Carbon emission data management method and system and readable storage medium

Abstract

The invention relates to the technical field of data processing, and discloses a carbon emission data management method and system and a readable storage medium. The method comprises the following steps: establishing a multi-channel data set; performing channel trust authentication of the data source, and establishing a first authentication result; reading carbon emission associated equipment of the enterprise, performing data authenticity authentication in the multi-channel data set, and establishing a second authentication result; establishing data association; performing cross authentication on the data in the multi-channel data set, and performing cross authentication check through the first authentication result and the second authentication result to generate a cross authentication result; and establishing carbon emission data of the enterprise according to a cross authentication result, and performing carbon emission management of the enterprise according to the carbon emission data. The technical problems that in the prior art, carbon emission data verification is low in efficiency and insufficient in accuracy are solved, the credibility and accuracy of data are improved through multi-channel data authentication, data association and cross verification, and the technical effect of improving the carbon emission management efficiency is achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to a carbon emission data management method, system and readable storage medium. Background Art

[0002] As global requirements for environmental protection continue to increase, carbon emission management has become a key area of ​​concern for all companies. In industrial and corporate operations, accurate monitoring and management of carbon emission data is an important step in achieving carbon neutrality goals. However, existing technologies have certain problems in carbon emission data management. For example, the comparative document (CN202310013804.4) proposes a carbon emission management data platform. Although the solution can achieve certain carbon emission data collection, storage, analysis and management functions, there are still some technical bottlenecks. The data verification and analysis process in the comparative document (CN202310013804.4) is relatively simple, mainly relying on manual settings and static data processing, lacking a dynamic cross-validation mechanism, which affects the accuracy and timeliness of the data. Especially in the integration and authenticity authentication of multi-channel data, it is difficult for the system to effectively handle complex data association and source verification issues. Secondly, although the platform has the functions of data analysis and rectification plan generation, it lacks a more sophisticated automated authentication and real-time feedback mechanism, resulting in low verification efficiency and insufficient processing accuracy, which cannot meet the needs of large-scale enterprises in real-time carbon emission monitoring. Therefore, improving the accuracy and verification efficiency of carbon emission data, especially in the context of multi-channel data sets and equipment authentication, has become an urgent need to solve current technical problems in carbon emission data management. Summary of the invention

[0003] In view of the above problems, an object of the present invention is to provide a carbon emission data management method, system and readable storage medium.

[0004] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0005] A carbon emission data management method, the method comprising:

[0006] Acquire the carbon emission related data of the enterprise through multiple channels and establish a multi-channel data set; perform channel trust authentication on the data source of the multi-channel data set to establish a first authentication result; read the carbon emission related equipment of the enterprise, perform data authenticity authentication in the multi-channel data set through the carbon emission related equipment, and establish a second authentication result; perform data analysis on the multi-channel data set to establish data association, and the data association includes horizontal data association and vertical data association; use the data association to cross-certify the data in the multi-channel data set, and perform cross-certification verification through the first authentication result and the second authentication result to generate a cross-certification result; establish the carbon emission data of the enterprise according to the cross-certification result, and manage the carbon emission of the enterprise according to the carbon emission data.

[0007] A carbon emission data management system, the system comprising: a memory and a processor, the memory comprising a carbon emission data management method program, the carbon emission data management method program when executed by the processor to implement the following steps:

[0008] Acquire the carbon emission related data of the enterprise through multiple channels and establish a multi-channel data set; perform channel trust authentication on the data source of the multi-channel data set to establish a first authentication result; read the carbon emission related equipment of the enterprise, perform data authenticity authentication in the multi-channel data set through the carbon emission related equipment, and establish a second authentication result; perform data analysis on the multi-channel data set to establish data association, and the data association includes horizontal data association and vertical data association; use the data association to cross-certify the data in the multi-channel data set, and perform cross-certification verification through the first authentication result and the second authentication result to generate a cross-certification result; establish the carbon emission data of the enterprise according to the cross-certification result, and manage the carbon emission of the enterprise according to the carbon emission data.

[0009] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a carbon emission data management method provided by the present invention.

[0010] Acquire the carbon emission related data of the enterprise through multiple channels and establish a multi-channel data set; perform channel trust authentication on the data source of the multi-channel data set to establish a first authentication result; read the carbon emission related equipment of the enterprise, perform data authenticity authentication in the multi-channel data set through the carbon emission related equipment, and establish a second authentication result; perform data analysis on the multi-channel data set to establish data association, and the data association includes horizontal data association and vertical data association; use the data association to cross-certify the data in the multi-channel data set, and perform cross-certification verification through the first authentication result and the second authentication result to generate a cross-certification result; establish the carbon emission data of the enterprise according to the cross-certification result, and manage the carbon emission of the enterprise according to the carbon emission data.

[0011] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0012] The present invention provides a carbon emission data management method, system and readable storage medium through multi-channel data collection and authentication, data analysis and association. First, multi-channel data sources are used to perform source trust authentication and device authentication to ensure the authenticity and accuracy of the data, and the credibility of the data is improved through a cross-certification mechanism. Secondly, horizontal and vertical data association technology is used to optimize the integration capability of multi-source data, which can comprehensively and accurately reflect the carbon emission status of the enterprise. The present invention not only improves the credibility and accuracy of the data, but also improves the efficiency of carbon emission management, reduces manual intervention, and promotes the intelligent development of carbon emission management. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of a carbon emission data management method provided by an embodiment of the present invention.

[0014] Figure 2 A schematic diagram of a process for trust transfer fitting in a carbon emission data management method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0017] Example 1

[0018] like Figure 1 As shown, this embodiment discloses a carbon emission data management method, wherein the method includes:

[0019] Obtain carbon emission-related data of enterprises through multiple channels and establish a multi-channel data set.

[0020] Carbon emission-related data is obtained through multiple channels, including the company's internal data system (such as energy management system, production records, etc.), carbon emission reports issued by authoritative institutions, monitoring data from third-party monitoring agencies, etc. The collected data is integrated into a unified data set to form a multi-channel data set.

[0021] Perform channel trust authentication on data sources of multi-channel data sets and establish the first authentication result.

[0022] Conduct channel trust authentication for data sources of multi-channel data sets, and establish the first authentication result by verifying whether the data source is reliable. Specifically, conduct trust authentication for each data source: conduct equipment and system authentication for internal enterprise data to ensure the legitimacy of the data source; conduct timeliness and accuracy verification for carbon emission reports issued by authoritative institutions to ensure the availability of reported data; conduct institutional qualification authentication and data collection method verification for third-party monitoring data to ensure the reliability of its data; based on the authentication results, assign a trust level to each data source and generate the first authentication result.

[0023] Based on the authentication results of each data source, a trust level is assigned to each data source. Specifically, by trust authenticating the internal data of the enterprise, the carbon emission reports issued by authoritative institutions, and the monitoring data of third-party monitoring institutions, the system will evaluate the reliability of each data source according to the various verification standards in the authentication process (such as legality, timeliness, accuracy, collection methods, etc.); according to the results of the authentication evaluation, the system will assign a trust level to each data source, which is usually divided into three levels: high trust, medium trust, and low trust. For example, for internal data of enterprises that have been strictly authenticated and have good data consistency, the system may give a high trust level; for reports from authoritative institutions with certain delays or occasional fluctuations in data sources, the system may give a medium trust level; for third-party data that may have deviations or imperfect collection methods, the system may give a low trust level; according to the trust level, the authentication results of each data source are summarized to generate a first authentication result, which contains the trust level and related authentication information of each data source.

[0024] Read the carbon emission-related equipment of the enterprise, authenticate the authenticity of the data in the multi-channel data set through the carbon emission-related equipment, and establish a second authentication result.

[0025] The system identifies and reads the company's carbon emission-related equipment, which includes energy meters, environmental monitoring sensors, carbon emission monitoring devices in the production process, etc.; reads real-time or historical data from carbon emission-related equipment, and compares the data read from the carbon emission-related equipment with the corresponding data in the multi-channel data set, including checking whether the data's numerical value, timestamp, unit and other information are consistent; through comparison, verifies whether the data in the multi-channel data set truly reflects the actual emissions of the equipment, and generates a second certification result.

[0026] Perform data analysis on multi-channel data sets and establish data associations, including horizontal and vertical data associations.

[0027] By analyzing multi-channel data sets, data associations are established, which are divided into horizontal associations (relationships between data of the same type) and vertical associations (relationships between data of different types). Through horizontal and vertical multi-dimensional data associations, the system can fully integrate data from different sources, reveal the complex relationships between data, and provide more accurate and sophisticated data support for carbon emission management.

[0028] Analyze and associate data from multiple channels, including:

[0029] Conduct cross-domain data linkage analysis on data in multi-channel data sets to establish data domain associations; conduct data association analysis on data in multi-channel data sets with the same timestamp to establish data time associations; conduct data type association analysis on data in multi-channel data sets to establish data type associations; construct data horizontal associations based on data domain associations, data time associations, and data type associations, and establish data associations based on data horizontal associations.

[0030] Cross-domain data linkage analysis refers to associating data from different fields and identifying the relationship between data in each field. The carbon emission data of an enterprise may come from multiple different fields, such as energy consumption, production process, equipment status, environmental monitoring, etc. For example, there may be a correlation between the energy management system (energy consumption data) and the production record (production process data), and changes in energy consumption may directly affect the carbon emissions in the production process.

[0031] Data time association refers to the association analysis of data from different sources at the same timestamp. Since multi-channel data usually comes from different devices, systems or monitoring platforms, their timestamps may be inconsistent. The system needs to align the timestamps of each data source through time synchronization technology to perform accurate time association. For example, emission data and energy consumption data at the same time point may be directly correlated, and an increase in energy use may lead to an increase in carbon emissions. Through time association analysis, the system can compare different data sources within a unified time frame to reveal the real-time trend of carbon emissions.

[0032] Data type association refers to analyzing different types of data and identifying the associations between them. Data types may include numerical data (such as energy consumption, temperature), text data (such as equipment status, production records), time data (such as timestamps), etc. Through data type association analysis, the system can establish connections between different data types to ensure the synergy of multiple types of data. For example, the relationship between the text data of equipment status and the numerical data of emissions can be established by analyzing the impact of abnormal conditions (such as failures or maintenance) during equipment operation on emissions.

[0033] Horizontal data association is to conduct horizontal linkage analysis on different data sources by combining multiple association methods such as data field association, data time association, data type association, and establish comprehensive horizontal data association. Based on the results of horizontal data association, a comprehensive association network between various types of data is established to ensure effective collaboration between different data sources and data types, providing a reliable analysis basis for subsequent carbon emission management and optimization.

[0034] Data analysis and data association for multi-channel data sets, including:

[0035] Send the multi-channel data set to the deep learning model, and establish data hierarchical association based on the deep learning results; perform time series analysis on the multi-channel data set to establish data time series association; establish data vertical association based on data hierarchical association and data time series association, and establish data association based on data vertical association and data horizontal association.

[0036] Further preprocess the multi-channel data sets, such as standardization and normalization, to meet the input requirements of the deep learning model; input the preprocessed data into the deep learning model (such as neural network, convolutional neural network, recurrent neural network, etc.) for training; the model automatically learns and trains the data to identify the different hierarchical relationships between different data features and establish data hierarchical associations. Data hierarchical association divides data into different levels according to importance, influence or different characteristics, which helps to reveal the hierarchical structure and internal connections between data. For example, production process data may be divided into different levels such as key parameter data and auxiliary data. The associations between these levels will help to analyze the data more accurately.

[0037] Time series analysis is performed on multi-channel data sets, and data at different time points are processed and analyzed in chronological order to reveal data trends, periodic fluctuations, and abnormal fluctuations. Through time series analysis, the system can identify the changing patterns of data such as carbon emissions and energy consumption over time; at this point, the data will form a data time series association, providing a basis for subsequent dynamic analysis.

[0038] Based on the hierarchical association of data and the association of data time series, the system further establishes data vertical association, which refers to the relationship between different time periods and data levels. Through vertical association, the system can analyze the impact between data at different time nodes and different levels. For example, the system can analyze whether the increase in energy consumption in a certain period of time directly leads to an increase in emissions, or whether changes in a certain production link affect the overall carbon emission level.

[0039] After completing the vertical data association, the system integrates the vertical data association and the horizontal data association to form the final data association. By combining horizontal and vertical associations, the system can comprehensively analyze and understand the multi-dimensional relationship of carbon emission data, providing accurate and reliable support for the company's carbon emission management and decision-making.

[0040] Data association is used to cross-certify data in multi-channel data sets, and cross-certification verification is performed through the first authentication result and the second authentication result to generate a cross-certification result.

[0041] Based on the established data associations (horizontal and vertical data associations), cross-certification is performed on multi-channel data sets. Cross-certification refers to verifying the consistency and accuracy of data through associations between different data sources. Based on the first authentication result (such as data source trust authentication) and the second authentication result (such as device authentication, data authenticity authentication), the results that have passed the data association verification are cross-certified and checked to generate a cross-certification result.

[0042] Use data association to cross-validate data in multi-channel datasets, including:

[0043] Determine whether there is isolated data during the cross-certification process; if there is isolated data, predict the data value of the isolated data and establish the data value prediction results, including:

[0044] Among them, V pred Characterize the data value prediction result, X isolated,i The i-th linear feature that characterizes isolated data, The jth secondary feature that characterizes isolated data, X isolated,k The kth nonlinear feature that characterizes isolated data, α i , β j , γ k , δ are the weight coefficients of linear features, quadratic features, nonlinear features and interaction effects, n represents the total number of linear features, i represents any linear feature, m represents the total number of quadratic features, j represents any quadratic feature, p represents the total number of nonlinear features, k represents any nonlinear feature, h(X isolated ,Y) characterizes the interaction function between isolated data and external factor Y; obtains the historical carbon emission data of the enterprise, conducts historical correlation analysis of isolated data based on the historical carbon emission data, and establishes the historical correlation analysis results; establishes data attention based on the data value prediction results and the historical correlation analysis results, and after conducting attention verification on isolated data through data attention, adds the attention verification results to the cross-certification results to establish the carbon emission data of the enterprise.

[0045] Perform cross-certification analysis on multi-channel data sets to check whether there are inconsistent or irrelevant data between data sources, that is, isolated data. Isolated data usually refers to data points that cannot be found in multiple data sources. If isolated data is found during the cross-certification process, the system will predict the data value of this data, which can be specifically predicted through the data value prediction formula: Quantify, where V pred Characterizes the data value prediction results and reflects the potential value of isolated data. isolated,i The i-th linear feature that characterizes isolated data, The jth secondary feature that characterizes isolated data, X isolated,k The kth nonlinear feature that characterizes isolated data, α i , β j , γ k , δ are the weight coefficients of linear features, quadratic features, nonlinear features and interaction effects, n represents the total number of linear features, i represents any linear feature, m represents the total number of quadratic features, j represents any quadratic feature, p represents the total number of nonlinear features, k represents any nonlinear feature, h(Xisolated ,Y) represents the interaction influence function between isolated data and external factor Y, which is used to capture the relationship between isolated data and the environment or other external factors.

[0046] Obtain the company's historical carbon emission data from the database, conduct historical correlation analysis on isolated data based on the historical carbon emission data, and judge the performance of isolated data in past data and whether the isolated data conforms to the historical emission pattern by analyzing the rules and trends in the historical carbon emission data. For example, historical data may show that the carbon emissions of a certain production link in a specific period of time fluctuate with the changes in equipment operation. By analyzing the trends in the historical data, the reasonable value of the isolated data can be inferred to determine whether it is consistent with the historical data.

[0047] According to the data value prediction results and historical correlation analysis results, data attention is established, that is, isolated data is marked as objects that need special attention, and a attention list is generated; through data attention, isolated data is checked for attention, that is, the relationship between isolated data and other data and the degree of conformity with historical data are checked to assess whether it can accurately reflect carbon emissions. After completing the attention verification of isolated data, the verification results (such as data correction, supplementary forecast values, etc.) are added to the cross-certification results; finally, based on the cross-certification results of all data, including the verification results of isolated data, the carbon emission data of the enterprise is established.

[0048] The first authentication result and the second authentication result are used to perform cross-authentication verification to generate a cross-authentication result, including:

[0049] A cross-certification chain is established, and a trust joint analysis of the cross-certification chain is performed through the first authentication result and the second authentication result, and a joint trust identifier mapped with the cross-certification chain is established, wherein the trust joint analysis includes: a: setting the chain length level of the cross-certification chain, performing a trigger analysis of the cross-certification chain according to the chain length level, and generating a level authentication factor; b: using the level authentication factor to perform a trust transfer fitting based on the first authentication result and the second authentication result, and completing the trust joint analysis; generating a cross-certification result according to the cross-certification chain and the mapped joint trust identifier.

[0050] A cross-certification chain is constructed based on the first authentication result (data source trust authentication) and the second authentication result (data authenticity authentication). The cross-certification chain is a trust chain formed by connecting multiple authentication results in series. Each authentication link corresponds to a different verification step of the data source, ensuring that the data forms a reliable data chain after passing through multiple verification levels. The structure of the cross-certification chain is connected by the trust information of each authentication link, providing a clear authentication track.

[0051] In order to enhance the credibility of the cross-certification chain, the trust joint analysis of the cross-certification chain is performed through the first authentication result and the second authentication result, and a joint trust mark is generated. Specifically, a chain length level is set according to the length of the cross-certification chain. The chain length refers to the number of authentication links involved in the cross-certification chain. The longer the chain length, the more authentication links the data has passed through, which usually means that the credibility of the data is higher; the authentication chain is further triggered according to the chain length level to evaluate the impact of chains of different lengths in trust transfer. The chain length level (such as short chain, medium chain, long chain) can be set according to the actual situation, and the corresponding analysis rules can be set. For example, a short chain may involve fewer authentication links and its trust is lower; a long chain involves more authentication links and has a higher trust; the trigger analysis analyzes the trigger conditions and influencing factors of the authentication chain according to the chain length level, and generates a level authentication factor for subsequent trust transfer and fitting. Using the level authentication factor, a trust transfer fitting based on the first authentication result and the second authentication result is performed. The trust transfer fitting is calculated by an algorithm (weighted average), and the trust of different authentication results is transferred through the cross-certification chain to calculate the final trust value of each data point. After completing the trust joint analysis, the system will generate a joint trust mark for each data point that is mapped to the cross-certification chain (by combining the trust values ​​of each link to form a comprehensive score to identify whether the data is trustworthy). The joint trust mark represents the overall credibility of the data in the certification chain and can reflect the performance of the data in all certification links. Based on the cross-certification chain and the mapped joint trust mark, the system finally generates a cross-certification result, which contains the trust score of the data and the information of the certification chain, indicating whether the data has passed the strict certification process and can be used for carbon emission management decisions.

[0052] like Figure 2 As shown, the trust transfer fitting based on the first authentication result and the second authentication result is performed using the level authentication factor, including:

[0053] Determine whether the level authentication factor is lower than the preset factor threshold; if the level authentication factor is lower than the preset factor threshold, generate additional verification instructions; perform additional authentication of the trust of the first authentication result and the second authentication result according to the additional verification instructions; if the additional authentication fails, generate an exception mark, perform a corresponding cross-certification chain mark according to the exception mark, and complete the trust joint analysis according to the exception mark.

[0054] Based on the calculated level certification factor, determine whether it is lower than the preset factor threshold. The level certification factor reflects the trust of each certification link in the cross-certification chain. If the level certification factor is low, it means that the credibility of the data is weak and there may be inconsistent or unreliable certification links.

[0055] If the level authentication factor is judged to be lower than the preset factor threshold, it means that the data may have potential risks or inconsistencies. At this time, an additional verification instruction will be automatically generated, indicating that the reliability of the data needs to be further verified. According to the additional verification instruction, the first authentication result and the second authentication result are additionally authenticated for trust, including re-examining the legitimacy and credibility of the data source to ensure that the data source is reliable and exclude any forged or erroneous sources; re-verifying the authenticity of the data, including reviewing the data collection equipment, collection process, calibration, etc., to ensure that the data itself has not been tampered with or misreported.

[0056] If the additional authentication fails, it means that there are inconsistencies or abnormalities in the data, and an abnormality mark will be generated. The abnormality mark is used to identify the problems with the data during the authentication process; the status of the cross-certification chain is updated according to the abnormality mark to mark the authentication link with the abnormality.

[0057] Establish the company's carbon emission data based on the cross-certification results, and manage the company's carbon emissions based on the carbon emission data.

[0058] Based on the results of cross-certification, the company's carbon emissions data is established, and effective carbon emissions management is carried out based on this data, including formulating emission control measures, optimizing emission processes, etc., to help companies better control and manage carbon emissions.

[0059] Carry out carbon emission management of enterprises based on carbon emission data, including:

[0060] Establish a carbon emission early warning indicator set; use the carbon emission early warning indicator set to perform trigger analysis on the carbon emission data, establish an early warning mark based on the trigger analysis result, and perform carbon emission management based on the early warning mark.

[0061] According to the carbon emission data and industry standards of enterprises, establish a set of carbon emission early warning indicators, which include but are not limited to total carbon emissions (monitor whether the total carbon emissions of enterprises exceed the preset limit), emission intensity (evaluate carbon emission efficiency based on emissions per unit of output or production activities), emission trends (analyze the trend of carbon emissions over time and detect whether there are signs of growth), and compliance indicators (check whether the enterprise meets the requirements according to the prescribed carbon emission standards); compare the carbon emission early warning indicator set with real-time carbon emission data, conduct trigger analysis, and evaluate whether any early warning events have been triggered by comparing real-time data with preset thresholds and historical trends. For example, if carbon emissions exceed the set upper limit, an over-limit warning will be triggered; if carbon emission intensity increases sharply in a certain production stage, an abnormal fluctuation warning will be issued; if the emissions of certain equipment change significantly compared with historical data, an equipment abnormality warning will be triggered.

[0062] Based on the results of the trigger analysis, a warning sign is established for each warning event. These signs can be divided into different levels, such as green sign (indicating that carbon emissions are in line with expectations and no measures are needed), yellow sign (indicating that carbon emissions are close to the set threshold and require attention and possible preventive measures), and red sign (indicating that carbon emissions exceed the preset threshold or there are serious abnormalities and immediate response measures are required).

[0063] Carbon emission management of enterprises based on carbon emission data also includes:

[0064] Establish additional attention to corporate carbon emissions based on early warning signs, and use the additional attention to conduct continuous carbon emissions monitoring and management of the company.

[0065] When a warning mark is generated through trigger analysis, the company's carbon emission status will be further marked and paid attention to. If a warning mark is yellow or red, the system will automatically establish additional attention, that is, to track and monitor the warning event at a deeper level. The additional attention is not limited to the monitoring of emissions, but also includes possible sources of abnormalities (such as equipment failure, production process abnormalities, etc.), and monitors and analyzes related parameters that may have an impact (such as energy consumption, raw material use, etc.). For example, when carbon emission data triggers a yellow warning, the system may require regular inspections of certain production lines or special monitoring of emission equipment; if it is a red warning, additional attention will include real-time monitoring of carbon emission levels, equipment operating status, and adjustments to production processes to ensure that problems are discovered and measures are taken in a timely manner. After establishing additional attention, the system will continuously monitor the company's carbon emissions to ensure that potential carbon emission anomalies or risks are discovered in a timely manner during the monitoring period.

[0066] In summary, this application has at least the following technical effects:

[0067] Acquire the carbon emission-related data of enterprises through multiple channels and establish a multi-channel data set; perform channel trust authentication of the data source of the multi-channel data set to establish the first authentication result; read the carbon emission-related equipment of the enterprise, authenticate the authenticity of the data in the multi-channel data set through the carbon emission-related equipment, and establish the second authentication result; perform data analysis on the multi-channel data set to establish data association, which includes horizontal data association and vertical data association; use data association to cross-certify the data in the multi-channel data set, and perform cross-certification verification through the first authentication result and the second authentication result to generate a cross-certification result; establish the carbon emission data of the enterprise based on the cross-certification result, and manage the carbon emission of the enterprise based on the carbon emission data. The technical problems of low efficiency and insufficient accuracy of carbon emission data verification in the existing technology are solved. Through multi-channel data authentication, data association and cross-validation, the credibility and accuracy of the data are improved, and the technical effect of improving the efficiency of carbon emission management is achieved.

[0068] Example 2

[0069] This embodiment discloses a carbon emission data management system, including a memory and a processor. The memory includes a carbon emission data management method program. When the carbon emission data management method program is executed by the processor, the above-mentioned carbon emission data management method is implemented.

[0070] Example 3

[0071] This embodiment discloses a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions corresponding to a carbon emission data management method in an embodiment of the present application. The processor executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory, that is, implementing the above-mentioned carbon emission data management method.

[0072] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0073] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0074] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0075] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0076] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A carbon emission data management method, characterized in that: The method comprises: Obtain carbon emission-related data of enterprises through multiple channels and establish a multi-channel data set; Performing channel trust authentication of data sources on the multi-channel data set to establish a first authentication result; Reading the carbon emission-related equipment of the enterprise, authenticating the authenticity of data in the multi-channel data set through the carbon emission-related equipment, and establishing a second authentication result; Performing data analysis on the multi-channel data sets to establish data associations, wherein the data associations include horizontal data associations and vertical data associations; Using the data association to cross-certify the data in the multi-channel data set, and performing cross-certification verification through the first authentication result and the second authentication result to generate a cross-certification result; The carbon emission data of the enterprise is established according to the cross-certification result, and the carbon emission management of the enterprise is performed according to the carbon emission data.

2. The carbon emission data management method according to claim 1, characterized in that: The cross-certification of the data in the multi-channel data set by using the data association includes: Determine whether there is isolated data during the cross-certification process; If isolated data exists, data value prediction is performed on the isolated data to establish a data value prediction result, including: Among them, V pred Characterize the data value prediction result, X isolated,i The i-th linear feature that characterizes isolated data, The jth secondary feature that characterizes isolated data, X isolated,k The kth nonlinear feature that characterizes isolated data, α i , β j , γ k , δ are the weight coefficients of linear features, quadratic features, nonlinear features and interaction effects, n represents the total number of linear features, i represents any linear feature, m represents the total number of quadratic features, j represents any quadratic feature, p represents the total number of nonlinear features, k represents any nonlinear feature, h(X isolated ,Y) represents the interactive influence function between isolated data and external factor Y; Acquire the historical carbon emission data of the enterprise, perform historical correlation analysis on the isolated data based on the historical carbon emission data, and establish historical correlation analysis results; Data attention is established according to the data value prediction result and the historical correlation analysis result. After the isolated data is checked through the data attention, the check result is added to the cross-certification result to establish the carbon emission data of the enterprise.

3. The carbon emission data management method according to claim 2, characterized in that: The performing data analysis on the multi-channel data sets and establishing data associations includes: Conduct cross-domain data linkage analysis on the data in the multi-channel data set to establish data domain associations; Performing data association analysis on the data in the multi-channel data set under the same timestamp to establish data time association; Performing data type association analysis on the data in the multi-channel data set to establish data type association; The data horizontal association is constructed by using the data domain association, the data time association and the data type association, and the data association is established based on the data horizontal association.

4. The carbon emission data management method according to claim 3, characterized in that: The method comprises: Sending the multi-channel data set to a deep learning model, and establishing data hierarchical associations based on deep learning results; Performing time series analysis on the multi-channel data set to establish data time series association; The data vertical association is established according to the data hierarchical association and the data time series association, and the data association is established based on the data vertical association and the data horizontal association.

5. The carbon emission data management method according to claim 1, characterized in that: The cross-certification verification is performed through the first authentication result and the second authentication result to generate a cross-certification result, including: Establish a cross-certification chain, and perform a trust joint analysis of the cross-certification chain through the first authentication result and the second authentication result, and establish a joint trust identifier mapped to the cross-certification chain, wherein the trust joint analysis includes: a: setting the chain length level of the cross-certification chain, performing trigger analysis of the cross-certification chain according to the chain length level, and generating a level authentication factor; b: using the level authentication factor to perform trust transfer fitting based on the first authentication result and the second authentication result to complete trust joint analysis; A cross-certification result is generated according to the cross-certification chain and the mapped joint trust identifier.

6. The carbon emission data management method according to claim 5, characterized in that: The step of using the level authentication factor to perform trust transfer fitting based on the first authentication result and the second authentication result includes: Determining whether the level authentication factor is lower than a preset factor threshold; If the level authentication factor is lower than the preset factor threshold, generating an additional verification instruction; Perform additional authentication of the trustworthiness of the first authentication result and the second authentication result according to the additional verification instruction; If the additional authentication fails, an exception mark is generated, a corresponding cross-authentication chain mark is generated according to the exception mark, and the trust joint analysis is completed according to the exception mark.

7. The carbon emission data management method according to claim 1, characterized in that: The method further comprises: Establish a set of carbon emission early warning indicators; The carbon emission early warning indicator set is used to perform a trigger analysis on the carbon emission data, an early warning mark is established according to the trigger analysis result, and carbon emission management is performed according to the early warning mark.

8. The carbon emission data management method according to claim 7, characterized in that: The method further comprises: Additional attention to the enterprise's carbon emissions is established based on the early warning mark, and the additional attention is used to carry out continuous carbon emission monitoring and management of the enterprise.

9. A carbon emission data management system, characterized in that: The device comprises a memory and a processor, wherein the memory comprises a carbon emission data management method program, and when the carbon emission data management method program is executed by the processor, the following steps are implemented: Obtain carbon emission-related data of enterprises through multiple channels and establish a multi-channel data set; Performing channel trust authentication of data sources on the multi-channel data set to establish a first authentication result; Reading the carbon emission-related equipment of the enterprise, authenticating the authenticity of data in the multi-channel data set through the carbon emission-related equipment, and establishing a second authentication result; Performing data analysis on the multi-channel data sets to establish data associations, wherein the data associations include horizontal data associations and vertical data associations; Using the data association to cross-certify the data in the multi-channel data set, and performing cross-certification verification through the first authentication result and the second authentication result to generate a cross-certification result; The carbon emission data of the enterprise is established according to the cross-certification result, and the carbon emission management of the enterprise is performed according to the carbon emission data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a carbon emission data management method program, and when the carbon emission data management method program is executed by a processor, the steps of a carbon emission data management method as described in any one of claims 1 to 8 are implemented.

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