiency of carbon footprint data. It can quickly output accurate analysis results and provide efficient data support for the carbon emission management of copper products throughout their entire life cycle. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for designing a big data warehouse table for the carbon footprint of copper products throughout their entire lifecycle, as provided in an embodiment of the present invention. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for designing a carbon footprint big data warehouse table for the entire lifecycle of copper products. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for designing a carbon footprint big data warehouse table for the entire lifecycle of copper products can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a method for designing a big data warehouse table for the carbon footprint of copper products throughout their entire lifecycle, according to an embodiment of the present invention. In this embodiment, the method for designing a big data warehouse table for the carbon footprint of copper products throughout their entire lifecycle includes: S1. Filter out abnormal data from the original carbon footprint dataset of the entire life cycle of copper products to obtain the standardized carbon footprint data of the entire life cycle of copper products. In this embodiment of the invention, the step of filtering out abnormal data from the original carbon footprint dataset of the entire life cycle of copper products to obtain standardized carbon footprint data of the entire life cycle of copper products includes: The original carbon footprint dataset of the entire life cycle of copper products is validated to obtain an abnormal data identifier set of the original carbon footprint dataset; Based on the abnormal data identifier set, the original carbon footprint dataset is cleaned to obtain the cleaned carbon footprint data of the original carbon footprint dataset. The cleaned carbon footprint data is normalized to obtain standardized carbon footprint data for the entire life cycle of the copper product.

[0021] Specifically, when conducting data validity verification of the original carbon footprint dataset of the entire life cycle of copper products, the necessary fields that the dataset must include are first identified. These fields cover key information of each stage of the entire life cycle of copper products, such as data collection time, data stage, carbon emission record, and collection equipment number. Then, each piece of data in the original dataset is checked to see if it contains all the necessary fields. If any piece of data is missing any necessary field, it is determined to be invalid data.

[0022] Furthermore, after completing the necessary field checks, based on the actual conditions of normal production at each stage of the copper product lifecycle, the reasonable range of each field's content is determined. For example, the reasonable range of carbon emissions in the mining stage is set based on the common production scale and energy consumption level of that stage. Then, the content of each field in each data entry is checked to see if it is within the corresponding reasonable range. If the content of a certain field exceeds the reasonable range, it is also judged as invalid data. Finally, all the data judged as invalid are summarized, a unique identifier number is assigned to each invalid data entry, and the reason for invalidity of each invalid data entry is recorded in detail, such as "missing field of the stage to which the data belongs" or "carbon emissions exceed the reasonable range of the smelting stage". This forms the abnormal data identifier set of the original carbon footprint dataset.

[0023] Furthermore, when cleaning the original carbon footprint dataset based on the abnormal data identifier set, all information in the abnormal data identifier set is first obtained completely, including the identifier number and invalid reason of each invalid data. Then, based on the identifier number, each corresponding invalid data is accurately searched and located in the original carbon footprint dataset to ensure that no invalid data recorded in the abnormal data identifier set is missed.

[0024] Furthermore, after locating invalid data, invalid data marked as missing necessary fields is directly removed from the original dataset; invalid data marked as field content exceeding the reasonable range is also removed from the original dataset. After removing invalid data, the remaining data is fully checked to confirm that the remaining data does not appear in the abnormal data identifier set, and that each remaining data contains all necessary fields and that the content of each field is within the reasonable range. This yields the cleaned carbon footprint data of the original carbon footprint dataset.

[0025] In general, when standardizing cleaned carbon footprint data, a unified standard for the carbon footprint data of copper products throughout their entire life cycle should be established first. This standard should clarify the unified expression of field names, such as unifying "collection time" as "data collection time" and "emissions" as "carbon emissions." It should also clarify the unified requirements for field formats, such as using "year-month-day hour:minute:second" for data collection time and using "kilograms of carbon dioxide equivalent" as the unit with two decimal places for carbon emissions. Furthermore, it should clarify the unified standard for data accuracy, such as unifying the measurement accuracy of carbon emissions at each stage to two decimal places.

[0026] In summary, after establishing a unified standard, each piece of data in the cleaned carbon footprint data is processed according to the standard. The field names of each piece of data are adjusted to a unified description, the field formats of each piece of data are converted to a unified requirement, and the data accuracy of each piece of data is calibrated to a unified standard. After processing, all data are verified for standard consistency to confirm that each piece of data fully complies with the unified standard in terms of field names, formats, and accuracy, and there are no inconsistencies. This yields standardized carbon footprint data for the entire life cycle of copper products.

[0027] S2. Based on the standardized carbon footprint data, optimize the time-series data storage of the big data warehouse table structure to obtain the dynamic partitioned table structure of the big data warehouse table structure. In this embodiment of the invention, the step of optimizing the time-series data storage of the big data warehouse table structure based on the standardized carbon footprint data to obtain a dynamic partitioned table structure for the big data warehouse table structure includes: The standardized carbon footprint data is parsed using time-series pattern analysis to obtain a time-series feature description set of the standardized carbon footprint data. Based on the time-series feature description set, the standardized carbon footprint data is classified by activity level to obtain a data lifecycle partitioning scheme for the standardized carbon footprint data. The data lifecycle partitioning scheme is mapped to a partitioning strategy to obtain the dynamic partitioning configuration parameters of the data lifecycle partitioning scheme; Based on the dynamic partitioning configuration parameters, the partitioning granularity of the big data warehouse table structure is optimized to obtain the optimized storage partition of the big data warehouse table structure. A partition metadata index is established for the optimized storage partition to obtain the dynamic partition table structure of the big data warehouse table structure.

[0028] The step of performing time-series pattern parsing on the standardized carbon footprint data to obtain a time-series feature description set of the standardized carbon footprint data includes: Identify the timestamp field in the standardized carbon footprint data to obtain the timestamp sequence of the standardized carbon footprint data; By analyzing the distribution density and periodic fluctuation patterns of data points in the timestamp sequence, the data acquisition frequency characteristics of the timestamp sequence are obtained. Based on the timestamp sequence and the data collection frequency characteristics, the generation mode of the standardized carbon footprint data is defined to obtain the time-series feature description set of the standardized carbon footprint data.

[0029] Specifically, when performing time-series pattern parsing on standardized carbon footprint data, each record in the standardized carbon footprint data is examined line by line to identify the field used to mark the time when the data was generated. This field is the timestamp field. The timestamp fields of all records are extracted and arranged in the order in which the data was generated to form the timestamp sequence of the standardized carbon footprint data.

[0030] Specifically, after obtaining the timestamp sequence, the number of data points in the timestamp sequence within a unit time period is counted, the distribution density of the data points is analyzed, and the changes in the number of data points in the timestamp sequence within a continuous time period are observed to determine whether there are fluctuations in the number at fixed intervals, thereby analyzing the data collection frequency characteristics of the timestamp sequence.

[0031] Specifically, by combining the obtained timestamp sequences and data collection frequency characteristics, the generation patterns of standardized carbon footprint data are analyzed to determine whether the data is generated continuously at fixed intervals or in batches at specific stages. Based on these patterns, the generation mode of standardized carbon footprint data is defined, and the timestamp sequences, data collection frequency characteristics, and generation modes are organized into structured descriptive information to obtain the time-series feature description set of standardized carbon footprint data.

[0032] Furthermore, when classifying the activity level of standardized carbon footprint data based on the time-series feature description set, the evaluation dimensions for activity level classification are first determined according to the generation pattern and data collection frequency characteristics in the time-series feature description set. The evaluation dimensions include the data access frequency and update frequency. The access frequency is the number of times the data is queried per unit time, and the update frequency is the number of times the data is modified or supplemented per unit time.

[0033] Furthermore, according to the established evaluation dimensions, each piece of data in the standardized carbon footprint data is statistically analyzed, and the number of visits and updates for each piece of data within a certain period of time is recorded. The data is classified according to the degree of visits and updates: data with high visits and updates are classified as high-activity data, data with medium visits and updates are classified as medium-activity data, and data with low visits and updates are classified as low-activity data.

[0034] Furthermore, based on the data activity classification results, the data lifecycle stages are divided. The stage corresponding to high-activity data is defined as the active period, the stage corresponding to medium-activity data is defined as the stable period, and the stage corresponding to low-activity data is defined as the dormant period. The definitions, corresponding activity ranges, and included data ranges of each lifecycle stage are compiled to obtain a data lifecycle classification scheme for standardized carbon footprint data.

[0035] Furthermore, when mapping the data lifecycle segmentation scheme to partition strategies, first determine the corresponding partition strategy type for each lifecycle stage in the scheme. During the active period, data needs to be accessed and updated frequently and requires a fast response, so a fine-grained partition strategy is adopted. During the stable period, data access and update frequency is moderate, so a medium-grained partition strategy is adopted. During the dormant period, data access and update frequency is low, so a coarse-grained partition strategy is adopted.

[0036] Furthermore, for each partitioning strategy type, specific settings are determined: a shorter partitioning interval is determined for fine-grained partitioning strategies, a medium-length partitioning interval is determined for medium-grained partitioning strategies, and a longer partitioning interval is determined for coarse-grained partitioning strategies. At the same time, the data storage location under each partitioning strategy is specified, and these settings are organized into parameter form.

[0037] Furthermore, the compiled parameters are standardized according to a unified format, specifying the name and corresponding value of each parameter, including the partitioning strategy type, partitioning time interval, and data storage location corresponding to each lifecycle stage, thereby obtaining the dynamic partitioning configuration parameters of the data lifecycle partitioning scheme.

[0038] Furthermore, when optimizing the partitioning granularity of the big data warehouse table structure based on dynamic partitioning configuration parameters, first check the partitioning time interval and partitioning strategy type corresponding to each lifecycle stage in the dynamic partitioning configuration parameters, and adjust the partitioning dimension of the big data warehouse table structure according to this information. If the original table is partitioned according to a single time dimension, then refine or coarse the time partitioning interval according to the configuration parameters.

[0039] Furthermore, after adjusting the partition dimensions, the expected data volume of each adjusted partition is calculated, and it is checked whether the data volume matches the carrying capacity corresponding to the partitioning strategy in the configuration parameters. If the data volume of a partition exceeds the carrying capacity, the partition is further split. If the data volume is too small, adjacent partitions are merged to ensure that the data volume of each partition meets the configuration requirements.

[0040] Furthermore, after completing the partition adjustment, the data query speed and update efficiency of each partition are tested to confirm that the query and update efficiency meet the data processing requirements of the corresponding lifecycle stage. After confirming that there are no errors, the optimized storage partition of the big data warehouse table structure is obtained.

[0041] Specifically, when identifying the timestamp field in standardized carbon footprint data, first examine each record in the standardized carbon footprint data line by line, read the name and corresponding description of each field in each record, and find the field used to mark the time when the data was generated or collected. This field is the timestamp field.

[0042] Specifically, the timestamp field content of each record is extracted, and the extracted timestamp field content is arranged according to the actual generation or collection order of the data to form an ordered sequence, thereby obtaining the timestamp sequence of standardized carbon footprint data.

[0043] Furthermore, when analyzing the distribution density of data points in a timestamp sequence, a fixed unit time interval is first determined, and the number of data points contained in the timestamp sequence within that unit time interval is counted. Then, the number of data points in other consecutive unit time intervals is counted in turn. By comparing the number of data points in different unit time intervals, the distribution of data points in the time dimension can be understood.

[0044] Furthermore, when analyzing the periodic fluctuation patterns of the timestamp sequence, observe the changes in the number of data points in each unit time interval over a longer period of time, and check whether the number of data points shows a similar increasing or decreasing trend after a fixed period of time. If such a trend change at a fixed interval exists, then the fixed interval is determined to be the fluctuation period. Combining the data point distribution and the fluctuation period, the data collection frequency characteristics of the timestamp sequence are summarized.

[0045] Furthermore, when defining the generation pattern of standardized carbon footprint data based on timestamp sequences and data collection frequency characteristics, we first observe whether the time intervals between data generation are uniform according to the order of the timestamp sequences, and then combine the data collection frequency characteristics to determine whether the data is continuously generated at fixed intervals or generated in a concentrated period of time followed by a longer time interval.

[0046] Furthermore, if data is continuously generated at fixed intervals, its generation mode is defined as a continuous periodic generation mode. If data is generated in a concentrated period of time and then regenerated after a long interval, its generation mode is defined as a phased batch generation mode. The arrangement of timestamp sequences, the specific manifestations of data collection frequency characteristics, and the defined generation modes are organized into a systematic description.

[0047] In summary, the organized system descriptions are structured in a unified manner to ensure that the content covers the ordered arrangement of timestamp sequences, the specific patterns of data collection frequencies, and the detailed definition of generation modes, forming a complete set of structured information to obtain a time-series feature description set of standardized carbon footprint data.

[0048] In general, when building a partition metadata index to optimize storage partitions, the metadata information of the optimized storage partitions is first collected, including the name of each partition, the copper product lifecycle stage to which the data contained in the partition belongs, the time range of the data, and the activity level of the data. This metadata information is recorded one by one.

[0049] In summary, the index structure is designed based on the collected metadata information. The index structure includes key fields such as partition name, data time range, and copper product life cycle stage. These key fields can be used to quickly locate the corresponding optimized storage partition, ensuring that the index can cover the metadata information of all optimized storage partitions.

[0050] In summary, by associating the designed index structure with optimized storage partitions, a mapping relationship between indexes and partitions is established in the big data warehouse system. This allows the corresponding optimized storage partitions to be quickly found by querying the index. The associated indexes and optimized storage partitions are then integrated into the original big data warehouse table structure, thereby obtaining a dynamic partitioned table structure for the big data warehouse.

[0051] S3. Based on the standardized carbon footprint data, perform data mapping on the dynamic partitioned table structure to obtain the initial data table for the entire life cycle of the copper product; In this embodiment of the invention, the step of mapping the dynamic partitioned table structure based on the standardized carbon footprint data to obtain the initial data table for the entire life cycle of the copper product includes: The mapping relationship between the fields of the standardized carbon footprint data and the target fields of the dynamic partitioned table structure is configured to obtain the field mapping rule set of the mapping relationship; Based on the field mapping rule set, the standardized carbon footprint data is structurally transformed to obtain the partitioned data mapping result of the standardized carbon footprint data; Perform a data integrity audit on the partition data mapping result to obtain the verified mapped data of the partition data mapping result; Based on the verified mapping data, a logical view is synthesized on the dynamic partitioned table structure to obtain the initial data table for the entire life cycle of the copper product.

[0052] Specifically, when configuring the mapping relationship, firstly, all fields in the standardized carbon footprint data are extracted completely, and the name, data type, and business meaning of each field are recorded in detail. For example, the "carbon emission value in the mining stage" field is numerical and records the carbon emissions generated during the copper mining stage. Next, all target fields in the dynamic partitioning table structure are extracted completely, and the name, data type, and business meaning of each target field are also recorded. For example, the "carbon emission amount in the mining stage" target field is numerical and used to store carbon emission data from the mining stage. Then, the fields in the standardized carbon footprint data are compared one by one with the target fields in the dynamic partitioning table structure. A unique correspondence is determined based on whether the business meaning of the fields matches completely and whether the data types are consistent, avoiding mapping errors due to meaning discrepancies or type mismatches. Finally, each determined correspondence is explicitly recorded in text form. Each record includes the standardized carbon footprint data field name, the dynamic partitioning table target field name, and the specific matching criteria. All records are summarized and organized into a structured rule set, thus obtaining the field mapping rule set for the corresponding relationship.

[0053] Furthermore, when performing structural transformation on standardized carbon footprint data based on the field mapping rule set, a single standardized carbon footprint data entry is first extracted. The target fields corresponding to each field of this data entry are then searched one by one in the field mapping rule set to ensure that each field has a unique matching target field. After finding the correspondence, the complete content of each field in the standardized carbon footprint data is extracted and filled into the target field corresponding to the dynamic partitioning table structure according to the format requirements of the target field. For example, if the "carbon emission time in the smelting process" field in the standardized carbon footprint data is in the format of "year-month-day", and the target field requires the format of "year-month-day hour:minute:second", then the specific hour, minute, and second information is added before filling it in, completing the structural transformation of a single data entry. This process is repeated for all standardized carbon footprint data entries, with the accuracy of the field content checked in real time during processing to avoid numerical omissions or format errors. All processed results are then summarized according to the field order of the dynamic partitioning table structure to obtain the partitioned data mapping results of the standardized carbon footprint data.

[0054] Furthermore, when auditing the data integrity of the partitioned data mapping results, a single partitioned data mapping result is first retrieved. Each target field in the result is checked against all target fields in the dynamic partitioned table structure to ensure it is filled with content. If a target field is empty or has no content, it is marked as a missing item. Simultaneously, the content filled in each target field is checked to ensure it matches the data type of that field. For example, if the target field is time-type, the content is checked to ensure it conforms to the format requirement of "year-month-day hour:minute:second". If the content format is incorrect or the data type does not match, it is marked as an anomaly. For the marked missing items, the content is supplemented by querying the original standardized carbon footprint data. For the anomalies, the content format is adjusted or the erroneous data is corrected according to the target field requirements. After correction, the data is reviewed again to confirm there are no missing or anomalies before processing the next data. After all data checks and corrections are completed, the verified mapped data of the partitioned data mapping results is obtained.

[0055] In summary, when synthesizing a logical view of a dynamic partitioned table structure based on verified mapping data, the partitioning logic of the dynamic partitioned table structure is first clearly defined, including partition dimensions and the specific scope definition of each partition, ensuring a clear understanding of the criteria for each partition's affiliation. Then, according to the partitioning logic, each piece of data in the verified mapping data is matched with the partition scope to determine the partition to which each piece of data should be assigned, and a partition identifier is added to each piece of data to record its affiliation and avoid data allocation errors. Finally, the verified mapping data from all partitions is integrated, and the data is arranged in an ordered manner according to the field order and partition order of the dynamic partitioned table structure, forming a unified view that contains all partition data and conforms to the table structure logic. This view covers the carbon footprint data of all stages of the copper product's entire lifecycle, thus obtaining the initial data table for the entire copper product lifecycle.

[0056] S4. Embed traceability information into the initial data table to obtain a big data warehouse table of the carbon footprint of the copper product throughout its entire life cycle. In this embodiment of the invention, embedding traceability information into the initial data table to obtain a big data warehouse table of the carbon footprint of the copper product throughout its entire life cycle includes: The initial data table is processed to generate traceability metadata, resulting in a traceability identifier set for the initial data table; Based on the traceability identifier set, metadata association and anchoring are performed on the initial data table to obtain the anchored traceability information data table of the initial data table; By deriving the data lineage relationship from the anchored traceability information data table, a big data warehouse table of the carbon footprint of the copper product throughout its entire life cycle is obtained.

[0057] Specifically, when generating traceability metadata for the initial data table, each data entry in the initial data table is examined one by one to extract the key traceability information corresponding to each data entry. This key information includes the copper product lifecycle stage to which the data belongs, the name of the data acquisition equipment, the information of the data acquisition personnel, the data acquisition time, and the time when the data was first entered into the system, ensuring that the key traceability information for each data entry is complete and accurate.

[0058] Specifically, after extracting key traceability information, a unique traceability identifier is generated for each piece of data. This traceability identifier is generated by combining the abbreviation of the life cycle stage to which the data belongs, the code of the collection time, and a fixed-length random character combination to ensure that the traceability identifiers of different data are completely unique. Then, the traceability identifier of each piece of data is recorded one by one with its corresponding key traceability information and organized into a structured set to obtain the traceability identifier set of the initial data table.

[0059] Furthermore, when anchoring metadata associations to the initial data table based on the traceability identifier set, a single data entry from the initial data table is first retrieved. Based on the content of this data, the corresponding traceability identifier and associated key traceability information are accurately matched in the traceability identifier set, ensuring that the matching result completely corresponds to the data itself.

[0060] Furthermore, after the matching is completed, two new fields are added to the row containing the data in the initial data table, which are used to store the traceability identifier and key traceability information respectively, so that the data is directly bound to the traceability metadata. Then, the above operation is repeated for all data in the initial data table, and finally a table containing the original data and the newly added traceability metadata is formed, thus obtaining the anchor traceability information data table of the initial data table.

[0061] In general, when deriving the data lineage relationship in the anchor traceability information data table, first examine the key traceability information of each data entry to determine its original source of collection, and then trace the entire process from the completion of data collection to its entry into the anchor traceability information data table, including whether it has undergone anomaly filtering, format adjustment, and the time, personnel, and basis of related operations.

[0062] In summary, after streamlining the process, the original source, flow path, and processing details of each piece of data are organized into data lineage relationships and added to the corresponding data rows. Finally, all records with added lineage relationships are integrated to form a table containing basic data content, traceability metadata, and complete lineage relationships, thereby obtaining a big data warehouse table of the carbon footprint of copper products throughout their entire life cycle.

[0063] S5. Reconstruct the storage structure of the carbon footprint big data warehouse table to obtain a high-efficiency storage data warehouse for the entire life cycle of the copper product; In this embodiment of the invention, the process of reconstructing the storage structure of the carbon footprint big data warehouse table to obtain a high-efficiency storage data warehouse for the entire lifecycle of copper products includes: Storage efficiency analysis is performed on the carbon footprint big data warehouse tables to obtain a set of storage optimization strategies for the carbon footprint big data warehouse tables; Based on the storage optimization strategy set, the carbon footprint big data warehouse table is divided into storage levels to obtain a multi-level storage architecture scheme for the carbon footprint big data warehouse table. Based on the multi-level storage architecture scheme, the carbon footprint big data warehouse table is redistributed to obtain the reconstructed storage structure of the carbon footprint big data warehouse table; The reconstructed storage structure is subjected to storage performance verification to obtain a high-efficiency storage data warehouse for the entire lifecycle of the copper product.

[0064] The storage efficiency analysis of the carbon footprint big data warehouse tables yields a set of storage optimization strategies, including: Access behavior features are extracted from the carbon footprint big data warehouse tables to obtain the access pattern feature set of the carbon footprint big data warehouse tables; Based on the access pattern feature set, the access popularity of the carbon footprint big data warehouse table is classified to obtain the data popularity level set of the carbon footprint big data warehouse table. Based on the data heat level set, a temperature distribution topology is constructed on the carbon footprint big data warehouse table to obtain the data temperature distribution map of the carbon footprint big data warehouse table. Based on the temperature distribution map of the data, a hierarchical storage strategy is synthesized for the carbon footprint big data warehouse table to obtain a set of storage optimization strategies for the carbon footprint big data warehouse table.

[0065] Specifically, when performing storage efficiency analysis on carbon footprint big data warehouse tables, a detailed access operation recording plan is first developed. A dedicated person or an automated logging tool is then assigned to continuously record all access behaviors to the table over a period of time. The recorded content includes the specific access time, accessed data segment, access type, and the terminal or system initiating the access, as well as the access duration, whether concurrent access occurred, and the operation results after the access. This ensures that the information in each access record is complete and traceable. Then, this access information is organized into an access log in chronological order to extract patterns in data access, such as whether a certain type of data is accessed intensively during daily production scheduling periods or whether specific data segments have not been accessed for extended periods. This forms the access pattern feature set of the carbon footprint big data warehouse tables.

[0066] Specifically, when classifying the access popularity of carbon footprint big data warehouse tables based on access pattern feature sets, the statistical period is first determined according to the business needs of copper product lifecycle management, usually one week or one month. Then, the total number of accesses, average access interval, and access continuity of each data segment within the period are counted. At the same time, the weight of access type is combined, with query access having a higher weight than export access and modification access having the highest weight. The popularity score of each data segment is calculated comprehensively, and the highest score is classified as high-popularity data, the medium score as medium-popularity data, and the lowest score as low-popularity data, forming a clear set of data popularity levels.

[0067] Specifically, when constructing a data temperature distribution map based on the data popularity level set, the existing storage paths of the carbon footprint big data warehouse tables are first sorted out. The locations of data with different popularity levels are marked in the map according to the storage paths, and the proportion of each popularity level data to the total data volume is also marked, so as to intuitively present the spatial distribution of data popularity. Then, a hierarchical storage strategy is formulated based on the map information. In addition to specifying that high popularity data uses solid-state drives, medium popularity data uses mechanical hard drives, and low popularity data uses tape libraries, it is also necessary to specify the backup frequency and data retention period of each popularity data. These strategies are structured in text form to obtain the storage optimization strategy set of the carbon footprint big data warehouse tables.

[0068] Furthermore, when dividing the carbon footprint big data warehouse tables into storage layers based on the storage optimization strategy set, we first deeply analyzed the storage requirements of each type of high-frequency data in the strategy set, clarifying that the storage layer must match the data access frequency and business priority—high-frequency data needs to respond quickly to queries and modifications, medium-frequency data needs to balance performance and storage costs, and low-frequency data needs to be stored long-term at low cost. Therefore, we determined to set up three layers: a high-speed storage layer, a normal storage layer, and an archive storage layer. Then, we refined the storage media characteristics of each layer. The high-speed storage layer uses enterprise-grade solid-state drives, whose continuous read and write speeds can meet the high-frequency, low-latency access requirements, and support multi-threaded concurrent operations, making them suitable for carrying high-frequency data related to real-time production. The ordinary storage layer uses high-capacity mechanical hard drives, which have large single-disk capacity, lower unit storage cost than solid-state drives, and read / write speeds sufficient for daily historical data query needs, making them suitable for storing moderately frequently accessed data. The archive storage layer uses tape libraries, which have high storage density, extremely low long-term preservation costs, and moisture and magnetic resistance, making them suitable for storing historical archive data with low access frequency. Simultaneously, considering the growth trend of copper product carbon footprint data, the initial storage capacity of each layer is planned, reserving some expansion space to avoid short-term content shortages. Finally, the layer definitions, storage media parameters, data popularity ranges corresponding to each layer, and capacity planning are compiled into a complete scheme, resulting in a multi-level storage architecture scheme for the carbon footprint big data warehouse table.

[0069] Furthermore, when redistributing data in the carbon footprint big data warehouse table according to the multi-level storage architecture scheme, the data is first extracted in batches from the original carbon footprint big data warehouse table based on the data source range corresponding to each storage level in the scheme and the data popularity tags through data filtering tools. During the extraction process, the data is classified and processed according to the copper product life cycle stage to which it belongs, so as to avoid data confusion between different stages. At the same time, the field integrity of the data is checked segment by segment to ensure that the extracted data and the correspondence between each level are accurate and error-free, with no errors or omissions.

[0070] Furthermore, a data migration channel was established, with a dedicated data transmission link configured for the high-speed storage layer to reduce transmission latency. High-frequency data was stored in a designated directory within the high-speed storage layer according to a "copper product batch - lifecycle stage" directory structure for easy subsequent location. Medium-frequency data was stored in the corresponding path within the ordinary storage layer according to a "year - quarter - stage" directory structure. Low-frequency data was stored in a compressed package format within the archive storage layer's preset slots. During the migration process, a breakpoint resume function was enabled; if transmission was interrupted, it could resume from the breakpoint without retransmitting all data. After migration, a hash value comparison tool was used to generate hash values ​​before and after migration for each data segment. If the two values ​​matched, the data was considered complete; otherwise, the segment was re-migrated until all data passed the consistency check after migration. This resulted in a new storage organization form with hierarchical layers based on frequency and clear directories, thus obtaining the reconstructed storage structure of the carbon footprint big data warehouse table.

[0071] Specifically, when extracting access behavior characteristics from the carbon footprint big data warehouse tables, a complete access behavior recording plan should be developed first, clarifying the information dimensions to be recorded, including the specific time of the access, the copper product life cycle stage and data batch corresponding to the accessed data segment, the access type, the terminal device number or system name that initiated the access, the duration of the access operation, whether there are concurrent accesses, and the final result of the access operation, to ensure that no key information is omitted.

[0072] Specifically, the logging is implemented by combining automated logging tools with regular manual checks. The logging tools capture access behavior at a frequency of minutes and generate raw logs according to set dimensions to avoid missing short-term accesses. The completeness of the logs is checked daily by humans, and special access scenarios not captured by the tools are supplemented to ensure that the logs cover all access behaviors.

[0073] Specifically, access logs over a continuous period of time are organized in chronological order. By analyzing the log content line by line, data access patterns are extracted. For example, whether a certain type of data is accessed intensively during the daily production report generation period, whether the access frequency of a specific batch of data decreases after carbon emission accounting is completed, and the differences in data access frequency at different life cycle stages. At the same time, the business scenarios corresponding to each pattern are labeled, and these patterns are organized into structured descriptive information to obtain the access pattern feature set of the carbon footprint big data warehouse table.

[0074] Furthermore, when classifying the access popularity of carbon footprint big data warehouse tables based on access pattern feature sets, the statistical period is first determined in conjunction with the business needs of copper product carbon footprint management. Typically, a business month is selected to ensure that the statistical results can reflect the data access situation in the real business scenario and avoid distortion of patterns due to too short a period.

[0075] Furthermore, the total number of visits, average access interval, and access continuity of each data segment within the statistical period are statistically analyzed. At the same time, weights are set for different access types. During the calculation, the number of visits for each access type is first multiplied by the corresponding weight, and then the results are accumulated to obtain the basic popularity value of each data segment, ensuring that the popularity calculation is aligned with the importance of the business.

[0076] Furthermore, the data is categorized into levels based on baseline values. The highest baseline value is classified as high-heat data, the medium baseline value as medium-heat data, and the lowest baseline value as low-heat data. The baseline value range and the list of data segments included for each level are clearly defined, and the typical uses of each level of data are also marked. This results in a set of data heat levels for the carbon footprint big data warehouse table.

[0077] Furthermore, when constructing the temperature distribution topology of the carbon footprint big data warehouse table based on the data heat level set, the existing storage architecture of the carbon footprint big data warehouse table is first sorted out, the specific path of the data in the physical storage medium is clarified, an initial storage topology diagram is drawn, and the location and relationship of each storage node are marked.

[0078] Furthermore, in the initial topology diagram, the data carried by each storage node is labeled according to the data popularity level. High-popularity data is marked with a dark color, medium-popularity data with a medium color, and low-popularity data with a light color. At the same time, the percentage of the popularity level of the data carried by each storage node is marked next to it. For example, in a certain hard disk partition, high-popularity data accounts for 35%, medium-popularity data accounts for 50%, and low-popularity data accounts for 15%, which intuitively presents the distribution of data with different popularity in the storage architecture.

[0079] Furthermore, the basic information of each storage node is supplemented, and the layout of the topology map is adjusted to ensure that the heat distribution and storage node information are clearly distinguishable, avoid information confusion caused by node overlap, and form a complete visual topology structure, thereby obtaining the data temperature distribution map of the carbon footprint big data warehouse table.

[0080] In summary, when synthesizing a hierarchical storage strategy for carbon footprint big data warehouse tables based on the data temperature distribution map, we first analyze the current storage status of data with different heat levels in the map, focusing on checking whether frequently accessed high-heat data is stored on low-speed media such as mechanical hard drives, and whether low-heat data occupies high-speed media such as solid-state drives, to determine whether there is a resource mismatch problem in the current storage and clarify the direction of strategy optimization.

[0081] In summary, storage media matching rules are established for different levels of data popularity: high-popularity data is matched with enterprise-grade solid-state drives with fast read and write speeds, medium-popularity data is matched with mechanical hard drives that balance performance and cost, and low-popularity data is matched with low-cost, high-capacity tape libraries, clearly defining the popularity level and applicable data range for each type of media.

[0082] In summary, supplementary management rules, including a dynamic adjustment mechanism for data popularity, backup frequency for data with different popularity levels, and retention period for low-popularity data, are added. The storage media rules and supporting management rules are then organized into a structured strategy document, thereby obtaining a set of storage optimization strategies for the carbon footprint big data warehouse tables.

[0083] In summary, when verifying the storage performance of the reconstructed storage structure, we first need to combine the business scenario of copper product carbon footprint management throughout its entire lifecycle and set comprehensive performance verification indicators. In addition to data query response time, data write speed, storage media utilization, and data stability, we need to add two new indicators: concurrent access support capability and data recovery speed. We need to clarify the acceptable range of each indicator. For example, the response time for querying high-volume data should meet the needs of real-time production scheduling, the data write speed should match the daily increase in data, the storage media utilization should be maintained within a reasonable range, there should be no request timeouts during concurrent access, the data recovery speed should be completed in a short time, and there should be no data damage or loss after a week of continuous monitoring.

[0084] In summary, multiple rounds of testing were conducted to simulate real business loads according to set indicators: simulating concurrent access scenarios during production periods by simultaneously initiating multiple access requests of different types, recording the response time and success rate of each request; writing data equivalent to the daily increase in volume to each level using batch writing tools, recording the writing time; regularly checking the usage of each storage medium and calculating utilization; randomly selecting a portion of data daily for integrity verification and simulating a data corruption scenario to test the recovery process and time; if any indicator fails to meet the standard, the cause is analyzed. If it is due to excessive storage medium load, some high-frequency data with slightly lower access frequency can be migrated to the edge area of ​​the ordinary storage layer; if it is due to an unreasonable directory structure, the directory classification method is optimized until all indicators meet the acceptable range and the results of multiple rounds of continuous testing are stable, thus obtaining a high-efficiency storage data warehouse for the entire lifecycle of copper products.

[0085] S6. Perform lifecycle carbon emission analysis on the high-efficiency storage data warehouse to obtain a structured carbon footprint analysis report of the copper product throughout its entire lifecycle.

[0086] In this embodiment of the invention, the step of performing lifecycle carbon emission analysis on the high-efficiency storage data warehouse to obtain a structured carbon footprint analysis report of the copper product throughout its entire lifecycle includes: Data from each stage of the lifecycle of the high-efficiency storage data warehouse is extracted to obtain a carbon footprint list for each stage of the high-efficiency storage data warehouse. Based on the carbon footprint inventory for each stage, the carbon emissions of the copper product throughout its entire life cycle are calculated to obtain the stage carbon emission indicators for the entire life cycle of the copper product. Based on the carbon emission indicators of the aforementioned stages, the carbon emission contribution of the copper product throughout its entire life cycle is deconstructed to obtain the carbon emission structure feature set of the copper product throughout its entire life cycle. The carbon emission structure feature set is standardized and transformed into a report to obtain a structured carbon footprint analysis report of the copper product's entire life cycle.

[0087] The formula for calculating the carbon emission targets for that stage is as follows: ; In the formula, The carbon emission indicators for the aforementioned stage. For the carbon footprint list of each stage, the first... The consumption of this type of energy, For the preset first The baseline emission factor for this energy source For the preset first The corresponding process energy efficiency coefficient for each type of energy source For the carbon footprint list of each stage, the first... The turnover rate of various materials. For the preset first The carbon content coefficient of the material. For the preset first The recycling rate of this material This is the summation symbol.

[0088] Specifically, we first need to identify the core stages of the entire life cycle of copper products, including mining, smelting, processing, transportation, and waste disposal. Each stage corresponds to key data collection dimensions for carbon footprint data. In the mining stage, we need to extract the amount of ore mined, the energy consumption of mining equipment, and the energy types, including diesel and electricity. We also need to extract the material loss during the mining process. In the smelting stage, we need to extract the smelting fuel consumption, and the fuel types, including coke and natural gas, as well as the electricity consumption and the amount of smelting slag generated. In the processing stage, we need to extract the energy consumption of processing equipment and the amount of processing auxiliary materials used. In the transportation stage, we need to extract the energy consumption of transportation vehicles and the transportation mileage. In the waste disposal stage, we need to extract the amount of waste copper products recovered and the energy consumption of processing equipment. This ensures that the key data dimensions of each stage fully cover the carbon footprint accounting needs.

[0089] Specifically, data is extracted from the high-efficiency storage data warehouse in stages, and the data for each stage is located according to the storage level of the warehouse—real-time smelting and processing data is stored in the high-speed storage layer, historical mining and transportation data is stored in the ordinary storage layer, and long-term archived waste disposal data is stored in the archive layer. Data at the corresponding level is retrieved one by one in stages, and the data collection time and data source are recorded simultaneously during extraction. Data sources include the sensor number of mining equipment, monthly energy consumption reports of smelters, etc., to ensure the complete traceability information of each piece of data.

[0090] Specifically, the completeness of the extracted data at each stage is checked to see if any key data dimensions are missing. For example, in the transportation stage, is there any missing data on the energy consumption of transportation vehicles? If any are missing, the data is traced back to the original data collection stage through the warehouse's traceability identifier to complete the data. After confirming that all data at all stages are complete, the data is categorized and organized by stage. The key data dimensions, corresponding values, and data sources for each stage are summarized into a clear table format, thereby obtaining a carbon footprint list for each stage of the efficient data warehouse.

[0091] Furthermore, The carbon footprint inventory for each stage is extracted and organized from a high-efficiency data warehouse. During extraction, the inventory is first divided according to the stages of the copper product's lifecycle, such as mining, smelting, and processing. Then, within each stage's inventory, different types of energy are distinguished, and the consumption value of each energy source in that stage is determined as the corresponding energy consumption amount, thus obtaining the carbon footprint inventory for each stage. The amount of energy consumed.

[0092] Furthermore, Derived from national carbon accounting standards or industry-recognized carbon emission accounting guidelines, the latest standard documents should be consulted before use, and should be based on the relevant provisions. The corresponding unit energy carbon emission benchmark value is found in the standard document for each type of energy. This benchmark value is the preset first value. The baseline emission factor for this type of energy.

[0093] Furthermore, The data is derived from actual process parameter monitoring data at each stage of copper product production. During monitoring, energy consumption and output metering devices are installed on the production equipment at each stage, and the data is recorded within a certain period. The total energy consumption and the quantity of qualified products produced by the energy-driven equipment are used to calculate the actual energy conversion efficiency by the ratio of the qualified product quantity to the total energy consumption. This efficiency value is the preset value for the first energy consumption. The process energy efficiency coefficient corresponding to each type of energy.

[0094] Furthermore, The carbon footprint inventory for each stage is extracted and organized from a high-efficiency data warehouse. Each stage's inventory distinguishes different types of materials, recording the input, output, and loss of each material at that stage. The total flow value of the material within the stage is determined as the corresponding material's flow volume, thus obtaining the carbon footprint inventory for that stage. The turnover rate of a certain type of material.

[0095] Furthermore, Sourced from industry-published material carbon content databases or laboratory test reports, for the first For any given material, if the database contains carbon content data for that material, it is used directly. If the database does not contain data, the material sample is sent to a professional laboratory where elemental analysis instruments are used to determine the mass percentage of carbon in the material. This analysis, combined with the material's density and unit conversion, yields the total carbon content per unit of material. This total carbon content is the preset target for the first material. The carbon content coefficient of a material.

[0096] Furthermore, Material recycling management records from copper product manufacturing companies, the records must include the first... The total consumption and recycling amount of a material within a certain period are used to calculate the proportion of material that is recycled and reused. This proportion is the preset value for the first cycle. The recycling rate of the materials.

[0097] Furthermore, this formula is used to calculate the total carbon emission index of a certain stage in the entire life cycle of copper products. The calculation process is divided into two parts: the first part is the calculation of energy carbon emissions, and the second part is the calculation of material carbon emissions. Finally, the results of the two parts are added together to obtain the total carbon emissions of the stage.

[0098] Furthermore, when calculating energy carbon emissions, the first stage is considered. For each type of energy, first multiply its consumption by the baseline emission factor to obtain the theoretical carbon emissions of that energy without considering process energy efficiency. Then multiply the theoretical carbon emissions by the corresponding process energy efficiency coefficient to obtain the actual energy carbon emissions considering the actual process energy conversion efficiency. Calculate the actual carbon emissions of all types of energy in this stage according to this logic.

[0099] Furthermore, when calculating the carbon emissions of materials, the first stage is considered. For each type of material, first multiply its turnover rate by the carbon content coefficient of the material to obtain the theoretical carbon emissions if the material is not recycled at all. Then subtract the recycling rate of the material from 1 to obtain the unrecycled proportion of the material. Multiply the theoretical carbon emissions by the unrecycled proportion to obtain the actual carbon emissions generated by the material. Calculate the actual carbon emissions of all types of materials in this stage according to this logic.

[0100] Furthermore, the actual carbon emissions of all energy sources and all materials in this stage are summed to obtain the total carbon emission index for this stage. This index can accurately reflect the carbon emission level of copper products in the corresponding life cycle stage, providing a data foundation for the subsequent deconstruction of carbon emission contribution and the generation of structured analysis reports.

[0101] Furthermore, when calculating carbon emissions based on the carbon footprint inventory at each stage, the basic basis required for accounting is first determined, including the industry benchmark emission factor for each energy source, which refers to the carbon emissions generated per unit of energy consumption; the process energy efficiency coefficient corresponding to each energy source, which refers to the proportion of energy actually converted into production utility; the carbon content coefficient for each material, which refers to the total amount of carbon contained in a unit of material; and the recycling rate for each material, which refers to the proportion of material that is recycled and reused. These bases are all determined with reference to national carbon accounting standards to ensure the authority of the accounting basis.

[0102] Furthermore, carbon emission calculations are conducted in stages. For the mining stage, energy carbon emissions are calculated first: the diesel consumption of mining equipment is multiplied by the diesel baseline emission factor, then multiplied by the corresponding process energy efficiency coefficient; electricity consumption is multiplied by the electricity baseline emission factor, then multiplied by the corresponding process energy efficiency coefficient, and the two are added together to obtain the energy carbon emissions for the mining stage. Next, material carbon emissions are calculated: ore material losses are multiplied by the ore carbon content coefficient, then multiplied by... The carbon emissions from materials during the mining stage are obtained; the total emissions from the mining stage are obtained by adding the carbon emissions from energy and materials.

[0103] Furthermore, the total emissions for each stage of smelting, processing, transportation, and waste disposal are calculated according to the above logic. Energy and material carbon emissions are calculated separately for each stage and then added together. The total emissions, energy carbon emissions, and material carbon emissions for each stage are organized into structured data, and the calculation basis for each stage is marked. The calculation basis includes the emission factor standards used, etc., so as to obtain the stage carbon emission indicators for the entire life cycle of copper products.

[0104] Furthermore, when deconstructing the contribution based on the stage carbon emission indicators, first calculate the total carbon emissions over the entire life cycle, and add up the total emissions of each stage to obtain the total value; then calculate the contribution ratio of each stage: divide the total emissions of each stage by the total value to obtain the proportion of emissions of that stage to the total emissions over the entire life cycle, and identify the core emission stage with the highest proportion, such as the smelting stage, the secondary stage with the medium proportion, such as processing and transportation, and the minor stage with the lowest proportion, such as waste disposal.

[0105] Furthermore, the emission composition within each stage is analyzed. In the mining stage, the proportion of diesel and electricity in energy carbon emissions is examined to identify the main energy emission sources. In the smelting stage, the proportion of fuel carbon emissions (including coke and natural gas) and electricity carbon emissions, as well as the contribution ratio of waste residue in material carbon emissions, are examined to identify the core emission sources within the stage. The internal composition of the processing, transportation, and waste treatment stages is analyzed in the same way to clarify the main emission source types of each stage.

[0106] Furthermore, the emissions, proportions, main emission source types, and core emission stage identifiers of each stage are compiled into a unified feature description, such as "Smelting stage: emissions account for a certain proportion of total emissions throughout the entire life cycle, with the main emission sources being coke combustion and electricity consumption." After summarizing the feature descriptions of all stages, the carbon emission structure feature set of copper products throughout their entire life cycle is obtained.

[0107] In general, when converting the carbon emission structure feature set into a standardized report, first determine the fixed format of the report. The header is "Copper Product Full Life Cycle Carbon Footprint Analysis Report". The core columns include "Life Cycle Stage", "Stage Emissions", "Percentage of Total Emissions", "Main Emission Sources", and "Emission Source Type". The emission source types are divided into two categories: energy and materials. The remarks column indicates the basis for data calculation, ensuring that the report structure conforms to industry analysis habits.

[0108] In general, fill in the data according to the report format: "Life cycle stage" column is filled with the name of mining, smelting, etc.; "Stage emissions" column is filled with the total emissions of each stage, with the unit indicated as tons of carbon dioxide equivalent; "Percentage" column is filled with the proportion of each stage to the total emissions, expressed as a percentage; "Major emission sources" column is filled with the core emission sources of each stage; "Emission source type" column is indicated with the corresponding energy or material.

[0109] In summary, the report data format is standardized, emissions are kept to a fixed number of decimal places, percentages are expressed uniformly, and the descriptions of major emission sources are concise and unambiguous. The report completeness is checked to ensure that all stage information is filled in, the data and structural feature sets are consistent, and the format is not chaotic. This results in a structured carbon footprint analysis report for the entire life cycle of copper products.

[0110] In the several embodiments provided by this invention, it should be understood that the disclosed method can be implemented in other ways.

[0111] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0112] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, and technology that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for designing a carbon footprint big data warehouse table for a full life cycle of a copper product, characterized in that, The method comprises: S1, filtering abnormal data of a raw carbon footprint dataset of a whole life cycle of a copper product to obtain standardized carbon footprint data of the whole life cycle of the copper product; S2, based on the standardized carbon footprint data, performing time series data storage optimization on a big data warehouse table structure to obtain a dynamic partition table structure of the big data warehouse table structure; S3, according to the standardized carbon footprint data, performing data mapping on the dynamic partition table structure to obtain an initial data table of the whole life cycle of the copper product; S4, embedding traceability information into the initial data table to obtain a carbon footprint big data warehouse table of the whole life cycle of the copper product; S5, reconstructing a storage structure of the carbon footprint big data warehouse table to obtain an efficient storage data warehouse of the whole life cycle of the copper product; S6, performing life cycle carbon emission analysis on the efficient storage data warehouse to obtain a structured carbon footprint analysis report of the whole life cycle of the copper product.

2. The method of claim 1, wherein the method further comprises: The method comprises: S1, filtering abnormal data of a raw carbon footprint dataset of a whole life cycle of a copper product to obtain standardized carbon footprint data of the whole life cycle of the copper product; S2, based on the standardized carbon footprint data, performing time series data storage optimization on a big data warehouse table structure to obtain a dynamic partition table structure of the big data warehouse table structure; S3, according to the standardized carbon footprint data, performing data mapping on the dynamic partition table structure to obtain an initial data table of the whole life cycle of the copper product; 3. The method of claim 1, wherein the method further comprises: S4, embedding traceability information into the initial data table to obtain a carbon footprint big data warehouse table of the whole life cycle of the copper product; S5, reconstructing a storage structure of the carbon footprint big data warehouse table to obtain an efficient storage data warehouse of the whole life cycle of the copper product; S6, performing life cycle carbon emission analysis on the efficient storage data warehouse to obtain a structured carbon footprint analysis report of the whole life cycle of the copper product. The method comprises: S1, filtering abnormal data of a raw carbon footprint dataset of a whole life cycle of a copper product to obtain standardized carbon footprint data of the whole life cycle of the copper product; S2, based on the standardized carbon footprint data, performing time series data storage optimization on a big data warehouse table structure to obtain a dynamic partition table structure of the big data warehouse table structure; 4. The method of claim 3, wherein the method further comprises: S3, according to the standardized carbon footprint data, performing data mapping on the dynamic partition table structure to obtain an initial data table of the whole life cycle of the copper product; S4, embedding traceability information into the initial data table to obtain a carbon footprint big data warehouse table of the whole life cycle of the copper product; S5, reconstructing a storage structure of the carbon footprint big data warehouse table to obtain an efficient storage data warehouse of the whole life cycle of the copper product; S6, performing life cycle carbon emission analysis on the efficient storage data warehouse to obtain a structured carbon footprint analysis report of the whole life cycle of the copper product. The method comprises: S1, filtering abnormal data of a raw carbon footprint dataset of a whole life cycle of a copper product to obtain standardized carbon footprint data of the whole life cycle of the copper product; S2, based on the standardized carbon footprint data, performing time series data storage optimization on a big data warehouse table structure to obtain a dynamic partition table structure of the big data warehouse table structure; S3, according to the standardized carbon footprint data, performing data mapping on the dynamic partition table structure to obtain an initial data table of the whole life cycle of the copper product; S4, embedding traceability information into the initial data table to obtain a carbon footprint big data warehouse table of the whole life cycle of the copper product; S5, reconstructing a storage structure of the carbon footprint big data warehouse table to obtain an efficient storage data warehouse of the whole life cycle of the copper product; S6, performing life cycle carbon emission analysis on the efficient storage data warehouse to obtain a structured carbon footprint analysis report of the whole life cycle of the copper product. Based on the timestamp sequence and the data acquisition frequency feature, a generation mode of the standardized carbon footprint data is defined, and a time sequence feature description set of the standardized carbon footprint data is obtained.

5. The copper product life cycle oriented carbon footprint big data warehouse table design method of claim 1, wherein, The data mapping of the dynamic partition table structure is performed according to the standardized carbon footprint data, and an initial data table of the whole life cycle of the copper product is obtained, including: The mapping relationship between the fields of the standardized carbon footprint data and the target fields of the dynamic partition table structure is configured, and a field mapping rule set of the corresponding relationship is obtained; Based on the field mapping rule set, the structure of the standardized carbon footprint data is converted, and a partition data mapping result of the standardized carbon footprint data is obtained; The data integrity of the partition data mapping result is audited, and the verified mapping data of the partition data mapping result is obtained; Based on the verified mapping data, a logical view synthesis of the dynamic partition table structure is performed, and an initial data table of the whole life cycle of the copper product is obtained.

6. The carbon footprint big data warehouse table design method for the full life cycle of copper-oriented products as claimed in claim 1, wherein, The initial data table is embedded with traceability information, and a carbon footprint big data warehouse table of the whole life cycle of the copper product is obtained, including: The initial data table is generated with traceability metadata, and a traceability identification set of the initial data table is obtained; Based on the traceability identification set, the metadata of the initial data table is associated and anchored, and an anchored traceability information data table of the initial data table is obtained; The data inheritance relationship of the anchored traceability information data table is derived, and a carbon footprint big data warehouse table of the whole life cycle of the copper product is obtained.

7. The copper product life cycle oriented carbon footprint big data warehouse table design method of claim 1, wherein, The storage structure of the carbon footprint big data warehouse table is restructured, and an efficient storage data warehouse of the whole life cycle of the copper product is obtained, including: The storage efficiency of the carbon footprint big data warehouse table is analyzed, and a storage optimization strategy set of the carbon footprint big data warehouse table is obtained; Based on the storage optimization strategy set, the carbon footprint big data warehouse table is divided into storage levels, and a multi-level storage architecture scheme of the carbon footprint big data warehouse table is obtained; According to the multi-level storage architecture scheme, the data of the carbon footprint big data warehouse table is redistributed, and a restructured storage structure of the carbon footprint big data warehouse table is obtained; The storage performance of the restructured storage structure is verified, and an efficient storage data warehouse of the whole life cycle of the copper product is obtained.

8. The method of claim 7, wherein the method further comprises: The storage efficiency of the carbon footprint big data warehouse table is analyzed, and a storage optimization strategy set of the carbon footprint big data warehouse table is obtained, including: The access behavior feature of the carbon footprint big data warehouse table is extracted, and an access mode feature set of the carbon footprint big data warehouse table is obtained; Based on the access mode feature set, the carbon footprint big data warehouse table is classified according to access frequency, and a data heat level set of the carbon footprint big data warehouse table is obtained; Based on the data heat level set, a temperature distribution topology of the carbon footprint big data warehouse table is constructed, and a data temperature distribution map of the carbon footprint big data warehouse table is obtained; According to the data temperature distribution map, a hierarchical storage strategy synthesis is performed on the carbon footprint big data warehouse table to obtain a storage optimization strategy set of the carbon footprint big data warehouse table.

9. The method for designing a big data warehouse table for the carbon footprint of copper products throughout their entire lifecycle, as described in claim 1, is characterized in that... The life cycle carbon emission analysis of the high-efficiency storage data warehouse obtains a structured carbon footprint analysis report of the copper product full life cycle, including: The life cycle stage data extraction of the high-efficiency storage data warehouse obtains a carbon footprint list of each stage of the high-efficiency storage data warehouse; Based on the carbon footprint list of each stage, the carbon emission amount calculation of the copper product full life cycle obtains a stage carbon emission index of the copper product full life cycle; Based on the stage carbon emission index, the carbon emission contribution degree decomposition of the copper product full life cycle obtains a carbon emission structure feature set of the copper product full life cycle; The carbon emission structure feature set is converted into a standardized report to obtain a structured carbon footprint analysis report of the copper product full life cycle.

10. The carbon footprint big data warehouse table design method for the full life cycle of copper-oriented products of claim 9, wherein, The calculation formula of the stage carbon emission index is as follows: ； In the formula, is the stage carbon emission index, is the first energy consumption in the stage carbon footprint list, is the first energy consumption in the stage carbon footprint list, is the preset reference emission factor of the first energy, is the preset reference emission factor of the first energy, is the preset process energy efficiency coefficient corresponding to the first energy, is the preset process energy efficiency coefficient corresponding to the first energy, is the flow amount of the first material in the stage carbon footprint list, is the flow amount of the first material in the stage carbon footprint list, is the preset carbon content coefficient of the first material, is the preset carbon content coefficient of the first material, is the preset recycling rate of the first material, is the preset recycling rate of the first material, is the summation symbol.

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