Carbon data credibility evaluation method and device, storage medium and program product

Abstract

The invention provides a credibility evaluation method and equipment for carbon data, a storage medium and a program product, relates to the technical field of communication, and is used for improving the reliability of carbon source data and the accuracy of carbon emission accounting statistics. The method comprises the following steps: collecting carbon source data; carrying out credibility verification on the carbon source data acquisition equipment so as to determine first credible data from the carbon source data; the collection device corresponding to the first trusted data is a collection device passing trusted verification; transmitting the first credible data to the first platform and the second platform respectively, and performing sensing detection on the transmission process to obtain second credible data; the second credible data is the first credible data which is successfully transmitted to the first platform and the second platform and passes sensing detection in the transmission process; performing data verification on the second credible data to obtain third credible data passing data verification; and carbon emission statistical accounting is carried out based on the third credible data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a method, device, storage medium and program product for credible evaluation of carbon data. BACKGROUND

[0002] With the gradual promotion and expansion of the national carbon trading market, communication enterprises in some regions and data centers have been gradually included in the compliance and industry supervision, and the carbon data of communication enterprises has become the management of enterprise carbon assets. Therefore, the quality of carbon emission data of communication enterprises is a key prerequisite for ensuring the monitoring and supervision of the communication industry, the management of enterprise carbon assets, and the participation in carbon market transactions. The authenticity and accuracy of the data not only affect the decision-making of communication enterprises (such as base station energy-saving reconstruction, data center energy efficiency improvement, and green network deployment), but also relate to the efficiency of resource allocation of enterprise carbon assets.

[0003] At present, the carbon verification means mainly relies on third-party verification institutions to verify the carbon accounting report submitted by enterprises. The technical means and steps are mainly that the verification personnel collects paper or electronic materials such as enterprise account books, verifies the logic and completeness of the data through on-site investigation and visit, and prepares a carbon verification report according to the accounting method and report guide to support the subsequent carbon compliance, carbon market transactions and other behaviors of enterprises.

[0004] However, the current carbon verification method is inefficient, and the reliability of the data is difficult to fully guarantee, so that the verification result cannot truly reflect the actual situation of enterprise carbon emission. SUMMARY

[0005] The present application provides a method, device, storage medium and program product for credible evaluation of carbon data, which is used to improve the reliability of carbon source data and the accuracy of carbon emission accounting statistics.

[0006] In a first aspect, the present application provides a method for credible evaluation of carbon data, comprising: collecting carbon source data; performing credible verification on the collection device of the carbon source data to determine first credible data from the carbon source data; the collection device corresponding to the first credible data is the collection device that passes the credible verification; transmitting the first credible data to a first platform and a second platform respectively, and performing sensing detection on the transmission process to obtain second credible data; the second credible data is the first credible data that is successfully transmitted to the first platform and the second platform and passes the sensing detection in the transmission process; performing data checking on the second credible data to obtain third credible data that passes the data checking; and performing carbon emission statistical accounting based on the third credible data.

[0007] The technical scheme provided by the application brings at least the following beneficial effects: first, the carbon source data collection device at the data collection source is subjected to trusted verification, ensuring the reliability of the data source, and first trusted data is generated; then, the first trusted data is transmitted to the first platform and the second platform in parallel, and sensing monitoring is implemented during the transmission process, effectively identifying and eliminating data with abnormal transmission, obtaining second trusted data; further, the second trusted data is subjected to data verification, further excluding the case that the data does not meet the requirements, and finally obtaining third trusted data with high credibility, based on which carbon emission statistics and accounting are performed, which can make the carbon emission statistical results more accurate and reliable, provide high-quality data support for enterprise carbon asset management, carbon verification and carbon market transaction, and guarantee the fairness of carbon-related work.

[0008] In a possible implementation, the trusted verification of each collection device includes at least one of the following: verifying whether the collection device is located within a preset carbon verification boundary; verifying whether the identification of the collection device is recorded in a trusted device database; verifying whether the collection device is in an online state; verifying whether the collection device is within a valid period of standardized measurement authentication; and verifying whether the clock of the collection device is synchronized with a standard time.

[0009] In another possible implementation, the sensing detection of the transmission process to obtain the second trusted data includes: during the transmission process, monitoring whether the collection device has an abnormal condition through a multi-modal sensing network; and taking the first trusted data that is successfully transmitted to the first platform and the second platform and the collection device has no abnormal condition during the transmission process as the second trusted data.

[0010] In another possible implementation, the data verification of the second trusted data includes at least one of the following: verifying whether the second trusted data in the first platform and the second platform is consistent; verifying whether the second trusted data has data loss; and verifying whether the second trusted data is within a preset consumption range.

[0011] In another possible implementation, the collection of the carbon source data includes: determining a data collection scheme of the carbon source data based on the type of the carbon source data.

[0012] In another possible implementation, in the case that the carbon source data can be directly collected through a measurement device, the data collection scheme includes: collecting the carbon source data through the measurement device in real time; in the case that the carbon source data cannot be directly collected through the measurement device, the data collection scheme includes at least one of the following: establishing an inventory account book, regularly obtaining energy inventories at different periods, and then calculating energy consumption based on the inventories at different periods to obtain the carbon source data; monitoring emission data of an emission source of energy consumption through a multi-modal sensing network, and determining the carbon source data based on the emission data and an energy consumption evaluation model.

[0013] In another possible implementation, the carbon emission statistical accounting is based on the third trusted data, and includes: data rectification is performed on the suspicious data to obtain rectified data; the data rectification includes retaining data within a tolerance range and eliminating data outside the tolerance range; the suspicious data includes at least one of the following: carbon source data collected by a collection device that fails the trusted verification, the first trusted data that fails the sensor monitoring, and the second trusted data that fails the data verification; and the carbon emission statistical accounting is based on the rectified data and the third trusted data.

[0014] In a second aspect, the present application provides a trusted evaluation device for carbon data, including: a collection module and a processing module; the collection module is configured to collect carbon source data; the processing module is configured to perform trusted verification on a collection device of the carbon source data to determine first trusted data from the carbon source data; the collection device corresponding to the first trusted data is a collection device that passes the trusted verification; the first trusted data is transmitted to a first platform and a second platform respectively, and sensor monitoring is performed on a transmission process to obtain second trusted data; the second trusted data is the first trusted data that is successfully transmitted to the first platform and the second platform and passes the sensor monitoring in the transmission process; the second trusted data is subjected to data verification to obtain third trusted data that passes the data verification; and the carbon emission statistical accounting is based on the third trusted data.

[0015] In a possible implementation, the processing module is specifically configured to verify whether the collection device is located within a preset carbon verification boundary; verify whether an identifier of the collection device is recorded in a trusted device database; verify whether the collection device is in an online state; verify whether the collection device is in an effective period of standardized measurement authentication; and verify whether a clock of the collection device is synchronized with a standard time.

[0016] In another possible implementation, the processing module is specifically configured to monitor, in the transmission process, whether the collection device has an abnormal situation through a multi-modal sensor network; and the first trusted data that is successfully transmitted to the first platform and the second platform and has no abnormal situation in the transmission process of the collection device is taken as the second trusted data.

[0017] In another possible implementation, the processing module is specifically configured to verify whether the second trusted data in the first platform and the second platform is consistent; verify whether the second trusted data has data loss; and verify whether the second trusted data is within a preset consumption range.

[0018] In another possible implementation, the processing module is specifically configured to determine a data collection scheme of the carbon source data based on a type of the carbon source data.

[0019] In another possible implementation, the processing module is specifically configured to, in a case where the carbon source data can be directly collected by the metering device, the data collection scheme comprises: collecting the carbon source data in real time by the metering device; in a case where the carbon source data cannot be directly collected by the metering device, the data collection scheme comprises at least one of the following: establishing an inventory account book, obtaining energy inventories at different time periods regularly, and then calculating energy consumption based on the inventories at different time periods to obtain the carbon source data; monitoring emission data of an emission source of energy consumption through a multi-modal sensor network, and determining the carbon source data based on the emission data and an energy consumption evaluation model.

[0020] In another possible implementation, the processing module is specifically configured to perform data rectification on the suspicious data to obtain rectified data; wherein the data rectification comprises retaining data within a tolerance range and eliminating data outside the tolerance range; the suspicious data comprises at least one of the following: carbon source data collected by a collection device that does not pass a trusted verification, first trusted data that does not pass a sensor monitoring, and second trusted data that does not pass a data check; and the carbon emission statistical accounting is performed based on the rectified data and third trusted data.

[0021] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory; the memory stores instructions executable by the processor; and the processor is configured to execute the instructions, so that the electronic device implements the method of the first aspect.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, comprising: computer software instructions; when the computer software instructions run in an electronic device, the electronic device implements the method of the first aspect.

[0023] In a fifth aspect, the present application provides a computer program product, comprising a computer program; when the computer program runs in an electronic device, the electronic device implements the method of the first aspect.

[0024] The beneficial effects of the second aspect to the fifth aspect are described with reference to the corresponding description of the first aspect, and will not be repeated. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 An application environment schematic diagram of the carbon data trusted evaluation method provided by the present application; Figure 2 A flowchart of the carbon data trusted evaluation method provided by the present application; Figure 3 A flowchart of the second trusted data data check provided by the present application; Figure 4 A flowchart of the carbon emission statistical accounting provided by the present application; Figure 5 Another flowchart of a method for evaluating the credibility of carbon data according to the present application is provided; Figure 6 Another flowchart of a method for evaluating the credibility of carbon data according to the present application is provided; Figure 7 A composition diagram of a device for evaluating the credibility of carbon data according to the present application is provided. Figure 8 A composition diagram of an electronic device according to the present application is provided. DETAILED DESCRIPTION

[0026] A method for evaluating the credibility of carbon data according to the present application will be described in detail below with reference to the accompanying drawings.

[0027] The term "and / or" in this document merely describes an association relationship of associated objects, and can represent three relationships, for example, A and / or B can represent three cases of A alone, A and B together, and B alone.

[0028] The terms "first" and "second" and the like in the specification and drawings of the present application are used to distinguish different objects or different treatments of the same object, and are not used to describe a specific order of the objects.

[0029] In addition, the terms "include" and "have" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but can optionally include other steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0030] It should be noted that in the embodiments of the present application, the words "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design schemes. Rather, the use of "exemplary" or "for example" is intended to present relevant concepts in a concrete manner.

[0031] In order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the same items or similar items with basically the same functions and effects are distinguished by "first", "second" and the like. Those skilled in the art can understand that "first", "second" and the like are not limited in number and execution order.

[0032] In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0033] With the steady progress and expansion of the national carbon market, the regional network facilities and large data centers of communication enterprises have been gradually included in the carbon compliance supervision system. In this context, carbon emission data has become an important object of asset management for communication enterprises, and its data quality is directly related to the effectiveness of industry supervision, the efficiency of enterprise carbon asset allocation, and the market transaction credibility. Currently, communication enterprises need to carry out systematic carbon data management for their core emission scenarios, including communication base stations, data centers, communication machine rooms, and business office sites, to support carbon inventory, carbon accounting, and carbon compliance, etc.

[0034] Currently, in the process of participating in carbon compliance and carbon trading, communication enterprises first need to carry out carbon inventory for their core carbon emission situations, submit carbon accounting reports, cover key scenarios such as communication base stations, data centers, communication machine rooms, and business offices, and clarify the carbon emission accounting boundaries and data sources of various energy consumptions in each scenario. In order to avoid the omission, calculation error, or even fraud of carbon emission data, carbon verification is also needed. The current carbon verification method mainly relies on third-party verification agencies to verify the carbon accounting reports submitted by enterprises. The technical means and steps are mainly to verify the logic and completeness of the data by collecting paper or electronic materials such as enterprise account books, and on-site investigation and verification of data by verification personnel. After verifying the data, carbon verification reports are prepared according to the accounting methods and report guidelines to support the subsequent carbon compliance and carbon market trading of enterprises. This third-party verification method effectively fills the gap of insufficient self-checking ability of enterprises in the early stage of carbon market development, provides a basic framework for standardized carbon data accounting, and plays a certain role in promoting the standardization of carbon data management in the early stage of market regulation.

[0035] However, with the continuous expansion of the scope of the national carbon market and the significant increase in the number of enterprises involved, as well as the increasing requirements of regulatory departments for data timeliness and credibility, the existing technical path mainly relying on third-party manual verification has exposed increasingly serious limitations. First, the verification efficiency is low. The current verification method highly depends on human resources, which is restricted by geographical limitations and the number of professionals. The complete verification cycle of a single enterprise usually takes 1 to 2 months, and on-site checking and comparing account books, investigating and visiting the production environment of enterprises also takes several days. For enterprises with large and diverse business scales, the time required is even longer, which cannot meet the demand for rapid verification of large-scale carbon data. Second, the reliability of verification is questionable. The verification results highly depend on the subjective judgment of the verification personnel, and the differences in their professional competence, the inconsistent execution of verification standards, and possible interest associations provide operational space for data fraud, such as false reporting and tampering with emission data. Such behaviors not only lead to the inability of verification data to truly reflect the carbon emission level of enterprises, but also distort the pricing basis of carbon credit assets and weaken the market credibility of trading products, ultimately impacting the fairness and credibility of the carbon market.

[0036] To address the aforementioned technical issues, this application provides a method for the reliable assessment of carbon data. The method involves: first, verifying the reliability of the carbon source data acquisition equipment at the data acquisition source to ensure the reliability of the data source and generate first reliable data; then, transmitting the first reliable data in parallel to a first platform and a second platform, implementing sensor monitoring during transmission to effectively identify and eliminate abnormal data, thus obtaining second reliable data; next, verifying the second reliable data to further eliminate data that does not meet requirements, ultimately obtaining highly reliable third reliable data. Carbon emission statistical accounting based on this third reliable data makes the carbon emission statistical results more accurate and reliable, providing high-quality data support for enterprise carbon asset management, carbon verification, and carbon market trading, and ensuring the fairness of carbon-related work.

[0037] The embodiments provided in this application will now be described in detail with reference to the accompanying drawings.

[0038] The carbon data reliability assessment method provided in this application can be applied to, for example... Figure 1 The application environment shown. For example... Figure 1 As shown, the application environment includes a data acquisition device 10 and a processing device 20. The data acquisition device 10 and the processing device 20 are interconnected.

[0039] In some embodiments, the data acquisition device 10 is responsible for sensing and measuring the state or parameters of the target object, capturing real and complete raw data. In this application, the data acquisition device 10 is used to collect carbon source data (such as electricity, gas, fuel consumption, etc.) to provide a data foundation for subsequent carbon verification.

[0040] In some embodiments, the data acquisition device 10 may be a metering device with communication functions, such as a smart meter, gas meter, or steam flow meter, deployed at a fixed emission source (such as a communication base station or data center), or it may be a fuel consumption monitoring terminal installed at a mobile emission source (such as a communication support vehicle). This application does not limit the specific device form of the data acquisition device 10.

[0041] In some embodiments, the processing device 20 receives raw data captured by the acquisition device 10 and verifies the credibility of the raw data to obtain highly credible data that can be used for accurate calculation. In this application, the processing device 20 verifies the identity and status of the acquisition device to filter out first credible data with reliable sources, then transmits the first credible data in parallel to the first platform and the second platform, and performs sensor monitoring during the transmission process to generate second credible data with tamper-proof characteristics. The second credible data is then verified to obtain highly credible third credible data, and finally, the carbon emission statistics and calculations are completed based on the third credible data.

[0042] In some embodiments, the processing device 20 can be a server cluster composed of multiple servers, or a single server, or a computer, or a processor or processing chip in a server or computer, etc. The embodiments of the present application do not limit the specific device form of the processing device 20.

[0043] It should be noted that the system architecture described in the embodiments of the present application is for more clearly illustrating the technical solutions of the embodiments of the present application, and does not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of the system architecture, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0044] Referring to Figure 2 The flowchart of a carbon data credibility evaluation method provided by the embodiments of the present application is shown in FIG. 2. As shown in FIG. 2, the carbon data credibility evaluation method provided by the present application specifically includes the following steps S201-S205. Figure 2

[0045] S201, collecting carbon source data.

[0046] The carbon source data mainly includes the consumption data of various types of energy and materials (such as the consumption of fuels such as electricity, natural gas, diesel, gasoline, etc.) in the communication enterprise operation process, and refers to the data of greenhouse gases such as carbon dioxide directly or indirectly caused by the enterprise in the production and operation activities.

[0047] In some embodiments, based on the type of carbon source data, a data collection scheme of the carbon source data is determined.

[0048] As an implementable manner, in the case that the carbon source data can be directly collected by a metering device, the data collection scheme includes: collecting the carbon source data in real time through the metering device.

[0049] For example, for the energy (such as electricity, heat, natural gas, etc.) that can be directly metered by a special meter, a real-time automatic collection scheme can be adopted, and an intelligent metering device (such as a smart meter, a heat meter, a gas meter, etc.) with communication function is installed at the energy input port to collect the carbon source data in real time.

[0050] As another implementable manner, in the case that the carbon source data cannot be directly collected by a metering device, the data collection scheme includes at least one of the following: Data collection scheme one: establishing an inventory account, regularly obtaining energy inventory at different periods, and then calculating energy consumption based on the inventory at different periods to obtain the carbon source data.

[0051] ​For example, for solid energy sources (such as coal) that cannot directly collect consumption through a meter, an indirect calculation method based on an intelligent inventory account is used. The initial inventory, the current inventory, and the final inventory data are automatically obtained or input periodically (such as daily or weekly), and the actual consumption in a specific period is automatically calculated according to the calculation method of "consumption = initial inventory + current inventory - final inventory - reasonable loss", thereby indirectly obtaining the carbon source data.

[0052] For example, for liquid energy sources (such as diesel, gasoline, etc.) that cannot directly collect consumption through a meter, an indirect calculation method based on intelligent liquid level monitoring is used. A liquid level sensor is deployed in the oil tank to automatically collect the initial and final liquid level data periodically (such as daily or weekly), and the liquid level difference is calculated. Combined with the volume calibration parameters of the tank (i.e. the volume corresponding to the unit height) and the oil density, the actual consumption in the statistical period is automatically calculated according to the calculation method of "consumption = liquid level difference x unit height volume x oil density", thereby indirectly obtaining the carbon source data.

[0053] Data collection scheme two: monitor the emission data of the emission source of the energy consumption through a multi-modal sensing network, determine the carbon source data based on the emission data and the energy consumption evaluation model.

[0054] For example, mobile emission sources that cannot directly collect energy consumption through a meter, such as mobile vehicles using gasoline and diesel. The vehicle is equipped with an intelligent oil consumption monitoring module to collect oil consumption and upload it to the algorithm platform through a multi-modal sensing network.

[0055] For example, for mobile emission sources (such as communication support vehicles and engineering vehicles), it is difficult to directly install a fixed meter, and an intelligent monitoring scheme based on a multi-modal sensing network can be used. By installing a high-precision oil consumption sensor in the vehicle oil system, real-time collection of oil consumption, mileage, engine operating conditions, and other multi-modal data is performed, and the original data is corrected and compensated by combining a vehicle energy consumption evaluation model (such as a dynamic oil consumption model based on mileage and load), and finally the accurate fuel consumption is output as the carbon source data.

[0056] S202, performing trusted verification on the carbon source data collection device to determine first trusted data from the carbon source data.

[0057] Among them, the collection device corresponding to the first trusted data is the collection device that passes the trusted verification.

[0058] In some embodiments, the trusted verification of each collection device includes verifying whether the collection device is located within a preset carbon verification boundary.

[0059] For example, the unique identity document (ID) reported by the collection device and the geographic coordinate information obtained by the built-in positioning module are compared with the electronic fence data of the carbon verification boundary. If the collection device coordinates fall within the boundary polygon, it is determined to be in compliance with the location, and the collected carbon source data is allowed to enter the subsequent processing flow. If the collection device coordinates are outside the boundary, the device is immediately marked as "untrusted", and all carbon source data collected by the collection device is rejected.

[0060] In some embodiments, the trusted verification of each collection device includes verifying whether the identity of the collection device is recorded in the trusted device database.

[0061] The trusted device identity database is a pre-established, strictly audited and certified collection device whitelist database for storing the identity information (such as identity identification) of legal devices authorized to participate in carbon data collection.

[0062] For example, the ID of the collection device is identified and matched with the existing trusted device ID database of the first platform (such as an algorithm platform that stores collection data and performs trusted verification). If the collection device ID exists in the database and is in normal state, it is determined to be a legal device, and the carbon source data can enter the subsequent processing flow. If the matching fails or the collection device ID is not included, the collection device is immediately marked as "unauthorized", and all carbon source data collected by the collection device is rejected.

[0063] In some embodiments, the trusted verification of each collection device includes verifying whether the collection device is in an online state.

[0064] For example, the connection between the collection device and the platform can be periodically determined to be normal through the communication link state. If the communication link of the collection device is smooth, it is determined to be in an online state, and the carbon source data can continue to participate in the subsequent processing flow. If the link of the collection device is interrupted for a continuous number of detection periods, it is determined to be in an offline state, and the carbon source data collected by the collection device during the fault period is rejected.

[0065] In some embodiments, the trusted verification of each collection device includes verifying whether the collection device is within the effective period of standardized metrological certification.

[0066] For example, the validity of the standardization measurement authentication of the collection device is verified by associating the authentication database of the authoritative measurement agency or checking the local certificate of the device. If the collection device is within the effective period of the standardization measurement authentication, it indicates that the measurement accuracy of the device meets the standard, and the carbon source data collected by the device can enter the subsequent process. If the collection device is not within the effective period of the standardization measurement authentication, the carbon source data collected by the collection device is not reliable, and all the carbon source data collected by the collection device needs to be marked as “to be verified” state, and then enters the data rectification process. It can be disposed by manual audit, reference to historical reliable data estimation or waiting for the collection device to re-calibrate and supplement the data, so as to ensure that the final carbon data used for accounting are all from the collection device with controlled measurement accuracy.

[0067] In some embodiments, the trusted verification of each collection device includes verifying whether the clock of the collection device is synchronized with the standard time.

[0068] For example, the deviation value of the clock of the collection device is periodically checked by accessing the standard time published by the official time center. If the clock deviation is within the allowable threshold (such as ±1 second), it is determined that the synchronization is successful, the data timestamp is reliable, and the carbon source data collected by the collection device can enter the subsequent processing flow. If the clock deviation is out of limit or the time signal cannot be obtained, it is determined that the step is lost, the collection device is marked as “clock abnormal”, and the carbon source data generated by the collection device is marked as “to be verified” state, and then enters the data rectification process.

[0069] S203, transmit the first trusted data to the first platform and the second platform respectively, and perform sensing detection on the transmission process to obtain second trusted data.

[0070] The second trusted data is the first trusted data that is successfully transmitted to the first platform and the second platform and passes the sensing detection in the transmission process.

[0071] In some embodiments, during the transmission process, whether the collection device has abnormal conditions is monitored through a multi-modal sensing network; the first trusted data that is successfully transmitted to the first platform and the second platform and does not have abnormal conditions in the transmission process of the collection device is taken as the second trusted data.

[0072] Exemplarily, the first trusted data can be synchronously copied by the routing nodes of the multi-modal sensor network into two independent data streams, transmitted to the first platform (such as an algorithm software platform) as a main business platform for real-time calculation and analysis, and transmitted to the second platform (such as a twin database independently deployed on different physical servers) as a secure backup. During the transmission and storage process, the system synchronously records the key traceability information of the first trusted data in the first platform and the second platform, including but not limited to the collection device ID, the physical location of the collection device, the data collection timestamp, etc. Through the double-path parallel transmission and full-link traceability information recording mechanism, the integrity and non-tamperability of the carbon source data can be guaranteed, and the basis for the trustworthiness of the carbon source data is provided.

[0073] In some embodiments, the transmission status of the two data stream transmission links is monitored in real time. Only the first trusted data that is confirmed to be successfully transmitted to the first platform and the second platform and has no abnormality in the whole transmission process is determined as the second trusted data. Through double-path redundant storage and transmission monitoring, the traceability and high trustworthiness of the data are ensured.

[0074] Exemplarily, the multi-modal sensors (such as vibration sensors, temperature sensors, and humidity sensors) deployed on the collection device monitor the device operating state and environmental parameters in real time. If abnormal vibration, over-limit temperature of the terminal, or excessive environmental humidity, which may cause measurement deviation or failure, is monitored, the collection device is marked as “sensing abnormality”, and the data is marked as “to be verified”. Subsequently, the abnormal data enters the rectification process. If there is no sensing abnormality, a trusted label is attached to the corresponding collection data to obtain the second trusted data, and the second trusted data is synchronously transmitted to the algorithm software platform and the independently deployed twin database through the “one number double sending” mechanism.

[0075] S204, data verification is performed on the second trusted data to obtain third trusted data passing the data verification.

[0076] In some embodiments, the data verification on the second trusted data includes verifying whether the second trusted data in the first platform and the second platform is consistent.

[0077] Exemplarily, the same data in the first platform (such as an algorithm software platform) and the second platform (such as a twin database) is compared, and the consistency of the data is verified to ensure the authenticity of the second trusted data. If the two are completely consistent, it is determined that the data has not been tampered with in the transmission and storage process, and is allowed to enter the subsequent verification process. If there is inconsistency, the second trusted data is marked as “untrusted”, and the data is excluded.

[0078] In some embodiments, the data verification on the second trusted data includes verifying whether the second trusted data has data missing.

[0079] For example, to check whether the second trusted data has data loss, the core fields of each data record can be checked for non-nullity, format, and reasonableness. Among them, the field non-nullity check ensures that key fields such as collection device ID, geographic coordinates, and core measurement values are not empty or illegal placeholders; the format and value range check verifies whether the timestamp conforms to the standard format and whether the numerical data (such as energy consumption value) is within a reasonable range; the time sequence completeness check checks the continuity of the time sequence of the data stream collected at a fixed frequency, such as detecting whether data is successfully received at the expected collection time point, identifying “faults” in the data sequence, and if there is a missing data point, marking the corresponding time period as “time sequence interruption”. If any of the checks fails, the data is marked as “field missing” or “value abnormal”, and the data is excluded.

[0080] In some embodiments, the data verification of the second trusted data includes: checking whether the second trusted data is within a preset consumption range.

[0081] For example, for carbon source data with multiple levels of measurement relationships (such as total circuit and branch electric quantity), the system automatically calculates the sum of each branch data and compares it with the total circuit data. If the deviation between the two exceeds a preset threshold (such as η), it is determined that the data is abnormal, indicating that there may be a measurement error or data loss, and the subsequent data correction process is entered.

[0082] For example, a dynamic correlation model is constructed for data reasonableness analysis in combination with key parameters such as collection time, environmental temperature and humidity, vehicle mileage, and device running time. For example, if the environmental temperature is high but the air conditioning system consumes significantly less power than expected, or the vehicle mileage and the fuel consumption calculated based on unit fuel consumption have a significant deviation, it indicates that the data may be abnormal, and the subsequent data correction process is entered.

[0083] As shown in Figure 3 The data verification process of the second trusted data is as follows: first, determine whether the same data in the algorithm software platform and the twin database is consistent. If not, it is determined that the data is inconsistent and the data is directly excluded. If it is consistent, check whether there is data loss. If there is a loss, trigger the data correction process. If the data is complete, further perform logical reasonableness check to determine whether the data is within the preset reasonable consumption range. If it exceeds the reasonable range, enter the data correction process. If all checks pass, it is determined that the third trusted data, and it is stored in the twin database for subsequent carbon emission statistical accounting.

[0084] S205, based on the third trusted data, perform carbon emission statistical accounting.

[0085] In some embodiments, asFigure 4 As shown in the flow of carbon emission statistical accounting, first, multi-source energy consumption statistics are performed, based on the third trusted data, the consumption of various types of energy and materials in the statistical period is classified and summarized, such as electricity, natural gas, diesel, etc., to form a standardized energy consumption statistical list; then, dynamic emission factor matching is performed, which can automatically match the applicable carbon emission factor from the standard emission factor library according to the energy consumption type, region and source, for example, the power consumption can select the corresponding regional power grid emission factor according to the source of the power grid, or select a more accurate factor according to the green power consumption certificate; then, standardized carbon emission accounting is performed, which can calculate the carbon emissions corresponding to various types of energy consumption according to the standard accounting model of “emission = activity data x emission factor”, and then add up to obtain the total carbon emissions in the accounting period; finally, the carbon emission statistical result is obtained, and the accounting result is stored in a trusted manner, and the detailed process data of carbon emission accounting (such as activity data, used emission factor, calculation model and result, etc.) is stored in a trusted manner. Hash storage to the second platform (such as the twin database), ensuring that the whole process is traceable and tamper-proof, and providing a trusted data basis for carbon verification, trading and disclosure.

[0086] Based on the above embodiments, first, the carbon source data collection device at the data collection source is verified for trustworthiness to ensure the reliability of the data source, and the first trusted data is generated; then, the first trusted data is transmitted to the first platform and the second platform in parallel, and sensing monitoring is implemented during transmission to effectively identify and eliminate abnormal data during transmission, obtaining the second trusted data; further, the second trusted data is checked for data, further excluding data that does not meet the requirements, and finally obtaining highly trusted third trusted data, based on which carbon emission statistical accounting is performed, which can make the carbon emission statistical result more accurate and reliable, and provide high-quality data support for enterprise carbon asset management, carbon verification and carbon market trading, and ensure the fairness of carbon-related work.

[0087] In some embodiments, as shown in Figure 5 The above step S205 of performing carbon emission statistical accounting based on the third trusted data can be specifically implemented as: S501, data correction is performed on suspicious data to obtain corrected data.

[0088] The data correction includes retaining data within the tolerance range and eliminating data outside the tolerance range; the suspicious data includes at least one of the following: carbon source data collected by a collection device that does not pass the trust verification, first trusted data that does not pass the sensing monitoring, and second trusted data that does not pass the data check.

[0089] In some embodiments, for carbon source data collected by a collection device whose standard measurement is not within the effective period or whose time is inconsistent with the standard time, data correction is performed.

[0090] Exemplarily, for the carbon source data suspected due to expired metering authentication, the historical trusted data collected by the device within the authentication validity period in the twin database is called as a reference to compare whether there is a significant deviation in the collection frequency, value distribution and other key characteristics of the current carbon source data. If the deviation is within the preset tolerance range, the current carbon source data is considered to be trusted, which is stored in the database and enters the accounting process. If the deviation is out of limit, the data is considered to be untrusted and is rejected and an alarm is triggered.

[0091] Exemplarily, for the data with different timestamps, the deviation value from the standard time is first quantified, and then the data timestamps are batch corrected based on the time mapping model. After correction, a% of the data is randomly extracted for time consistency verification, checking whether the time sequence is continuous, without jumping and without repetition. The corrected data that passes the verification is marked as trusted data and stored in the twin database and enters the accounting process. The carbon source data that does not pass the verification is rejected and an alarm is triggered.

[0092] In some embodiments, for the first trusted data that does not pass the sensor monitoring, data correction is performed.

[0093] Exemplarily, the historical third trusted data generated by the collection device in the normal state in the twin database is called as a reference to analyze the deviation of the current data from the historical reference by comparing the collection frequency, value fluctuation range and other key characteristics of the current data. If the deviation is within the preset tolerance range, the data is considered to be trusted, and the data is stored in the twin database and then enters the carbon emission statistical accounting process. If the deviation is out of limit, the data is considered to be untrusted, and the data is rejected and an alarm is triggered.

[0094] In some embodiments, for the second trusted data with missing data in the data verification or consumption amount not within the preset range, data correction is performed.

[0095] Exemplarily, for the suspicious data with "field missing" or "data missing", the following correction process is performed: first, it is detected whether the same type of missing occurs in the subsequent collection period. If the subsequent data is complete, it is determined to be an accidental missing, and the data is marked as trusted data and enters the subsequent processing process. If the missing continues to occur, the criticality of the missing field is further analyzed. For the missing of non-critical fields, the historical third trusted data in the twin database is used to perform data completion by using a linear interpolation algorithm, and the completion result is marked as trusted data. If the missing field is a key information such as device ID, timestamp or core metering value, the data is directly rejected and an alarm mechanism is triggered.

[0096] Exemplarily, for the second trusted data whose consumption amount is not within the preset range, the historical third trusted data generated by the collection device in the normal state in the twin database is called as a reference, combined with power quality analyzer data, payment electronic account book, etc. for joint analysis to identify the root cause of data anomaly (such as harmonic interference or equipment failure). If the problem point is in the lower loop, the total loop data is marked with a trusted label. If the problem point is in the total loop, the sum of the lower loop data is marked with a trusted label. The data collected by the collection device with problems is removed and an alarm is prompted.

[0097] Exemplarily, for the second trusted data whose consumption amount is not within the preset range, a rectification method combining theoretical calculation and manual verification is adopted. The theoretical energy consumption value is calculated according to the collection time, environmental temperature and humidity, driving mileage, running time length and other key parameters, and is compared with the actual collection value. For data with significant deviation, manual on-site verification is required to confirm the abnormal reason (such as equipment failure or input error). The data confirmed by manual audit to be correct is marked as trusted data. The data confirmed to be abnormal is removed.

[0098] S502, based on the rectified data and the third trusted data, carbon emission statistical accounting is performed.

[0099] In some embodiments, first, the rectified data and the third trusted data directly verified are integrated to form a complete trusted carbon emission source data set. Subsequently, according to the national or industry standard accounting guidelines (such as “Greenhouse Gas Emission Accounting and Reporting Requirements”), the emission factors corresponding to each type of energy consumption (such as electricity, natural gas, diesel, etc.) are matched, and the carbon emission is calculated through the calculation model of “activity data x emission factor”. Finally, the system aggregates the classification accounting results into total emissions, and synchronously stores the accounting process data, emission factor source and results to the twin database, ensuring that the accounting process is traceable and providing an authoritative data basis for carbon trading.

[0100] Based on the above embodiments, through data rectification, the utilization rate of data and the accuracy of carbon emission statistical accounting are improved. The suspicious data that does not pass the device trusted verification, sensor monitoring or data verification is not simply removed, but is disposed through tolerance judgment or repair strategy. For data with deviation within the tolerance range, it can be repaired and restored based on the historical third trusted data model, time series analysis, etc. to convert it into trusted data. For abnormal data exceeding the tolerance range, it is removed decisively to avoid interference with the overall accounting result. This rectification process effectively retains valid data while effectively removing invalid interference. Finally, based on the optimized data set formed by the rectified data and the third trusted data, carbon emission statistical accounting is performed, which can improve the accuracy and reliability of the accounting result.

[0101] The method for evaluating the credibility of carbon data according to the embodiments of the present application will be described below with reference to a specific embodiment. The specific implementation process of the method is shown in Figure 6

[0102] S601, according to the type of carbon source data, determine the data collection scheme of the carbon source data, and collect the carbon source data.

[0103] S602, judge whether the collection equipment for collecting the carbon source data is within the carbon accounting boundary.

[0104] For example, if the collection equipment for collecting the carbon source data is within the carbon accounting boundary, jump to step S603; if the collection equipment for collecting the carbon source data is not within the carbon accounting boundary, jump to step S6014.

[0105] S603, judge whether the identification of the collection equipment for collecting the carbon source data is recorded in the database of the trusted equipment.

[0106] For example, if the identification of the collection equipment for collecting the carbon source data is recorded in the database of the trusted equipment, jump to step S604; if the identification of the collection equipment for collecting the carbon source data is not recorded in the database of the trusted equipment, jump to step S6014.

[0107] S604, judge whether the collection equipment for collecting the carbon source data is in an online state.

[0108] For example, if the collection equipment for collecting the carbon source data is in an online state, jump to step S605; if the collection equipment for collecting the carbon source data is not in an online state, jump to step S6014.

[0109] S605, judge whether the collection equipment for collecting the carbon source data is within the effective period of the standardized measurement authentication.

[0110] For example, if the collection equipment for collecting the carbon source data is within the effective period of the standardized measurement authentication, jump to step S606; if the collection equipment for collecting the carbon source data is not within the effective period of the standardized measurement authentication, jump to step S6012 for data rectification.

[0111] S606, judge whether the clock of the collection equipment for collecting the carbon source data is synchronized with the standard time.

[0112] For example, if the clock of the collection equipment for collecting the carbon source data is synchronized with the standard time, jump to step S607; if the clock of the collection equipment for collecting the carbon source data is not synchronized with the standard time, jump to step S6012 for data rectification.

[0113] S607, obtain first trusted data, transmit and store the first trusted data to an algorithm software platform and a twin database, and perform sensing monitoring on the transmission process.​

[0114] S608, judging whether the sensor monitoring is abnormal.

[0115] For example, if the sensor monitoring is not abnormal, jump to step S609; if the sensor monitoring is abnormal, jump to step S6012 to perform data correction.

[0116] S609, obtaining second trusted data and storing the second trusted data in the twin database, and performing data verification on the second trusted data.

[0117] S6010, judging whether the data verification is abnormal.

[0118] For example, if the data verification is not abnormal, jump to step S6011; if the data verification is abnormal, jump to step S6012 to perform data correction.

[0119] S6011, obtaining third trusted data and storing the third trusted data in the twin database.

[0120] S6012, data correction process.

[0121] S6013, judging whether the corrected data is available.

[0122] For example, if the corrected data is available, jump to step S6015; if the corrected data is not available, jump to step S6014.

[0123] S6014, discarding the data.

[0124] S6015, performing carbon emission statistical accounting based on the corrected trusted data and the third trusted data.

[0125] It can be seen that the above mainly introduces the scheme provided by the embodiments of the present application from the perspective of method. In order to realize the above functions, the embodiments of the present application provide corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should easily realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in the present text can be realized in the form of hardware or combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driven hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0126] The embodiments of the present application can divide the functional modules of the carbon data trusted evaluation device according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated in one processing module. The above integrated module can be realized in the form of hardware or in the form of a software functional module. Optionally, the division of the modules in the embodiments of the present application is illustrative, and is only a logical function division. In actual implementation, there can be another division manner.

[0127] In some embodiments, the present application also provides a carbon data trusted evaluation device. The carbon data trusted evaluation device can include one or more functional modules for implementing the carbon data trusted evaluation method of the above method embodiments.

[0128] For example, Figure 7 A composition schematic diagram of a carbon data trusted evaluation device provided by the embodiments of the present application is shown in FIG. 7. Figure 7 As shown in FIG. 7, the carbon data trusted evaluation device 700 includes a collection module 701 and a processing module 702.

[0129] The collection module 701 is configured to collect carbon source data. The processing module 702 is configured to perform trusted verification on a collection device of the carbon source data, to determine first trusted data from the carbon source data. The collection device corresponding to the first trusted data is a collection device that passes the trusted verification. The processing module 702 is configured to transmit the first trusted data to a first platform and a second platform respectively, and perform sensing detection on the transmission process to obtain second trusted data. The second trusted data is the first trusted data that is successfully transmitted to the first platform and the second platform, and the transmission process passes the sensing detection. The processing module 702 is further configured to perform data verification on the second trusted data to obtain third trusted data that passes the data verification. The processing module 702 is further configured to perform carbon emission statistical accounting based on the third trusted data.

[0130] In some embodiments, the processing module 702 is specifically configured to verify whether the collection device is located within a preset carbon verification boundary; verify whether an identifier of the collection device is recorded in a trusted device database; verify whether the collection device is in an online state; verify whether the collection device is in an effective period of standardized measurement authentication; and verify whether a clock of the collection device is synchronized with a standard time.

[0131] In other embodiments, the processing module 702 is specifically configured to monitor, in the transmission process, whether the collection device has an abnormal situation through a multi-modal sensing network; and take, as the second trusted data, the first trusted data that is successfully transmitted to the first platform and the second platform, and the collection device does not have an abnormal situation in the transmission process.

[0132] In some embodiments, the processing module 702 is specifically configured to verify whether the second trusted data in the first platform and the second platform is consistent; verify whether the second trusted data is missing; and verify whether the second trusted data is within a preset consumption range.

[0133] In some embodiments, the processing module 702 is specifically configured to determine a data collection scheme of the carbon source data based on a type of the carbon source data.

[0134] In some embodiments, the processing module 702 is specifically configured to, in a case that the carbon source data can be directly collected by the metering device, the data collection scheme comprises: collecting the carbon source data in real time by the metering device; in a case that the carbon source data cannot be directly collected by the metering device, the data collection scheme comprises at least one of the following: establishing an inventory account, periodically obtaining energy inventories in different periods, and then calculating energy consumption based on the inventories in different periods to obtain the carbon source data; monitoring emission data of an emission source of energy consumption through a multi-modal sensor network, and determining the carbon source data based on the emission data and an energy consumption evaluation model.

[0135] In some embodiments, the processing module 702 is specifically configured to perform data rectification on the suspicious data to obtain rectified data; wherein the data rectification comprises retaining data within a tolerance range and eliminating data outside the tolerance range; the suspicious data comprises at least one of the following: carbon source data collected by a collection device that does not pass the trusted verification, first trusted data that does not pass the sensor monitoring, and second trusted data that does not pass the data verification; and performing carbon emission statistical accounting based on the rectified data and third trusted data.

[0136] In a case that the functions of the above integrated modules are implemented in the form of hardware, the embodiments of the present application provide a possible structural diagram of the electronic device involved in the above embodiments. As shown in the figure, the electronic device 800 comprises a processor 802, a communication interface 803, and a bus 804. Optionally, the electronic device 800 can further comprise a memory 801. Figure 8

[0137] The processor 802 can be various exemplary logical blocks, modules and circuits described in combination with the disclosure of the present application. The processor 802 can be a central processor, a general processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure of the present application. The processor 802 can also be a combination of computing functions, such as one or more microprocessor combinations, DSP and microprocessor combinations, etc.

[0138] ​The communication interface 803 is configured to connect with other devices through a communication network. The communication network can be an Ethernet, a wireless access network, a wireless local area network (WLAN), etc.

[0139] The memory 801 can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to this.

[0140] As a possible implementation, the memory 801 can exist independently of the processor 802, and the memory 801 can be connected with the processor 802 through the bus 804, for storing instructions or program codes. When the processor 802 invokes and executes the instructions or program codes stored in the memory 801, the trusted evaluation method of carbon data provided by the embodiments of the present application can be implemented.

[0141] In another possible implementation, the memory 801 can also be integrated with the processor 802.

[0142] The bus 804 can be an extended industry standard architecture (EISA) bus, etc. The bus 804 can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 8 Only one thick line is used in the figure to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0143] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of functional modules is taken as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the service calling device is divided into different functional modules to complete all or part of the functions described above.

[0144] The embodiments of the present application further provide a computer readable storage medium. All or part of the processes in the method embodiments above can be instructed by computer instructions to complete relevant hardware, and the program can be stored in the computer readable storage medium. When the program is executed, the program can include the processes of the method embodiments above. The computer readable storage medium can be the memory of any of the foregoing embodiments. The computer readable storage medium can also be an external storage device of the service calling device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the computer readable storage medium can include both the internal storage unit of the service calling device and the external storage device. The computer readable storage medium is used to store the computer program and other programs and data required by the service calling device. The computer readable storage medium can also be used to temporarily store data that has been output or will be output.

[0145] The embodiments of the present application further provide a computer program product, which contains a computer program, and when the computer program product runs on a computer, the computer executes any one of the carbon data trusted evaluation methods provided in the embodiments above.

[0146] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any change or replacement within the technical scope disclosed in the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for reliable evaluation of carbon data, characterized by, The method comprises the following steps: collecting carbon source data; performing trusted verification on the collection equipment of the carbon source data to determine first trusted data from the carbon source data; the collection equipment corresponding to the first trusted data is the collection equipment that passes the trusted verification; transmitting the first trusted data to a first platform and a second platform respectively, and performing sensing detection on the transmission process to obtain second trusted data; the second trusted data is the first trusted data that is successfully transmitted to the first platform and the second platform, and the transmission process passes the sensing detection; performing data verification on the second trusted data to obtain third trusted data that passes the data verification; based on the third trusted data, carbon emission statistical accounting is performed.

2. The method of claim 1, wherein, The trusted verification on each collection equipment comprises at least one of the following: verify whether the collection equipment is located within a preset carbon verification boundary; verify whether the identification of the collection equipment is recorded in a trusted equipment database; verify whether the collection equipment is in an online state; verify whether the collection equipment is within a valid period of standardized measurement authentication; verify whether the clock of the collection equipment is synchronized with a standard time.

3. The method of claim 1, wherein, The sensing detection on the transmission process to obtain second trusted data comprises: during the transmission process, monitor whether the collection equipment has an abnormal condition through a multi-modal sensing network; the first trusted data that is successfully transmitted to the first platform and the second platform and has no abnormal condition during the transmission process is taken as the second trusted data.

4. The method of claim 1, wherein, The data verification on the second trusted data comprises at least one of the following: verify whether the second trusted data in the first platform and the second platform is consistent; verify whether the second trusted data has data loss; verify whether the second trusted data is within a preset consumption range.

5. The method of claim 1, wherein, The collection of carbon source data comprises: based on the type of the carbon source data, determine a data collection scheme of the carbon source data.

6. The method of claim 5, wherein: in the case that the carbon source data can be directly collected by a measurement device, the data collection scheme comprises: collecting the carbon source data in real time by a measurement device; in the case that the carbon source data cannot be directly collected by a measurement device, the data collection scheme comprises at least one of the following: establish an inventory account book, regularly obtain energy inventories at different periods, and then calculate energy consumption based on the inventories at different periods to obtain the carbon source data; monitor emission data of an emission source of energy consumption through a multi-modal sensing network, and determine the carbon source data based on the emission data and an energy consumption evaluation model.

7. The method of claim 1, wherein, The carbon emission statistical accounting based on the third trusted data comprises: performing data rectification on suspicious data to obtain rectified data; wherein the data rectification comprises retaining data within a tolerance range and eliminating data outside the tolerance range; the suspicious data comprises at least one of the following: the carbon source data collected by the collection equipment that does not pass the trusted verification, the first trusted data that does not pass the sensing monitoring, and the second trusted data that does not pass the data verification. Based on the corrected data and the third trusted data, carbon emission statistical accounting is performed.

8. An electronic device, comprising: The computer device comprises a processor and a memory, the processor is coupled with the memory; the memory is used for storing computer instructions, the computer instructions are loaded and executed by the processor to enable the computer device to implement the trusted evaluation method of carbon data according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises computer execution instructions, when the computer execution instructions run on the computer, the computer execution instructions enable the computer to execute the trusted evaluation method of carbon data according to any one of claims 1 to 7.

10. A computer program product, characterised in that, The computer program product comprises a computer program, when the computer program runs on the electronic device, the computer program enables the electronic device to execute the trusted evaluation method of carbon data according to any one of claims 1 to 7.

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