Carbon emission database data processing method and system, product and medium

Abstract

The invention discloses a carbon emission database data processing method and system, a product and a medium. The method comprises the following steps: calculating a deviation value between a real-time working condition parameter and a preset threshold value, and determining a life loss acceleration coefficient; when the predicted residual life is lower than a warning value, triggering early warning and multiplying the carbon emission calculation coefficient by the acceleration coefficient to obtain a real-time carbon emission calculation coefficient; calculating the accumulated carbon emission according to the real-time carbon emission calculation coefficient and the carbon emission reference value, and predicting the carbon emission during the remaining life period; and finally, adding the carbon emissions in the production, use, prediction and waste stages to obtain the full-life-cycle carbon footprint. By implementing the technical scheme provided by the invention, the accuracy of carbon footprint tracking is improved under a special working condition.

Description

Technical Field

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

[0002] Currently, carbon emission management has become an important part of all industries. With the continuous improvement of global energy conservation and emission reduction requirements, various organizations have put forward higher requirements for carbon footprint tracking in their operation processes. Especially in the field of carbon emission accounting for the entire life cycle of products and equipment, accurate and refined carbon footprint tracking is of great significance for sustainable development and environmental protection.

[0003] In the related art, the life cycle assessment method is usually adopted for carbon footprint tracking. This method will count the carbon emissions generated during the entire life cycle of a product or equipment from raw material acquisition, manufacturing and assembly, use and maintenance until scrapping and disposal. Specifically, first collect data such as energy consumption in each stage, then multiply by the corresponding carbon emission coefficient, and finally sum up the carbon emissions in all stages to obtain the carbon footprint.

[0004] However, in the related art, the selection of carbon footprint tracking objects has limitations. Due to the standardized characteristics of the calculation method, the related art mainly targets conventional objects with stable operating environments and predictable service lives, and it is difficult to meet the carbon footprint tracking requirements under special working conditions. When a device or product is used under special working conditions, its actual life may be significantly shortened due to environmental accelerated aging, and the actual carbon emissions will be greater than those under normal conditions. Using the method of evenly distributing carbon emissions according to the designed life in the related art results in a significant deviation between the carbon emission calculation result and the actual situation, and it cannot accurately reflect the impact of special working conditions on the carbon footprint. Summary of the Invention

[0005] This application provides a method, system, product, and medium for processing carbon emission database data, which improves the accuracy of carbon footprint tracking under special working conditions.

[0006] In the first aspect of this application, a method for processing carbon emission database data is provided. The method includes: Calculate the deviation value between the collected real-time operating condition parameters and the preset operating condition parameter thresholds, and determine the life loss acceleration coefficient according to the deviation value; when the predicted remaining life calculated according to the life loss acceleration coefficient and the preset standard life is lower than the preset standard life warning value, trigger a 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 according to the real-time carbon emission calculation coefficient and the carbon emission baseline value per unit time under the preset standard operating condition, 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 according to the real-time carbon emission calculation coefficient, the carbon emission baseline value per unit time, and the predicted remaining life; add the carbon emissions during the equipment production stage, the cumulative carbon emissions, the predicted total carbon emissions, and the predicted carbon emissions during the waste treatment stage to obtain the full life cycle carbon footprint.

[0007] In the above embodiment, through the comparative analysis of the real-time operating condition parameters and the standard parameters, a quantitative index reflecting the operating condition deviation is obtained and converted into a life loss acceleration coefficient to evaluate the impact of non-standard operating conditions on the equipment life; when the predicted remaining life is lower than the warning value, a warning is issued in a timely manner, and at the same time, the life loss condition 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, and at the same time, the future carbon emission trend is predicted based on the current operating state. Finally, the carbon emission data of each stage of equipment production, operation, prediction, and waste treatment are integrated to evaluate the complete full life cycle carbon footprint. In the above steps, by combining the change of operating condition parameters with the prediction of equipment life, an adaptable carbon emission evaluation mechanism is established. By dynamically adjusting the carbon emission calculation parameters through the life loss acceleration coefficient, it can not only reflect the impact of environmental accelerated aging on the equipment life, but also timely capture the carbon emission increment caused by abnormal operating conditions, solving the problem of carbon footprint calculation deviation caused by shortened life under special operating conditions.

[0008] Combined with some embodiments of the first aspect, in some embodiments, after calculating the deviation value between the collected real-time operating condition parameters and the preset operating condition parameter thresholds and determining the life loss acceleration coefficient according to the deviation value, it further includes: Emit detection sound waves and receive the reflected sound waves to obtain the waveform parameters of the reflected sound waves. The waveform parameters include the sound wave propagation time, amplitude attenuation rate, and frequency shift. Calculate the ratio of the frequency shift 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. Calculate the material density change based on the amplitude attenuation rate. Substitute the acoustoelastic effect coefficient, material deformation rate, and material density change into the preset stress-strain relationship equation to calculate the stress-strain value. Repeatedly emit detection sound waves at preset intervals along the preset direction to obtain the stress-strain distribution. Perform integral operation on the stress-strain distribution and calculate the average value to obtain the overall deformation amount. Take the ratio of the overall deformation amount to the standard deformation amount as the pressure damage coefficient. Multiply the life loss acceleration coefficient by the pressure damage coefficient to obtain the corrected acceleration coefficient and replace the life loss acceleration coefficient.

[0009] In the above embodiments, the material state information is extracted from the changes in the sound wave propagation characteristics through sound wave detection, and the correlation between the acoustic characteristics and the material mechanical properties is established. 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 realized through the stress-strain relationship equation. Through continuous scanning measurement, a complete stress-strain distribution map is obtained, and then the overall deformation amount is obtained through integral operation and calculation of the average value. Finally, the pressure damage coefficient is obtained by comparing with the standard deformation amount. A corrected acceleration coefficient considering the influence of pressure load is constructed. By combining sound wave detection and stress analysis, a damage assessment mechanism is established, which can capture the influence of pressure load on the equipment and dynamically adjust the life prediction parameters, improving the accuracy of equipment life assessment under special working conditions.

[0010] Combined with some embodiments of the first aspect, in some embodiments, add the carbon emissions during the equipment production stage, cumulative carbon emissions, predicted total carbon emissions, and expected carbon emissions during the waste treatment stage to obtain the life cycle carbon footprint, specifically including: When it is detected that there is a covering on the equipment surface, simultaneously detect the temperature of the covered area and the temperature of the uncovered area on the equipment surface. Obtain the temperature-power consumption characteristic curve of the equipment, substitute the temperature of the covered area and the temperature of the uncovered area 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. Take the difference between the power consumption of the covered area and the power consumption of the uncovered area as the additional power consumption increase value. According to the preset carbon emission factor of the covered area where the equipment is located, multiply the power consumption increase value by the carbon emission factor and the obtained covering duration of the covering to obtain the covering carbon footprint increment caused by the covering layer. Add the carbon emissions during the equipment production stage, cumulative carbon emissions, predicted total carbon emissions, covering carbon footprint increment, and expected carbon emissions during the waste treatment stage to obtain the life cycle carbon footprint.

[0011] In the above embodiments, by detecting the temperature difference between the covered area and the uncovered area, the influence of the covering on the temperature distribution of the device is obtained; the temperature difference is converted into a power consumption increment by using the temperature-power consumption characteristic curve, and a quantitative relationship between the covering and the energy consumption change is established; the power consumption increment is combined with the regional carbon emission factor and the covering duration to calculate the carbon footprint increment caused by the covering; finally, by integrating the carbon emission data in each stage of device production, operation, covering influence, predicted operation and waste treatment, the complete life cycle carbon footprint is evaluated. An adaptable carbon emission assessment mechanism is established, which can not only accurately quantify the influence of the covering on the device energy consumption, but also dynamically track the resulting carbon emission changes, and finally improve the accuracy of carbon footprint tracking under special working conditions.

[0012] Combined with some embodiments of the first aspect, in some embodiments, after calculating the actual carbon emissions per unit time according to the real-time carbon emission calculation coefficient and the carbon emission benchmark value per unit time under the preset standard working condition, and accumulating the actual carbon emissions to obtain the cumulative carbon emissions of the device, it further includes: Obtain the cumulative carbon emissions and the collection time; generate a blockchain data structure including the current block hash value, the previous block hash value and the timestamp according to the cumulative carbon emissions and the collection time; divide the blockchain data structure into blocks at a preset time interval to obtain a plurality of data blocks; use the consensus mechanism to verify the validity of each data block; add the verified data blocks to the blockchain.

[0013] In the above embodiments, by combining the cumulative carbon emissions with the collection time into a blockchain data structure including the hash value and the timestamp, the correlation and timeliness between the data are established; the continuous carbon emission data is divided into discrete data blocks, which is convenient for subsequent verification and storage; the validity of each data block is verified to ensure the authenticity and consistency of the data, and the verified data blocks are added to the blockchain to form an immutable data record. By closely combining blockchain technology with carbon emission data records, a decentralized data storage mechanism is established, which can not only ensure the authenticity and traceability of the data, but also improve the security of the data through distributed storage, and improve the credibility and management efficiency of the carbon emission data.

[0014] Combined with some embodiments of the first aspect, in some embodiments, after adding the verified data blocks to the blockchain, it further includes: Record the deposit time and verification node information of the data block; generate a smart contract including the deposit time and verification node information; deploy the smart contract to the blockchain network.

[0015] In the above embodiments, by recording the time of deposit certification and the information of verification nodes for each data block, an integrity proof of data deposit certification is established; these key information are encapsulated into a smart contract to form an automatically executable programmed contract, realizing the automation of data verification and management; by deploying the smart contract to the blockchain network, a distributed contract execution environment is established, enabling the data deposit process and verification process to be automatically completed and forming a consensus in the network. Combining the smart contract with blockchain deposit certification constructs an automated data verification and management mechanism, which can not only ensure the transparency and traceability of the data deposit process, but also improve the operation efficiency through the automatic execution of the smart contract, enhancing the credibility of carbon emission data and the automation level of management.

[0016] Combined with some embodiments of the first aspect, in some embodiments, the deviation value between the collected real-time working condition parameters and the preset working condition parameter threshold is calculated, and the life loss acceleration coefficient is determined according to the deviation value, specifically including: Collect real-time working condition parameters; store the real-time working condition parameters in the data lake in the original format; establish a unified metadata index for the data in the data lake; calculate the deviation value between the real-time working condition parameters and the preset working condition parameter threshold, and multiply the ratio of the real-time working condition parameters to the deviation value by the preset acceleration coefficient weight to obtain the life loss acceleration coefficient.

[0017] In the above embodiments, by collecting parameters and storing them in the data lake in the original format, the integrity and authenticity of the data are maintained, avoiding information loss that may be caused by traditional data preprocessing; establishing a unified metadata index realizes the efficient management and rapid retrieval of massive heterogeneous data; calculating the deviation between the real-time working condition parameters and the preset threshold, and performing normalization processing in combination with the characteristics of the parameters themselves, and finally adjusting through the acceleration coefficient weight to establish a mapping relationship from working condition deviation to life loss. An adaptable state evaluation mechanism is established, which can not only ensure the integrity and availability of the data, but also accurately quantify the impact of working condition deviation on the equipment life, improving the accuracy of equipment state evaluation under special working conditions.

[0018] Combined with some embodiments of the first aspect, in some embodiments, after establishing a unified metadata index for the data in the data lake, it further includes: Obtain the real-time data access frequency and record the data access frequency of each data in the data lake; store the data in the data lake with a data access frequency higher than the preset data access frequency threshold in the real-time database; migrate the data in the data lake with a data access frequency equal to or lower than the data access frequency threshold to the near-line storage database, and maintain the consistency of the metadata index during data migration.

[0019] In the above embodiments, by monitoring the data access frequency in real time, the high-frequency access data is stored in the real-time database to ensure fast access performance, while the low-frequency access data is migrated to the near-line storage database to reduce the storage cost. During the data migration process, the traceability and integrity of the data are ensured by maintaining the consistency of the metadata index. 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 the storage cost while ensuring the data availability. Finally, the efficient utilization of data storage resources and the overall improvement of system performance are achieved.

[0020] In a 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. 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 manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions. When the computer program product runs on the carbon emission database data processing system, the carbon emission database data processing system is enabled to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium including instructions. When the instructions run on the carbon emission database data processing system, the carbon emission database data processing system is enabled to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the carbon emission 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 emission database data processing method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Through the comparative analysis of real-time operating parameters and standard parameters, this application obtains a quantitative index reflecting the operating deviation, converts it into a life loss acceleration coefficient, and evaluates the impact of non-standard operating conditions on the equipment life. When the predicted remaining life is lower than the warning value, a warning is issued in a timely manner. At the same time, the life loss situation is associated with carbon emission calculation. By adjusting the carbon emission calculation coefficient, the acceleration effect of non-standard operating conditions on carbon emissions is reflected. Combining with the carbon emission benchmark value per unit time, the actual carbon emissions are calculated and accumulated. Meanwhile, based on the current operating state, the future carbon emission trend is predicted. Finally, by integrating the carbon emission data in each stage of equipment production, operation, prediction, and waste treatment, the complete life cycle carbon footprint is evaluated. In the above steps, by combining the change of operating parameters with equipment life prediction, an adaptable carbon emission evaluation mechanism is established. By dynamically adjusting the carbon emission calculation parameters through the life loss acceleration coefficient, it can not only reflect the impact of environmental accelerated aging on equipment life, but also capture the carbon emission increment caused by abnormal operating conditions in a timely manner, solving the problem of carbon footprint calculation deviation caused by shortened life under special operating conditions.

[0025] 2. This application extracts material state information from the change of acoustic wave propagation characteristics through acoustic wave detection, and establishes the correlation between acoustic characteristics and material mechanical properties. By calculating the photoelastic effect coefficient, material deformation rate, and density change, the acoustic parameters are converted into quantifiable material property indicators, and the mapping from acoustic characteristics to mechanical state is realized through the stress-strain relationship equation. Through continuous scanning measurement, a complete stress-strain distribution map is obtained. Then, through integral operation and averaging, the overall deformation amount is obtained. Finally, the pressure damage coefficient is obtained by comparing with the standard deformation amount. A modified acceleration coefficient considering 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 influence of pressure load on the equipment and dynamically adjust the life prediction parameters, improving the accuracy of equipment life assessment under special operating conditions.

[0026] 3. This application obtains the influence of the covering on the equipment temperature distribution by detecting the temperature difference between the covered area and the uncovered area. Using the temperature-power consumption characteristic curve, the temperature difference is converted into a power consumption increment, and a quantitative relationship between the covering and the energy consumption change is established. By combining the power consumption increment with the regional carbon emission factor and the covering duration, the carbon footprint increment caused by the covering is calculated. Finally, by integrating the carbon emission data in each stage of equipment production, operation, covering influence, predicted operation, and waste treatment, the complete life cycle carbon footprint is evaluated. An adaptable carbon emission evaluation mechanism is established, which can not only accurately quantify the influence of the covering on the equipment energy consumption, but also dynamically track the resulting carbon emission change, ultimately improving the accuracy of carbon footprint tracking under special operating conditions. Description of the Drawings

[0027] Figure 1It is a schematic flowchart of a data processing method for a carbon emission database in an embodiment of the present application; Figure 2 It is another schematic flowchart of a data processing method for a carbon emission database in an embodiment of the present application; Figure 3 It is a schematic diagram of an exemplary hardware structure of a carbon emission database data processing system in an embodiment of the present application. Detailed implementation manners

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or indicating relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0030] In the related art, carbon footprint tracking mainly adopts a static assessment method based on the life cycle. This method collects the energy consumption data of the device at each stage such as production, use, and disposal, multiplies it by the standard carbon emission coefficient, and then accumulates it to obtain the life cycle carbon footprint. However, this method has obvious limitations: First, its carbon emission calculation uses a fixed carbon emission coefficient and cannot reflect the dynamic impact of the actual operating conditions on carbon emissions; Second, the life prediction is based on the design life under standard conditions and ignores 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 detect and respond to the carbon emission increment caused by abnormal operating conditions in a timely manner, resulting in a significant deviation between the carbon footprint calculation result and the actual situation.

[0031] In an embodiment of the present application, a data processing method for a carbon emission database based on working condition perception is proposed. By real-time monitoring the deviation between the working condition parameters and the standard threshold values, a quantitative relationship between the degree of working condition abnormality and life loss is established, and then the carbon emission calculation coefficient is dynamically adjusted. When it is detected that the equipment may undergo 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 working condition monitoring, life prediction, and carbon emission calculation, a closed-loop carbon footprint assessment system is constructed, which can accurately reflect the impact of special working conditions on equipment life and carbon emissions.

[0032] Figure 1 It is a flow schematic diagram of using the data processing method for the carbon emission database in the embodiment of the present application, including the following steps: S101. Calculate the deviation value between the collected real-time working condition parameters and the preset working condition parameter threshold, and determine the life loss acceleration coefficient according to the deviation value.

[0033] Specifically, first, the working condition parameter data of the equipment are collected in real time through the sensor network. These parameters may include operation state data such as temperature, humidity, vibration, and pressure. The collected real-time working condition parameters are compared with the pre-set standard working condition parameter threshold to calculate the deviation value. This deviation calculation can be carried out in various ways: it can be a simple difference calculation, a weighted average deviation, or a deviation analysis using a specific mathematical model. For the multi-parameter case, a comprehensive evaluation method can be adopted, and different weights are assigned to the deviations of each parameter according to their importance, and finally a comprehensive deviation value is obtained.

[0034] According to the calculated deviation value, the life loss acceleration coefficient is determined by using the pre-established corresponding relationship. This corresponding relationship is established based on engineering experience and theoretical analysis, and usually shows a linear relationship, that is, the life loss acceleration coefficient is proportional to the deviation value. This linear relationship can be used to determine the specific proportional coefficient through experimental data and statistical analysis.

[0035] In some embodiments, when the deviation of operating condition parameters causes qualitative changes in materials, their performance attenuation exhibits a characteristic of severe non-linear variation. In such a case, to determine the life loss acceleration coefficient, it is first necessary to collect in real time the key performance parameters of the materials, including index data such as physical strength, chemical stability, and structural integrity. Compare the collected real-time performance parameters with the performance parameters under the preset standard operating conditions to calculate the comprehensive deviation value. Among them, calculating the comprehensive deviation value is to classify and weight the collected performance parameters according to the degree of influence of the corresponding performance parameters on the equipment life. For each performance parameter, calculate the difference between its real-time value and the performance parameter under the corresponding standard operating conditions, then divide by the performance parameter value under the standard operating conditions to obtain the normalized deviation. Then, sum up all the weighted normalized deviations. At the same time, introduce a non-linear correction coefficient to compensate for the interaction effects between the real-time performance parameters. Finally, obtain the comprehensive deviation value reflecting the overall performance deviation of the equipment. When calculating, dynamically adjust the weight coefficients of the real-time performance parameters so that they can be adaptively adjusted with the change of the equipment operating state, thereby ensuring that the calculation results can accurately reflect the actual operating conditions of the equipment.

[0036] When this comprehensive deviation value exceeds the qualitative change critical point, the calculation of the life loss acceleration coefficient needs to adopt a high-order power function model, that is, substitute the comprehensive deviation value into the power function calculation formula with the base of two and the comprehensive deviation value as the exponent. At the same time, considering the irreversibility in the process of material qualitative change, it is also necessary to introduce a cumulative damage factor to incorporate the influence of historical operating conditions on the material performance into the calculation scope. This calculation method can reflect the rapid attenuation characteristics of materials in the qualitative change state through the combined effects of real-time data, qualitative change critical point, power function relationship, and cumulative damage factor. The sharp decrease in material performance in the qualitative change state will lead to a rapid increase in equipment energy consumption and a significant decrease in operating efficiency. At the same time, the material itself may release additional greenhouse gases during the qualitative change process. These factors will all have a significant impact on carbon emissions. Therefore, by accurately depicting the qualitative change characteristics of materials, the additional carbon emissions of equipment under abnormal operating conditions can be more accurately evaluated, which can further improve the accuracy of carbon footprint tracking.

[0037] In some other embodiments, a data management system combining a data lake and a hierarchical storage architecture can be constructed, and unified management of data can be achieved through metadata indexing. At the same time, intelligent hierarchical storage is performed based on data access frequency, thereby improving data processing efficiency and storage resource utilization rate.

[0038] First, collect the real-time operating condition parameters of the device through a distributed sensor network, including key operation data such as temperature, pressure, and rotational speed, and store these data in the data lake in their original format. This schema-less storage method preserves the original characteristics and integrity of the data, avoiding the limitations brought by the fixed schema of traditional databases. The adoption of the data lake enables the system to store and process various types of structured, semi-structured, and unstructured data, providing a complete data foundation for subsequent in-depth analysis and mining.

[0039] Secondly, establish a unified metadata index system for the data in the data lake. This index system contains key information such as the timestamp, source, format, and correlation relationships between data. Through standardized metadata descriptions, unified management of massive heterogeneous data is achieved. The establishment of the metadata index not only improves the data retrieval efficiency but also provides basic support for data traceability and data governance, ensuring the manageability and traceability of the data.

[0040] Monitor and record the access frequency of each piece of data in real time. By establishing a data access log, record the time, frequency, and method of access for each piece of data to form a heat map of data usage. This dynamic monitoring mechanism can accurately reflect the usage value and importance of the data, providing a decision-making basis for subsequent hierarchical storage strategies.

[0041] For data with an access frequency higher than the preset threshold, store it in a real-time database. These high-frequency access data are quickly responded to through an in-memory database or a caching mechanism, significantly improving the access efficiency of frequently used data. The use of the real-time database ensures the data access performance in critical business scenarios and meets the requirements of real-time analysis and decision-making.

[0042] Migrate the data with an access frequency equal to or lower than the preset data access frequency threshold to a near-line storage database. During the data migration process, strictly maintain the consistency of the metadata index to ensure that the data migration does not affect the traceability and integrity of the data. This hierarchical storage strategy not only ensures the accessibility of the data but also optimizes the usage efficiency of storage resources and reduces the storage cost.

[0043] Calculate the difference between the collected real-time operating condition parameters and the preset operating condition parameter thresholds, and combine the ratio of the real-time operating condition parameters to the deviation value, and multiply by the preset acceleration coefficient weight to obtain a life loss acceleration coefficient that reflects the current operating state of the device. This calculation process takes into account the impact of the operating condition deviation on the device life. Through a scientific weight system, the impact degree of non-standard operating conditions on the device life is accurately quantified.

[0044] By constructing a data management system, intelligent management of the entire process from data collection, storage to analysis has been achieved. Based on the unified storage of the data lake and the unified management of metadata indexing, combined with the dynamic hierarchical storage strategy according to data access frequency, it not only improves the data processing efficiency, but also optimizes the storage resource allocation, while ensuring the integrity and traceability of the data.

[0045] S102. When the predicted remaining life calculated according to 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 the real-time carbon emission calculation coefficient.

[0046] Specifically, according to the standard life of the equipment and the currently calculated life loss acceleration coefficient, the predicted remaining life is calculated through a mathematical model. This calculation process takes into account the impact of real-time working conditions on the life and the current life loss acceleration coefficient, and obtains the remaining life prediction value. Among them, there are various situations for the mathematical model of calculating the predicted remaining life, and different calculation methods can be adopted according to actual needs and application scenarios: First, divide the standard remaining life by the current life loss acceleration coefficient. This method is applicable to scenarios where the equipment operating state is relatively stable and the working conditions change little. However, this linear calculation method may not accurately reflect the actual life loss of the equipment under complex working conditions.

[0047] Secondly, establish a dynamic integral model, discretize the equipment operation cycle into multiple time periods, and record and dynamically track the life loss acceleration coefficient of each time period. First, calculate the actual life loss amount in each time period, then accumulate to obtain the total loss amount, and finally subtract the cumulative loss amount from the standard life to obtain a more accurate remaining life prediction value. This method takes into account the cumulative impact of the working conditions experienced by the equipment at different times on the life.

[0048] In scenarios where the equipment is often in severe working condition fluctuations, a more advanced non-linear prediction model can be adopted. This non-linear prediction model not only considers the life loss acceleration coefficient, but also introduces factors such as the change rate of working condition parameters and the duration of working conditions, and describes the comprehensive impact of working condition changes on the equipment life through a multi-dimensional mathematical model. At the same time, the non-linear prediction model also includes correction parameters such as material fatigue characteristics and environmental factor impacts, which can more comprehensively reflect the life loss law of the equipment in a complex operating environment.

[0049] When the equipment enters the performance qualitative change stage, a special life prediction algorithm is started. At this time, the mutation characteristics of material performance need to be considered, the life loss laws before and after the qualitative change are respectively modeled, and a smooth transition is processed at the critical point. This segmented calculation method can accurately reflect the life loss characteristics of the equipment during the period of drastic performance changes.

[0050] When the calculated remaining life expectancy is lower than the pre-set warning threshold, a warning signal will be automatically triggered. Such warnings can be divided into multiple levels, and different warning levels are set according to different intervals of the remaining life.

[0051] At the same time, multiply the carbon emission calculation coefficient under the pre-set standard working conditions by the current life loss acceleration coefficient to obtain the carbon emission calculation coefficient reflecting the actual working conditions. This calculation method is based on the principle that when the equipment operates under non-standard working conditions, energy consumption and carbon emissions will increase with performance degradation. When the equipment is in an accelerated loss state, its energy utilization efficiency decreases, resulting in an increase in carbon emissions per unit time, and this increase is positively correlated with the life loss acceleration coefficient.

[0052] S103. Calculate the actual carbon emissions per unit time according to the real-time carbon emission calculation coefficient and the carbon emission benchmark value per unit time under the pre-set standard working conditions, and accumulate the actual carbon emissions to obtain the cumulative carbon emissions of the equipment.

[0053] Specifically, multiply the carbon emission benchmark value per unit time under the pre-set standard working conditions by the real-time calculated carbon emission calculation coefficient to obtain the carbon emissions per unit time reflecting the current actual working conditions. This calculation takes into account the impact of performance changes on carbon emissions when the equipment operates under non-standard working conditions and can reflect the carbon emission level under the actual operating state.

[0054] Adopt the data integration method to continuously accumulate the actual carbon emissions obtained in each calculation period, starting from the initial moment when the equipment is put into use until the current moment, to obtain the total carbon emissions of the equipment. This cumulative calculation can adopt different time resolutions, which can be accumulated by seconds, minutes, hours or days. The specific choice depends on the characteristics of the equipment and the monitoring requirements. A shorter calculation period can provide a more refined carbon emission change trend, but requires more data storage and computing resources.

[0055] In practical applications, a hierarchical accumulation mechanism is usually established. First, calculate and store carbon emission data on a shorter time scale, and then aggregate the data according to different time dimensions (such as hours, days, months, years), which not only ensures the accuracy of the data but also improves the efficiency of long-term trend analysis. At the same time, abnormal data will be identified and processed to ensure the accuracy of the cumulative calculation.

[0056] In some embodiments, in the working condition scenario where the device starts and stops frequently, since the device requires additional energy to reach the stable working state during the startup phase and there is also residual energy consumption during the shutdown phase, the carbon emission characteristics in these special phases are significantly different from those during stable operation. Therefore, a dynamic segmented calculation model is established. This dynamic segmented calculation model automatically identifies and divides the startup, stable operation, and shutdown phases by real-time monitoring the operating state of the device, and sets corresponding carbon emission calculation parameters and correction coefficients for each phase. During the startup phase, since parameters such as the internal temperature and pressure of the device have not reached the optimal working state and the energy utilization efficiency is low, a higher carbon emission calculation coefficient is adopted; during the stable operation phase, the device is in the optimal working state and standard calculation parameters are used; during the shutdown phase, the influence of the device's inertial operation and waste heat consumption is considered, and decreasing calculation parameters are used. This segmented and refined calculation method improves the accuracy of carbon emission data.

[0057] In other embodiments, after obtaining the cumulative carbon emissions of the device, the carbon emission data can also be stored and verified through blockchain technology, and automated management of the data can be achieved through smart contracts, thereby ensuring the authenticity, immutability, and traceability of the carbon emission data.

[0058] First, obtain the cumulative carbon emission data of the device and the corresponding collection time in real time. This is completed through the data collection module on the device. The collection module continuously collects data according to the preset sampling frequency to ensure the continuity and integrity of the data, and can timely capture the changes in the device's carbon emissions, providing accurate data for subsequent data processing and analysis.

[0059] Second, based on the obtained cumulative carbon emissions and collection time, construct a blockchain data structure. This data structure includes three key elements: the current block hash value, the previous block hash value, and the timestamp. The current block hash value is obtained by encrypting and calculating the block data to ensure that the data is not tampered with; the previous block hash value establishes the link relationship between blocks to ensure the continuity of the data; the timestamp records the exact time when the data is generated, facilitating subsequent traceability and verification.

[0060] Perform block processing on the blockchain data structure. According to the preset time interval, the continuous data stream is cut into multiple data blocks. This block mechanism not only considers the timeliness of the data but also improves the processing efficiency of the blockchain network. A reasonable block size can balance the data processing efficiency and storage cost while ensuring the integrity of the data is not affected.

[0061] Start the consensus mechanism to verify the validity of each data block. The verification process involves multiple nodes in the network, and the authenticity, integrity, and timeliness of the data are verified through the preset consensus algorithm, which can effectively prevent data tampering and improve the credibility.

[0062] For the data blocks that pass the verification, add them to the blockchain. The 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.

[0063] Record the deposit time of each data block and the information of the nodes participating in the verification. These metadata can prove the source of the data and the reliability of the verification process.

[0064] Generate a smart contract containing the deposit time and verification node information. The smart contract not only contains these basic information, but also defines data access rules, verification rules, and business processing logic. Through the smart contract, preset business rules can be automatically executed, improving the automation level of data management.

[0065] Deploy the generated smart contract to the blockchain network. The deployment process includes the verification, optimization, and distribution of the contract code to ensure that the contract can run normally in the blockchain network. After deployment, the smart contract can automatically respond to events in the network and execute the corresponding business logic.

[0066] By introducing blockchain technology and smart contract mechanisms, a decentralized and immutable carbon emission data management system has been established. This technical solution records data through a distributed ledger, ensures authenticity through multi-node consensus, and realizes automated management through smart contracts. Ultimately, the goal of trustworthy deposit, full-chain traceability, and all-round supervision of carbon emission data throughout the whole process is achieved, providing reliable data support for carbon footprint tracking.

[0067] S104. Calculate the predicted total carbon emissions during the remaining life based on the real-time carbon emission calculation coefficient, the unit-time carbon emission benchmark value, and the predicted remaining life.

[0068] Specifically, when predicting the total carbon emissions during the remaining life, a multi-dimensional prediction model is adopted. This prediction model comprehensively considers the deterioration trend of equipment performance, the variation law of working conditions, and the influence of environmental factors.

[0069] 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 working conditions, and the predicted remaining life. However, in order to improve the prediction accuracy, more complex prediction algorithms can also be used.

[0070] The prediction model first establishes a curve of equipment performance deterioration based on historical data and analyzes the variation trend of the carbon emission calculation coefficient. At the same time, the predicted remaining life period is divided into multiple time periods, and corresponding performance deterioration coefficients are set for each time period, so as to obtain a more realistic carbon emission prediction value. This segmented prediction method takes into account the performance characteristics of the equipment at different life stages and can more accurately reflect the dynamic changes of carbon emissions.

[0071] An adaptive prediction mechanism is established. By continuously comparing the deviation between the actual carbon emission data and the predicted value, the parameters of the prediction model are continuously optimized to improve the prediction accuracy. At the same time, the prediction model also includes correction parameters for influencing factors such as seasonal factors and load changes, making the prediction results closer to the actual operating conditions.

[0072] In some embodiments, for the special working conditions of seasonally operating equipment, a dynamic prediction model based on ambient temperature is established. First, by analyzing historical operating data, the correlation between the equipment's carbon emissions and 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 unique carbon emission benchmark parameters and correction coefficients are set for each interval to adapt to the performance fluctuations caused by temperature changes. For possible extreme weather conditions, by setting 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 coefficients and efficiency correction values. This dynamic prediction method not only improves the accuracy of carbon emission prediction but also helps operators identify high-carbon emission risk periods in advance, providing a scientific basis for adjusting equipment operation strategies and formulating maintenance plans, and ultimately achieving precise carbon emission management throughout the equipment's life cycle. At the same time, the adaptive nature of the prediction model enables it to continuously optimize the prediction algorithm with the accumulation of data, improving the prediction accuracy of carbon emission behavior under abnormal weather conditions.

[0073] In some other embodiments, in the carbon emission prediction of equipment that requires regular maintenance, a prediction model including maintenance factors is constructed. This prediction model analyzes the equipment performance degradation curve and the improvement effect of maintenance activities to establish a performance recovery coefficient matrix. During the prediction process, first, the performance degradation trend of the equipment is identified, and the carbon emission growth curve without maintenance is calculated. Then, according to the preset maintenance plan, a performance recovery calculation module is inserted at each maintenance node. This performance recovery calculation module considers the improvement degree of different types of maintenance activities (such as daily maintenance, regular overhaul, major overhaul, etc.) on the equipment performance, and dynamically adjusts the predicted value through the performance recovery coefficient. At the same time, the prediction model also establishes a maintenance effect decay curve to describe the decreasing law of the performance improvement effect over time after maintenance, so as to more accurately predict the carbon emission changes during the maintenance cycle. This prediction method not only improves the accuracy of long-term carbon emission prediction but also provides data support for optimizing the maintenance plan, helping to determine the best maintenance timing, and achieving a balance between carbon emission control and equipment maintenance costs.

[0074] S105. Add the carbon emissions during the equipment production stage, the cumulative carbon emissions, the predicted total carbon emissions, and the expected carbon emissions during the waste treatment stage to obtain the carbon footprint of the entire life cycle.

[0075] Specifically, the full-life-cycle carbon footprint calculation adopts a complete life-cycle assessment method to integrate the carbon emissions in each stage of the equipment from production and manufacturing, operation and use to final waste treatment. First, obtain the carbon emission data in the production stage of the equipment, which includes the carbon emissions generated in links such as raw material acquisition, component manufacturing, and assembly; second, incorporate the actual cumulative carbon emissions after the equipment is put into use into the calculation; third, add the expected carbon emissions during the remaining life calculated based on the prediction model; finally, consider the expected carbon emissions generated in links such as recycling, disassembly, and disposal after the equipment is scrapped.

[0076] Adopt a standardized data processing method to unify the carbon emission data in different stages to the same measurement unit and time scale. At the same time, a data traceability mechanism is established to ensure the accuracy and integrity of the carbon emission data in each stage. During the integration process, consider the associated impacts between different stages, such as the impact of the production process selection on the energy consumption in the subsequent use stage, and the impact of the maintenance method in the use stage on the carbon emissions in the final disposal stage.

[0077] In the above embodiment, through the comparative analysis of real-time operating condition parameters and standard parameters, a quantitative index reflecting the operating condition deviation is obtained and converted into a life loss acceleration coefficient to evaluate the impact of non-standard operating conditions on the equipment life; when the predicted remaining life is lower than the warning value, a warning is issued in a timely manner, and at the same time, the life loss condition 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, and at the same time, the future carbon emission trend is predicted based on the current operating state. Finally, the carbon emission data in each stage of equipment production, operation, prediction, and waste treatment are integrated to evaluate the complete full-life-cycle carbon footprint. In the above steps, by combining the change of operating condition parameters with the equipment life prediction, an adaptable carbon emission assessment mechanism is established. By dynamically adjusting the carbon emission calculation parameters through the life loss acceleration coefficient, it can not only reflect the impact of environmental accelerated aging on the equipment life, but also timely capture the carbon emission increment caused by abnormal operating conditions, solving the problem of carbon footprint calculation deviation caused by shortened life under special operating conditions.

[0078] In some other embodiments of the present application, when the equipment bears complex pressure loads, it may cause deformation on the surface and require additional energy consumption, resulting in a greater deviation in the equipment life prediction calculation. By using the data processing method of the carbon emission database provided in the present application, the stress-strain distribution of the material can be obtained through acoustic wave detection to realize the assessment of the equipment state under special operating conditions and the accurate calculation of carbon emissions.

[0079] As Figure 2 shown, it is another process schematic diagram of the data processing method of the carbon emission database provided by the embodiment of the present application. This method can be used for Figure 1In the application scenario shown, the following steps are included: S201. Calculate the deviation value between the collected real-time working condition parameters and the preset working condition parameter thresholds, and determine the life loss acceleration coefficient according to the deviation value.

[0080] S202. Transmit detection sound waves and receive the reflected sound waves to obtain the waveform parameters of the reflected sound waves. The waveform parameters include the sound wave propagation time, the amplitude attenuation rate, and the frequency shift.

[0081] Specifically, the acoustic wave detection technology emits detection sound waves with a specific frequency through an ultrasonic sensor and captures the reflected acoustic wave signals through a receiving device. First, an electrical signal is generated and converted into ultrasonic waves through a transducer. The detection sound waves are emitted at a preset detection frequency and energy level. When the sound waves encounter the object to be measured, part of the energy is reflected back to the receiving device. The receiving device converts the received acoustic wave signals into electrical signals, and extracts the key waveform parameters through a signal processing system.

[0082] The measurement of the sound wave propagation time is achieved by recording the time difference between the emission moment and the reception moment. A high-precision clock and a synchronous trigger mechanism are used to ensure the accuracy of the time measurement. The calculation of the amplitude attenuation rate is carried out by comparing the energy amplitudes of the emitted sound waves and the received sound waves, and is standardized considering the propagation distance and the medium characteristics. The measurement of the frequency shift is realized by performing spectral analysis on the received signal, comparing the differences between the emission frequency and the reception frequency, and adopting signal processing methods such as Fourier transform to achieve accurate frequency measurement.

[0083] S203. Calculate the ratio of the frequency shift 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 according to the amplitude attenuation rate.

[0084] Specifically, the calculation of the acoustoelastic effect coefficient is based on the frequency change characteristics of sound waves under stress. When the material is under stress, the internal lattice structure undergoes microscopic changes, resulting in a change in the sound wave propagation speed, and thus causing a frequency shift. By calculating the ratio of the actually measured frequency shift to the preset emission frequency, the stress state of the material can be quantitatively characterized.

[0085] The calculation of the material deformation rate utilizes the propagation characteristics of sound waves in the material. Measure the actual sound wave propagation time and calculate the ratio with the pre-calibrated standard propagation time. This ratio reflects the degree of material deformation because when the material deforms, the change in its internal structure will cause a change in the sound wave propagation time. By establishing a sound speed-strain relationship model, the time ratio is converted into the material deformation rate.

[0086] The calculation of the material density change is based on the energy attenuation characteristics of sound waves during propagation. By analyzing the amplitude attenuation rate and combining with the attenuation theory of sound waves in the medium, a correlation model between the attenuation rate and the density change is established. This model takes into account the acoustic impedance characteristics and scattering losses of the material, and obtains the change value of the material density through mathematical transformation.

[0087] S204. Substitute the acoustoelastic effect coefficient, the material deformation rate, and the material density change into the preset stress-strain relationship equation to calculate the stress-strain value.

[0088] 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 the density change.

[0089] A step-by-step calculation strategy is adopted. First, an initial solution of the stress field is established using the acoustoelastic effect coefficient, which reflects the influence of the internal stress of the material on the propagation characteristics of sound waves. Then, the material deformation rate information is integrated into the model, and the actual strain state is calculated through the strain-displacement relationship equation. Finally, combined with the material density change data, considering the influence of volume change on the stress distribution, the final stress-strain value is obtained through iterative calculation.

[0090] The entire calculation process is solved using numerical methods. Through numerical calculation techniques such as finite element analysis or boundary element method, the discrete measurement data is transformed into a continuous stress-strain field distribution. The nonlinear characteristics of the material and the influence of boundary conditions are considered during the calculation process to ensure the accuracy of the calculation results.

[0091] S205. Repeat the emission of detection sound waves at preset intervals along the preset direction to obtain the stress-strain distribution.

[0092] Specifically, first, determine the scanning direction and the measuring point spacing based on the detection requirements, and establish a standardized scanning grid. Use a precision positioning mechanism to control the movement of the probe to ensure the accuracy of each measuring point position. At each measuring point position, emit detection sound waves according to the preset emission parameters and record the received reflection signals.

[0093] The sound wave scanning adopts a step-by-step moving method, and stays at each measuring point for enough time to complete data acquisition. The timing accuracy of emission and reception is ensured through a synchronous trigger mechanism, and digital signal processing technology is used to process the sound wave signals of each measuring point in real time. For the data of each measuring point, multiple samplings are averaged to improve the signal-to-noise ratio and measurement reliability.

[0094] Through the spatial correlation analysis of data from multiple measurement points, a complete stress-strain distribution field was established. The discrete measurement point data was reconstructed using an interpolation algorithm to obtain a continuous stress-strain distribution map. The accuracy and reliability of the distribution map were also improved through methods such as data smoothing and outlier processing.

[0095] S206. Integrate and average the stress-strain distribution to obtain the overall deformation amount. Take the ratio of the overall deformation amount to the standard deformation amount as the pressure damage coefficient, and multiply the life loss acceleration coefficient by the pressure damage coefficient to obtain the corrected acceleration coefficient and replace the life loss acceleration coefficient.

[0096] Specifically, first, select an appropriate integration method according to the geometric characteristics of the detection area. For a linear detection area, use line integral, and weight and sum the stress-strain values of discrete measurement points according to the measurement point spacing; for an area detection area, double integration is required, that is, first perform line integral in a certain direction and then perform integral operation in the perpendicular direction. During the integration process, the continuity of the stress-strain distribution needs to be considered, and interpolation is performed on the data between adjacent measurement points to ensure the accuracy of the integration result. After obtaining the integration result, divide it by the total length or total area of the detection area to obtain the average stress-strain value, which is the overall deformation amount representing the deformation degree of the entire area.

[0097] After calculating the overall deformation amount, compare it with the pre-determined standard deformation amount. The standard deformation amount is a reference value obtained based on theoretical calculations or experimental measurements under normal operating conditions of the equipment. By calculating the ratio of the actual overall deformation amount to the standard deformation amount, the pressure damage coefficient reflecting the influence degree of the pressure load is obtained.

[0098] Finally, multiply the previously obtained life loss acceleration coefficient by the pressure damage coefficient to obtain the corrected acceleration coefficient. The corrected acceleration coefficient replaces the original life loss acceleration coefficient for subsequent life assessment calculations.

[0099] In some embodiments, under high-temperature environmental conditions, mechanical property changes such as thermal expansion, elastic modulus variation, and yield strength reduction may occur in materials, and these changes will directly affect the calculation accuracy of stress-strain distribution. To address this situation, first, a material property database covering different temperature ranges is established, including temperature-related material parameters such as elastic modulus, Poisson's ratio, and coefficient of thermal expansion. Based on this database, a temperature-material property relationship model is constructed, and this temperature-material property relationship model describes the variation law of material parameters with temperature through mathematical functions. During the actual calculation process, the operating temperature data is obtained in real-time and substituted into the temperature-material property relationship model to obtain the corrected material parameters at the current temperature. These corrected parameters are used to update the stress-strain calculation model. An iterative calculation method is adopted, and the influence of temperature on material properties is considered in each calculation step to dynamically adjust the calculation parameters. Through this temperature compensation mechanism, the actual mechanical response of materials under high-temperature environments can be accurately reflected, the accuracy of deformation calculation is improved, and the finally obtained corrected acceleration coefficient is more in line with the actual working conditions, providing reliable technical support for the equipment life assessment under high-temperature environments.

[0100] S207. When the predicted remaining life calculated according to 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 the real-time carbon emission calculation coefficient.

[0101] S208. Calculate the actual carbon emissions per unit time according to the real-time carbon emission calculation coefficient and the carbon emission benchmark 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.

[0102] S209. Calculate the predicted total carbon emissions during the remaining life according to the real-time carbon emission calculation coefficient, the carbon emission benchmark value per unit time, and the predicted remaining life.

[0103] S210. When a covering is detected on the surface of the equipment, the temperature of the covered area and the temperature of the uncovered area on the surface of the equipment are detected simultaneously.

[0104] Specifically, first, the surface of the equipment is scanned through a sensor network to identify the presence and distribution of the covering. This identification process adopts the collaborative work of multiple sensing technologies, including optical sensing, infrared sensing, and ultrasonic sensing, etc. Through a multi-source data fusion algorithm, the position and range of the covering are accurately judged, a surface feature map is established, and the surface of the equipment is divided into a covered area and an uncovered area.

[0105] Temperature detection adopts a sub-region synchronous measurement strategy. Temperature sensors are arranged in the covered area and the uncovered area respectively to ensure simultaneous acquisition of temperature data in both areas. The arrangement of temperature sensors takes into account the uniformity and representativeness of heat distribution, and the measurement accuracy is improved by optimizing the sensor positions. Real-time data acquisition and processing technologies are used to ensure the timeliness and accuracy of temperature data.

[0106] S211. Obtain the temperature-power consumption characteristic curve of the device. Substitute the temperature in the covered area and the temperature in the uncovered area into the temperature-power consumption characteristic curve respectively to obtain the power consumption in the covered area and the power consumption in the uncovered area. Take the difference between the power consumption in the covered area and the power consumption in the uncovered area as the additional power consumption increase value.

[0107] Specifically, first, obtain the temperature-power consumption characteristic curve of the device. The temperature-power consumption characteristic curve describes the power consumption change law of the device under different temperature conditions. This curve is established through a large amount of experimental data and theoretical models, considering multiple factors such as the thermodynamic characteristics, material characteristics, and operating characteristics of the device. The curve establishment process uses data fitting technology, and through mathematical methods such as polynomial regression or piecewise functions, an accurate mapping relationship between temperature and power consumption is achieved.

[0108] Substitute the synchronously collected temperature in the covered area and the temperature in the uncovered area into the temperature-power consumption characteristic curve respectively. This calculation process uses the function mapping method, and calculates the power consumption value corresponding to the corresponding temperature through the characteristic curve equation. Considering the error range of temperature measurement, error compensation technology is used to improve the calculation accuracy.

[0109] Finally, calculate the difference between the power consumption in the covered area and the power consumption in the uncovered area to obtain the additional power consumption increase value. This difference calculation takes into account the timeliness and continuity of the data, and through methods such as data smoothing and outlier processing, ensures the reliability of the calculation result. A dynamic monitoring mechanism for power consumption difference is also established to track the power consumption change trend in real time.

[0110] S212. According to the preset carbon emission factor of the covered area where the device is located, multiply the power consumption increase value by the carbon emission factor and the obtained covering duration of the covering to obtain the covering carbon footprint increment caused by the covering layer.

[0111] Specifically, determine the preset carbon emission factor according to the energy structure and energy consumption characteristics of the area where the device is located. This factor reflects the carbon emissions corresponding to unit energy consumption, and its determination process takes into account multiple factors such as the composition of the regional energy structure, energy conversion efficiency, and emission characteristics. A complete carbon emission factor database is established, including emission coefficients of different energy types and different regions.

[0112] The calculation process adopts the multi-factor multiplication method, multiplying the previously obtained power consumption increase value by the carbon emission factor and the covering duration of the covering. This calculation process takes into account the influence of the time scale. Through the time accumulation algorithm, the carbon emissions in different time periods are accumulated. The timeliness and continuity of the data are also considered in the calculation, and the accuracy of the calculation results is ensured through data compensation and smoothing processing.

[0113] S213. Add the carbon emissions during the equipment production stage, the cumulative carbon emissions, the predicted total carbon emissions, the incremental carbon footprint of the covering, and the predicted carbon emissions during the waste treatment stage to obtain the full-life cycle carbon footprint.

[0114] Steps S201, S207 - S209, and S213 are similar to Figure 1 Steps S101 - S105 in the illustrated embodiment. Refer to the descriptions in Steps S101 - S105, and details are not repeated here.

[0115] In the above embodiment, waveform parameters are obtained through acoustic wave detection, material state information is extracted from the changes in the acoustic wave propagation characteristics, and the correlation between the acoustic characteristics and the material mechanical properties is established; by calculating the photoelastic effect coefficient, the material deformation rate, and the density change, the acoustic parameters are converted into quantifiable material property indicators, and the mapping from the acoustic characteristics to the mechanical state is realized through the stress-strain relationship equation; by detecting the temperature difference caused by the covering on the equipment surface and combining with the temperature-power consumption characteristic curve, the additional energy consumption increase caused by the covering is quantified. By combining the acoustic wave detection and the covering influence assessment, a highly adaptable state monitoring mechanism is established, which can not only capture the influence of the pressure load on the equipment but also evaluate the energy consumption change caused by the covering, ultimately improving the accuracy of the equipment state assessment and carbon footprint tracking under special working conditions.

[0116] The following introduces the exemplary carbon emission database data processing system 300 provided by the embodiments of the present application. Figure 3 It is an exemplary hardware structure schematic diagram of the carbon emission database data processing system 300 provided by the embodiments of the present application.

[0117] 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. Among them, 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 via 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. The computer program, when executed by the processor, implements the method in the embodiments of the present application.

[0118] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures 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 those shown in the figure, or combine certain components, or have different component arrangements.

[0119] As mentioned 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

[0120] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if..." or "after..." or "in response to determining..." or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted as "if determining..." or "in response to determining..." or "when detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)".

[0121] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive), etc.

[0122] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware with a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes various media that can store program codes such as ROM or random access memory RAM, magnetic disks, or optical discs.

Claims

1. A method for processing data of a carbon emission database, characterized in that, Including: Calculating the deviation value between the collected real-time operating condition parameters and the preset operating condition parameter thresholds, and determining the life loss acceleration coefficient according to the deviation value; the life loss acceleration coefficient is proportional to the deviation value; When the predicted remaining life calculated according to the life loss acceleration coefficient and the preset standard life is lower than the preset standard life warning value, triggering an early warning signal, and multiplying the preset carbon emission calculation coefficient by the life loss acceleration coefficient to obtain the real-time carbon emission calculation coefficient; 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 operating condition, and accumulating 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 of being put into use to the current moment; Calculating the predicted total carbon emissions during the remaining life according to the real-time carbon emission calculation coefficient, the carbon emission baseline value per unit time, and the predicted remaining life; Adding the carbon emissions during the equipment production stage, the cumulative carbon emissions, the predicted total carbon emissions, and the predicted carbon emissions during the waste treatment stage to obtain the life cycle carbon footprint.

2. The method according to claim 1, characterized in that, After calculating the deviation value between the collected real-time operating condition parameters and the preset operating condition parameter thresholds and determining the life loss acceleration coefficient according to the deviation value, it further includes: Emitting a detection sound wave and receiving the reflected sound wave to obtain the waveform parameters of the reflected sound wave, where the waveform parameters include the sound wave propagation time, the amplitude attenuation rate, and the frequency shift; Calculating the ratio of the frequency shift 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 according to the amplitude attenuation rate; Substituting the acoustoelastic effect coefficient, the material deformation rate, and the material density change into the preset stress-strain relationship equation to calculate the stress-strain value; Repeating the emission of the detection sound wave at preset intervals along a preset direction to obtain the stress-strain distribution; Performing an integral operation on the stress-strain distribution and taking the average value to obtain the overall deformation amount, taking the ratio of the overall deformation amount to the standard deformation amount as the pressure damage coefficient, and multiplying the life loss acceleration coefficient by the pressure damage coefficient to obtain the corrected acceleration coefficient and replacing the life loss acceleration coefficient with it.

3. The method according to claim 1, wherein The adding the carbon emissions during the equipment production stage, the cumulative carbon emissions, the predicted total carbon emissions, and the predicted carbon emissions during the waste treatment stage to obtain the life cycle carbon footprint specifically includes: When detecting a covering on the surface of the equipment, simultaneously detecting the temperature of the covered area and the temperature of the uncovered area on the surface of the equipment; Obtaining the temperature-power consumption characteristic curve of the equipment, substituting the temperature of the covered area and the temperature of the uncovered area 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 taking the difference between the power consumption of the covered area and the power consumption of the uncovered area as the additional power consumption increase value; According to the preset carbon emission factor of the covered area where the equipment is located, multiplying the power consumption increase value by the carbon emission factor and the obtained covering duration of the covering to obtain the covering carbon footprint increment caused by the covering layer; Add the carbon emissions during the equipment production stage, the cumulative carbon emissions, the predicted total carbon emissions, the covered carbon footprint increment, and the expected carbon emissions during the waste treatment stage to obtain the full-life cycle carbon footprint.

4. The method according to claim 1, wherein After calculating the actual carbon emissions per unit time based on the real-time carbon emission calculation coefficient and the carbon emission benchmark 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, it further includes: Obtain the cumulative carbon emissions and the collection time. Generate a blockchain data structure including the current block hash value, the previous block hash value, and the timestamp based on the cumulative carbon emissions and the collection time. Chunk the blockchain data structure at preset time intervals to obtain multiple data blocks. Verify the validity of each data block using a consensus mechanism. Add the verified data blocks to the blockchain.

5. The method according to claim 4, wherein After adding the verified data blocks to the blockchain, it further includes: Record the deposit time of the data block and the verification node information. Generate a smart contract including the deposit time and the verification node information. Deploy the smart contract to the blockchain network.

6. The method according to claim 1, characterized in that, Calculating the deviation value between the collected real-time working condition parameters and the preset working condition parameter threshold, and determining the life loss acceleration coefficient according to the deviation value, specifically includes: Collect real-time working condition parameters. Store the real-time working condition parameters in the data lake in their original format. Establish a unified metadata index for the data lake data in the data lake. Calculate the deviation value by subtracting the preset working condition parameter threshold from the real-time working condition parameters, and multiply the ratio of the real-time working condition parameters to the deviation value by a preset acceleration coefficient weight to obtain the life loss acceleration coefficient.

7. The method according to claim 1, wherein After establishing a unified metadata index for the data in the data lake, it further includes: Obtain the real-time data access frequency and record the data access frequency of each data lake data. Store the data lake data with a data access frequency higher than the preset data access frequency threshold in the real-time database. Migrate the data lake data with a data access frequency equal to or lower than the data access frequency threshold to the near-line storage database, and maintain the consistency of the metadata index during data migration.

8. A data processing system for a carbon emission database, 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 according to any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that, When the computer program product runs on the carbon emission database data processing system, it enables the carbon emission database data processing system to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the carbon emission database data processing system, it enables the carbon emission database data processing system to execute the method according to any one of claims 1-7.

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