A blockchain-based carbon footprint monitoring analysis and evaluation method and system

Abstract

The application provides a kind of carbon footprint monitoring analysis and evaluation method and system based on blockchain, related to carbon footprint field, solve the technical problem that the result is distorted due to the dependence of carbon emission accounting on static or regional average emission factor in prior art, so that it is difficult to achieve high-precision carbon footprint quantification.The method comprises: collecting energy consumption data and production activity data, and attaching a timestamp to the collected data and applying a private key digital signature of the data collection device to form monitoring data; based on historical emission related data, an emission factor calibration model is constructed, and an emission factor is output, and the energy consumption data and production activity data corresponding to each emission source are subjected to carbon footprint quantification calculation to obtain carbon emissions; according to a preset carbon performance evaluation rule set, the carbon footprint results in the carbon accounting record are evaluated.The application is used in the process of carbon footprint monitoring.

Description

Technical Field

[0001] This application relates to the field of carbon footprint, and in particular to a blockchain-based method and system for carbon footprint monitoring, analysis and assessment. Background Technology

[0002] Existing accounting methods, which collect energy consumption data and use fixed or regional average emission factors, fail to reflect the dynamic changes in energy consumption over time and space. For example, the carbon intensity of the power grid in the same region varies significantly with daily green electricity output. If annual average factors are still used for accounting, carbon emissions will be systematically overestimated or underestimated.

[0003] The aforementioned shortcomings result in insufficient accuracy and limited credibility of existing carbon footprint assessments, making it difficult to support refined carbon management and the issuance of high-value carbon credits. There is an urgent need for a technical solution that can integrate dynamic emission factor modeling, reliable source data collection, and a verifiable mechanism throughout the entire process. Summary of the Invention

[0004] This application provides a blockchain-based method and system for carbon footprint monitoring, analysis and assessment, which solves the technical problem that existing carbon emission accounting relies on static or regional average emission factors, resulting in distorted results and making it difficult to achieve high-precision carbon footprint quantification.

[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, a blockchain-based method for monitoring, analyzing, and assessing carbon footprint is provided, including: Collect energy consumption data and production activity data, add timestamps to the collected data and apply a digital signature of the data collection device's private key to form monitoring data; The monitoring data is uploaded to the blockchain network, and after the consensus nodes verify the validity of the monitoring data signature and the consistency of the time sequence, it is written into the distributed ledger. Based on the historical emission-related data stored in the distributed ledger, an emission factor calibration model is constructed. The emission factor calibration model outputs the corresponding emission factor according to geographical location, time window and energy type. Based on the emission factors, carbon footprint quantification calculations are performed on the energy consumption data and production activity data corresponding to each emission source to obtain carbon emissions. The carbon emissions, emission factors and their emission factor calibration model information are packaged to generate carbon accounting records and written into the blockchain. Based on a pre-defined set of carbon performance assessment rules, the carbon footprint results in the carbon accounting records are evaluated to generate a structured assessment report, including carbon emission intensity level, compliance status identifier and emission reduction potential index, and the structured assessment report is linked to the on-chain identity of the corresponding emission entity.

[0006] Based on the above technical solutions, in the blockchain-based carbon footprint monitoring, analysis, and assessment method provided in this application, existing carbon footprint accounting systems generally rely on manual reporting, static default emission factors, and centralized databases, leading to two core defects: first, emission factors cannot reflect the spatiotemporal dynamic changes of power grid structure, fuel composition, or meteorological conditions, causing systematic bias in the accounting results; second, the original monitoring data lacks anti-tampering and verifiable mechanisms, making it easy to forge or reuse, and difficult to support highly credible carbon credit issuance and regulatory audits. To address these issues, this application proposes a blockchain-based carbon footprint monitoring, analysis, and assessment method. This solution ensures the credibility of the data source through device-side digital signatures and timestamps, and uses the blockchain consensus mechanism to verify and store monitoring data, fundamentally guaranteeing data authenticity and temporal consistency; simultaneously, it constructs a dynamic emission factor calibration model based on on-chain historical data and multi-dimensional environmental covariates, achieving precise quantification of "one factor per location, one time"; finally, the accounting logic, results, and assessment report are all uploaded to the blockchain, forming an immutable, traceable, and verifiable carbon management closed loop. Its advantages include significantly improving the accuracy of carbon accounting.

[0007] In conjunction with the first aspect above, in one possible implementation, the method for constructing the emission factor calibration model includes: Multiple verified carbon emission event records are extracted from the distributed ledger, each record containing the geographical location of the emission source. timestamp Activity data and corresponding carbon emissions Calculate the observed unit active carbon intensity Where i is the sequence number of the carbon emission event recorded; Get and various Corresponding covariates The covariates include the real-time proportion of coal-fired power in the regional power grid, the calorific value of natural gas, the penetration rate of wind / solar power generation, and the near-surface air temperature; Constructing with spatiotemporal coordinates Main input, covariates The Gaussian process regression model with auxiliary features, the mean function of this model Implemented by a shallow neural network; where, This is the set of learnable parameters for a shallow neural network. The hyperparameters of the Gaussian process regression model are optimized by maximizing the marginal likelihood function with a physical regularization term. The marginal likelihood function is: ; For the log-marginal likelihood term, For carbon emission factor vectors, The input matrix corresponds to the spatiotemporal coordinates. It is a covariate matrix; For the set of learnable parameters Physical violation penalty items, Regularization weights; After training, an emission factor calibration model is obtained for any new spatiotemporal location. and the corresponding new covariates The emission factor calibration model outputs the emission factor.

[0008] In conjunction with the first aspect above, in one possible implementation, the covariance function of the Gaussian process regression model... ,include: ; in, It is a space-time composite kernel, employing a combination of exponential decay and periodic terms. It is a covariate similarity kernel based on Mahalanobis distance.

[0009] In conjunction with the first aspect above, in one possible implementation, the physical constraints of the shallow neural network include: Carbon emission factor is non-negative: ; Fuel substitution monotonicity: As the proportion of clean energy increases, .

[0010] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the carbon emissions includes: Acquire production activity data of the target emission source within a preset time window, wherein the production activity data is categorized by energy type. Class, number Production activity data includes its collection timestamp. Spatial location ; Each and corresponding covariates The emission factor is input into the emission factor calibration model, and the corresponding emission factor is output. ; For each type of energy activity, calculate its carbon emission component. ;in, For the first Production activity data; Based on energy properties, each By performing hierarchical weighted aggregation, carbon emissions can be obtained. : ; Among them, weight It is dynamically determined by the clean energy properties.

[0011] In conjunction with the first aspect above, in one possible implementation, the evaluation of the carbon footprint results in the carbon accounting record includes: Extract historical carbon accounting records of target emission sources from the blockchain to construct a time series of carbon emission intensity. ;in, For time period Total carbon emissions within the region For the corresponding output indicators; Current carbon emission intensity Deviation analysis was performed at the same spatiotemporal granularity as the carbon emission benchmark to obtain the deviation vector. Where j = 1, 2, ..., m, and m is a positive integer; Based on the preset membership function and weight vector The carbon performance comprehensive score is calculated using a fuzzy comprehensive evaluation model. : ; in, For the first The S-type or Z-type membership function corresponding to the class deviation characterizes the degree of compliance. according to The interval is mapped to generate carbon emission intensity levels; if any benchmark deviation If the preset warning threshold is exceeded, it is considered non-compliant. Emission Reduction Potential Index Defined as: ; in, This represents the theoretical minimum carbon emissions. This represents the current carbon emissions.

[0012] In conjunction with the first aspect above, in one possible implementation, the verification of the validity and temporal consistency of the monitoring data signature includes: Receive raw monitoring data packets from IoT sensing devices, the raw monitoring data packets including device public key certificates, digital signatures, and timestamps. The hash value of the previous data packet and current monitoring data; The validity of the digital signature is verified based on the device's public key certificate, and it is confirmed that the device has been authorized to access the carbon monitoring blockchain network. Verify current timestamp Satisfy global timing constraints: ;in, The local time of the consensus node. The preset maximum allowable clock skew threshold; ;in, The timestamp of the most recent valid record for this device on the chain; Calculate the hash value of the monitoring data and metadata in the current data packet. And verify whether it matches the next pointer recorded on the chain. If a continuous hash chain is formed, the monitoring data has signature validity and time sequence consistency, and is allowed to be written to the block; otherwise, it is rejected from being added to the chain and a device anomaly alarm is triggered.

[0013] In conjunction with the first aspect above, in one possible implementation, the method for acquiring the monitoring data includes: The data acquisition device's identifier, timestamp, raw sensor data, and the hash value of the previous valid data are concatenated into a structured message, and a hash operation is performed on the message; the hash value is digitally signed using the device's private key; the raw data, timestamp, device identifier, preceding hash, and digital signature are packaged and encapsulated to form monitoring data.

[0014] In conjunction with the first aspect mentioned above, in one possible implementation, upon receiving a third-party verification request, a verification smart contract deployed on the blockchain is triggered. The verification smart contract, in conjunction with a zero-knowledge proof protocol, generates a cryptographically verifiable credential for the assessment report, which is then used by the third party to verify the authenticity and compliance of the assessment result.

[0015] Secondly, this application provides a blockchain-based carbon footprint monitoring, analysis, and assessment system, comprising: a data acquisition module, a carbon emission analysis module, and a carbon assessment module; wherein, the data acquisition module is used to collect energy consumption data and production activity data, and to attach timestamps to the collected data and apply a digital signature of the private key of the data acquisition device to form monitoring data; the monitoring data is uploaded to the blockchain network, and after the consensus node verifies the validity and time sequence consistency of the monitoring data signature, it is written into the distributed ledger; the carbon emission analysis module is used to construct an emission factor calibration model based on the historical emission-related data stored in the distributed ledger, and the emission factor calibration model outputs the corresponding emission factor according to geographical location, time window, and energy type; based on the emission factor, the carbon footprint is quantified and calculated for the energy consumption data and production activity data corresponding to each emission source to obtain the carbon emission amount, and the carbon emission amount, emission factor, and emission factor calibration model information are packaged to generate a carbon accounting record and written into the blockchain; the carbon assessment module is used to evaluate the carbon footprint results in the carbon accounting record according to a preset carbon performance assessment rule set, generate a structured assessment report, and associate the structured assessment report with the on-chain identity of the corresponding emission subject.

[0016] This application provides a blockchain-based method and system for carbon footprint monitoring, analysis, and assessment, enabling the entire process of carbon emission data collection, accounting, and assessment to be trustworthy, dynamic, and intelligent. Addressing the core issues of existing technologies, such as accounting distortion, data unreliability, and lack of transparency due to reliance on static emission factors and centralized data management, this solution ensures the authenticity and temporal irreversibility of monitoring data by applying device private key digital signatures and attaching high-precision timestamps to energy consumption and production activity data at the data source. After verifying the signature validity and temporal consistency using a blockchain consensus mechanism, the data is written into a distributed ledger, constructing a tamper-proof and traceable data foundation. Based on this, historical emission data on the blockchain is integrated with multi-dimensional environmental covariates such as regional power grid structure, fuel composition, and meteorology to construct a spatiotemporally adaptive emission factor calibration model, achieving precise quantification of "one factor per location, one time." Furthermore, carbon emissions, dynamic emission factors, and model information are packaged to generate structured carbon accounting records on the blockchain, and an assessment report containing carbon intensity level, compliance status, and emission reduction potential is automatically generated according to a preset rule set, linked to the on-chain identity of the emitting entity. This application significantly improves the accuracy of carbon footprint accounting.

[0017] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0018] Figure 1 A system architecture diagram of a blockchain-based carbon footprint monitoring, analysis, and assessment system provided for embodiments of this application; Figure 2 A flowchart illustrating a blockchain-based carbon footprint monitoring, analysis, and assessment method provided in this application embodiment; Figure 3 This is a flowchart illustrating a method for constructing an emission factor calibration model provided in an embodiment of this application. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The blockchain-based carbon footprint monitoring, analysis, and assessment method provided in this application can be applied to, for example... Figure 1 The blockchain-based carbon footprint monitoring, analysis, and assessment system shown includes a data acquisition module, a carbon emission analysis module, and a carbon assessment module.

[0021] The data acquisition module is used to collect energy consumption data and production activity data, and to add timestamps to the collected data and apply the private key digital signature of the data acquisition device to form monitoring data; The monitoring data is uploaded to the blockchain network, and after the consensus nodes verify the validity of the monitoring data signature and the consistency of the time sequence, it is written into the distributed ledger. The carbon emission analysis module is used to build an emission factor calibration model based on historical emission-related data stored in the distributed ledger. The emission factor calibration model outputs the corresponding emission factor according to geographical location, time window and energy type. Based on emission factors, carbon footprint quantification calculations are performed on energy consumption data and production activity data corresponding to each emission source to obtain carbon emissions. The carbon emissions, emission factors and their emission factor calibration model information are packaged to generate carbon accounting records and written into the blockchain. The carbon assessment module is used to evaluate the carbon footprint results in the carbon accounting records based on a preset set of carbon performance assessment rules, generate a structured assessment report, and link the structured assessment report to the on-chain identity of the corresponding emission entity.

[0022] To address the technical problem that existing carbon emission accounting technologies rely on static or regional average emission factors, leading to distorted results and hindering high-precision carbon footprint quantification, this application provides a blockchain-based carbon footprint monitoring, analysis, and assessment method. This method includes: collecting energy consumption data and production activity data, adding timestamps to the collected data, and applying a digital signature using the private key of the data acquisition device to form monitoring data; uploading the monitoring data to a blockchain network, where consensus nodes verify the validity and temporal consistency of the monitoring data signature before writing it into a distributed ledger; and constructing an emission factor calibration model based on historical emission-related data stored in the distributed ledger, the emission factor calibration model being adjusted according to geographical location, time window, and energy type. The system outputs corresponding emission factors. Based on these emission factors, it quantifies the carbon footprint of each emission source's energy consumption and production activity data to obtain carbon emissions. The carbon emissions, emission factors, and their calibration model information are packaged to generate carbon accounting records, which are then written to the blockchain. According to a pre-defined set of carbon performance assessment rules, the carbon footprint results in the carbon accounting records are evaluated, generating a structured assessment report. This report is then linked to the corresponding emission entity's on-chain identity. Based on this, the application uses device-side private key signing and timestamp binding, and verifies the data through blockchain consensus before uploading it to the chain. This ensures that the monitoring data is tamper-proof, non-repudiable, and time-consistent, fundamentally solving the problems of data falsification and difficulty in traceability in traditional carbon accounting. It abandons static emission factors and, based on historical on-chain data, integrates multi-dimensional covariates such as power grid structure, fuel composition, and meteorology to construct a spatiotemporally adaptive emission factor calibration model, outputting dynamic values ​​for "one factor per location at one time," significantly improving accounting accuracy.

[0023] like Figure 2 As shown in the embodiment of this application, a blockchain-based carbon footprint monitoring, analysis, and assessment method is provided, including: S201. Collect energy consumption data and production activity data, add timestamps to the collected data and apply a digital signature of the data collection device's private key to form monitoring data.

[0024] In some implementations, raw data related to carbon emissions is acquired in real time by IoT sensing devices deployed at emission source sites. This raw data includes the consumption of electricity, natural gas, coal, or steam, as well as process parameters characterizing production load. High-precision timestamps calibrated by a Network Time Protocol (NTP) server or Global Navigation Satellite System (GNSS) are simultaneously acquired to ensure global consistency of time information. A pre-set device unique identifier and corresponding asymmetric encryption private key are read from the device's built-in security chip or Trusted Execution Environment (TEE). The raw data, timestamp, device identifier, and hash value of the previous valid monitoring data are concatenated into a structured message, and a hash operation is performed on this message. A digital signature operation is performed on the hash value using the private key to generate a digital signature conforming to the ECDSA or SM2 national cryptographic standards. The raw data, timestamp, device identifier, preceding hash, and digital signature are encapsulated into a complete monitoring data packet. This data packet possesses security features such as traceable identity, tamper-proof content, irreversible time sequence, and resistance to replay attacks, and can serve as a trusted data unit for subsequent on-chain verification.

[0025] S202. The monitoring data is uploaded to the blockchain network. After the consensus nodes verify the validity of the monitoring data signature and the consistency of the time sequence, it is written into the distributed ledger.

[0026] Distributed ledgers refer to immutable data storage systems that are jointly maintained by multiple nodes and whose consistency is guaranteed through a consensus mechanism.

[0027] S203. Based on historical emission-related data stored in the distributed ledger, construct an emission factor calibration model. The emission factor calibration model outputs the corresponding emission factor according to geographical location, time window and energy type.

[0028] S204. Based on emission factors, perform carbon footprint quantification calculations on the energy consumption data and production activity data corresponding to each emission source to obtain carbon emissions. Then, package the carbon emissions, emission factors, and emission factor calibration model information to generate carbon accounting records and write them into the blockchain.

[0029] S205. Based on the preset carbon performance assessment rule set, evaluate the carbon footprint results in the carbon accounting record, generate a structured assessment report, including carbon emission intensity level, compliance status identifier and emission reduction potential index, and link the structured assessment report to the on-chain identity of the corresponding emission entity.

[0030] S206. Upon receiving a third-party verification request, a verification smart contract deployed on the blockchain is triggered. The verification smart contract, in conjunction with a zero-knowledge proof protocol, generates a cryptographically verifiable credential for the assessment report, which is then used by the third party to verify the authenticity and compliance of the assessment result.

[0031] Based on the above technical solutions, this application provides a blockchain-based carbon footprint monitoring, analysis, and assessment method. Traditional methods use fixed or regional annual average emission factors, which cannot respond to spatiotemporal dynamic changes such as the proportion of green electricity in the power grid, fuel composition, or meteorological conditions, leading to overestimation or underestimation of carbon emissions. This application, by integrating historical on-chain data with real-time environmental covariates (such as power grid structure and weather), constructs a dynamic emission factor calibration model based on geographical location, time window, and industry category, achieving precise mapping of "one factor per location and time," significantly improving the accuracy of carbon footprint quantification.

[0032] Furthermore, in this embodiment, existing systems largely rely on centralized storage and manual data entry, making the original data susceptible to tampering and lacking verifiability, thus failing to meet the stringent requirements for data authenticity in regulatory and carbon credit issuance processes. This solution adds device private key signatures and high-precision timestamps at the data collection end, and then verifies the signature validity and temporal consistency through blockchain consensus nodes before storing the data on the blockchain. Its advantages lie in ensuring data is unforgeable, non-repudiable, and fully traceable from the source, providing a trusted data foundation for advanced applications such as third-party verification, auditing, and zero-knowledge proofs, effectively solving the trust deficiency problem in the carbon accounting system.

[0033] In one possible implementation of this application embodiment, the above-mentioned S202 can be specifically implemented by the following S301, S302, S303 and S304, which are described in detail below: S301. Receive raw monitoring data packets from IoT sensor devices. The raw monitoring data packets include device public key certificate, digital signature, and timestamp. The hash value of the previous data packet and current monitoring data; S302. Verify the validity of the digital signature based on the device's public key certificate, and confirm that the device has been authorized to access the carbon monitoring blockchain network; S303, Verify the current timestamp Satisfy global timing constraints: ;in, The local time of the consensus node. This is the preset maximum allowable clock deviation threshold.

[0034] It should be noted that this condition is used to detect whether the deviation between the device's local clock and the consensus node's network time is within an acceptable range. If the device clock drifts significantly (e.g., due to failure to synchronize after a power outage and restart), it may generate data packets that are much earlier or later than the current time. Allowing such data to be uploaded to the chain would lead to historical data being forged as future events (time rollback attack), or future data being submitted prematurely (time jump attack). By setting a maximum allowed offset threshold... (For example, 5 minutes) can enable lightweight time reliability verification without relying on high-precision GPS time synchronization.

[0035] ;in, The timestamp of the most recent valid record for this device on the chain; S304. Calculate the hash value of the monitoring data and metadata in the current data packet. And verify whether it matches the next pointer recorded on the chain. If a continuous hash chain is formed, the monitoring data has signature validity and time sequence consistency, and is allowed to be written to the block; otherwise, it is rejected from being added to the chain and a device anomaly alarm is triggered.

[0036] It should be noted that this condition is used to prevent the same device from repeatedly submitting the same or old data records, i.e., to prevent replay attacks. In some scenarios, attackers may intercept and repeatedly send a legitimate piece of monitoring data, attempting to mislead the system into believing it is a newly generated emission; this can be prevented by comparing the current timestamp with the timestamp of the device's most recent valid record on the chain. This ensures the time-incremental nature of each data entry; combined with the preceding hash chain structure, it further strengthens the immutability and sequential integrity of the data.

[0037] Based on the above technical solutions, in carbon emission monitoring scenarios, if the raw data is tampered with or forged, it will directly lead to distorted carbon accounting and even trigger compliance risks. Therefore, it is urgent to solve the three core security issues of data authenticity, signature validity, and time sequence consistency of IoT devices. Traditional centralized verification methods are vulnerable to single-point attacks and lack traceability, making it difficult to meet the data trustworthiness requirements of carbon regulation. This technical solution achieves closed-loop protection through a multi-layer verification mechanism: using device public key certificates to verify digital signatures to ensure the authenticity and non-repudiation of data sources; and using timestamp deviation verification (… To prevent replay attacks caused by clock drift; through a forward hash chain ( and A lightweight anti-tampering mechanism (continuity verification) is constructed to ensure that data is generated in sequence and cannot be inserted or deleted. This scheme deeply integrates cryptography, temporal constraints, and a chain structure to achieve triple protection of "identity authentication, time synchronization, and data continuity." Its advantages are: first, it can complete highly reliable verification without relying on a central server; second, the hash chain design effectively resists man-in-the-middle attacks and data backtracking; and third, it supports efficient verification under large-scale device access, significantly improving the security and attack resistance of the carbon monitoring system.

[0038] In one possible implementation of the embodiments of this application, such as Figure 3 As shown, the above S203 can be specifically implemented through the following S401, S402, S403 and S404, which are explained in detail below: S401. Extract multiple verified carbon emission event records from the distributed ledger, each record containing the geographical location of the emission source. timestamp Activity data and corresponding carbon emissions Calculate the observed unit active carbon intensity Where i is the sequence number of the carbon emission event recorded.

[0039] Get and various Corresponding covariates Among them, covariates include the real-time proportion of coal-fired power in the regional power grid, the calorific value of natural gas, the penetration rate of wind / solar power generation, and near-surface temperature.

[0040] S402, Constructing a spatiotemporal coordinate system Main input, covariates The Gaussian process regression model with auxiliary features, the mean function of this model It is implemented using a shallow neural network.

[0041] in, This is the set of learnable parameters for a shallow neural network.

[0042] The physical constraints of shallow neural networks include: Carbon emission factor is non-negative: To ensure that the model output is physically reasonable, carbon emissions cannot be negative, and to avoid unreasonable predictions caused by overfitting.

[0043] Fuel substitution monotonicity: As the proportion of clean energy increases, It reflects the common sense of energy systems that "the more green electricity, the lower the carbon intensity," and prevents the model from exhibiting the counterintuitive behavior of "increasing green electricity actually increasing emissions."

[0044] It should be noted that traditional methods using shallow neural networks to implement the mean function often assume that the emission factor is a constant or a linear function, making it difficult to capture complex spatiotemporal variation patterns. However, this application, through neural network modeling, can learn spatial heterogeneity (such as regional power grid differences), temporal periodicity (such as seasonal electricity consumption fluctuations), and multi-factor coupling effects (such as weather and load linkage), significantly improving the model's expressive power.

[0045] Covariance function of Gaussian process regression model ,include: ; in, It is a space-time composite kernel, employing a combination of exponential decay and periodic terms. It is a covariate similarity kernel based on Mahalanobis distance.

[0046] It should be noted that the covariance function is used to support environmental factor transfer learning across regions and time periods.

[0047] S403. The hyperparameters of the Gaussian process regression model are optimized by maximizing the marginal likelihood function with a physical regularization term. The marginal likelihood function is: ; in, For the log-marginal likelihood term, For carbon emission factor vectors, The input matrix corresponds to the spatiotemporal coordinates. It is a covariate matrix; For the set of learnable parameters Physical violation penalty items, This is the regularization weight.

[0048] It should be noted that traditional Gaussian process regression has limitations; traditional GPR only maximizes the marginal likelihood. Estimating hyperparameters aims to fit observed data, but this method is prone to overfitting or results that violate physical principles (such as predicting negative emission factors, increased green electricity ratios, and increased carbon intensity). This "black box" modeling is unacceptable in high-reliability applications like carbon accounting.

[0049] Introducing physical regularization terms The core function of this penalty item: The current model parameters are explicitly quantized. The degree of violation of physical constraints; for example, if the neural network output ,but If the proportion of clean energy increases ,but By subtracting During the optimization process, penalties are imposed on parameter configurations that are "non-physical" to guide the model to converge in a direction that conforms to the operating laws of the energy system.

[0050] Regular weights The regulatory effect, The trade-off between controlling physical constraints and data fitting: when When the size is large, physical rationality is prioritized, which may sacrifice some fitting accuracy; when When the scale is smaller, more emphasis is placed on data matching, but this increases the risk in practical applications. The optimal balance between data fidelity and physical consistency can be achieved through cross-validation or Bayesian optimization.

[0051] S404. After training, the emission factor calibration model is obtained for any new spatiotemporal location. and the corresponding new covariates The emission factor calibration model outputs the emission factor.

[0052] Based on the aforementioned technical solutions, traditional carbon emission factor calculations rely on static default values ​​or regional averages, failing to reflect spatiotemporal dynamic changes (such as fluctuations in the proportion of green electricity in the power grid and weather influences), leading to distorted calculation results. Especially under the demand for high-precision carbon management, there is an urgent need to construct dynamic emission factor models that can respond to real-time environmental conditions. This technical solution constructs a Gaussian process regression model with spatiotemporal coordinates as the main input and covariates as auxiliary features, combined with a shallow neural network to implement the mean function. This retains the advantages of GPR in modeling uncertainty while enhancing its nonlinear expressive power. More importantly, it introduces a marginal likelihood function with a physical regularization term, maximizing data fit while constraining the model output to conform to physical laws (such as the emission factor decreasing as the proportion of green electricity increases), avoiding "black box" predictions that violate common sense. This method effectively solves the core problem of "conflict between data-driven and physical consistency," achieving a leap from empirical estimation to scientific deduction. Its advantages are: first, it significantly improves the spatiotemporal resolution and accuracy of emission factors; second, it ensures the credibility of the model through physical regularization terms and enhances regulatory acceptance; and third, it supports online prediction at any new location and is suitable for carbon accounting systems in multi-source heterogeneous scenarios.

[0053] In one possible implementation of this application embodiment, the above-mentioned S204 can be specifically implemented by the following S501, S502, S503 and S504, which are described in detail below: S501. Obtain production activity data of the target emission source within a preset time window.

[0054] Among them, production activity data is divided into energy types. Class, number Production activity data includes its collection timestamp. Spatial location .

[0055] Energy type classification refers to categorizing the collected raw energy consumption data into several mutually exclusive and fully covered categories based on the physical properties, sources, carbon emission characteristics, or accounting standards of energy, so that the corresponding dynamic emission factor can be applied to each category.

[0056] In some implementations, the energy type is categorized as follows: (1) purchased electricity; (2) purchased heat or steam; (3) fossil fuels, including coal, natural gas, and oil; (4) self-generated renewable energy, such as solar and wind power; and (5) purchased green electricity with blockchain-based verification. Each type of energy activity data is automatically classified by equipment type, energy flow identifier, or purchase contract information, and assigned a unique category index. This is to ensure the accuracy of subsequent dynamic emission factor matching.

[0057] S502, each and corresponding covariates Input emission factor into the calibration model, output the corresponding emission factor .

[0058] S503. For each type of energy activity, calculate its carbon emission component. .

[0059] in, For the first Production activity data.

[0060] S504, Based on energy attributes, each By performing hierarchical weighted aggregation, carbon emissions can be obtained. : ; Among them, weight It is dynamically determined by the clean energy properties.

[0061] It should be noted that if the first If the energy source is purchased from the external grid, then the real-time green electricity ratio of the regional power grid stored on the blockchain will be used. The electricity is split into clean and fossil fuel components, and carbon emissions are calculated only for the fossil fuel component (i.e., The clean portion is considered to have zero emissions; If the first If the energy source is a self-generated renewable energy source (such as rooftop solar power or wind power), then its weight will be directly assigned. This indicates that its carbon emissions are zero; If the first If the energy source is a fossil fuel (such as coal, natural gas, or oil), then set... All of these are included in carbon emissions; Other energy types (such as biomass energy, hydrogen energy, etc.) can have their cleanliness attributes determined according to industry standards, and corresponding settings can be made. .

[0062] Based on the aforementioned technical solutions, traditional carbon emission accounting generally employs static emission factors, neglecting the spatiotemporal dynamics of energy consumption and differences in clean energy attributes. This leads to significant biases in the accounting results, failing to reflect the true carbon footprint. Especially given the complex grid structure and frequent fluctuations in the proportion of green electricity, there is an urgent need to address the technical challenge of "how to achieve accurate, dynamic, and traceable carbon emission quantification." This solution constructs a hierarchical weighted aggregation model, classifying production activity data according to energy type. Classes, and input spatiotemporal coordinates for each class. with covariates The emission factor calibration model based on Gaussian process regression is called to output dynamic factors. This ensures that it matches the actual carbon intensity. The carbon emission components of each energy activity are then calculated. The weights are dynamically set based on the cleanliness of the energy source (such as whether purchased electricity includes green electricity and whether self-generated energy is renewable). First, it only calculates emissions for fossil fuels, achieving "precise emissions calculation and green offsetting." This method overcomes the limitations of "uniform factors and comprehensive inclusion," significantly improving the accuracy and compliance of accounting, and supporting differentiated processing for multiple energy types. Second, it achieves refined accounting through dynamic factors and weighting mechanisms.

[0063] In one possible implementation of this application embodiment, the above-mentioned S205 can be specifically implemented by the following S601, S602 and S603, which are described in detail below: S601. Extract historical carbon accounting records of the target emission sources from the blockchain and construct a time series of carbon emission intensity. It is used to characterize the carbon performance level of a target emission source at different time periods.

[0064] in, For time period Total carbon emissions within the region This corresponds to output indicators such as output, output value, and total energy consumption.

[0065] In some implementations, historical carbon accounting records are extracted from the blockchain network, including carbon emissions verified by consensus nodes and written into the ledger. Corresponding production activity data and evaluation reports; output indicators Real-time production data can be obtained from enterprise ERP systems, MES systems, or IoT sensors, and its authenticity is ensured through digital signatures and timestamps; carbon emission intensity time series. During the construction process, time alignment and outlier filtering are performed simultaneously to ensure data consistency and analysis reliability.

[0066] S602, Current carbon emission intensity Deviation analysis was performed at the same spatiotemporal granularity as the carbon emission benchmark to obtain the deviation vector. ; where j=1,2,…,m, and m is a positive integer.

[0067] Among them, the carbon emission benchmark is used to measure the current carbon emission intensity. A set of dynamic reference values ​​for whether compliance is achieved includes industry benchmarks, policy benchmarks, and scientific benchmarks, corresponding respectively to the quantile carbon intensity curves of enterprises of the same size in the same region, the annual reduction paths in national or local carbon peaking action plans, and the remaining carbon budget allocation curve derived from the temperature control path recommended by the SBTi (Scientific Carbon Targets Initiative); deviation analysis is performed at the same spatiotemporal granularity... By comparing each point with a benchmark, the deviation vector is obtained. Each element Indicates the current carbon intensity relative to the first The degree of deviation from the benchmark.

[0068] In some implementations, carbon emission benchmark data is stored in a trusted database on a blockchain network and updated regularly by regulatory agencies or third-party certification bodies; deviation analysis is automatically executed through smart contracts deployed on the chain, ensuring the calculation process is transparent and traceable; deviation vectors... Each element in Defined using the form of relative deviation, for example ;in, For the first This allows for the comparison of carbon intensity values ​​across different time periods and units, using benchmarks within the same time window.

[0069] It should be noted that the aforementioned deviation analysis method is not a simple threshold comparison, but rather constructs a multi-dimensional, dynamic, and interpretable carbon performance assessment framework. Traditional methods typically use only a single standard to determine compliance, while this invention, by introducing three types of benchmarks and generating a deviation vector, achieves a comprehensive diagnosis of carbon emission levels: it can identify whether a company is lagging behind the industry average, determine whether it meets policy requirements, and assess whether it conforms to climate science goals, significantly improving the depth and guidance of the assessment.

[0070] For example, the carbon emission intensity of a steel company in Q1 of a certain year. The comparison revealed that: (1) the average CO2e / t steel of the top 25% of companies in the same industry was 1.6tCO2e / t steel → deviation (2) Local policy requires a reduction to 1.7tCO2e / t steel → deviation (3) SBTi recommends a target of 1.4tCO2e / t steel → deviation This yields the deviation vector. This indicates that although the company did not exceed the standards, there is still a significant gap between its performance and the scientific goals, and it needs to focus on optimizing its processes and energy structure.

[0071] Based on the preset membership function and weight vector The carbon performance comprehensive score is calculated using a fuzzy comprehensive evaluation model. : ; in, For the first The S-type or Z-type membership function corresponding to the class deviation characterizes the degree of compliance.

[0072] according to The interval is mapped to generate carbon emission intensity levels; if any benchmark deviation If the preset warning threshold is exceeded, it is considered non-compliant.

[0073] It should be noted that this formula uses a fuzzy comprehensive evaluation model to weight and fuse multi-dimensional carbon emission deviations, aiming to address the problem that traditional "hard threshold" judgment methods are too rigid and cannot reflect the gradual compliance process. Its core idea is to weight and fuse each benchmark deviation... Mapped to a membership value between 0 and 1 Used to characterize the current carbon intensity relative to the first The degree of "compliance" with each benchmark.

[0074] For example, when (When fully met) ,when When the warning threshold is exceeded The intermediate region then transitions smoothly using an S-shaped or Z-shaped function. This is achieved through weight vectors. The weighted sum of each membership degree is used to obtain the overall score. This score not only quantifies the overall carbon performance level but also preserves the differences in sensitivity to different benchmarks. The range is mapped to intensity levels such as "excellent / good / medium / poor", and a safety mechanism is set to trigger "non-compliance" if any deviation exceeds the limit. This realizes the upgrade from "binary judgment" to "continuous assessment + risk warning", which significantly improves the scientificity and practicality of carbon performance assessment.

[0075] S603, Emission Reduction Potential Index Defined as: ; in, This represents the theoretical minimum carbon emissions. This represents the current carbon emissions.

[0076] It should be noted that the emission reduction potential index defined by this formula... This study aims to quantify the theoretical maximum emission reduction potential that enterprises can achieve by maintaining their current production scale and output levels through technological optimization, energy substitution, or process improvement. Its design logic is based on the principle of "relative achievability": using current actual carbon emissions as a benchmark. Based on this, subtract the theoretical minimum carbon emissions predicted under the same operating conditions using a lightweight simulator (such as a regression model or rule engine trained on historical data). This data is then normalized to a percentage, resulting in an intuitive and comparable indicator. This avoids the limitations of absolute value comparisons, emphasizing "how much can be saved" rather than "how much has already been emitted," enabling companies to clearly identify their emission reduction bottlenecks and priority paths. Simultaneously, because... These are limit values ​​calculated based on the boundaries of existing technology, not ideal assumptions. It has practical guiding significance and can be used to formulate short-term emission reduction targets, assess the return on investment in green technology upgrades, and support the design and accounting of carbon credit projects. It is a key bridge connecting "current situation analysis" and "future planning".

[0077] Based on the above technical solutions, traditional carbon performance assessments often rely on a single threshold to determine compliance, lacking comprehensive consideration of multi-dimensional benchmarks and failing to reflect a company's true emission reduction level and future potential. Therefore, there is an urgent need to address the technical problem of "how to achieve multi-dimensional, dynamic, and interpretable intelligent carbon performance assessment." This solution constructs a carbon emission intensity time series... This process transforms historical accounting data into continuously comparable performance indicators, supporting trend analysis and horizontal comparisons; furthermore, it introduces three types of benchmarks (industry, policy, and science) to perform deviation vector analysis. The analysis comprehensively depicts the gap between the current state and each objective. Based on preset membership functions and weight vectors, a fuzzy comprehensive evaluation model is used to calculate the overall score. This quantifies qualitative judgments into continuous numerical values, avoiding rigid "black and white" determinations and improving the flexibility and scientific rigor of the assessment. Simultaneously, it defines an emissions reduction potential index. By quantifying the gap between the theoretical minimum emissions and current emissions, enterprises can gain a clear direction for improvement. This solution integrates time-series analysis, multi-source benchmarking, and intelligent decision-making, achieving an upgrade from "static accounting" to "dynamic assessment + risk warning + path guidance." Its advantages include: first, supporting multi-dimensional and differentiated assessments; second, enhancing the interpretability of results through fuzzy models; and third, combining potential indices to promote proactive emission reduction, significantly improving the intelligence and foresight of carbon management.

[0078] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

Claims

1. A blockchain-based method for monitoring, analyzing, and assessing carbon footprint, characterized in that, include: Collect energy consumption data and production activity data, add timestamps to the collected data and apply a digital signature of the data collection device's private key to form monitoring data; The monitoring data is uploaded to the blockchain network, and after the consensus nodes verify the validity of the monitoring data signature and the consistency of the time sequence, it is written into the distributed ledger. Based on the historical emission-related data stored in the distributed ledger, an emission factor calibration model is constructed. The emission factor calibration model outputs the corresponding emission factor according to geographical location, time window and energy type. The method for constructing the emission factor calibration model includes: Multiple verified carbon emission event records are extracted from the distributed ledger, each record containing the geographical location of the emission source. timestamp Activity data and corresponding carbon emissions Calculate the observed unit active carbon intensity Where i is the sequence number of the carbon emission event recorded; Get and various Corresponding covariates The covariates include the real-time proportion of coal-fired power in the regional power grid, the calorific value of natural gas, the penetration rate of wind / solar power generation, and the near-surface air temperature; Constructing with spatiotemporal coordinates Main input, covariates The Gaussian process regression model with auxiliary features, the mean function of this model Implemented by a shallow neural network; where, This is the set of learnable parameters for a shallow neural network. The hyperparameters of the Gaussian process regression model are optimized by maximizing the marginal likelihood function with a physical regularization term. The marginal likelihood function is: ； in, For the log-marginal likelihood term, For carbon emission factor vectors, The input matrix corresponds to the spatiotemporal coordinates. It is a covariate matrix; For the set of learnable parameters Physical violation penalty items, Regularization weights; After training, an emission factor calibration model is obtained for any new spatiotemporal location. and the corresponding new covariates The emission factor calibration model outputs the emission factor; The covariance function of the Gaussian process regression model ,include: ； in, It is a space-time composite kernel, employing a combination of exponential decay and periodic terms. The covariate similarity kernel is based on Mahalanobis distance; The physical constraints of the shallow neural network include: Carbon emission factor is non-negative: ; Fuel substitution monotonicity: As the proportion of clean energy increases, ; Based on the emission factors, carbon footprint quantification calculations are performed on the energy consumption data and production activity data corresponding to each emission source to obtain carbon emissions. The carbon emissions, emission factors and their emission factor calibration model information are packaged to generate carbon accounting records and written into the blockchain. Based on the pre-set carbon performance assessment rule set, the carbon footprint results in the carbon accounting record are evaluated to generate a structured assessment report, including carbon emission intensity level, compliance status identifier and emission reduction potential index, and the structured assessment report is linked to the on-chain identity of the corresponding emission entity. The evaluation of the carbon footprint results in the carbon accounting records includes: Extract historical carbon accounting records of target emission sources from the blockchain to construct a time series of carbon emission intensity. ;in, For time period Total carbon emissions within the region For corresponding output indicators; Current carbon emission intensity Deviation analysis was performed at the same spatiotemporal granularity as the carbon emission benchmark to obtain the deviation vector. Where j = 1, 2, ..., m, and m is a positive integer; Based on the preset membership function and weight vector The carbon performance comprehensive score is calculated using a fuzzy comprehensive evaluation model. : ； in, For the first The S-type or Z-type membership function corresponding to the class deviation characterizes the degree of compliance. according to The interval is mapped to generate carbon emission intensity levels; if any benchmark deviation If the preset warning threshold is exceeded, it is considered non-compliant. Emission Reduction Potential Index Defined as: ； in, This represents the theoretical minimum carbon emissions. This represents the current carbon emissions.

2. The method for monitoring, analyzing, and evaluating carbon footprint based on blockchain according to claim 1, characterized in that, The method for obtaining the carbon emissions includes: Acquire production activity data of the target emission source within a preset time window, wherein the production activity data is categorized by energy type. Class, number Production activity data includes its collection timestamp. Spatial location ; Each and corresponding covariates The emission factor is input into the emission factor calibration model, and the corresponding emission factor is output. ; For each type of energy activity, calculate its carbon emission component. ;in, For the first Production activity data; Based on energy properties, each By performing hierarchical weighted aggregation, carbon emissions can be obtained. : ； Among them, weight It is dynamically determined by the clean energy properties.

3. The method for monitoring, analyzing, and evaluating carbon footprint based on blockchain according to claim 1, characterized in that, The verification of the validity and temporal consistency of the monitoring data signature includes: Receive raw monitoring data packets from IoT sensing devices, the raw monitoring data packets including device public key certificates, digital signatures, and timestamps. The hash value of the previous data packet and current monitoring data; The validity of the digital signature is verified based on the device's public key certificate, and it is confirmed that the device has been authorized to access the carbon monitoring blockchain network. Verify current timestamp Satisfy global timing constraints: ;in, The local time of the consensus node. The preset maximum allowable clock skew threshold; ;in, The timestamp of the most recent valid record for this device on the chain; Calculate the hash value of the monitoring data and metadata in the current data packet. And verify whether it matches the next pointer recorded on the chain. If a continuous hash chain is formed, the monitoring data has signature validity and time sequence consistency, and is allowed to be written to the block; otherwise, it is rejected from being added to the chain and a device anomaly alarm is triggered.

4. The method for monitoring, analyzing, and evaluating carbon footprint based on blockchain according to claim 1, characterized in that, The method for acquiring the monitoring data includes: The data acquisition device's identifier, timestamp, raw sensor data, and the hash value of the previous valid data are concatenated into a structured message, and a hash operation is performed on the message; the hash value is digitally signed using the device's private key; the raw data, timestamp, device identifier, preceding hash, and digital signature are packaged and encapsulated to form monitoring data.

5. The method for monitoring, analyzing, and evaluating carbon footprint based on blockchain according to claim 1, characterized in that, Upon receiving a third-party verification request, a verification smart contract deployed on the blockchain is triggered. The verification smart contract, in conjunction with a zero-knowledge proof protocol, generates a cryptographically verifiable credential for the assessment report, which is then used by the third party to verify the authenticity and compliance of the assessment results.

6. A blockchain-based carbon footprint monitoring, analysis, and assessment system, operating based on the blockchain-based carbon footprint monitoring, analysis, and assessment method according to any one of claims 1-5, characterized in that, It includes a carbon emission analysis module, as well as a data acquisition module and a carbon assessment module connected to it; The data acquisition module is used to collect energy consumption data and production activity data, and to add timestamps to the collected data and apply the private key digital signature of the data acquisition device to form monitoring data. The monitoring data is uploaded to the blockchain network, and after the consensus nodes verify the validity of the monitoring data signature and the consistency of the time sequence, it is written into the distributed ledger. The carbon emission analysis module is used to construct an emission factor calibration model based on historical emission-related data stored in the distributed ledger. The emission factor calibration model outputs the corresponding emission factor according to geographical location, time window and energy type. Based on the emission factors, carbon footprint quantification calculations are performed on the energy consumption data and production activity data corresponding to each emission source to obtain carbon emissions. The carbon emissions, emission factors and their emission factor calibration model information are packaged to generate carbon accounting records and written into the blockchain. The carbon assessment module is used to assess the carbon footprint results in the carbon accounting record according to a preset set of carbon performance assessment rules, generate a structured assessment report, and associate the structured assessment report with the on-chain identity of the corresponding emission entity.

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