A method and system for integrating and storing carbon emission data of building materials for ultra-high voltage power transmission projects

Abstract

This invention discloses a method and system for integrating and storing carbon emission data of building materials for ultra-high voltage (UHV) power transmission projects. The method includes: collecting multi-dimensional carbon data throughout the entire lifecycle of building materials; calculating the data using a lifecycle carbon measurement model and calibrating a multi-source carbon footprint coupling calculation model to obtain a standardized carbon data set; then using a time-series carbon data incremental synchronization algorithm to achieve time-series data updates and consistency verification; and finally, after structured processing by a carbon data visualization and decision analysis platform, completing modular and categorized storage according to a dedicated storage architecture. The system's corresponding method sets up various functional units, breaking the limitations of single-dimensional calculation through deep fusion of multi-source data, and addressing the problem of insufficient adaptability with a dedicated time-series synchronization mechanism and structured storage architecture. This achieves accurate carbon data calculation, efficient synchronization, and secure integration, providing comprehensive and reliable data support for carbon emission control in UHV projects and adapting to the green and low-carbon transformation needs of these projects.

Description

Technical Field

[0001] This invention relates to the field of carbon emission technology for building materials used in ultra-high voltage (UHV) engineering projects, and in particular to a method and system for integrating and storing carbon emission data of building materials used in UHV engineering projects. Background Technology

[0002] As a core component of the energy internet, ultra-high voltage (UHV) power transmission projects involve the large-scale use of various building materials such as steel, concrete, and insulators. These materials generate carbon emissions at every stage of their lifecycle, from production and transportation to construction, operation and maintenance, and recycling. Accurate accounting and efficient storage of relevant carbon data are crucial for promoting the green and low-carbon transformation of these projects. Currently, UHV projects are characterized by dispersed construction sites, diverse building material types, and a wide lifecycle. Carbon data sources include multiple dimensions such as raw material consumption, energy utilization, process emissions, and transportation parameters, resulting in a massive volume of data with strong temporal characteristics. Simultaneously, the need for carbon data visualization for decision-making and cross-stage data traceability is increasingly urgent. Therefore, it is necessary to establish a carbon data integration and storage system adapted to the characteristics of UHV projects to achieve standardized integration, temporal synchronization, and secure storage of multi-source carbon data, providing data support for carbon emission control of these projects.

[0003] Existing technologies have significant shortcomings in the integrated storage of carbon emission data from building materials used in ultra-high voltage (UHV) projects. On the one hand, carbon data accounting lacks a deep integration mechanism encompassing the entire lifecycle and multi-source data. It relies solely on accounting methods from a single stage or a single data source, failing to fully integrate monitoring data from various stages of building materials and supplementary data across different scenarios. This results in discrepancies between carbon accounting results and actual emissions, making it difficult to meet data standardization requirements. On the other hand, data synchronization and storage architecture adaptability are insufficient. No dedicated synchronization algorithm has been designed for the time-series incremental characteristics of UHV project carbon data, and the storage system lacks a structured storage mechanism with modular classification. This makes it impossible to achieve efficient incremental updates and secure integration of carbon data, while also hindering the integration with visualization and decision analysis needs, thus impacting the subsequent application value of the data. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method and system for integrating and storing carbon emission data of building materials for ultra-high voltage projects.

[0005] The technical solution adopted in this invention is a method for integrating and storing carbon emission data of building materials for ultra-high voltage (UHV) projects, comprising the following steps: S1, collecting multi-dimensional raw carbon data, including raw material consumption intensity, energy consumption type, process emission coefficient, transportation distance, and recycling rate, based on online monitoring nodes of carbon emissions at each stage of UHV project building material production, transportation, construction, operation and maintenance, and recycling; S2, calculating the staged carbon emission amount of the multi-dimensional raw carbon data through a building material life cycle carbon measurement model to form an initial carbon accounting result; S3, using a multi-source carbon footprint coupling accounting model to compare the initial carbon accounting result with cross-scenario carbon data. S4. Based on the supplementary source, coupling calibration is performed to generate a standardized carbon dataset; S5. The time-series carbon data incremental synchronization algorithm is used to perform time-dimensional incremental updates and data consistency checks on the standardized carbon dataset to obtain a time-series carbon data sequence; S6. The time-series carbon data sequence is transmitted to the carbon data visualization decision analysis platform for data structuring processing to generate target carbon data that conforms to the integrated storage specifications; S7. Based on the dedicated storage architecture for carbon emission data of building materials in UHV projects, the target carbon data is classified and stored in modules, and an associated index and data access permission mechanism are established to achieve secure and efficient integrated storage of carbon data.

[0006] Furthermore, the expression for the carbon measurement model of the entire life cycle of building materials is as follows: ,in, This refers to the carbon emissions throughout the entire life cycle of building materials used in ultra-high voltage power transmission projects. The carbon emission conversion factor for the i-th type of building material raw materials is... Let i be the consumption of building material raw materials of type i. Let be the carbon emission correction factor for the i-th type of building material production process. The carbon emission factor for the energy type corresponding to the i-th type of building material. For the energy consumption of the production of building materials of type i, Let be the energy utilization efficiency impact coefficient of the i-th type of building materials. Let be the carbon emission time decay coefficient of the i-th type of building material in the j-th stage. The duration of stage j for building material of type i is given. This represents the total number of building material types. This represents the total number of stages throughout the entire lifecycle.

[0007] Furthermore, the expression for the multi-source carbon footprint coupled accounting model is as follows: ,in, This represents the carbon emissions after coupling calibration. The weighting of the full lifecycle accounting results. To supplement the weights of multi-source data, Adjust the weights for the scene. To supplement the carbon source emission intensity for category k, The contribution percentage of the supplementary carbon source of type k. This is a carbon emission adjustment factor for ultra-high voltage power transmission projects. For regional environmental impact parameters, This is the error correction factor for the coupled accounting calculation. To supplement the number of carbon source types.

[0008] Furthermore, the expression for the time-series carbon data incremental synchronization algorithm is as follows: ,in, The carbon data is synchronized at time t. For the first Real-time historical carbon data For data attenuation coefficient, Let be the increment value of carbon data at time t. Let be the data reliability coefficient at time t. Let be the synchronization priority coefficient of the q-th type of data at time t. Let q be the data synchronization efficiency factor. The number of dimensions for data synchronization.

[0009] Furthermore, the data structuring processing model expression of the carbon data visualization decision analysis platform is as follows: ,in, For structured target carbon data, These are the data matrix transformation coefficients. This is a structured weight matrix for carbon data of ultra-high voltage building materials. For time series data integration coefficients, Let s be the structured label vector of the data at time s. Adjust the matrix to adapt to data storage. Let be the transpose matrix of the carbon data at time t.

[0010] Furthermore, the expression for the optimized model for the integrated storage of carbon emission data of building materials in ultra-high voltage power transmission projects is: Storage Where Storage is the optimized integrated storage data volume. Let u be the amount of structured data in the u-th storage module. Let be the storage priority coefficient of the u-th storage module. Let be the space utilization coefficient of the u-th storage module. This is a storage redundancy adjustment factor. The data access frequency impact factor. This represents the number of storage modules.

[0011] Further, S3 includes the following steps: S31, based on the multi-source characteristics of carbon emission data from building materials in UHV projects, select carbon emission monitoring data from raw material production, carbon emission statistics data from transportation, carbon emission sensor data from construction, and carbon emission record data from operation and maintenance to supplement data sources, and establish multi-source data association mapping rules; S32, match the initial carbon accounting results with the data dimensions of each supplementary data source, and perform homogenization processing of multi-source data through field alignment, format conversion, and logical verification; S33, substitute into the multi-source carbon footprint coupling accounting model, input the weight parameters of each data source, scenario adjustment coefficients, and error correction factors, and complete the coupling calculation of multi-source data; S34, identify and remove outliers from the coupling calculation results to generate a standardized carbon data set that meets the data consistency requirements.

[0012] Further, S4 includes the following steps: S41, extracting timestamp information from the standardized carbon dataset, establishing a time-series data sequence index, and dividing the data incremental update cycle and update granularity; S42, based on the time-series carbon data incremental synchronization algorithm, calculating the carbon data increment value in each cycle, and determining the data update weight by combining the historical data decay coefficient and the synchronization priority coefficient; S43, performing cross-cycle consistency verification on the incrementally updated data, and verifying the data validity through data correlation analysis and logical conflict detection; S44, correcting the verification-unqualified data, supplementing the missing time-series data segments, and forming a complete time-series carbon data sequence.

[0013] Further, S5 includes the following steps: S51, transmitting the time-series carbon data sequence to the carbon data visualization decision analysis platform, parsing the data structure and data type, and determining the structured processing rules and storage format requirements; S52, classifying the time-series carbon data according to building material type, life cycle stage, and carbon emission intensity level based on the platform's built-in data classification algorithm; S53, using the data structured processing model, performing matrix transformation, feature extraction, and labeling on the classified data to generate structured intermediate data; S54, according to the integrated storage specifications, performing format adaptation and redundancy optimization on the structured intermediate data to obtain target carbon data that meets storage requirements.

[0014] A carbon emission data integration and storage system for ultra-high voltage (UHV) power transmission (UHV) engineering building materials, utilizing a method for integrating and storing UHV engineering building material carbon emission data, includes: a multi-dimensional carbon data acquisition unit, connected to monitoring nodes at various stages of UHV engineering building materials, collecting carbon data related to raw material consumption, energy consumption, process emissions, transportation parameters, and recycling, and transmitting the data to a building material lifecycle carbon metering unit; a building material lifecycle carbon metering unit, receiving the data transmitted from the multi-dimensional carbon data acquisition unit, completing staged carbon emission calculations through a built-in carbon metering model, and outputting initial carbon calculation results to a multi-source carbon footprint coupling calculation unit; and a multi-source carbon footprint coupling calculation unit, connecting the building material lifecycle carbon metering unit to supplementary data sources, and performing calculations through a coupling model. The initial carbon accounting results are calibrated with supplementary data, and a standardized carbon data set is sent to the time-series carbon data incremental synchronization unit. The time-series carbon data incremental synchronization unit receives the standardized carbon data set, uses an incremental synchronization algorithm to update the data time-series and verify consistency, and transmits the time-series carbon data sequence to the carbon data visualization and decision analysis unit. The carbon data visualization and decision analysis unit performs structured processing on the time-series carbon data sequence, generates target carbon data, and transmits it to the integrated storage unit. The UHV building materials carbon data integrated storage unit receives the target carbon data, completes data storage according to a modular classification storage architecture, establishes associated indexes and access permission mechanisms, and performs secure integrated data storage. Each unit performs bidirectional data interaction and command transmission through a data bus.

[0015] Beneficial Effects: This invention proposes a method and system for integrating and storing carbon emission data of building materials for ultra-high voltage (UHV) projects. Utilizing a collaborative mechanism of carbon measurement throughout the entire lifecycle of building materials and coupled accounting with multi-source carbon footprints, it integrates monitoring data from various stages of UHV project building materials with supplementary data across different scenarios. This breaks through the limitations of single-dimensional accounting. Through deep fusion and calibration of multi-source data, the carbon accounting results are made more consistent with actual emissions, meeting data standardization requirements. By employing a time-series carbon data incremental synchronization algorithm, it specifically adapts to the time-series incremental characteristics of carbon data, achieving efficient data updates and consistency verification. Combined with a modular and categorized structured storage architecture, along with associated indexes and permission mechanisms, it solves the problem of insufficient storage adaptability, achieving secure and efficient integrated storage of carbon data. Through the structured processing function of the carbon data visualization decision analysis platform, it achieves seamless integration of data with visualization applications and cross-stage traceability needs, enhancing the subsequent application value of the data. The entire system forms a complete link from multi-source data collection, accurate accounting, time-series synchronization to standardized storage, providing comprehensive and reliable data support for carbon emission control in UHV projects and promoting the green and low-carbon transformation of these projects. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention.

[0017] Figure 2This is a flowchart of method step S3 of the present invention; Figure 3 This is a flowchart of method step S4 of the present invention; Figure 4 This is a flowchart of step S5 of the method of the present invention; Figure 5 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown, a method for integrating and storing carbon emission data of building materials for ultra-high voltage (UHV) projects includes the following steps: S1, collecting multi-dimensional raw carbon data, including raw material consumption intensity, energy consumption type, process emission coefficient, transportation distance, and recycling rate, based on online monitoring nodes of carbon emissions at each stage of UHV project building material production, transportation, construction, operation and maintenance, and recycling; S2, calculating the staged carbon emission amounts of the multi-dimensional raw carbon data using a building material life-cycle carbon measurement model to form initial carbon accounting results; S3, supplementing the initial carbon accounting results with cross-scenario carbon data using a multi-source carbon footprint coupling accounting model. S4. Linear coupling calibration generates a standardized carbon dataset; S5. The time-series carbon data incremental synchronization algorithm is used to perform time-dimensional incremental updates and data consistency checks on the standardized carbon dataset to obtain a time-series carbon data sequence; S6. The time-series carbon data sequence is transmitted to the carbon data visualization decision analysis platform for data structuring processing to generate target carbon data that conforms to the integrated storage specifications; S7. Based on the dedicated storage architecture for carbon emission data of building materials in UHV projects, the target carbon data is classified and stored in modules, and an associated index and data access permission mechanism are established to achieve secure and efficient integrated storage of carbon data.

[0020] Step S1 requires the deployment of no fewer than 30 online monitoring nodes across the five core stages of UHV power transmission project building material production, transportation, construction, operation and maintenance, and recycling. Each node is equipped with high-precision sensing equipment and a data acquisition module to achieve real-time collection of multi-dimensional raw carbon data. During the production stage, the focus is on collecting the consumption intensity of various building material raw materials. Specifically, the consumption intensity of steel raw materials is controlled within the range of 1.2 to 1.8 per unit of building material, and the consumption intensity of concrete and cement raw materials is set within the range of 2.5 to 3.2 per unit of building material. Energy consumption types include the three core energy sources: coal, electricity, and natural gas. The consumption duration and rate of each energy source are recorded simultaneously. Process emission coefficients are set separately for different production processes, with the emission coefficient for smelting processes maintained between 1.1 and 1.5, and the emission coefficient for casting processes set between 0.8 and 1.0. During the transportation stage, data is collected for transportation via road, rail, and waterway. For distance data collection, the accuracy is as follows: road transport distance is measured to 1 kilometer, and rail and waterway transport distance is measured to 5 kilometers. During the recycling phase, the focus is on collecting the recycling rates of various building materials, with steel recycling rates ranging from 80% to 95% and concrete recycling rates ranging from 30% to 50%. All collected data is transmitted in real time to the data aggregation node via a 5G communication module to ensure the timeliness and completeness of data collection. The collection frequency is set to once every 15 minutes, and the amount of data collected in a single session is controlled to be between 10 and 20 megabytes. During the collection process, a data verification mechanism is used to remove abnormal fluctuation data to ensure the reliability of the original carbon data.

[0021] In step S2, the multi-dimensional raw carbon data collected in step S1 is first categorized into five types based on building material: steel, concrete, insulators, cables, and towers. An independent data processing channel is established for each type of building material. Then, the building material lifecycle carbon accounting model is used to calculate the staged carbon emissions. During the calculation, for the production stage, the basic carbon emissions are calculated by combining raw material consumption intensity and process emission coefficients. Simultaneously, based on the emission characteristics corresponding to the energy consumption type, the carbon emissions generated by energy consumption are added. The carbon emission accounting weight for coal energy consumption is set at 0.4, electricity at 0.3, and natural gas at 0.3. For the transportation stage, transportation carbon emissions are calculated based on the transportation distance and the unit distance emission parameters of the corresponding transportation mode. The unit distance emission parameters for road transportation are taken in the range of 0.8 to 1.2 per kilometer, for railways at 0.3 to 0.5 per kilometer, and for waterways at 0.2 to 0.4 per kilometer. The calculation during the construction phase combines the energy consumption of construction equipment with the construction duration. The energy consumption of construction equipment is calculated in the range of 2.0 to 3.5 per hour, and the construction duration is accurate to the hour. The calculation during the operation and maintenance phase is based on the number of monthly operation and maintenance operations and the energy consumption per operation and maintenance operation. The number of monthly operation and maintenance operations is controlled to be 2 to 4 times, and the energy consumption per operation and maintenance operation is set to be 1.5 to 2.5. The carbon emission reduction during the recycling phase is calculated based on the recycling rate and the energy consumption of recycling and processing. The energy consumption of recycling and processing is taken as 0.3 to 0.6 per unit of recycled building materials. After the phased calculation, the initial carbon accounting results of various building materials are obtained by summarizing. The data processing delay during the calculation process is controlled within 30 seconds to ensure the efficiency of the calculation.

[0022] Step S3 involves first clarifying the specific types of cross-scenario carbon data supplementation sources, including three categories: carbon emission statistics from the building materials industry, regional environmental monitoring data, and reference data on carbon emissions from similar projects. The carbon emission statistics for the building materials industry are derived from annual reports released by the industry's regulatory authorities; regional environmental monitoring data is provided in real-time by local environmental monitoring stations; and reference data on carbon emissions from similar projects is selected from relevant data from 3 to 5 completed ultra-high voltage power transmission projects. Subsequently, multi-source data association mapping rules are established, and data matching is performed according to three dimensions: building material type, life cycle stage, and data collection time, ensuring the correspondence between the supplemented data and the initial carbon accounting results. After matching, the supplemented data is preprocessed, removing invalid data with a missing rate exceeding 10% and smoothing abnormal data with fluctuations exceeding 20%. The preprocessed supplemented data and the initial carbon accounting results are then substituted into the multi-source carbon footprint coupled accounting model for calibration. During the calibration process, the initial carbon accounting results are weighted at 0.6, the carbon emission statistics data of the building materials industry at 0.2, the regional environmental monitoring data at 0.1, and the carbon emission reference data of similar projects at 0.1. Through weighted calculation, deep integration of multi-source data is achieved. After calibration, a standardized carbon data set is generated. Each data point in the set includes core fields such as building material identifier, stage identifier, carbon emission value, and data credibility. The data credibility must reach above 90 to ensure the quality of the standardized data.

[0023] In step S4, the standardized carbon data set generated in step S3 is first divided into time-series segments, establishing a time-series data framework at three levels: daily, weekly, and monthly. Daily data is divided into 24 time slices, weekly data into 7 time units, and monthly data into 30 time nodes. Then, a time-series carbon data incremental synchronization algorithm is used to extract incremental carbon data information within each time unit. Incremental data extraction uses timestamps as indexes, comparing the data differences between the current and previous time units to filter out newly added, modified, and deleted data records. During synchronization, data consistency verification rules are set, including three types: data range verification, logical relationship verification, and cross-time unit correlation verification. Data range verification limits carbon emission values ​​to the range of 0 to 10; data exceeding this range is marked as abnormal. Logical relationship verification ensures that the cumulative relationship of carbon emission data from different stages of the same building material is reasonable, and the deviation between the sum of stage carbon emission data and the total life-cycle carbon emission data does not exceed 5%. Cross-time unit correlation verification ensures that the data fluctuation range between adjacent time units does not exceed 30%. Data that passes verification is updated in a time-series manner. During the update process, incremental storage is used to save only the changed data, reducing storage resource consumption. Data that fails verification is marked and returned to the previous step for reprocessing until the data meets the requirements, ultimately forming a complete time-series carbon data sequence. The sequence data is arranged in chronological order and supports data querying and extraction at any time interval, with a query response time of no more than 2 seconds.

[0024] In step S5, the time-series carbon data sequence is first transmitted to the carbon data visualization and decision analysis platform via a dedicated data transmission protocol. Encryption is used during transmission to ensure data security, and the bandwidth is set to 10-20 megabits per second to avoid congestion. Upon receiving the data, the platform first parses the data structure, identifying core fields such as building material type, life cycle stage, timestamp, and carbon emission values. Data is then categorized by field type, storing numerical, character, and time-based data in their respective data buffers. Subsequently, data structuring is performed, establishing a three-level data index based on the UHV project construction number, building material batch number, and life cycle stage code. The time-series carbon data sequence is converted into a structured data table using a row-column storage structure, with each row corresponding to one carbon data record and each column corresponding to one data field. Simultaneously, the data format is standardized according to integrated storage specifications, unifying the data encoding format, time format, and numerical precision. The time format uniformly adopts the standard year-month-day-hour-minute-second format, and the numerical precision retains two decimal places. During the processing, the platform has a built-in data quality assessment module to evaluate the completeness, consistency, and accuracy of the structured data. The completeness requirement is that the data missing rate is less than 3%, the consistency requirement is that the data format of the same field is consistent, and the accuracy requirement is that the data error does not exceed 2%. After passing the assessment, the target carbon data that conforms to the integrated storage specification is generated. After the data is generated, it is confirmed to be correct through the data verification mechanism before being transmitted to the integrated storage unit.

[0025] In step S6, storage operations are conducted based on a dedicated storage architecture for carbon emission data of building materials used in ultra-high voltage (UHV) projects. This architecture combines distributed storage with centralized management, deploying no fewer than eight storage nodes. Each storage node has a storage capacity of 500 to 1000 gigabytes, and the nodes are connected via a high-speed local area network, achieving a data transmission rate of over 1 gigabits per second. First, the target carbon data is categorized and stored in modules according to building material type and lifecycle stage. Steel, concrete, insulators, cables, and towers each correspond to an independent storage module. Each module is further divided into storage partitions based on five stages: production, transportation, construction, operation and maintenance, and recycling, ensuring the orderly storage of data. During storage, a data association index is established, including key information such as project number, building material number, stage number, timestamp, and storage node address. This index enables rapid querying and association of data across different modules and partitions. A data access permission mechanism is implemented, assigning different access permissions to four roles: administrator, data entry clerk, data analyst, and general query user. Administrators have full access, data entry clerks only have data upload and modification permissions, data analysts have data query and analysis permissions, and general query users have limited data query permissions. Permission allocation is achieved through a dual mechanism of account password and role authentication. After storage is completed, a data backup mechanism is initiated, employing a combination of real-time and scheduled backups. Real-time backups synchronously back up newly added data to the backup storage node, while scheduled backups perform a full backup once a day. The backup data retention period is set to 3 years to ensure the secure and efficient integrated storage of carbon data. It also supports rapid data retrieval and recovery, with a data retrieval response time of no more than 3 seconds and a data recovery success rate of over 99.9%.

[0026] Preferably, the expression for the carbon metering model of the building materials' entire life cycle is: ,in, This refers to the carbon emissions throughout the entire life cycle of building materials used in ultra-high voltage power transmission projects. The carbon emission conversion factor for the i-th type of building material raw materials is... Let i be the consumption of building material raw materials of type i. Let be the carbon emission correction factor for the i-th type of building material production process. The carbon emission factor for the energy type corresponding to the i-th type of building material. For the energy consumption of the production of building materials of type i, Let be the energy utilization efficiency impact coefficient of the i-th type of building materials. Let be the carbon emission time decay coefficient of the i-th type of building material in the j-th stage. The duration of stage j for building material of type i is given. This represents the total number of building material types. This represents the total number of stages throughout the entire lifecycle.

[0027] Specifically, the carbon emission measurement model for the entire life cycle of building materials is based on the logic of carbon emission composition at each stage of the life cycle of building materials in ultra-high voltage power transmission projects. It first identifies raw material consumption and energy consumption as the core sources of carbon emissions, then considers the impact of time decay at different stages on carbon emissions, and constructs the model using a combination of superposition and product methods. When establishing the model, it first separates the carbon emissions from raw material consumption into two core modules: carbon emissions from raw material consumption and carbon emissions from energy consumption. Carbon emissions from raw material consumption are calculated by multiplying the raw material consumption amount by a conversion factor, and then adjusting for process correction factors. Similarly, carbon emissions from energy consumption are calculated by multiplying the energy consumption amount by an emission factor, and then calibrated using an energy utilization efficiency impact coefficient. Subsequently, time decay coefficients for each stage are introduced, and the cumulative impact of stage duration on carbon emissions is reflected through a product, ultimately achieving accurate accounting of carbon emissions throughout the entire life cycle. The carbon emission conversion factor for raw materials is set between 0.8 and 2.2 based on the type of building material. The carbon emission correction factor for processes is set between 0.9 and 1.6 based on the complexity of the production process. The carbon emission factor for energy type is set between 1.0 and 3.5 with reference to industry standards. The energy utilization efficiency impact factor is set between 0.7 and 1.1 based on the level of energy utilization technology. The time decay coefficient is set between 0.01 and 0.05 based on the characteristics of each stage. The total number of stages in the entire life cycle is fixed at five: production, transportation, construction, operation and maintenance, and recycling. In implementation, the collected data on raw material consumption, energy consumption, etc., are first substituted into the corresponding modules to calculate the carbon emissions of each type. Then, the stage carbon emissions are superimposed to obtain the stage carbon emissions. Finally, the total life cycle carbon emissions are obtained by correcting with the time decay coefficient. This model can comprehensively cover the carbon emission influencing factors at each stage, improving the accuracy and comprehensiveness of the calculation.

[0028] Preferably, the expression for the multi-source carbon footprint coupled accounting model is: ,in, This represents the carbon emissions after coupling calibration. The weighting of the full lifecycle accounting results. To supplement the weights of multi-source data, Adjust the weights for the scene. To supplement the carbon source emission intensity for category k, The contribution percentage of the supplementary carbon source of type k. This is a carbon emission adjustment factor for ultra-high voltage power transmission projects. For regional environmental impact parameters, This is the error correction factor for the coupled accounting calculation. To supplement the number of carbon source types.

[0029] Specifically, the multi-source carbon footprint coupled accounting model is based on the complementary calibration logic of multi-source data. Addressing the issue of bias in accounting results from a single data source, it achieves collaborative optimization of multi-source data through weighted fusion. When establishing the model, three core input items are first determined: initial carbon accounting results, supplementary multi-source data, and scenario-corrected data. Then, weights are assigned according to the reliability and relevance of each type of data. The initial carbon accounting results, as basic data, have the highest weight. Supplementary multi-source data is used to supplement insufficient basic data, and scenario-corrected data is adapted to the specific environment of ultra-high voltage projects. Finally, a coupled accounting error correction factor is introduced to calibrate the weighted calculation results, ensuring data consistency. The weights for the full life-cycle accounting results are set at 0.5 to 0.7, the weights for multi-source supplementary data are set at 0.2 to 0.3 based on the reliability of the supplementary sources, the weights for scenario correction are set at 0.1 to 0.2, the supplementary carbon source emission intensity is set at 0.5 to 3.0 based on the characteristics of the supplementary source data, the regional environmental impact parameters are set at 0.8 to 1.3 according to the environmental differences in the project area, the coupled accounting error correction factor is set at 0.95 to 1.05 based on historical accounting error statistics, and the number of supplementary carbon source types is determined to be 3 to 8 categories based on the project scale. During implementation, various types of supplementary data are first collected and homogenized, then substituted into the model according to the set weights to calculate the weighted average, and finally calibrated through the error correction factor to generate a standardized carbon dataset. This effectively integrates the advantages of multi-source data and reduces the accounting bias caused by a single data source.

[0030] Preferably, the expression for the time-series carbon data incremental synchronization algorithm is: ,in, The carbon data is synchronized at time t. For the first Real-time historical carbon data For data attenuation coefficient, Let be the increment value of carbon data at time t. Let be the data reliability coefficient at time t. Let be the synchronization priority coefficient of the q-th type of data at time t. Let q be the data synchronization efficiency factor. The number of dimensions for data synchronization.

[0031] Specifically, the incremental synchronization algorithm model for time-series carbon data is based on the update characteristics of time-series data, considering the continuity of historical data and the reliability of incremental data. It is constructed using a weighted fusion method of historical data decay and incremental data. When building the model, the fundamental role of historical carbon data from the previous moment is first retained. A data decay coefficient is set to reflect the time-sensitivity decay of historical data. Then, the incremental value of carbon data at the current moment is introduced. The weight of incremental data is adjusted by combining the data reliability coefficient and the synchronization priority coefficient. The synchronization efficiency factor is used to optimize the balance between synchronization speed and accuracy, ultimately achieving smooth updates of time-series data. The data decay coefficient is set between 0.02 and 0.08 based on data timeliness; the data reliability coefficient is set between 0.85 and 0.99 based on the accuracy of the data acquisition equipment; the synchronization priority coefficient is set between 0.6 and 1.0 based on data importance; and the synchronization efficiency factor is set between 0.9 and 1.2 based on the level of data synchronization technology. The number of data synchronization dimensions includes 3 to 6 dimensions such as time, type, and intensity. In practice, historical data from the previous moment is first extracted and adjusted according to the attenuation coefficient. Then, the incremental value of the current moment is calculated. The weight of the incremental data is determined by combining the reliability coefficient, priority coefficient and efficiency factor. Finally, the synchronized data at the current moment is obtained through weighted fusion. This model can achieve efficient time-series updates of data and ensure the continuity and consistency of data.

[0032] Preferably, the data structured processing model expression of the carbon data visualization decision analysis platform is as follows: ,in, For structured target carbon data, These are the data matrix transformation coefficients. This is a structured weight matrix for carbon data of ultra-high voltage building materials. For time series data integration coefficients, Let s be the structured label vector of the data at time s. Adjust the matrix to adapt to data storage. Let be the transpose matrix of the carbon data at time t.

[0033] Specifically, the data structuring processing model derivation of the carbon data visualization decision analysis platform is based on the requirements of data structuring and time-series integration. To achieve the conversion of time-series carbon data to a storage-adaptive format, a model is constructed by combining matrix operations and summation operations. Through matrix transformation, time-series carbon data is converted into a structured data matrix. A weight matrix is ​​used to highlight the importance of key data fields. Then, time-series data summation is used to integrate data from multiple time points. Finally, a data storage adaptation adjustment matrix is ​​introduced to ensure that the processed data conforms to integrated storage specifications. Parameter values ​​are determined according to the data structure characteristics and storage requirements. The data matrix transformation coefficient is set between 0.1 and 0.3 based on the data dimension; the structured weight matrix elements are set between 0.5 and 1.0 based on field importance; the time-series data integration coefficient is set between 0.8 and 1.2 based on the data time span; the data structuring label vector is set to 0 or 1 based on the label type; and the storage adaptation adjustment matrix elements are set between 0.9 and 1.1 according to storage format requirements. In practice, the time-series carbon data sequence is first converted into a data matrix, which is then multiplied with the weight matrix to obtain a weighted data matrix. The data from multiple time points are then summed and integrated. Finally, the data format is calibrated by adapting and adjusting the matrix to generate structured target carbon data. This model can quickly complete data structuring and provides adaptability support for subsequent integrated storage.

[0034] Preferably, the expression for the optimized model for the integrated storage of carbon emission data of building materials in ultra-high voltage power transmission projects is: Storage Where Storage is the optimized integrated storage data volume. Let u be the amount of structured data in the u-th storage module. Let be the storage priority coefficient of the u-th storage module. Let be the space utilization coefficient of the u-th storage module. This is a storage redundancy adjustment factor. The data access frequency impact factor. This represents the number of storage modules.

[0035] Specifically, the integrated storage optimization model for carbon emission data of building materials in ultra-high voltage power transmission projects is based on the requirements of efficient utilization of storage resources and convenient data access. The model is constructed through weighted averaging and adjustment factors. During model building, the structured data volume is first divided according to storage modules. The effective data volume of each module is calculated by combining storage priority coefficients and space utilization coefficients. Then, a weighted average is used to obtain the basic storage data volume. Finally, a storage redundancy adjustment coefficient and a data access frequency impact factor are introduced to optimize storage resource allocation and data access efficiency. Parameter values ​​are determined through storage system testing and engineering practice. The storage priority coefficient is set between 0.6 and 1.0 according to data importance; the space utilization coefficient is set between 0.7 and 0.95 based on storage module configuration; the storage redundancy adjustment coefficient is set between 0.05 and 0.15 based on data security requirements; the data access frequency impact factor is set between 0.8 and 1.3 with reference to historical access data; and the number of storage modules is determined to be 5 to 10 based on data classification. During implementation, the structured data volume of each storage module is first counted, and the effective data volume is calculated by combining the priority coefficient and the space utilization coefficient. Then, the basic storage data volume is obtained by weighted averaging. Finally, the optimized integrated storage data volume is obtained by adjusting the adjustment factor. This model can achieve reasonable allocation of storage resources and improve access efficiency while ensuring data security.

[0036] Preferred, such as Figure 2 As shown, S3 includes the following steps: S31, based on the multi-source characteristics of carbon emission data from building materials in UHV projects, select carbon emission monitoring data from raw material production, carbon emission statistics data from transportation, carbon emission sensor data from construction, and carbon emission record data from operation and maintenance to supplement data sources, and establish multi-source data association mapping rules; S32, match the initial carbon accounting results with the data dimensions of each supplementary data source, and perform homogenization processing of multi-source data through field alignment, format conversion, and logical verification; S33, substitute into the multi-source carbon footprint coupling accounting model, input the weight parameters of each data source, scenario adjustment coefficients, and error correction factors, and complete the coupling calculation of multi-source data; S34, identify and remove outliers from the coupling calculation results to generate a standardized carbon data set that meets the data consistency requirements.

[0037] Specifically, step S3 involves precise calibration of the multi-source carbon footprint coupling accounting, achieved through steps S31 to S34 to deeply integrate and standardize multi-source data. S31 first comprehensively reviews supplementary data sources related to carbon emissions from building materials in the UHV project, clarifying the collection frequencies for raw material production carbon emission monitoring data, transportation carbon emission statistics, construction process carbon emission sensor data, and operation and maintenance phase carbon emission records: every 10 minutes, every 30 minutes, every 15 minutes, and every 24 hours, respectively. Simultaneously, multi-source data association mapping rules are established, including three core dimensions: building material type coding matching, lifecycle phase time alignment, and data field attribute correspondence, ensuring the correlation of data from different sources. S32 matches the initial carbon accounting results generated in step S2 with the data dimensions of each supplementary data source, performing unified format conversion for text, numerical, and time-based data of different formats. A field alignment algorithm is used to achieve precise correspondence of eight core fields. After three rounds of logical verification to eliminate data conflicts, a conflict threshold is set at 15%, achieving homogenization of multi-source data. S33 substitutes the multi-source carbon footprint coupled accounting model, setting the initial carbon accounting result weight to 0.6, the carbon emission statistics data of the building materials industry weight to 0.2, the regional environmental monitoring data weight to 0.1, and the carbon emission reference data of similar projects weight to 0.1. The input carbon emission adjustment coefficient for the UHV project scenario is 0.9 to 1.2, and the regional environmental impact parameters are set from 0.85 to 1.35 according to different regions. The coupled accounting error correction factor is fixed at 1.02, completing the coupled calculation of multi-source data. S34 uses the standard deviation detection method to identify outliers in the coupled calculation results, setting the outlier judgment threshold to ±2 standard deviations. Identified outlier data is directly removed, and the remaining data undergoes a second consistency check. The pass rate of the check must reach more than 95%, finally generating a standardized carbon data set, providing a high-quality and highly consistent data foundation for subsequent time-series synchronization.

[0038] Preferred, such as Figure 3 As shown, step S4 includes the following steps: S41, extracting timestamp information from the standardized carbon dataset, establishing a time-series data sequence index, and dividing the data incremental update cycle and update granularity; S42, based on the time-series carbon data incremental synchronization algorithm, calculating the carbon data increment value in each cycle, and determining the data update weight by combining the historical data decay coefficient and the synchronization priority coefficient; S43, performing cross-cycle consistency verification on the incrementally updated data, and verifying the data validity through data correlation analysis and logical conflict detection; S44, correcting the verification-unqualified data, supplementing the missing time-series data segments, and forming a complete time-series carbon data sequence.

[0039] Specifically, step S4 ensures the accuracy and continuity of incremental synchronization of time-series carbon data, proceeding systematically through steps S41 to S44. S41 first extracts the timestamp information embedded in the standardized carbon dataset, establishing a time-series data sequence index with 15-minute time slices, dividing the data into 24 basic update cycles per day. The data update granularity is determined to be a single-building-material, single-stage carbon emission record, with the data processing volume controlled between 500 and 800 records per update cycle to ensure fine-grained segmentation and processing efficiency in the time-series dimension. S42, based on the incremental synchronization algorithm for time-series carbon data, calculates the incremental carbon data value within each update cycle, setting the historical data decay coefficient to 0.05, and the data reliability coefficient to three levels (0.9, 0.95, and 0.99) based on the accuracy of the acquisition equipment. The synchronization priority coefficient is set from 0.7 to 1.0 according to data importance, with a priority coefficient of 1.0 for production stage data and 0.7 for recycling stage data. The synchronization efficiency factor is fixed at 1.1, and the update weight of each incremental data is determined through weighted calculation to ensure priority synchronization of important data. S43 conducts cross-cycle consistency verification, setting a reasonable range for carbon emission values ​​from 0 to 10. The deviation threshold between the cumulative carbon emission data of the same building material at different stages and the full life-cycle data is set at 5%, and the data fluctuation threshold between adjacent time units is set at 30%. Data validity is verified through a triple-rule system of data range verification, logical relationship verification, and cross-time unit correlation verification, with verification time controlled within 20 seconds. S44 marks unqualified data in red and returns it to S3 for reprocessing. Missing time-series data fragments are supplemented using the same-source data interpolation method, with a missing data supplementation accuracy rate set at over 98%, ensuring the data integrity of each time unit. This ultimately forms a continuous and consistent time-series carbon data sequence, providing time-complete and logically rigorous data support for subsequent structured processing.

[0040] Preferred, such as Figure 4 As shown, step S5 includes the following steps: S51, transmitting the time-series carbon data sequence to the carbon data visualization decision analysis platform, parsing the data structure and data type, and determining the structured processing rules and storage format requirements; S52, classifying the time-series carbon data according to building material type, life cycle stage, and carbon emission intensity level based on the platform's built-in data classification algorithm; S53, using the data structured processing model, performing matrix transformation, feature extraction, and labeling on the classified data to generate structured intermediate data; S54, performing format adaptation and redundancy optimization on the structured intermediate data according to the integrated storage specifications to obtain target carbon data that meets storage requirements.

[0041] Specifically, step S5 involves implementing structured processing for the carbon data visualization decision analysis platform, through steps S51 to S54 to achieve data format adaptation and optimization. S51 uses a dedicated encrypted transmission protocol to transmit the time-series carbon data sequence to the platform, with a transmission bandwidth set at 15 megabits per second and data transmission latency controlled within 100 milliseconds. After receiving the data, the platform parses the data structure, identifying eight core fields, including building material type, life cycle stage, timestamp, and carbon emission value. These fields are stored in corresponding data buffers according to their type, with a buffer capacity set at 50 megabytes. A circular overwrite mechanism is used to prevent data overflow. S52, based on the platform's built-in classification algorithm, performs three-dimensional classification of the time-series carbon data according to five building material types (steel, concrete, insulators, cables, and towers), five life cycle stages (production, transportation, construction, operation and maintenance, and recycling), and three carbon emission intensity levels (0-2, 2-5, and 5-10). The classification process takes no more than 30 seconds, with a classification accuracy set at over 98%. S53 utilizes a data structuring model to establish a relationship between project number, building material batch number, and stage code according to a three-level data indexing rule. This transforms time-series data into a row-column structured data table, with each record including 12 core fields. Field lengths are fixed at a preset standard of 8 to 32 characters to ensure data structure consistency. S54, based on integrated storage specifications, standardizes the data table format to a standard storage format. Time is expressed using a unified year-month-day-hour-minute-second format, with numerical precision retained to two decimal places. A 3% data missing rate threshold is set, and a redundant data removal algorithm deletes data entries with a duplication rate exceeding 90%. The optimized data volume is compressed to 60% to 70% of the original, ultimately generating target carbon data that meets storage requirements. This achieves efficient conversion of time-series data to a storage-compatible format, ensuring smooth data storage and subsequent application integration.

[0042] like Figure 5As shown, a carbon emission data integration and storage system for building materials in ultra-high voltage (UHV) power transmission projects is disclosed. This system utilizes a method for integrating and storing carbon emission data for UHV power transmission project building materials, comprising: a multi-dimensional carbon data acquisition unit, connected to monitoring nodes at various stages of the UHV power transmission project building materials, collecting carbon data related to raw material consumption, energy consumption, process emissions, transportation parameters, and recycling, and transmitting the data to a carbon metering unit covering the entire life cycle of building materials; a carbon metering unit covering the entire life cycle of building materials, receiving the data transmitted from the multi-dimensional carbon data acquisition unit, completing staged carbon emission calculations through a built-in carbon metering model, and outputting initial carbon calculation results to a multi-source carbon footprint coupling calculation unit; and a multi-source carbon footprint coupling calculation unit, connecting the carbon metering unit covering the entire life cycle of building materials with supplementary data sources, and using a coupling calculation model... The system calibrates the initial carbon accounting results and supplementary data, and sends a standardized carbon data set to the time-series carbon data incremental synchronization unit. The time-series carbon data incremental synchronization unit receives the standardized carbon data set, uses the incremental synchronization algorithm to update the data time-series and verify consistency, and transmits the time-series carbon data sequence to the carbon data visualization and decision analysis unit. The carbon data visualization and decision analysis unit performs structured processing on the time-series carbon data sequence, generates target carbon data, and transmits it to the integrated storage unit. The UHV building materials carbon data integrated storage unit receives the target carbon data, completes data storage according to the modular classification storage architecture, establishes associated indexes and access permission mechanisms, and performs secure integrated storage of data. Each unit performs bidirectional data interaction and command transmission through the data bus.

[0043] This invention integrates different scalar and vector parameters for unified calculation, constructing a multi-dimensional parameter normalization adaptation mechanism and logical association mapping system. Through pre-set unified data dimension conversion rules, weight allocation models, and coupling calibration factors, it achieves collaborative computation of heterogeneous parameters. First, for scalar parameters such as raw material consumption intensity and process emission coefficients (which lack directional attributes), corresponding weight coefficients are directly assigned according to their physical meaning to adapt them to the formula's computational dimensions. For vector parameters such as transportation distance and data increment changes (which contain directional or temporal characteristics), their magnitude or temporal change amplitude is extracted and converted into a dimensionless scalar equivalent form using a directional influence coefficient, eliminating dimensional differences. For example, transportation distance, as a vector parameter, has its direction corrected by a regional environmental influence coefficient, and its amplitude converted into an equivalent emission contribution value based on the unit distance emission benchmark. Temporal carbon data increments, as vector parameters, have their effective amplitude over time extracted using a time decay coefficient, transforming them into computational units that can be superimposed on scalar parameters. Meanwhile, cross-parameter logical correlation factors are embedded in the formula. For example, a one-to-one correspondence is established between the building material type code and the carbon emission conversion factor, so that the classification vector parameter and the numerical scalar parameter form a computational correlation. Then, through weighted fusion, product calibration and other computational logic, different types of parameters can achieve synergistic effects within a unified computational framework. This not only preserves the essential characteristics of each parameter, but also ensures the accuracy and rationality of the computational results, ultimately realizing the integrated accounting and processing of multi-source heterogeneous parameters.

[0044] A method and system for integrating and storing carbon emission data from building materials used in ultra-high voltage (UHV) power transmission projects has been developed. This system constructs an accounting system that deeply integrates data from the entire lifecycle and multiple sources. By combining carbon data collection at each stage of building material production with specialized carbon measurement models and coupled accounting models, it overcomes the limitations of existing technologies that rely on single-stage or single-source data. The system and method comprehensively cover the entire process of building material production, transportation, construction, operation and maintenance, and recycling. It integrates multi-dimensional data such as raw material consumption, energy utilization, and process emissions, along with supplementary data from different scenarios. Through multi-source data association mapping, homogenization processing, and coupled calibration, it achieves standardized integration of carbon accounting data. This fundamentally solves the problem of discrepancies between accounting results and actual emissions, and the difficulty in meeting data standardization requirements in existing technologies, thus improving the accuracy and completeness of carbon data.

[0045] Furthermore, the system and method possess time-series synchronization and integrated storage capabilities adapted to the characteristics of carbon data from UHV projects. Its specially designed incremental synchronization mechanism enables efficient updates and consistency checks for the time-series incremental characteristics of carbon data. Combined with a modular, categorized structured storage architecture and associated indexing mechanism, it effectively overcomes the shortcomings of insufficient data synchronization and storage architecture adaptability in existing technologies. Simultaneously, through structured processing in the carbon data visualization and decision analysis phase, it achieves seamless integration of data with subsequent application requirements. This ensures both the secure and efficient storage of carbon data and fully leverages its application value, forming a closed-loop process from data acquisition, calculation, synchronization to storage, providing reliable technical support for carbon emission control in UHV projects.

[0046] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0047] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for integrating and storing carbon emission data of building materials used in ultra-high voltage power transmission projects, characterized in that, The process includes the following steps: S1, collecting multi-dimensional raw carbon data, including raw material consumption intensity, energy consumption type, process emission coefficient, transportation distance, and recycling rate, based on online monitoring nodes of carbon emissions at each stage of the production, transportation, construction, operation and maintenance, and recycling of building materials for UHV projects; S2, calculating the staged carbon emissions from the multi-dimensional raw carbon data using a building materials life-cycle carbon measurement model to form initial carbon accounting results; S3, using a multi-source carbon footprint coupling accounting model to couple and calibrate the initial carbon accounting results with supplementary carbon data sources from cross-scenario scenarios to generate a standardized carbon data set; S4, using a time-series carbon data incremental synchronization algorithm to perform time-dimensional incremental updates and data consistency checks on the standardized carbon data set to obtain a time-series carbon data sequence; S5, transmitting the time-series carbon data sequence to a carbon data visualization decision analysis platform for data structuring processing to generate target carbon data that conforms to integrated storage specifications; S6, based on a dedicated storage architecture for UHV project building material carbon emission data, classifying and storing the target carbon data into modules, establishing associated indexes and data access permission mechanisms, and performing secure and efficient integrated storage of carbon data.

2. The method for integrating and storing carbon emission data of building materials for ultra-high voltage power transmission projects according to claim 1, characterized in that, The expression for the carbon metering model of the entire life cycle of building materials is as follows: ,in, This refers to the carbon emissions throughout the entire life cycle of building materials used in ultra-high voltage power transmission projects. The carbon emission conversion factor for the i-th type of building material raw materials is... For the consumption of raw materials of type i, Let be the carbon emission correction factor for the i-th type of building material production process. The carbon emission factor for the energy type corresponding to the i-th type of building material. For the energy consumption of the production of building materials of type i, Let be the energy utilization efficiency impact coefficient of the i-th type of building materials. Let be the carbon emission time decay coefficient of the i-th type of building material in the j-th stage. The duration of stage j for building material of type i is given. This represents the total number of building material types. This represents the total number of stages throughout the entire lifecycle.

3. The method for integrating and storing carbon emission data of building materials for ultra-high voltage power transmission projects according to claim 1, characterized in that, The expression for the multi-source carbon footprint coupled accounting model is as follows: ,in, This represents the carbon emissions after coupling calibration. The weighting of the full lifecycle accounting results. To supplement the weights of multi-source data, Adjust the weights for the scene. To supplement the carbon source emission intensity for category k, The contribution percentage of the supplementary carbon source of type k. This is a carbon emission adjustment factor for ultra-high voltage power transmission projects. For regional environmental impact parameters, This is a correction factor for the coupling calculation error. To supplement the number of carbon source types.

4. The method for integrating and storing carbon emission data of building materials for ultra-high voltage power transmission projects according to claim 1, characterized in that, The expression for the time-series carbon data incremental synchronization algorithm is as follows: ,in, The carbon data is synchronized at time t. For the first Real-time historical carbon data For data attenuation coefficient, Let be the increment value of carbon data at time t. Let be the data reliability coefficient at time t. Let be the synchronization priority coefficient of the q-th type of data at time t. Let q be the data synchronization efficiency factor. The number of dimensions for data synchronization.

5. The method for integrating and storing carbon emission data of building materials for ultra-high voltage projects according to claim 1, characterized in that, The data structure processing model expression of the carbon data visualization decision analysis platform is as follows: ,in, For structured target carbon data, These are the data matrix transformation coefficients. This is a structured weight matrix for carbon data of ultra-high voltage building materials. For time series data integration coefficients, Let s be the structured label vector of the data at time s. Adjust the matrix to adapt to data storage. Let be the transpose matrix of the carbon data at time t.

6. The method for integrating and storing carbon emission data of building materials for ultra-high voltage projects according to claim 1, characterized in that, The expression for the integrated storage optimization model of carbon emission data from building materials in ultra-high voltage power transmission projects is: Storage Where Storage is the optimized integrated storage data volume. Let u be the amount of structured data in the u-th storage module. Let be the storage priority coefficient of the u-th storage module. Let be the space utilization coefficient of the u-th storage module. This is a storage redundancy adjustment factor. The data access frequency impact factor. This represents the number of storage modules.

7. The method for integrating and storing carbon emission data of building materials for ultra-high voltage power transmission projects according to claim 1, characterized in that, S3 includes the following steps: S31, based on the multi-source characteristics of carbon emission data from building materials in UHV projects, select carbon emission monitoring data from raw material production, carbon emission statistics from transportation, carbon emission sensor data from construction, and carbon emission record data from operation and maintenance to supplement data sources, and establish multi-source data association mapping rules; S32, match the initial carbon accounting results with the data dimensions of each supplementary data source, and perform homogenization processing of multi-source data through field alignment, format conversion, and logical verification; S33, substitute into the multi-source carbon footprint coupling accounting model, input the weight parameters of each data source, scenario adjustment coefficients, and error correction factors, and complete the coupling calculation of multi-source data; S34, identify and remove outliers from the coupling calculation results to generate a standardized carbon data set that meets the data consistency requirements.

8. A method for integrating and storing carbon emission data of building materials for ultra-high voltage power transmission projects according to claim 1, characterized in that, S4 includes the following steps: S41, extracting timestamp information from the standardized carbon dataset, establishing a time-series data sequence index, and dividing the data incremental update cycle and update granularity; S42, based on the time-series carbon data incremental synchronization algorithm, calculating the carbon data incremental value in each cycle, and determining the data update weight by combining the historical data decay coefficient and the synchronization priority coefficient. S43, perform cross-cycle consistency verification on the incrementally updated data, and verify the validity of the data through data correlation analysis and logical conflict detection; S44, correct the unqualified data, supplement the missing time series data segments, and form a complete time series carbon data sequence.

9. A method for integrating and storing carbon emission data of building materials for ultra-high voltage power transmission projects according to claim 1, characterized in that, S5 includes the following steps: S51, transmitting the time-series carbon data sequence to the carbon data visualization decision analysis platform, parsing the data structure and data type, and determining the structured processing rules and storage format requirements; S52, classifying the time-series carbon data according to building material type, life cycle stage, and carbon emission intensity level based on the platform's built-in data classification algorithm; S53, using the data structured processing model, performing matrix transformation, feature extraction, and labeling on the classified data to generate structured intermediate data; S54, performing format adaptation and redundancy optimization on the structured intermediate data according to the integrated storage specifications to obtain target carbon data that meets storage requirements.

10. A data integration and storage system for carbon emission data of building materials used in ultra-high voltage power transmission projects, characterized in that, This system utilizes the carbon emission data integration and storage method for building materials in ultra-high voltage (UHV) engineering projects as described in claim 1, comprising: a multi-dimensional carbon data acquisition unit, connected to monitoring nodes at various stages of UHV engineering building materials, collecting carbon data related to raw material consumption, energy consumption, process emissions, transportation parameters, and recycling, and transmitting the data to a building materials lifecycle carbon metering unit; a building materials lifecycle carbon metering unit, receiving the data transmitted by the multi-dimensional carbon data acquisition unit, completing staged carbon emission calculations through a built-in carbon metering model, and outputting initial carbon calculation results to a multi-source carbon footprint coupling calculation unit; and a multi-source carbon footprint coupling calculation unit, connecting the building materials lifecycle carbon metering unit with supplementary data sources, and using a coupling calculation model to process the initial carbon calculation results. The system calibrates the results and supplementary data, and sends a standardized carbon data set to the time-series carbon data incremental synchronization unit. The time-series carbon data incremental synchronization unit receives the standardized carbon data set, uses the incremental synchronization algorithm to update the data time sequence and verify consistency, and transmits the time-series carbon data sequence to the carbon data visualization and decision analysis unit. The carbon data visualization and decision analysis unit performs structured processing on the time-series carbon data sequence, generates target carbon data, and transmits it to the integrated storage unit. The UHV building materials carbon data integrated storage unit receives the target carbon data, completes data storage according to the modular classification storage architecture, establishes associated indexes and access permission mechanisms, and performs secure integrated storage of data. Each unit performs bidirectional data interaction and command transmission through the data bus.

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