A carbon emissions database data processing method, system, product and medium

Abstract

A carbon emissions database data processing method, system, product, and medium. The method includes: calculating the deviation between real-time operating parameters and a preset threshold value to determine a life loss acceleration factor; when the estimated remaining life falls below a warning value, triggering an early warning and multiplying the carbon emission calculation factor by the acceleration factor to obtain a real-time carbon emission calculation factor; calculating cumulative carbon emissions based on the real-time carbon emission calculation factor and a carbon emission baseline value, and predicting carbon emissions during the remaining life; and finally, summing the carbon emissions from the production, use, prediction, and disposal stages to obtain a full life cycle carbon footprint. Implementing the technical solution provided in this application improves the accuracy of carbon footprint tracking under special operating conditions.

Description

Technical Field

[0001] The present application relates to the field of electronic digital data processing, and in particular to a carbon emission database data processing method, system, product and medium. Background Art

[0002] Carbon emissions management has become a critical component across all industries. With the increasing global demand for energy conservation and emissions reduction, organizations are placing higher demands on carbon footprint tracking throughout their operations. Accurate and detailed carbon footprint tracking is particularly important for sustainable development and environmental protection, particularly in the area of ​​carbon emissions accounting throughout the lifecycle of products and equipment.

[0003] Carbon footprint tracking typically uses a life cycle assessment (LICA) approach. This method calculates the carbon emissions generated throughout a product or device's entire life cycle, from raw material acquisition, manufacturing and assembly, use and maintenance, to disposal. Specifically, data on energy consumption and other factors at each stage is collected, multiplied by a corresponding carbon emission factor, and finally, the total carbon emissions from all stages are added together to produce the carbon footprint.

[0004] However, the relevant technologies have limitations in the selection of carbon footprint tracking objects. Due to the standardized nature of the calculation methods, the relevant technologies are mainly targeted at conventional objects with stable operating environments and predictable service lives, making it difficult to meet the carbon footprint tracking needs of equipment or products used under special operating conditions. When equipment or products are used under special operating conditions, their actual lifespan may be significantly shortened due to accelerated aging, and actual carbon emissions will be greater than those under normal conditions. The method used in relevant technologies to evenly distribute carbon emissions based on the design life results in significant deviations from the actual carbon emissions calculation results, and cannot accurately reflect the impact of special operating conditions on the carbon footprint. Summary of the Invention

[0005] The present application provides a carbon emissions database data processing method, system, product and medium, which improve the accuracy of carbon footprint tracking under special working conditions.

[0006] In a first aspect of the present application, a method for processing carbon emission database data is provided, the method comprising:

[0007] Calculate the deviation between the collected real-time operating parameters and the preset operating parameter threshold, and determine the life loss acceleration coefficient based on the deviation; when the estimated remaining life calculated based on the life loss acceleration coefficient and the preset standard life is lower than the preset standard life warning value, trigger the early warning signal, and multiply the preset carbon emission calculation coefficient by the life loss acceleration coefficient to obtain the real-time carbon emission calculation coefficient; calculate the actual carbon emissions per unit time based on the real-time carbon emission calculation coefficient and the carbon emission baseline value per unit time under the preset standard operating conditions, and accumulate the actual carbon emissions to obtain the cumulative carbon emissions of the equipment; calculate the predicted total carbon emissions during the remaining life based on the real-time carbon emission calculation coefficient, the carbon emission baseline value per unit time and the expected remaining life; add up the carbon emissions of the equipment in the production stage, the cumulative carbon emissions, the predicted total carbon emissions and the predicted carbon emissions in the waste treatment stage to obtain the carbon footprint of the entire life cycle.

[0008] In the above embodiment, by comparing and analyzing real-time operating parameters with standard parameters, a quantitative indicator reflecting the operating condition deviation is obtained, and the indicator is converted into a life loss acceleration coefficient to evaluate the impact of non-standard operating conditions on the equipment life. When the estimated remaining life is lower than the warning value, a warning is issued in a timely manner. At the same time, the life loss status is associated with the carbon emission calculation. By adjusting the carbon emission calculation coefficient, the acceleration effect of non-standard operating conditions on carbon emissions is reflected. Combined with the carbon emission baseline value per unit time, the actual carbon emissions are calculated and accumulated. At the same time, based on the current operating status, the future carbon emission trend is predicted. Finally, the carbon emission data of each stage of equipment production, operation, prediction and waste treatment are integrated to evaluate the complete carbon footprint of the entire life cycle. In the above steps, the changes in operating parameters are combined with the equipment life prediction to establish a highly adaptable carbon emission assessment mechanism. The carbon emission calculation parameters are dynamically adjusted through the life loss acceleration coefficient. This can not only reflect the impact of accelerated environmental aging on equipment life, but also timely capture the carbon emission increase caused by abnormal operating conditions, solving the problem of carbon footprint calculation deviation caused by shortened life under special operating conditions.

[0009] In conjunction with some embodiments of the first aspect, in some embodiments, after calculating the deviation between the collected real-time operating condition parameter and the preset operating condition parameter threshold, and determining the life loss acceleration coefficient based on the deviation, the method further includes:

[0010] A detection sound wave is emitted and a reflected sound wave is received to obtain the waveform parameters of the reflected sound wave, which include the sound wave propagation time, amplitude attenuation rate and frequency displacement; the ratio of the frequency displacement to the preset sound wave emission frequency is calculated to obtain the acousto-elastic effect coefficient, the ratio of the sound wave propagation time to the preset standard propagation time is calculated to obtain the material deformation rate, and the material density change is calculated based on the amplitude attenuation rate; the acousto-elastic effect coefficient, the material deformation rate and the material density change are substituted into the preset stress-strain relationship equation to calculate the stress-strain value; the detection sound wave is repeatedly emitted at preset distances along a preset direction to obtain the stress-strain distribution; the stress-strain distribution is integrated and averaged to obtain the overall deformation variable, the ratio of the overall deformation variable to the standard deformation variable is used as the pressure damage coefficient, the life loss acceleration coefficient is multiplied by the pressure damage coefficient to obtain the corrected acceleration coefficient and replace the life loss acceleration coefficient.

[0011] In the above embodiment, acoustic wave detection is used to extract material state information from changes in acoustic wave propagation characteristics, establishing a correlation between acoustic characteristics and material mechanical properties. By calculating the acoustoelastic effect coefficient, material deformation rate, and density change, acoustic parameters are converted into quantifiable material performance indicators, and the mapping from acoustic characteristics to mechanical state is achieved through the stress-strain relationship equation. Through continuous scanning measurement, a complete stress-strain distribution diagram is obtained, and then the overall deformation variable is obtained through integration and averaging. Finally, the pressure damage coefficient is obtained by comparison with the standard deformation variable. A modified acceleration coefficient that takes into account the influence of pressure load is constructed. By combining acoustic wave detection with stress analysis, a damage assessment mechanism is established that can capture the impact of pressure load on equipment and dynamically adjust life prediction parameters, thereby improving the accuracy of equipment life assessment under special working conditions.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the carbon emissions during the equipment production phase, the cumulative carbon emissions, the predicted total carbon emissions, and the estimated carbon emissions during the disposal phase are added together to obtain a full life cycle carbon footprint, specifically including:

[0013] When it is detected that there is a covering on the surface of the device, the temperature of the covered area and the temperature of the uncovered area on the surface of the device are detected at the same time; the temperature power consumption characteristic curve of the device is obtained, and the temperature of the covered area and the temperature of the uncovered area are substituted into the temperature power consumption characteristic curve respectively to obtain the power consumption of the covered area and the power consumption of the uncovered area, and the difference between the power consumption of the covered area and the power consumption of the uncovered area is used as the additional power consumption increase value; according to the preset carbon emission factor of the coverage area where the device is located, the power consumption increase value is multiplied by the carbon emission factor and the obtained coverage time of the covering to obtain the coverage carbon footprint increment caused by the covering layer; the carbon emissions of the equipment in the production stage, the cumulative carbon emissions, the predicted total carbon emissions, the coverage carbon footprint increment and the expected carbon emissions in the disposal stage are added together to obtain the carbon footprint of the entire life cycle.

[0014] In the above embodiment, the impact of the covering on the device's temperature distribution is determined by detecting the temperature difference between covered and uncovered areas. The temperature difference is converted into power consumption increments using the temperature-power consumption characteristic curve, establishing a quantitative relationship between the covering and energy consumption changes. The power consumption increment is combined with the regional carbon emission factor and the duration of the covering to calculate the incremental carbon footprint caused by the covering. Finally, the complete lifecycle carbon footprint is assessed by integrating carbon emission data from each stage of equipment production, operation, coverage impact, predicted operation, and disposal. This establishes a highly adaptable carbon emission assessment mechanism that not only accurately quantifies the impact of coverings on device energy consumption but also dynamically tracks the resulting changes in carbon emissions, ultimately improving the accuracy of carbon footprint tracking under specific operating conditions.

[0015] In conjunction with some embodiments of the first aspect, in some embodiments, after calculating the actual carbon emissions per unit time based on the real-time carbon emissions calculation coefficient and the carbon emissions per unit time baseline value under preset standard operating conditions, and accumulating the actual carbon emissions to obtain the cumulative carbon emissions of the device, the method further includes:

[0016] Obtain the cumulative carbon emissions and collection time; generate a blockchain data structure containing the current block hash value, the previous block hash value and the timestamp based on the cumulative carbon emissions and collection time; divide the blockchain data structure into blocks according to preset time intervals to obtain multiple data blocks; use the consensus mechanism to verify the validity of each data block; and add the verified data blocks to the blockchain.

[0017] In the above embodiment, cumulative carbon emissions and collection times are combined into a blockchain data structure containing a hash value and timestamp, establishing data relevance and temporal order. Continuous carbon emissions data is divided into discrete data blocks to facilitate subsequent verification and storage. Each data block is validated to ensure authenticity and consistency, and validated data blocks are added to the blockchain, forming an immutable data record. By tightly integrating blockchain technology with carbon emissions data records, a decentralized data storage mechanism is established. This not only ensures data authenticity and traceability, but also enhances data security through distributed storage, improving the credibility and management efficiency of carbon emissions data.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after adding the verified data block to the blockchain, the method further includes:

[0019] Record the storage time and verification node information of the data block; generate a smart contract containing the storage time and verification node information; deploy the smart contract to the blockchain network.

[0020] In the above-mentioned embodiment, by recording the storage time and verification node information of each data block, a proof of integrity for data storage is established. This critical information is encapsulated into a smart contract, forming an automatically executable programmatic contract, which realizes the automation of data verification and management. By deploying the smart contract to the blockchain network, a distributed contract execution environment is established, which enables the data storage and verification process to be automated and reach consensus within the network. Combining smart contracts with blockchain storage establishes an automated data verification and management mechanism, which not only ensures the transparency and traceability of the data storage process, but also improves operational efficiency through the automatic execution of smart contracts, thereby enhancing the credibility of carbon emission data and the level of automation of management.

[0021] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the deviation between the collected real-time operating condition parameter and the preset operating condition parameter threshold, and determining the life loss acceleration coefficient based on the deviation, specifically includes:

[0022] Collect real-time operating parameters; store the real-time operating parameters in the data lake in their original format; establish a unified metadata index for the data lake data in the data lake; subtract the real-time operating parameters from the preset operating parameter threshold to obtain a deviation value, and multiply the ratio of the real-time operating parameters to the deviation value by the preset acceleration factor weight to obtain the life loss acceleration factor.

[0023] In the above-described embodiment, by collecting parameters and storing them in their original format in the data lake, data integrity and authenticity are maintained, avoiding information loss that may result from traditional data preprocessing. A unified metadata index is established, enabling efficient management and rapid retrieval of massive amounts of heterogeneous data. The deviation between real-time operating parameters and preset thresholds is calculated and normalized based on the parameters' own characteristics. Finally, by adjusting the acceleration coefficient weights, a mapping relationship between operating condition deviations and life loss is established. This establishes a highly adaptable condition assessment mechanism that not only ensures data integrity and availability but also accurately quantifies the impact of operating condition deviations on equipment life, improving the accuracy of equipment condition assessments under specific operating conditions.

[0024] In conjunction with some embodiments of the first aspect, in some embodiments, after establishing a unified metadata index for the data in the data lake, the process further includes:

[0025] Obtain real-time data access frequency and record the data access frequency of each data lake data; store data lake data with a data access frequency higher than the preset data access frequency threshold into the real-time database; migrate data lake data with a data access frequency equal to or lower than the data access frequency threshold to the nearline storage database, and maintain the consistency of metadata indexes during data migration.

[0026] In the above embodiment, by real-time monitoring of data access frequency, high-frequency access data is stored in a real-time database to ensure fast access performance, and low-frequency access data is migrated to a near-line storage database to reduce storage costs. During the data migration process, the traceability and integrity of the data are ensured by maintaining metadata index consistency. A dynamic data management mechanism is established, which can not only automatically optimize the storage location according to the data access pattern, but also significantly reduce storage costs while ensuring data availability, ultimately achieving efficient utilization of data storage resources and overall improvement of system performance.

[0027] In second aspect, an embodiment of the present application provides a carbon emission database data processing system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the carbon emission database data processing system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0028] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product is run on a carbon emission database data processing system, the above-mentioned carbon emission database data processing system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0029] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a carbon emission database data processing system, the carbon emission database data processing system executes the method described in the first aspect and any possible implementation of the first aspect.

[0030] It is understood that the carbon emissions database data processing system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the carbon emissions database data processing method provided in the embodiments of this application. Therefore, the beneficial effects achieved by these methods can be referenced to the beneficial effects of the corresponding methods and will not be further elaborated here.

[0031] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0032] 1. This application obtains quantitative indicators reflecting operating condition deviations through comparative analysis of real-time operating condition parameters and standard parameters, and converts them into life loss acceleration coefficients to evaluate the impact of non-standard operating conditions on equipment life; when the estimated remaining life is lower than the warning value, a warning is issued in a timely manner, and the life loss status is linked to the carbon emission calculation. By adjusting the carbon emission calculation coefficient, the acceleration effect of non-standard operating conditions on carbon emissions is reflected; combined with the unit time carbon emission baseline value, the actual carbon emissions are calculated and accumulated, and the future carbon emission trend is predicted based on the current operating status. Finally, the carbon emission data of each stage of equipment production, operation, prediction and waste treatment are integrated to evaluate the complete carbon footprint of the entire life cycle. In the above steps, the changes in operating condition parameters are combined with the equipment life prediction to establish a highly adaptable carbon emission assessment mechanism. The carbon emission calculation parameters are dynamically adjusted through the life loss acceleration coefficient, which can not only reflect the impact of accelerated environmental aging on equipment life, but also timely capture the increase in carbon emissions caused by abnormal operating conditions, solving the problem of carbon footprint calculation deviation caused by shortened life under special operating conditions.

[0033] 2. This application uses acoustic wave detection to extract material state information from changes in acoustic wave propagation characteristics, establishing a correlation between acoustic characteristics and material mechanical properties; by calculating the acoustoelastic effect coefficient, material deformation rate, and density change, the acoustic parameters are converted into quantifiable material performance indicators, and the mapping from acoustic characteristics to mechanical state is achieved through the stress-strain relationship equation; through continuous scanning measurement, a complete stress-strain distribution diagram is obtained, and then the overall deformation variable is obtained by integration operation and averaging, and finally the pressure damage coefficient is obtained by comparison with the standard deformation variable. A modified acceleration coefficient that takes into account the influence of pressure load is constructed. By combining acoustic wave detection with stress analysis, a damage assessment mechanism is established, which can capture the impact of pressure load on equipment and dynamically adjust life prediction parameters, thereby improving the accuracy of equipment life assessment under special working conditions.

[0034] 3. This application detects the temperature difference between covered and uncovered areas to determine the impact of coverings on the temperature distribution of equipment. It uses the temperature power consumption characteristic curve to convert temperature differences into power consumption increments, establishing a quantitative relationship between coverings and energy consumption changes. It combines the power consumption increment with the regional carbon emission factor and the duration of coverage to calculate the incremental carbon footprint caused by coverings. Finally, by integrating carbon emission data from each stage of equipment production, operation, coverage impact, predicted operation, and waste disposal, it evaluates the complete lifecycle carbon footprint. A highly adaptable carbon emission assessment mechanism has been established that can not only accurately quantify the impact of coverings on equipment energy consumption, but also dynamically track the resulting changes in carbon emissions, ultimately improving the accuracy of carbon footprint tracking under special operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1This is a flow chart of a method for processing carbon emission database data in an embodiment of the present application;

[0036] Figure 2 This is another flowchart of the carbon emission database data processing method in an embodiment of the present application;

[0037] Figure 3 This is a schematic diagram of an exemplary hardware structure of the carbon emission database data processing system in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The terms used in the following examples of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to and encompasses any or all possible combinations of one or more of the listed items.

[0039] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0040] In related technologies, carbon footprint tracking mainly adopts a static assessment method based on the life cycle. This method collects energy consumption data of equipment at various stages such as production, use and disposal, multiplies it by a standard carbon emission coefficient, and then accumulates it to obtain the carbon footprint of the entire life cycle. However, this method has obvious limitations: first, its carbon emission calculation uses a fixed carbon emission coefficient, which cannot reflect the dynamic impact of actual operating conditions on carbon emissions; second, the life prediction is based on the design life under standard operating conditions, ignoring the accelerated aging effect that may be caused by special operating conditions; finally, due to the lack of real-time operating condition monitoring and dynamic assessment mechanisms, it is difficult to promptly detect and respond to the increase in carbon emissions caused by abnormal operating conditions, resulting in a significant deviation between the carbon footprint calculation results and the actual situation.

[0041] In an embodiment of the present application, a method for processing data from a carbon emission database based on operating condition perception is proposed. This method establishes a quantitative relationship between the degree of operating condition abnormality and life loss by monitoring the deviation of operating condition parameters from standard thresholds in real time, and then dynamically adjusts the carbon emission calculation coefficient. When it is detected that the equipment may be experiencing accelerated aging, the early warning mechanism is triggered in a timely manner, and the carbon emission calculation parameters are adjusted accordingly. By combining real-time operating condition monitoring, life prediction, and carbon emission calculation, a closed-loop carbon footprint assessment system is constructed that can accurately reflect the impact of special operating conditions on equipment life and carbon emissions.

[0042] Figure 1 This is a flow chart of a method for processing carbon emission database data using an embodiment of the present application, including the following steps:

[0043] S101. Calculate the deviation between the collected real-time operating condition parameter and the preset operating condition parameter threshold, and determine the life loss acceleration coefficient according to the deviation.

[0044] Specifically, first, a sensor network collects real-time operating parameter data for the equipment. These parameters may include operating status data such as temperature, humidity, vibration, and pressure. The collected real-time operating parameters are compared with pre-set thresholds for standard operating parameters to calculate deviations. This deviation calculation can be performed in a variety of ways: from simple difference calculations to weighted average deviations, or using specific mathematical models for deviation analysis. For multi-parameter scenarios, a comprehensive evaluation method can be used, assigning different weights to the deviations of each parameter based on their importance, ultimately resulting in a comprehensive deviation value.

[0045] Based on the calculated deviation, a pre-established relationship is used to determine the life loss acceleration factor. This relationship, based on engineering experience and theoretical analysis, typically exhibits a linear relationship, where the life loss acceleration factor is proportional to the deviation. This linear relationship can be used to determine the specific proportionality factor through experimental data and statistical analysis.

[0046] In some embodiments, when deviations in operating parameters cause a material to undergo a qualitative change, its performance decay exhibits dramatic nonlinear variations. In this case, determining the life loss acceleration factor first requires real-time collection of key material performance parameters, including physical strength, chemical stability, and structural integrity. The collected real-time performance parameters are compared with the performance parameters under preset standard operating conditions to calculate a comprehensive deviation value. Calculating the comprehensive deviation value involves weighting each collected performance parameter according to its impact on the device lifespan. For each performance parameter, the difference between its real-time value and the corresponding standard operating parameter is calculated and divided by the standard operating parameter value to obtain a normalized deviation. All weighted normalized deviations are then summed, and a nonlinear correction factor is introduced to compensate for the interactions between the real-time performance parameters. Ultimately, a comprehensive deviation value is obtained that reflects the overall performance deviation of the device. During the calculation, the weight coefficients of each real-time performance parameter are dynamically adjusted to adapt to changes in the device's operating status, ensuring that the calculation results accurately reflect the device's actual operating conditions.

[0047] When this combined deviation exceeds the critical point of qualitative change, the calculation of the life loss acceleration factor requires a high-order power function model. This involves substituting the combined deviation into a power function with a base of two and using the combined deviation as the exponent. Furthermore, given the irreversibility of material qualitative change, a cumulative damage factor is also required to account for the impact of historical operating conditions on material properties. This calculation method, through the combined effects of real-time data, the critical point of qualitative change, the power function relationship, and the cumulative damage factor, can reflect the rapid decay characteristics of materials during qualitative change. The drastic reduction in material performance during this qualitative change can lead to a rapid increase in equipment energy consumption and a significant decrease in operating efficiency. Furthermore, the material itself may release additional greenhouse gases during the qualitative change process. These factors significantly impact carbon emissions. Therefore, accurately characterizing the material's qualitative change characteristics allows for a more precise assessment of the equipment's additional carbon emissions under abnormal operating conditions, further improving the accuracy of carbon footprint tracking.

[0048] In other embodiments, a data management system combining a data lake with a hierarchical storage architecture can be constructed, and unified data management can be achieved through metadata indexing. At the same time, intelligent hierarchical storage can be performed based on data access frequency, thereby improving data processing efficiency and storage resource utilization.

[0049] First, a distributed sensor network collects real-time equipment operating parameters, including key operational data such as temperature, pressure, and speed, and stores this data in its original format in a data lake. This schema-free storage method preserves the data's original characteristics and integrity, avoiding the limitations of traditional databases' fixed schemas. The data lake enables the system to store and process a wide range of structured, semi-structured, and unstructured data, providing a comprehensive data foundation for subsequent in-depth analysis and mining.

[0050] Secondly, a unified metadata indexing system is established for the data in the data lake. This indexing system includes key information such as data timestamps, sources, formats, and relationships between data. Through standardized metadata descriptions, unified management of massive amounts of heterogeneous data is achieved. The establishment of metadata indexing not only improves data retrieval efficiency but also provides foundational support for data traceability and governance, ensuring data manageability and traceability.

[0051] Monitor and record the access frequency of each piece of data in real time. By establishing a data access log, recording the time, frequency, and method of access for each piece of data, a profile of data usage trends is generated. This dynamic monitoring mechanism accurately reflects the value and importance of data, providing a basis for decision-making regarding subsequent tiered storage strategies.

[0052] Data accessed more frequently than a preset threshold is stored in a real-time database. This frequently accessed data is quickly responded to through an in-memory database or cache mechanism, significantly improving access efficiency for frequently used data. The use of a real-time database ensures data access performance in critical business scenarios and meets the needs of real-time analysis and decision-making.

[0053] Data with access frequencies equal to or below the preset data access frequency threshold is migrated to a nearline storage database. During the data migration process, metadata index consistency is strictly maintained to ensure that the migration does not affect data traceability and integrity. This tiered storage strategy ensures data accessibility while optimizing storage resource utilization and reducing storage costs.

[0054] The difference between the collected real-time operating parameters and the preset operating parameter thresholds is calculated. The ratio of the real-time operating parameters to the deviation value is multiplied by the preset acceleration factor weight to obtain the life loss acceleration factor that reflects the current operating status of the equipment. This calculation process takes into account the impact of the degree of operating condition deviation on equipment life. Through a scientific weighting system, it accurately quantifies the impact of non-standard operating conditions on equipment life.

[0055] By building a data management system, we achieve intelligent management of the entire data process, from data collection and storage to analysis. Unified storage and metadata indexing within the data lake, combined with a dynamic tiered storage strategy based on data access frequency, not only improves data processing efficiency but also optimizes storage resource allocation while ensuring data integrity and traceability.

[0056] S102. When the estimated remaining life calculated based on the life loss acceleration coefficient and the preset standard life is lower than the preset standard life warning value, an early warning signal is triggered, and the preset carbon emission calculation coefficient is multiplied by the life loss acceleration coefficient to obtain a real-time carbon emission calculation coefficient.

[0057] Specifically, a mathematical model is used to calculate the estimated remaining life based on the equipment's standard lifespan and the currently calculated lifespan acceleration factor. This calculation process takes into account the impact of real-time operating conditions on the lifespan and the current lifespan acceleration factor to produce a predicted remaining lifespan. There are various mathematical models for calculating the estimated remaining lifespan, and different calculation methods can be used based on actual needs and application scenarios:

[0058] First, the standard remaining life is divided by the current life loss acceleration factor. This method is suitable for scenarios where the equipment's operating status is relatively stable and operating conditions do not change much. However, this linear calculation method may not accurately reflect the actual life loss of equipment under complex operating conditions.

[0059] The second approach involves establishing a dynamic integration model, discretizing the equipment's operating cycle into multiple time periods. The life loss acceleration factor for each time period is historically recorded and dynamically tracked. The actual life loss in each time period is first calculated and then accumulated to obtain the total loss. Finally, the cumulative loss is subtracted from the standard lifespan to obtain a more accurate remaining lifespan prediction. This approach accounts for the cumulative impact of varying operating conditions experienced by the equipment over time on its lifespan.

[0060] In scenarios where equipment frequently experiences drastic operating fluctuations, a more advanced nonlinear prediction model can be employed. This nonlinear prediction model not only considers the life loss acceleration factor but also incorporates factors such as the rate of change of operating parameters and the duration of the operating condition. By establishing a multi-dimensional mathematical model, it describes the comprehensive impact of operating condition changes on equipment life. Furthermore, the nonlinear prediction model incorporates correction parameters such as material fatigue properties and the impact of environmental factors, enabling a more comprehensive reflection of equipment life loss patterns in complex operating environments.

[0061] When a device enters a phase of dramatic performance change, a specialized lifespan prediction algorithm is activated. This algorithm takes into account the sudden change in material properties, modeling the lifespan loss patterns before and after the change separately and applying a smooth transition at critical points. This segmented calculation method accurately reflects the lifespan loss characteristics of a device during periods of dramatic performance change.

[0062] When the calculated estimated remaining life is lower than the preset warning threshold, an early warning signal will be automatically triggered. This warning can be divided into multiple levels, and different warning levels can be set according to different intervals of remaining life.

[0063] At the same time, the carbon emission calculation coefficient under pre-set standard operating conditions is multiplied by the current life loss acceleration factor to obtain the carbon emission calculation coefficient reflecting actual operating conditions. This calculation method is based on the principle that when equipment operates under non-standard operating conditions, energy consumption and carbon emissions increase as performance deteriorates. When equipment is in a state of accelerated loss, its energy utilization efficiency decreases, resulting in an increase in carbon emissions per unit time. This increase is positively correlated with the life loss acceleration factor.

[0064] S103, calculating the actual carbon emissions per unit time according to the real-time carbon emission calculation coefficient and the carbon emission baseline value per unit time under the preset standard working conditions, and accumulating the actual carbon emissions to obtain the cumulative carbon emissions of the equipment.

[0065] Specifically, the pre-set carbon emissions per unit time baseline under standard operating conditions is multiplied by the real-time calculated carbon emissions coefficient to obtain the carbon emissions per unit time under the current actual operating conditions. This calculation takes into account the impact of performance changes on carbon emissions when the equipment operates under non-standard operating conditions, and can reflect the carbon emissions level under actual operating conditions.

[0066] Using a data integration method, the actual carbon emissions obtained within each calculation cycle are continuously accumulated, starting from the initial moment the equipment is put into operation until the current moment, to obtain the total carbon emissions of the equipment. This accumulation calculation can adopt different time resolutions, such as seconds, minutes, hours, or days, depending on the characteristics of the equipment and monitoring requirements. Shorter calculation cycles can provide more detailed carbon emission trends, but require greater data storage and computing resources.

[0067] In practical applications, a hierarchical accumulation mechanism is typically established. Carbon emissions data is first calculated and stored on a shorter time scale, and then aggregated according to different time dimensions (such as hours, days, months, and years). This ensures data accuracy and improves the efficiency of long-term trend analysis. At the same time, abnormal data is identified and processed to ensure the accuracy of the cumulative calculation.

[0068] In some embodiments, in operating scenarios where equipment is frequently started and stopped, the equipment requires additional energy to reach a stable working state during the startup phase, and there is residual energy consumption during the shutdown phase. The carbon emission characteristics of these special phases are significantly different from those during stable operation, so a dynamic segmented calculation model is established. This dynamic segmented calculation model automatically identifies and divides the three different phases of startup, stable operation, and shutdown by monitoring the operating status of the equipment in real time, and sets corresponding carbon emission calculation parameters and correction coefficients for each phase. During the startup phase, since the internal temperature, pressure and other parameters of the equipment have not yet reached the optimal working state, the energy utilization efficiency is low, and a higher carbon emission calculation coefficient will be used; during the stable operation phase, the equipment is in the optimal working state, and standard calculation parameters are used; during the shutdown phase, the effects of equipment inertia and waste heat consumption are considered, and decreasing calculation parameters are used. This segmented and refined calculation method improves the accuracy of carbon emission data.

[0069] In other embodiments, after obtaining the cumulative carbon emissions of the equipment, the carbon emission data can also be stored and verified through blockchain technology, and the data can be automatically managed through smart contracts, thereby ensuring the authenticity, immutability and traceability of the carbon emission data.

[0070] First, the device's cumulative carbon emissions data and corresponding collection time are acquired in real time. This is accomplished through the device's data acquisition module, which continuously collects data at a preset sampling frequency to ensure data continuity and integrity. This module can promptly capture changes in the device's carbon emissions, providing accurate data for subsequent data processing and analysis.

[0071] Next, a blockchain data structure is constructed based on the accumulated carbon emissions and the time of collection. This data structure consists of three key elements: the current block hash value, the previous block hash value, and a timestamp. The current block hash value is calculated by encrypting the block data to ensure data is tamper-proof; the previous block hash value establishes a link between blocks, ensuring data continuity; and the timestamp records the exact time the data was generated, facilitating subsequent traceability and verification.

[0072] Blockchain data structures are processed in chunks. Based on preset time intervals, the continuous data stream is divided into multiple data chunks. This chunking mechanism considers the temporal nature of data while improving the processing efficiency of the blockchain network. A reasonable chunk size balances data processing efficiency and storage costs while ensuring data integrity.

[0073] The consensus mechanism is activated to verify the validity of each data block. The verification process involves multiple nodes in the network. The authenticity, integrity, and timeliness of the data are verified through a preset consensus algorithm, which can effectively prevent data tampering and improve credibility.

[0074] Once a block of data has been verified, it is added to the blockchain. This addition process follows a strict protocol to ensure that the new block is correctly linked to the existing blockchain, maintaining the integrity and consistency of the blockchain.

[0075] Record the storage time of each data block and the node information involved in the verification. These metadata can prove the source of the data and the reliability of the verification process.

[0076] Generate a smart contract that includes the deposit time and verification node information. The smart contract not only contains this basic information but also defines data access rules, verification rules, and business processing logic. Smart contracts can automatically execute pre-set business rules, improving the automation level of data management.

[0077] Deploy the generated smart contract to the blockchain network. The deployment process includes verification, optimization, and distribution of the contract code to ensure that the contract can function properly within the blockchain network. Once deployed, the smart contract can automatically respond to network events and execute the corresponding business logic.

[0078] By introducing blockchain technology and smart contract mechanisms, a decentralized, tamper-proof carbon emissions data management system has been established. This technical solution uses distributed ledgers to record data, multi-node consensus to ensure authenticity, and smart contracts to achieve automated management. Ultimately, this achieves the goals of reliable evidence storage, full traceability, and comprehensive oversight of carbon emissions data throughout the entire process, providing reliable data support for carbon footprint tracking.

[0079] S104. Calculate the predicted total carbon emissions during the remaining lifespan based on the real-time carbon emission calculation coefficient, the carbon emission baseline value per unit time, and the estimated remaining lifespan.

[0080] Specifically, when predicting the total carbon emissions during the remaining life, a multi-dimensional prediction model is adopted, which comprehensively considers the equipment performance degradation trend, operating condition change law and environmental factors.

[0081] The basic prediction method is to perform mathematical operations on the current real-time carbon emission calculation coefficient, the unit time carbon emission benchmark value under standard operating conditions and the expected remaining life, but in order to improve the prediction accuracy, a more complex prediction algorithm can also be used.

[0082] The prediction model first establishes a performance degradation curve for the equipment based on historical data and analyzes the changing trends of the carbon emission calculation coefficient. It then divides the estimated remaining lifespan into multiple time periods, assigning corresponding performance degradation coefficients to each time period to achieve a more realistic carbon emission forecast. This segmented prediction approach considers the performance characteristics of the equipment at different lifespan stages and more accurately reflects the dynamic changes in carbon emissions.

[0083] An adaptive forecasting mechanism has been established. By continuously comparing the deviation between actual carbon emissions data and predicted values, the forecasting model parameters are continuously optimized to improve forecast accuracy. Furthermore, the forecasting model also incorporates correction parameters for factors such as seasonality and load variations, ensuring that the forecast results are more closely aligned with actual operating conditions.

[0084] In some embodiments, a dynamic prediction model based on ambient temperature is established for the special operating conditions of seasonally operating equipment. First, by analyzing historical operating data, the correlation between the carbon emissions of the equipment and the ambient temperature is identified, and a seasonal change curve is established. On this basis, the annual operating cycle is divided into different seasonal intervals, and each interval is set with unique carbon emission baseline parameters and correction coefficients to adapt to performance fluctuations caused by temperature changes. For possible extreme weather conditions, by setting early warning thresholds, when the ambient temperature exceeds the normal range, the calculation parameters in the prediction model are adjusted, including key indicators such as energy consumption coefficient and efficiency correction value. This dynamic prediction method not only improves the accuracy of carbon emission predictions, but also helps operators identify high carbon emission risk periods in advance, providing a scientific basis for equipment operation strategy adjustments and maintenance plan formulation, and ultimately achieving accurate carbon emission management throughout the equipment life cycle. At the same time, the adaptive characteristics of the prediction model enable it to continuously optimize the prediction algorithm as data accumulates, thereby improving the accuracy of predictions of carbon emission behavior under abnormal weather conditions.

[0085] In other embodiments, a prediction model incorporating maintenance factors is constructed for carbon emissions forecasting of equipment requiring regular maintenance. This model analyzes the equipment's performance degradation curve and the improvement effects of maintenance activities to establish a performance recovery coefficient matrix. During the prediction process, the equipment's performance degradation trend is first identified, and the carbon emissions growth curve under a no-maintenance scenario is calculated. Then, based on the preset maintenance plan, a performance recovery calculation module is inserted at each maintenance node. This performance recovery calculation module considers the degree of improvement in equipment performance resulting from different types of maintenance activities (such as routine maintenance, scheduled overhauls, and overhauls) and dynamically adjusts the predicted value using the performance recovery coefficient. Furthermore, the prediction model establishes a maintenance effect decay curve to describe the time-dependent decline in performance improvement after maintenance, thereby more accurately predicting carbon emissions changes within the maintenance cycle. This prediction method not only improves the accuracy of long-term carbon emissions forecasts but also provides data support for optimizing maintenance plans, helping to determine the optimal maintenance timing and achieve a balance between carbon emissions control and equipment maintenance costs.

[0086] S105. Add the carbon emissions during the equipment production phase, the cumulative carbon emissions, the predicted total carbon emissions, and the estimated carbon emissions during the waste disposal phase to obtain the carbon footprint of the entire life cycle.

[0087] Specifically, the full lifecycle carbon footprint calculation uses a comprehensive lifecycle assessment approach, integrating the carbon emissions of equipment at every stage, from manufacturing and operation to final disposal. First, carbon emission data for the equipment's production phase is obtained, including carbon emissions generated by raw material procurement, component manufacturing, and assembly. Second, the actual cumulative carbon emissions after the equipment is put into operation are included in the calculation. Third, the expected carbon emissions during the remaining lifespan calculated based on a predictive model are added. Finally, the expected carbon emissions generated by the equipment's recycling, disassembly, and disposal after it is scrapped are considered.

[0088] Standardized data processing methods are used to unify carbon emission data from different stages into the same measurement units and timescales. A data traceability mechanism is also established to ensure the accuracy and completeness of carbon emission data at each stage. During the integration process, the interdependencies between different stages are considered, such as the impact of production process selection on energy consumption during the subsequent use phase, and the impact of maintenance methods during the use phase on carbon emissions during the final disposal phase.

[0089] In the above embodiment, by comparing and analyzing real-time operating parameters with standard parameters, a quantitative indicator reflecting the operating condition deviation is obtained, and the indicator is converted into a life loss acceleration coefficient to evaluate the impact of non-standard operating conditions on the equipment life. When the estimated remaining life is lower than the warning value, a warning is issued in a timely manner. At the same time, the life loss status is associated with the carbon emission calculation. By adjusting the carbon emission calculation coefficient, the acceleration effect of non-standard operating conditions on carbon emissions is reflected. Combined with the carbon emission baseline value per unit time, the actual carbon emissions are calculated and accumulated. At the same time, based on the current operating status, the future carbon emission trend is predicted. Finally, the carbon emission data of each stage of equipment production, operation, prediction and waste treatment are integrated to evaluate the complete carbon footprint of the entire life cycle. In the above steps, the changes in operating parameters are combined with the equipment life prediction to establish a highly adaptable carbon emission assessment mechanism. The carbon emission calculation parameters are dynamically adjusted through the life loss acceleration coefficient. This can not only reflect the impact of accelerated environmental aging on equipment life, but also timely capture the carbon emission increase caused by abnormal operating conditions, solving the problem of carbon footprint calculation deviation caused by shortened life under special operating conditions.

[0090] In other embodiments of this application, when equipment is subjected to complex pressure loads, surface deformation may occur, requiring additional energy consumption, which may increase the deviation in the equipment life prediction calculation. Using the carbon emissions database data processing method provided in this application, material stress and strain distribution can be obtained through acoustic detection, enabling the assessment of equipment status under special operating conditions and accurate calculation of carbon emissions.

[0091] like Figure 2 FIG. 1 is another flow chart of the carbon emission database data processing method provided in an embodiment of the present application, which can be used to Figure 1The application scenario shown includes the following steps:

[0092] S201. Calculate the deviation between the collected real-time operating condition parameter and the preset operating condition parameter threshold, and determine the life loss acceleration coefficient according to the deviation.

[0093] S202 , transmitting a detection sound wave and receiving a reflected sound wave, and obtaining waveform parameters of the reflected sound wave, the waveform parameters including sound wave propagation time, amplitude attenuation rate, and frequency shift.

[0094] Specifically, acoustic wave detection technology uses an ultrasonic sensor to transmit a detection sound wave of a specific frequency, and a receiver captures the reflected sound wave signal. First, an electrical signal is generated and converted into ultrasonic waves by a transducer. The detection sound wave is emitted at a preset detection frequency and energy level. When the sound wave encounters the object being measured, some of the energy is reflected back to the receiver. The receiver converts the received sound wave signal into an electrical signal and uses a signal processing system to extract key waveform parameters.

[0095] Sound wave propagation time is measured by recording the time difference between transmission and reception. A high-precision clock and synchronous trigger mechanism are used to ensure accurate time measurement. The amplitude decay rate is calculated by comparing the energy amplitudes of the transmitted and received sound waves, normalizing them based on propagation distance and medium characteristics. Frequency shift is measured by performing spectral analysis on the received signal, comparing the difference between the transmitted and received frequencies, and employing signal processing methods such as Fourier transforms to achieve precise frequency measurement.

[0096] S203. Calculate the ratio of the frequency displacement to the preset sound wave emission frequency to obtain the acoustoelastic effect coefficient, calculate the ratio of the sound wave propagation time to the preset standard propagation time to obtain the material deformation rate, and calculate the material density change based on the amplitude attenuation rate.

[0097] Specifically, the calculation of the acoustoelastic effect coefficient is based on the frequency change characteristics of sound waves under stress. When the material is subjected to stress, its internal lattice structure undergoes microscopic changes, resulting in a change in the propagation speed of the sound wave, which in turn causes a frequency displacement. By calculating the ratio of the actual measured frequency displacement to the preset emission frequency, the stress state of the material can be quantitatively characterized.

[0098] The calculation of material deformation rate utilizes the propagation characteristics of sound waves in materials. The actual sound wave propagation time is measured and then compared to a pre-calibrated standard propagation time. This ratio reflects the degree of material deformation, as deformation alters the sound wave propagation time due to changes in the material's internal structure. By establishing a sound velocity-strain relationship model, the time ratio is converted into the material deformation rate.

[0099] The calculation of material density changes is based on the energy attenuation characteristics of sound waves during propagation. By analyzing the amplitude attenuation rate and combining it with the theory of sound wave attenuation in a medium, a model correlating the attenuation rate with density changes is established. This model takes into account the acoustic impedance characteristics and scattering losses of the material, and mathematically converts the change in material density.

[0100] S204. Substitute the acoustoelastic effect coefficient, the material deformation rate, and the material density change into a preset stress-strain relationship equation to calculate the stress-strain value.

[0101] Specifically, first, a complete stress-strain relationship model is established based on the theory of material mechanics. This model includes basic material parameters such as elastic modulus and Poisson's ratio, and takes into account the stress correction brought by the acoustoelastic effect coefficient, the strain state reflected by the material deformation rate, and the volume effect caused by density change.

[0102] A step-by-step calculation strategy is employed. First, an initial stress field solution is established using the acoustoelastic coefficient, which reflects the influence of internal material stress on acoustic wave propagation. Next, the material deformation rate is integrated into the model, and the actual strain state is calculated using the strain-displacement equation. Finally, the final stress and strain values ​​are obtained through iterative calculations, combining material density data and considering the influence of volume changes on the stress distribution.

[0103] The entire calculation process is solved numerically, using numerical calculation techniques such as finite element analysis or boundary element method to convert discrete measurement data into a continuous stress and strain field distribution. The calculation process takes into account the nonlinear characteristics of the material and the influence of boundary conditions to ensure the accuracy of the calculation results.

[0104] S205 , repeatedly transmitting detection sound waves at preset distances along a preset direction to obtain stress and strain distribution.

[0105] Specifically, the scanning direction and measurement point spacing are determined based on the inspection requirements, creating a standardized scanning grid. A precision positioning mechanism is used to control the probe's movement, ensuring the accuracy of each measurement point. At each measurement point, a sound wave is emitted according to the preset transmission parameters, and the received reflected signal is recorded.

[0106] The acoustic wave scanning method uses a step-by-step motion, pausing at each measurement point for sufficient time to complete data acquisition. A synchronous trigger mechanism ensures the timing accuracy of transmission and reception, and digital signal processing technology is used to process the acoustic wave signal at each measurement point in real time. The data at each measurement point is sampled and averaged multiple times to improve the signal-to-noise ratio and measurement reliability.

[0107] By analyzing the spatial correlation of data from multiple measurement points, a complete stress and strain distribution field was established. Interpolation algorithms were used to reconstruct the discrete measurement point data to obtain a continuous stress and strain distribution map. Data smoothing and outlier processing were also used to improve the accuracy and reliability of the distribution map.

[0108] S206. Integrate the stress-strain distribution and calculate the average value to obtain the overall deformation variable. The ratio of the overall deformation variable to the standard deformation variable is used as the pressure damage coefficient. The life loss acceleration coefficient is multiplied by the pressure damage coefficient to obtain a corrected acceleration coefficient and replace the life loss acceleration coefficient.

[0109] Specifically, first, select a suitable integration method based on the geometric characteristics of the detection area. For linear detection areas, the line integration method is used to perform weighted summation of the stress and strain values ​​of discrete measuring points according to the distance between the measuring points; for area detection areas, double integration is required, that is, first perform line integration in a certain direction, and then perform integration in the vertical direction. The continuity of the stress and strain distribution needs to be considered in the integration process, and the data between adjacent measuring points must be interpolated to ensure the accuracy of the integration results. After obtaining the integration result, divide it by the total length or total area of ​​the detection area to obtain the average stress and strain value, which is the overall deformation variable that characterizes the degree of deformation of the entire area.

[0110] After the overall deformation is calculated, it is compared with a predetermined standard deformation. The standard deformation is a baseline value obtained through theoretical calculation or experimental measurement under normal equipment operating conditions. By calculating the ratio of the actual overall deformation to the standard deformation, the pressure damage coefficient, which reflects the degree of pressure load influence, is obtained.

[0111] Finally, the previously obtained life loss acceleration coefficient is multiplied by the pressure damage coefficient to obtain a revised acceleration coefficient. This revised acceleration coefficient replaces the original life loss acceleration coefficient and is used in subsequent life assessment calculations.

[0112] In some embodiments, materials can experience changes in mechanical properties such as thermal expansion, changes in elastic modulus, and reduced yield strength under high-temperature operating conditions. These changes can directly affect the accuracy of stress-strain distribution calculations. To address this, a material property database covering different temperature ranges is first established, containing temperature-dependent material parameters such as elastic modulus, Poisson's ratio, and thermal expansion coefficient. Based on this database, a temperature-material property relationship model is constructed. This temperature-material property relationship model uses mathematical functions to describe how material parameters vary with temperature. During the actual calculation process, operating temperature data is acquired in real time and substituted into the temperature-material property relationship model to obtain corrected material parameters at the current temperature. These corrected parameters are used to update the stress-strain calculation model. An iterative calculation method is employed, in which the temperature effect on material properties is considered at each calculation step, dynamically adjusting the calculation parameters. This temperature compensation mechanism accurately reflects the actual mechanical response of the material under high-temperature conditions, improves the accuracy of deformation calculations, and ensures that the resulting corrected acceleration factor is more consistent with actual operating conditions, providing reliable technical support for equipment life assessment in high-temperature environments.

[0113] S207. When the estimated remaining life calculated based on the corrected acceleration coefficient and the preset standard life is lower than the preset standard life warning value, a warning signal is triggered, and the preset carbon emission calculation coefficient is multiplied by the corrected acceleration coefficient to obtain a real-time carbon emission calculation coefficient.

[0114] S208. Calculate the actual carbon emissions per unit time based on the real-time carbon emission calculation coefficient and the carbon emission baseline value per unit time under the preset standard working conditions, and accumulate the actual carbon emissions to obtain the cumulative carbon emissions of the equipment.

[0115] S209. Calculate the predicted total carbon emissions during the remaining lifespan based on the real-time carbon emission calculation coefficient, the carbon emission baseline value per unit time, and the estimated remaining lifespan.

[0116] S210: When it is detected that there is a covering on the surface of the device, the temperature of the covered area and the temperature of the uncovered area on the surface of the device are detected simultaneously.

[0117] Specifically, the sensor network first scans the device surface to identify the presence and distribution of covered objects. This identification process utilizes the collaborative work of multiple sensing technologies, including optical, infrared, and ultrasonic sensors. A multi-source data fusion algorithm accurately determines the location and range of covered objects, creates a surface feature map, and divides the device surface into covered and uncovered areas.

[0118] Temperature detection utilizes a synchronized measurement strategy across different areas. Temperature sensors are placed in both covered and uncovered areas to ensure simultaneous temperature data collection in both areas. The placement of temperature sensors takes into account the uniformity and representativeness of heat distribution, improving measurement accuracy through optimized sensor placement. Real-time data acquisition and processing technologies ensure the timeliness and accuracy of temperature data.

[0119] S211. Obtain a temperature power consumption characteristic curve of the device, substitute the temperature of the coverage area and the temperature of the uncovered area into the temperature power consumption characteristic curve respectively, obtain the power consumption of the coverage area and the power consumption of the uncovered area, and use the difference between the power consumption of the coverage area and the power consumption of the uncovered area as the additional power consumption increase value.

[0120] Specifically, the device's temperature-power consumption characteristic curve is first obtained. This curve describes how the device's power consumption changes under different temperature conditions. This curve is built using extensive experimental data and theoretical models, taking into account multiple factors such as the device's thermodynamic properties, material properties, and operational characteristics. The curve is constructed using data fitting techniques, using mathematical methods such as polynomial regression or piecewise functions, to achieve a precise mapping between temperature and power consumption.

[0121] The temperatures of the covered and uncovered areas collected simultaneously are substituted into the temperature-power consumption characteristic curve. This calculation process uses a function mapping method to calculate the power consumption value at the corresponding temperature using the characteristic curve equation. This calculation also considers the error range of temperature measurement and uses error compensation technology to improve calculation accuracy.

[0122] Finally, the difference between the power consumption in the covered area and the non-covered area is calculated to obtain the additional power consumption. This difference calculation takes into account the timeliness and continuity of the data, and uses methods such as data smoothing and outlier processing to ensure the reliability of the calculation results. A dynamic monitoring mechanism for power consumption difference is also established to track power consumption trends in real time.

[0123] S212. According to a preset carbon emission factor of the coverage area where the device is located, multiply the power consumption increase by the carbon emission factor and the obtained coverage duration of the coverage to obtain the coverage carbon footprint increment caused by the coverage layer.

[0124] Specifically, a preset carbon emission factor is determined based on the energy structure and energy consumption characteristics of the region where the equipment is located. This factor reflects the carbon emissions corresponding to a unit of energy consumption. Its determination takes into account multiple factors, including the regional energy structure, energy conversion efficiency, and emission characteristics. A comprehensive carbon emission factor database has been established, encompassing emission coefficients for different energy types and regions.

[0125] The calculation process uses a multi-factor product method, multiplying the previously calculated power consumption increase by the carbon emission factor and the duration of the cover. This calculation takes into account the influence of time scale, using a time accumulation algorithm to accumulate carbon emissions over different time periods. The calculation also considers the timeliness and continuity of the data, using data compensation and smoothing to ensure the accuracy of the results.

[0126] S213. Add the carbon emissions during the equipment production phase, the cumulative carbon emissions, the predicted total carbon emissions, the covered carbon footprint increment, and the estimated carbon emissions during the waste disposal phase to obtain the full life cycle carbon footprint.

[0127] Steps S201, S207-S209, S213 and Figure 1 Steps S101 to S105 in the illustrated embodiment are similar, and reference may be made to the description of steps S101 to S105 , which will not be repeated here.

[0128] In the above-mentioned embodiment, waveform parameters are acquired through acoustic wave detection, and material state information is extracted from changes in acoustic wave propagation characteristics, establishing a correlation between acoustic characteristics and material mechanical properties. Acoustic parameters are converted into quantifiable material performance indicators by calculating the acoustoelastic effect coefficient, material deformation rate, and density change, and the mapping from acoustic characteristics to mechanical state is achieved through the stress-strain relationship equation. By detecting the temperature difference caused by the covering on the equipment surface and combining it with the temperature power consumption characteristic curve, the additional energy consumption caused by the covering is quantified. Combining acoustic wave detection with covering impact assessment establishes a highly adaptable condition monitoring mechanism that can not only capture the impact of pressure loads on equipment, but also evaluate the energy consumption changes caused by the covering, ultimately improving the accuracy of equipment condition assessment and carbon footprint tracking under special working conditions.

[0129] The following introduces an exemplary carbon emission database data processing system 300 provided in an embodiment of the present application. Figure 3 Schematic diagram of an exemplary hardware structure of the carbon emission database data processing system 300 provided in an embodiment of the present application.

[0130] In some embodiments, the carbon emission database data processing system 300 is a computer device or the carbon emission database data processing system 300 includes a computer device. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, the method in the embodiment of the present application is implemented.

[0131] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0132] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0133] As used in the above embodiments, the term “when…” may be interpreted as “if…” or “after…” or “in response to determining…” or “in response to detecting…”, depending on the context. Similarly, the phrases “upon determining…” or “if (stated condition or event) is detected” may be interpreted as “if determining…” or “in response to determining…” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0134] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product comprises one or more computer instructions. When loaded and executed on a computer, the computer program instructions fully or partially perform the processes or functions described in the embodiments of this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be magnetic media (e.g., floppy disk, hard disk, magnetic tape), optical media (e.g., DVD), or semiconductor media (e.g., solid-state drive).

[0135] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for processing carbon emission database data, characterized in that: include: Calculating the deviation between the collected real-time operating condition parameter and the preset operating condition parameter threshold, and determining a life loss acceleration coefficient based on the deviation; the life loss acceleration coefficient is proportional to the deviation; When the estimated remaining life calculated based on the life loss acceleration coefficient and the preset standard life is lower than the preset standard life warning value, an early warning signal is triggered, and a preset carbon emission calculation coefficient is multiplied by the life loss acceleration coefficient to obtain a real-time carbon emission calculation coefficient; Transmitting a detection sound wave and receiving a reflected sound wave to obtain waveform parameters of the reflected sound wave, wherein the waveform parameters include sound wave propagation time, amplitude attenuation rate, and frequency shift; Calculating the ratio of the frequency displacement to the preset sound wave emission frequency to obtain the acoustoelastic effect coefficient, calculating the ratio of the sound wave propagation time to the preset standard propagation time to obtain the material deformation rate, and calculating the material density change based on the amplitude attenuation rate; Substituting the acoustoelastic effect coefficient, the material deformation rate, and the material density change into a preset stress-strain relationship equation to calculate the stress-strain value; Repeatedly emitting the detection sound wave at a preset distance along a preset direction to obtain stress and strain distribution; Integrating the stress-strain distribution and averaging it to obtain an overall deformation, taking the ratio of the overall deformation to the standard deformation as a pressure damage coefficient, multiplying the life loss acceleration coefficient by the pressure damage coefficient to obtain a corrected acceleration coefficient and replacing the life loss acceleration coefficient; Calculate the actual carbon emissions per unit time based on the real-time carbon emissions calculation coefficient and the carbon emissions baseline value per unit time under the preset standard operating conditions, and accumulate the actual carbon emissions to obtain the cumulative carbon emissions of the equipment; the cumulative carbon emissions represent the total carbon emissions of the equipment from the time it was put into use to the current moment; Calculate the predicted total carbon emissions during the remaining life according to the real-time carbon emission calculation coefficient, the unit time carbon emission baseline value and the estimated remaining life; When it is detected that there is a covering on the surface of the device, the temperature of the covered area and the uncovered area of ​​the device surface are detected at the same time; Obtaining a temperature and power consumption characteristic curve of the device, substituting the temperature of the coverage area and the temperature of the uncovered area into the temperature and power consumption characteristic curve respectively, obtaining the power consumption of the coverage area and the power consumption of the uncovered area, and using the difference between the power consumption of the coverage area and the power consumption of the uncovered area as the additional power consumption increase value; According to a preset carbon emission factor of the coverage area where the device is located, multiplying the power consumption increase by the carbon emission factor and the obtained coverage duration of the coverage to obtain the coverage carbon footprint increment caused by the coverage layer; The carbon emissions during the equipment production phase, the cumulative carbon emissions, the predicted total carbon emissions, the covered carbon footprint increment, and the estimated carbon emissions during the waste disposal phase are added together to obtain the full life cycle carbon footprint.

2. The method according to claim 1, characterized in that After calculating the actual carbon emissions per unit time based on the real-time carbon emissions calculation coefficient and the carbon emissions baseline value per unit time under the preset standard operating conditions, and accumulating the actual carbon emissions to obtain the cumulative carbon emissions of the device, the method further includes: Obtaining the cumulative carbon emissions and collection time; Generate a blockchain data structure including a current block hash value, a previous block hash value, and a timestamp based on the accumulated carbon emissions and the collection time; Dividing the blockchain data structure into blocks according to preset time intervals to obtain multiple data blocks; Use the consensus mechanism to verify the validity of each data block; Add the verified data blocks to the blockchain.

3. The method according to claim 2, characterized in that After adding the verified data block to the blockchain, the method further includes: Record the storage time of the data block and the verification node information; Generate a smart contract containing the said evidence storage time and verification node information; Deploy the smart contract to the blockchain network.

4. The method according to claim 1, wherein The calculating of the deviation between the collected real-time operating condition parameter and the preset operating condition parameter threshold, and determining the life loss acceleration coefficient according to the deviation, specifically includes: Collect real-time working parameters; Storing the real-time operating condition parameters in the data lake in the original format; Establishing a unified metadata index for the data lake data in the data lake; The real-time operating condition parameter is subtracted from the preset operating condition parameter threshold to obtain a deviation value, and the life loss acceleration coefficient is obtained by multiplying the ratio of the real-time operating condition parameter to the deviation value by a preset acceleration coefficient weight.

5. The method according to claim 4, characterized in that After establishing a unified metadata index for the data in the data lake, the method further includes: Obtaining real-time data access frequency and recording the data access frequency of each data lake data; Storing the data lake data whose data access frequency is higher than a preset data access frequency threshold in a real-time database; The data lake data whose data access frequency is equal to or lower than the data access frequency threshold is migrated to a nearline storage database, and the consistency of the metadata index is maintained during the data migration.

6. A carbon emission database data processing system, characterized in that: The carbon emission database data processing system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the carbon emission database data processing system to execute the method as described in any one of claims 1-5.

7. A computer program product comprising instructions, characterized in that When the computer program product is run on a carbon emission database data processing system, the carbon emission database data processing system is enabled to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a carbon emission database data processing system, the carbon emission database data processing system is caused to execute the method according to any one of claims 1 to 5.

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