Enterprise green electricity consumption ranking evaluation method based on block chain

Abstract

The embodiment of the invention provides an enterprise green power consumption ranking evaluation method based on a block chain, and relates to the technical field of block chains. The method comprises the following steps: acquiring first data of a plurality of enterprises in a first area from a preset data source based on an intelligent contract; verifying the integrity and consistency of the first data based on a preset logic to obtain a first result; under the condition that the first result is qualified, recording the first data on a block chain to obtain second data; an accounting list is generated based on the second data, and the accounting list comprises the total green power consumption amount, the green power source, the consumption time and the energy conservation and emission reduction effect; and calculating the ranking level of the enterprise based on the accounting list and a first preset algorithm. Through the method and the device, the problem of low accuracy of green electricity consumption ranking of an enterprise caused by the authenticity problem of data in related technologies is solved, and the effect of improving the accuracy of a ranking result is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of blockchain, and in particular to a method for ranking and evaluating corporate green electricity consumption based on blockchain. Background Art

[0002] Green power consumption refers to the process of effectively connecting green power (mainly from renewable energy sources such as wind power, solar power, hydropower, etc.) to the power grid and actually using it by end users. This process not only involves the production of electricity, but more importantly, the efficient connection of its transmission, distribution and final consumption. The purpose of green power consumption is to promote the widespread use of clean energy and reduce negative impacts on the environment. It is a key step in achieving energy structure transformation and sustainable development.

[0003] The ranking of corporate green power consumption is an evaluation mechanism that quantitatively evaluates the consumption of green power, source diversity, consumption time, and energy conservation and emission reduction effects of enterprises over a certain period of time, and ranks enterprises accordingly. This ranking mechanism aims to encourage enterprises to increase the proportion of green energy use, improve energy efficiency, and promote green and low-carbon production and lifestyles. Through the data on green power consumption, an objective evaluation can be made of the contribution of enterprises to green energy transformation and environmental protection.

[0004] In related technologies, data on green electricity consumption involves the participation of multiple parties, including power generation companies, power grid companies, and users. In particular, for green certificates purchased on the National Renewable Energy Information Management Center platform and green electricity data for self-use uploaded by users, there is a possibility of data falsification due to the lack of an effective supervision and verification mechanism. The authenticity of this data leads to low accuracy in the ranking of corporate green electricity consumption. Inaccurate data may cause distortion of the ranking results and fail to truly reflect the performance of the company in green electricity consumption, thereby affecting the fairness and incentive effect of the ranking mechanism. In addition, the inconsistency and incompleteness of the data will also lead to deviations in the evaluation results, reducing the reliability and effectiveness of the evaluation system. Therefore, how to ensure the authenticity and accuracy of green electricity consumption data has become a key issue in improving the accuracy of corporate green electricity consumption rankings. Summary of the invention

[0005] The embodiment of the present invention provides a method for evaluating the ranking of corporate green electricity consumption based on blockchain, so as to at least solve the problem of data authenticity existing in the related technology, which leads to low accuracy of the ranking of corporate green electricity consumption.

[0006] According to one embodiment of the present invention, there is provided a method for evaluating the ranking of green electricity consumption of enterprises based on blockchain, comprising: obtaining first data of multiple enterprises in a first region from a preset data source based on a smart contract; verifying the integrity and consistency of the first data based on a preset logic to obtain a first result; if the first result is qualified, recording the first data on a blockchain to obtain second data; generating an accounting list based on the second data, wherein the accounting list includes: the total amount of green electricity consumption, the source of green electricity, the consumption time, and the energy-saving and emission reduction effects; and calculating the ranking level of the enterprise based on the accounting list and a first preset algorithm.

[0007] Through the present invention, due to the combination of the immutability of blockchain and the automatic verification of smart contracts, the problem of difficulty in determining the authenticity of data in the background technology is effectively solved, and the accuracy and reliability of the green electricity consumption ranking of enterprises are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a flowchart of a method for evaluating the ranking of green electricity consumption of enterprises based on blockchain according to an embodiment of the present invention; Figure 2 is a flowchart of a method for calculating a ranking level of an enterprise based on a calculation list and a first preset algorithm according to an embodiment of the present invention; Figure 3 It is a flowchart of a method for recording first data on a blockchain to obtain second data when a first result is qualified according to an embodiment of the present invention. DETAILED DESCRIPTION

[0009] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0010] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0011] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0012] In this embodiment, a method for evaluating the ranking of green electricity consumption of enterprises based on blockchain is provided. Figure 1 is a flowchart of a method for evaluating the ranking of green electricity consumption of enterprises based on blockchain according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps: Step S101, obtaining first data of multiple enterprises in a first region from a preset data source based on a smart contract; In this step, the smart contract automatically obtains the original data of the company's green electricity consumption from the preset data source (such as the power grid company, the renewable energy information management center, etc.). These data include key information such as the total amount of green electricity consumption, the source of green electricity, the time of consumption, the effect of energy conservation and emission reduction, and the green certificate. Compared with the related art, this step uses the smart contract to automate the data collection process, reduces human intervention, and improves the efficiency and accuracy of data collection. The related art may need to rely on manual collection and verification, which is prone to errors and inefficient. In particular, since some data (such as green certificates purchased on the National Renewable Energy Information Management Center platform and self-used green electricity data) may need to be uploaded by the user himself, there is a lack of effective supervision and verification mechanism, and there is a possibility of data falsification. However, the present invention automatically retrieves data from officially certified platforms such as power grid companies and renewable energy information management centers based on smart contracts to make the authenticity of the data more reliable.

[0013] In an exemplary implementation, to implement step S101, the following solution may be adopted: Define data interface: Smart contracts need to interact with the API of the data source. First, the contract developer needs to negotiate with the data source provider (such as the power grid company, renewable energy information management center) to define a secure API interface for smart contracts to query and obtain data.

[0014] Authorization and authentication: To protect the security and privacy of data, the smart contract needs to use an authentication mechanism to ensure that it has the right to obtain data from the data source. This can involve the use of API keys, digital certificates, or other forms of authentication.

[0015] Data acquisition function: One or more functions are implemented in the smart contract to call the API interface of the data source. These functions will contain the necessary logic to format the request, process the request parameters, and receive the response data.

[0016] Data validation: In smart contracts, implement basic validation of acquired data, such as checking whether the data is in the expected format and whether it contains all required fields.

[0017] Data update mechanism: Smart contracts should contain logic to obtain updated data from data sources regularly or on demand. This can be achieved by listening to specific events on the blockchain or by timed triggered smart contract functions.

[0018] For example, a power grid company provides an API interface that allows authorized smart contracts to query the green power consumption data of enterprises in a specific area. The following is an example process for a smart contract to obtain data from a preset data source: Contract deployment: The smart contract is deployed to the blockchain and includes an authentication key for accessing the grid company’s API.

[0019] User request: A user (e.g., a company or regulator participating in the ranking) triggers a function in the smart contract to request the latest green power consumption data for multiple companies in a specific region.

[0020] API call: The smart contract uses the API key provided by the grid company to call the grid company’s API through its internally implemented function. The request can include parameters such as a specific time period, region identifier, and company list.

[0021] Processing response: The grid company’s API returns the required data, and the smart contract parses the data and performs necessary verification to ensure data integrity and consistency.

[0022] Data storage: Once the data is verified, the smart contract stores the data on the blockchain, and a mapping can be used to associate the company identifier and its green electricity consumption data.

[0023] Event triggering: Smart contracts can be designed to trigger an event when data is updated, which can be listened to by other smart contracts or front-end applications for further processing or notification to users.

[0024] In this way, smart contracts can automatically, securely and reliably obtain the first data of the enterprise from the preset data source, providing a solid foundation for subsequent data processing and ranking evaluation.

[0025] Step S102, verifying the integrity and consistency of the first data based on a preset logic to obtain a first result; In this step, the integrity and consistency of the collected data are verified using preset logical rules. The integrity check ensures that all necessary data fields have been collected, and the consistency check ensures that the logical relationship between the data is reasonable. Compared with the related art, this step improves the level of data quality control through the automated verification process. In the related art, data verification may not be strict or comprehensive enough, resulting in data quality problems.

[0026] In an exemplary implementation, to implement step S102, the following solution may be adopted: Define validation rules: Integrity rules: ensure that necessary data fields such as the total amount of green electricity consumed, the source of green electricity, the time of consumption, the effect of energy conservation and emission reduction, green certificates, etc. have been collected and are not empty.

[0027] Consistency rules: Ensure that the logical relationship between data is reasonable, for example, the total amount of green electricity consumption should be non-negative, and the energy-saving and emission reduction effects should be within a reasonable range of 0% to 100%.

[0028] Implement verification algorithms: Develop algorithms for each integrity and consistency rule that can be automatically enforced in smart contracts.

[0029] Data field check: For each data field, check whether it exists and is not empty. For example, check whether the total amount of green power consumption is a non-negative number.

[0030] Correlation verification: Verify the logical relationship between data items, such as whether there is a reasonable correspondence between the total amount of green electricity consumption and the energy-saving and emission reduction effects.

[0031] Threshold check: For data with a predetermined range, check whether it is within the acceptable threshold.

[0032] Error handling: If the data fails validation, the error message is recorded and a decision is made based on preset logic whether to reject the data or require resubmission.

[0033] For example, the power grid companies and the Renewable Energy Information Management Center provide the following data: The total green electricity consumption of Enterprise A is 1,000 MWh; the source of green electricity of Enterprise B is hydropower; the consumption time of Enterprise C is January 2023; the energy-saving and emission reduction effect of Enterprise D is 20%.

[0034] In the smart contract, implement the following verification logic: Integrity verification: Check whether the total green electricity consumption field of enterprise A exists and is not empty, check whether the green electricity source field of enterprise B exists and is not empty, and so on.

[0035] Consistency verification: Check whether the total amount of green electricity consumed by enterprise A is a non-negative number.

[0036] Check whether the energy conservation and emission reduction effect of Enterprise D is within the range of 0% to 100%.

[0037] Relevance verification: If the total amount of green electricity consumed by company A increases, but the energy conservation and emission reduction effects of company D do not increase accordingly during the same period, the smart contract may flag this inconsistency and require further review or additional explanation.

[0038] Threshold check: Checks whether all percentage type data is within the range of 0 to 100.

[0039] Error handling: If the green electricity source field of enterprise B is empty, the smart contract will record this error and may refuse to accept the data until the data provider completes the information.

[0040] In this way, smart contracts can automatically verify the integrity and consistency of data, ensuring that only accurate data is recorded on the blockchain and used for subsequent accounting list generation and ranking calculations. This approach improves the credibility of data and provides a fair and transparent evaluation environment for enterprises.

[0041] Step S103, if the first result is qualified, the first data is recorded on the blockchain to obtain the second data; In this step, if the data passes the integrity and consistency verification, the smart contract records the data on the blockchain. The immutable nature of the blockchain ensures that the data cannot be changed once it is on the chain. Compared with related technologies, this step uses blockchain technology to improve the security and credibility of the data. However, in related technologies, the data may be tampered with, resulting in inaccurate ranking results.

[0042] In an exemplary implementation, to implement step S103, the following solution may be adopted: Data preparation: Once the data passes the integrity and consistency verification in the smart contract (step S102), the data will be marked as "qualified" and ready to be written to the blockchain.

[0043] Data Packaging: The smart contract packages qualified data into a block. This block will contain a block header, which will contain the hash value of the previous block (to ensure the continuity of the blockchain), as well as timestamp, nonce (a random number used for proof of work) and other information.

[0044] Hash function: The block header is hashed using a hash function (such as SHA-256) to generate a unique identifier for the block (block hash).

[0045] Broadcast to the network: The smart contract broadcasts this new block to other nodes (secondary nodes) in the blockchain network.

[0046] Block verification: After receiving this block, other nodes in the network will verify it to ensure the accuracy of the data and the legitimacy of the block.

[0047] Consensus mechanism: Nodes in the network reach a consensus through a consensus mechanism (such as Proof of Work, PoW, or Proof of Stake, PoS) to confirm the validity of the new block.

[0048] Add to blockchain: Once a new block is verified and approved by the majority of nodes in the network, it will be added to the blockchain and become an unchangeable part of the chain.

[0049] For example, the first data of enterprises A, B, C and D have passed the integrity and consistency verification. These data include their respective total green electricity consumption, green electricity sources, consumption time and energy conservation and emission reduction effects.

[0050] Data packaging: Smart contracts package this data into a block, which contains the data records of these enterprises.

[0051] Hash processing: Hash the block header to generate the hash value of the block.

[0052] Broadcast: Broadcast this block to the blockchain network and wait for verification by other nodes.

[0053] Verification and consensus: Nodes in the network receive this block and verify the validity of the block. For example, they check whether the block hash is correct and whether the data complies with the preset rules.

[0054] On-chain: If the block passes verification, the nodes in the network will reach a consensus through the consensus mechanism and add the block to the blockchain.

[0055] In this way, data is permanently recorded on the blockchain, ensuring the data is tamper-proof and transparent. This provides a reliable data basis for subsequent accounting list generation and ranking calculations, while also improving the credibility and effectiveness of the entire corporate green power consumption ranking evaluation method.

[0056] Step S104, generating a calculation list based on the second data, wherein the calculation list includes: the total amount of green electricity consumed, the source of green electricity, the consumption time, and the energy-saving and emission-reduction effect; In this step, based on the verified data (second data) on the blockchain, the system automatically generates an accounting list, which records in detail the total amount of green electricity consumed by each enterprise, the source of green electricity, the time of consumption, and the energy conservation and emission reduction effect. Compared with related technologies, this step ensures the accuracy and reliability of the accounting list because all data has been verified and recorded on the blockchain, which cannot be tampered with.

[0057] In an exemplary implementation, to implement step S104, the following solution may be adopted: Extract blockchain data: Extract verified and recorded secondary data from the blockchain. This data has passed the integrity and consistency check of the smart contract and is permanently stored on the blockchain.

[0058] Data parsing: Parsing the extracted data into a readable format for further processing, for example, converting transaction data on a blockchain into structured database entries.

[0059] Accounting list generation: Use the parsed data to generate an accounting list. This list will record in detail the total amount of green electricity consumed by each enterprise, the source of green electricity, the consumption time and the energy-saving and emission reduction effect.

[0060] Data verification: During the process of generating the accounting list, data verification is performed to ensure that all data is consistent with the records on the blockchain.

[0061] Checklist review: The generated checklist is reviewed to check for any omissions or errors. This can be an automated check or include a manual review.

[0062] Checklist Updates: If any issues are discovered during the audit, update the accounting checklist and re-audit until the checklist is completely accurate.

[0063] Inventory publication: Once the inventory is confirmed to be accurate, it is published to relevant parties such as companies, regulators or other stakeholders.

[0064] For example, the data of companies A, B, C, and D have been verified by smart contracts and recorded on the blockchain. These data include the total amount of green electricity consumed by each company, the source of green electricity, the consumption time, and the energy-saving and emission reduction effects.

[0065] Extract data: A smart contract or a dedicated system component extracts the data of companies A, B, C, and D from the blockchain.

[0066] Data parsing: The system converts the raw data in blockchain transactions into a structured format, for example, converting the consumption time from a timestamp to a readable date format.

[0067] Generate accounting list: The system automatically generates an accounting list, listing the data of each enterprise: Company A: The total amount of green electricity consumed is 1000 MWh, the source of green electricity is "solar energy", the consumption time is "January 2023", and the energy-saving and emission reduction effect is "15%".

[0068] Company B: The total amount of green electricity consumed is 800 MWh, the source of green electricity is "wind power", the consumption time is "February 2023", and the energy-saving and emission reduction effect is "12%".

[0069] Company C: The total amount of green electricity consumed is 1,200 MWh, the source of green electricity is "hydropower", the consumption time is "March 2023", and the energy-saving and emission reduction effect is "18%".

[0070] Company D: Total green electricity consumption is 900 MWh, source of green electricity is “solar energy”, consumption time is “April 2023”, energy saving and emission reduction effect is “14%”.

[0071] Data verification: The system automatically checks whether each data point in the list is completely consistent with the record on the blockchain.

[0072] Checklist Review: A review team checks the accounting checklist to ensure that all data is accurate.

[0073] List Update: If any errors are found, such as data entry errors or calculation errors, the system will update the accounting list and re-audit it.

[0074] List Publication: Once the accounting list is confirmed to be accurate, it is published to a secure online platform where all relevant parties can access and view it.

[0075] In this way, the process of generating the accounting list is not only automated and efficient, but also highly reliable and transparent because the data is derived from the tamper-proof blockchain. This provides a solid foundation for the subsequent ranking calculation.

[0076] Step S105, calculating the ranking level of the enterprise based on the accounting list and the first preset algorithm.

[0077] In an exemplary embodiment, a preset algorithm (first preset algorithm) is used to calculate the green power consumption ranking of each enterprise based on the data in the accounting list. Compared with related technologies, this step provides a fair and transparent ranking calculation process. Since all data has been strictly verified and recorded, the ranking results are more accurate and reliable.

[0078] In an exemplary implementation, to implement step S105, the following solution may be adopted: Preparation of accounting lists: Extract verified secondary data from the blockchain and generate accounting lists. These lists record in detail the total amount of green electricity consumed by each enterprise, the source of green electricity, the time of consumption, and the effect of energy conservation and emission reduction.

[0079] Weight distribution model application: Use weight distribution models (such as random forest and principal component analysis models) to assign weights to each indicator in the accounting list. These weights reflect the importance of each indicator in the ranking calculation.

[0080] Calculation factor calculation: For each enterprise, multiply each indicator value in the accounting list by its corresponding weight to obtain a series of calculation factors.

[0081] Score summary: Sum up the calculation factors of each enterprise to obtain the total score of the enterprise.

[0082] Ranking determination: All enterprises are sorted according to their total scores to determine the ranking level of each enterprise.

[0083] For example: For example, there are four companies A, B, C and D, and their accounting list data are as follows: Company A: The total amount of green electricity consumed is 1000 MWh, the source of green electricity is "solar energy", the consumption time is "January 2023", and the energy-saving and emission reduction effect is "15%".

[0084] Company B: The total amount of green electricity consumed is 800 MWh, the source of green electricity is "wind power", the consumption time is "February 2023", and the energy-saving and emission reduction effect is "12%".

[0085] Company C: The total amount of green electricity consumed is 1,200 MWh, the source of green electricity is "hydropower", the consumption time is "March 2023", and the energy-saving and emission reduction effect is "18%".

[0086] Company D: Total green electricity consumption is 900 MWh, source of green electricity is “solar energy”, consumption time is “April 2023”, energy saving and emission reduction effect is “14%”.

[0087] For example, the weights obtained by the weight distribution model are as follows: Weight of total green electricity consumption: 0.4; Weight of green electricity source: 0.2; Weight of consumption time: 0.2; Weight of energy conservation and emission reduction effect: 0.2.

[0088] And, to simplify the calculation, it is assumed that the scores of green electricity sources are as follows (the actual scores should be based on more complex analysis): Solar energy: 1.0; Wind energy: 0.8; Hydropower: 0.9; Others: 0.7.

[0089] The calculation process is as follows: Calculate the weighted value of each indicator: Company A: 1000×0.4+1.0×0.2+1×0.2+0.15×0.2=400+0.2+0.2+0.03=400.431000×0.4+1.0×0.2+1×0.2+0.15×0.2=400+0.2+0.2+0.03=400.43; Company B: 800×0.4+0.8×0.2+1×0.2+0.12×0.2=320+0.16+0.2+0.024=320.384800×0.4+0.8×0.2+1×0.2+0.12×0.2=320+0.16+0.2+0.024=320.384; Company C: 1200×0.4+0.9×0.2+1×0.2+0.18×0.2=480+0.18+0.2+0.036=480.5161200×0.4+0.9×0.2+1×0.2+0.18×0.2=480+0.18+0.2+0.036=480.516; Company D: 900×0.4+1.0×0.2+1×0.2+0.14×0.2=360+0.2+0.2+0.028=360.428900×0.4+1.0×0.2+1×0.2+0.14×0.2=360+0.2+0.2+0.028=360.428.

[0090] Sort by weighted value: Enterprise C: 480.516; Enterprise A: 400.43; Enterprise D: 360.428; Enterprise B: 320.384.

[0091] Determine ranking: Company C ranks first; Company A ranks second; Company D ranks third; and Company B ranks fourth.

[0092] In this way, the corporate green electricity consumption ranking evaluation method can automatically and impartially calculate the ranking of each company, improving the transparency and credibility of the evaluation process.

[0093] Through the above steps S101 to S105, this embodiment significantly improves the accuracy, reliability and transparency of the corporate green electricity consumption ranking evaluation by combining smart contracts, preset logic verification, blockchain technology and automated accounting list generation, and solves the data authenticity and accuracy problems existing in related technologies.

[0094] Among them, it should be noted that a smart contract can be a program running on the blockchain, which can automatically execute the terms of the contract when the preset conditions are met and can be set based on actual conditions.

[0095] Figure 2 is a flowchart of a method for calculating a ranking level of an enterprise based on a calculation list and a first preset algorithm according to an embodiment of the present invention. In one implementation, Figure 2 As shown, the ranking level of the enterprise is calculated based on the accounting list and the first preset algorithm, including: Step S201, assigning weights to the total amount of green electricity consumption, the source of green electricity, the consumption time, and the energy conservation and emission reduction effect based on a weight allocation model; In an exemplary embodiment, for example, statistical methods such as random forest models and principal component analysis models can be used to evaluate the influence of each factor (total amount of green electricity consumption, source of green electricity, consumption time, energy conservation and emission reduction effect) on the company ranking based on historical data. Therefore, the weight of each factor is determined by a scientific method, which increases the transparency and fairness of the ranking calculation. In one example, for example, the weights determined by model analysis are: total amount of green electricity consumption (0.4), source of green electricity (0.2), consumption time (0.2), energy conservation and emission reduction effect (0.2).

[0096] Step S202, taking the product of the total amount of green electricity consumption, the source of green electricity, the consumption time, the energy-saving and emission-reduction effect and the weights assigned to each as a calculation factor; In an exemplary implementation, for each enterprise, the actual values ​​of its total green electricity consumption, green electricity source, consumption time, and energy-saving and emission reduction effects are multiplied by the corresponding weights. Therefore, the qualitative data is converted into quantitative calculation factors to provide a basis for subsequent score calculations. In one example, for example, the data of Enterprise A is: total green electricity consumption of 1000 MWh, green electricity source "solar energy" (score 1.0), consumption time "January 2023" (score 1.0), energy-saving and emission reduction effect 15% (score 0.15). The calculation factors are: 1000×0.4+1.0×0.2+1.0×0.2+0.15×0.2=400+0.2+0.2+0.03=400.431000×0.4+1.0×0.2+1.0×0.2+0.15×0.2=400+0.2+0.2+0.03=400.43.

[0097] Step S203, taking the sum of multiple calculation factors as the score of the enterprise; In an exemplary embodiment, the calculation factors calculated in step S202 are added together to obtain the total score of the enterprise. Therefore, a score that comprehensively considers various factors is provided for comparison and ranking between enterprises. In an example, for example, the sum of the calculation factors of enterprise A is 400.43, which will become the score of enterprise A.

[0098] Step S204, determining the ranking level of the enterprise based on the score.

[0099] In an exemplary implementation, the scores of all enterprises are sorted, and the ranking is determined according to the scores. Different ranking levels can be set, such as gold, silver, bronze, etc. Therefore, a clear ranking of green power consumption performance is provided to enterprises, which motivates enterprises to improve their use and consumption of green power. In one example, for example, among all the enterprises participating in the ranking, enterprise A has the highest score, so enterprise A is awarded the gold level, and the rankings of other enterprises are determined by analogy.

[0100] Through the above steps S101 to S105, the method not only provides a fair and transparent corporate green power consumption ranking evaluation mechanism, but also ensures the scientificity and rationality of the evaluation results through weight allocation and score calculation. This mechanism can effectively motivate enterprises to improve their use and consumption of green power and promote the development of green energy and environmental protection.

[0101] In one embodiment, the weight allocation model includes: The random forest model is used to obtain historical data, including the total amount of green electricity consumption, sources of green electricity, consumption time, energy conservation and emission reduction effects, and historical data of enterprise rankings; and Build multiple decision trees based on historical data and evaluate the importance of each feature to obtain an importance score; In an exemplary embodiment, the random forest model mainly performs the following steps: Data collection: Collect historical data, including the total amount of green electricity consumed, the source of green electricity, the time of consumption, the effect of energy conservation and emission reduction, and the ranking of enterprises.

[0102] Model training: Use the random forest model to train the data. The model constructs multiple decision trees to evaluate the importance of each feature.

[0103] Therefore, the random forest model can give an importance score for each feature (total green electricity consumption, source of green electricity, etc.), which reflects the influence of each feature on the company ranking.

[0104] For example, the feature importance scores obtained after running the random forest model are: total green electricity consumption 0.45, green electricity source 0.15, consumption time 0.20, and energy conservation and emission reduction effect 0.20.

[0105] The principal component analysis model is used to obtain the corresponding number of principal components from the importance scores; In an exemplary embodiment, the principal component analysis model takes the feature importance score as input, applies the principal component analysis model to reduce the dimension, and extracts the main influencing factors. Therefore, it can help to transform multiple related features into a few unrelated principal components, which can explain most of the data variation. For example, after the principal component analysis model is analyzed, two main components are obtained, which explain 70% and 20% of the variance respectively.

[0106] Sort based on the variance value of the principal component to get the sorting number; In an exemplary embodiment, the principal component analysis model calculates the variance ratio explained by each principal component and sorts the principal components according to the variance ratio. Therefore, principal components with larger variance ratios are given higher weights, which reflects their importance in explaining data variation. For example, principal component 1 explains 70% of the variance and principal component 2 explains 20% of the variance, so the sorting number of principal component 1 is 1 and the sorting number of principal component 2 is 2.

[0107] Based on the sorting sequence, weights are assigned to the total amount of green electricity consumption, source of green electricity, consumption time, and energy-saving and emission reduction effects.

[0108] In one implementation, the plurality of weights are an arithmetic progression, and the sum of the weights is 1.

[0109] By adopting the above technical means, it can be ensured that each factor is fairly and automatically weighted according to its contribution to the company ranking.

[0110] In an exemplary implementation, for example, the serial number of enterprise A is 1, the serial number of enterprise B is 2, the serial number of enterprise C is 3, and the serial number of enterprise D is 4. Based on the order of the serial numbers, the weight of enterprise A is automatically configured as 0.4, the weight of enterprise B is automatically configured as 0.3, the weight of enterprise C is automatically configured as 0.2, and the weight of enterprise D is automatically configured as 0.1.

[0111] Through this method, the weight allocation model can scientifically determine the weight of each factor in the ranking of corporate green electricity consumption, thereby improving the accuracy and fairness of the evaluation.

[0112] In one implementation, each field of the first data is checked item by item.

[0113] In an exemplary embodiment, a check logic is implemented in a smart contract, which will traverse each field in the data model. For each field, check whether its value exists. This can be achieved through simple conditional judgments. For example, in a Solidity smart contract, a require statement can be used to ensure that the field is not empty. Check whether the data type of the field value is correct, such as ensuring that a numeric field is a numeric type and a date field is a date type. For fields of numeric type, check whether its value is within a reasonable range, such as whether the consumption time is within a specific date range.

[0114] In one implementation, when a field in the first data has a null value, the first data is deleted.

[0115] In an exemplary implementation, if any field is found to be empty or non-compliant during the check, the smart contract will reject the data. The smart contract records the details of the invalid data and provides feedback to the data provider, indicating why the data was rejected. The data will only be retained after all fields pass the integrity and consistency check.

[0116] In one implementation, verifying the consistency of the first data based on a preset logic includes: In the case where the first data includes a plurality of elements, association between the plurality of elements is verified.

[0117] In an exemplary implementation, the consistency of the first data is verified based on a preset logic to ensure that the logical relationship between multiple elements in the data is reasonable. The following are specific steps and examples: Steps: Define association rules: determine the logical relationship between the elements in the data. For example, the total amount of green electricity consumption should be positively correlated with the energy conservation and emission reduction effect, that is, enterprises with a large total amount of consumption should generally have a higher energy conservation and emission reduction effect.

[0118] Implement the correlation check function: Implement a function in the smart contract to check whether the correlation between the elements in the data conforms to the preset logical rules.

[0119] Perform relevance check: For each data set, perform a relevance check to verify whether the data meets all preset relevance rules.

[0120] Handling inconsistent data: If the correlation between data elements does not meet the preset rules, the smart contract will reject the data set and record the reason for the inconsistency.

[0121] For example, set the following preset logic rules: Rule 1: The ratio of total green electricity consumption to energy conservation and emission reduction effect should be within a reasonable range. For example, if a company's total green electricity consumption is 1,000 MWh, its energy conservation and emission reduction effect should be at least 10%.

[0122] Rule 2: Consistency between the source of green electricity and the time of consumption. For example, if the green electricity comes from "solar energy", the consumption time should be during the peak period of solar power generation, such as noon.

[0123] Example data: Data of Company A: total green electricity consumption is 1000 MWh, source of green electricity is "solar energy", consumption time is "January 15, 2023 14:00", energy-saving and emission reduction effect is "15%".

[0124] Data of Company B: total green electricity consumption is 800 MWh, source of green electricity is "wind power", consumption time is "03:00 on January 15, 2023", energy-saving and emission reduction effect is "5%".

[0125] Regarding the relevance check: Inspection of Company A: Check the ratio of total green electricity consumption to energy conservation and emission reduction effects: 1000 MWh corresponds to 15%, which complies with Rule 1.

[0126] Check the consistency between the source of green electricity and the time of consumption: Solar energy has high power generation efficiency during the noon period, which complies with Rule 2.

[0127] Inspection of Company B: Check the ratio of total green electricity consumption to energy conservation and emission reduction effects: 800 MWh corresponds to 5%, which does not comply with Rule 1 (energy conservation and emission reduction effects are too low).

[0128] Check the consistency between the source of green electricity and the time of consumption: wind power may not be at its peak at 3 a.m., which requires further verification.

[0129] Handling inconsistent data: Enterprise B’s data does not comply with Rule 1, so the smart contract will reject Enterprise B’s data and require a correction or explanation.

[0130] In this way, smart contracts can ensure that only logically consistent and reasonable data is recorded on the blockchain, thereby improving the quality and reliability of the data and providing a solid foundation for subsequent accounting list generation and ranking calculations.

[0131] In one embodiment, the method further includes: when the correlation is less than a preset threshold, deleting the first data.

[0132] In an exemplary implementation, a preset threshold is set to determine whether the data relevance is strong enough, thereby determining whether to retain or delete the first data. The following is a detailed description with reference to an example: 1. Situation where the first data needs to be deleted: For example, the following preset thresholds are set for the relevance check: Threshold 1: The ratio of total green electricity consumption to energy conservation and emission reduction effect should be greater than or equal to 10%. That is, if the total green electricity consumption of an enterprise is 1,000 MWh, the energy conservation and emission reduction effect should be at least 10%.

[0133] Threshold 2: If the green electricity comes from "solar energy", the consumption time should be between 12 noon and 4 pm to coincide with the peak period of solar power generation.

[0134] Example data: Data of Company B: total green electricity consumption is 800 MWh, source of green electricity is "wind power", consumption time is "03:00 on January 15, 2023", energy-saving and emission reduction effect is "5%".

[0135] Regarding the relevance check: Inspection of Company B: Check the ratio of total green electricity consumption to energy conservation and emission reduction effects: 800 MWh corresponds to 5%, which does not meet the threshold of 1 (energy conservation and emission reduction effects are too low).

[0136] Check the consistency between the source of green electricity and the time of consumption: Wind power is not at its peak at 3 a.m., and this period is not related to the peak period of solar power generation, so the data does not match the preset logic.

[0137] Handling inconsistent data: Since the data of enterprise B does not meet threshold 1, the smart contract will reject the data of enterprise B and delete it from the data set to be uploaded to the chain.

[0138] 2. Circumstances for retaining the first data: Example data: Data of Company A: total green electricity consumption is 1000 MWh, source of green electricity is "solar energy", consumption time is "January 15, 2023 14:00", energy-saving and emission reduction effect is "15%".

[0139] Relevance check: Inspection of Company A: Check the ratio of total green electricity consumption to energy conservation and emission reduction effects: 1000 MWh corresponds to 15%, which meets the threshold of 1.

[0140] Check the consistency between the source of green electricity and the time of consumption: Solar energy has high power generation efficiency during the noon period, which meets the threshold 2.

[0141] Processing consistent data: Since company A’s data meets all preset relevance rules and thresholds, the smart contract will accept company A’s data and retain it in the dataset to be uploaded to the chain.

[0142] In this way, smart contracts ensure that only logically consistent and reasonable data is recorded on the blockchain. This not only improves the quality and reliability of the data, but also ensures the accuracy and fairness of subsequent accounting list generation and ranking calculations. Deleting data that does not meet the preset threshold helps maintain the integrity and effectiveness of the entire system.

[0143] Figure 3 is a flowchart of a method for recording first data on a blockchain to obtain second data when a first result is qualified according to an embodiment of the present invention. In one implementation, Figure 3 As shown, when the first result is qualified, the first data is recorded on the blockchain to obtain the second data, including: Step S301, obtaining first data of each first node; In an exemplary embodiment, a smart contract or a centralized / decentralized system component is responsible for collecting first data from a preset data source (such as a power grid company, a renewable energy information management center, etc.). These data have been verified for integrity and consistency to ensure their accuracy and reliability. Therefore, it is ensured that only verified data is used for blockchain records, which enhances the authenticity and credibility of the data. For example, the data of companies A, B, and C have been verified and qualified, and the system extracts these data from the database or API and prepares to upload them to the chain.

[0144] Step S302, packing the first data into blocks based on a hash function; In an exemplary embodiment, a cryptographic hash function (such as SHA-256) is used to process the data to generate a hash value of the data. The processed data is packaged into a block, which contains a block header and a block body. The block header contains the hash value, timestamp, nonce and other information of the previous block. Therefore, the hash function ensures the immutability of the data, and once the data is packaged into a block, any changes will be detected. For example, the system packages the data of enterprises A, B, and C into a block, calculates the hash value of this block, and links it with the hash value of the previous block.

[0145] Step S303, broadcasting the block to a second node in the blockchain network; In an exemplary embodiment, the packaged blocks are sent to other nodes in the blockchain network. These nodes receive the blocks and start the verification process. Therefore, by broadcasting the blocks, it is ensured that all participants in the network can receive the information of the new blocks, which increases the transparency and decentralization of the system. For example, the system sends a block containing the data of companies A, B, and C to other nodes in the network and requests them to verify the block.

[0146] Step S304, verifying the validity of the block based on the second node and reaching a consensus through a consensus mechanism; In an exemplary embodiment, nodes in the network verify blocks, including checking the hash value of the block, the integrity and consistency of the data. A consensus mechanism (such as Proof of Work, PoW) is used to reach a consensus on the validity of the block. Therefore, the consensus mechanism ensures that all participants in the network reach a consensus on the acceptance and rejection of the block, enhancing the security and reliability of the blockchain.

[0147] Step S305: When consensus is reached, the block is added to the blockchain to obtain the second data.

[0148] In an exemplary embodiment, once a block is verified by the majority of nodes in the network, it will be added to the blockchain and become an unchangeable part of the chain. This block now contains the verified data, i.e., the second data. Therefore, permanently recording the data on the blockchain ensures the persistence and immutability of the data, providing a solid foundation for subsequent data analysis and decision-making. For example, the block containing the data of companies A, B, and C is added to the blockchain, and this data now becomes part of the blockchain, and anyone can view and verify this data.

[0149] Through steps S301 to S305, the method ensures that only strictly verified data is recorded on the blockchain, improves the security, reliability and transparency of the data, and provides a solid data foundation for the blockchain-based corporate green electricity consumption ranking evaluation system.

[0150] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by adding the necessary general hardware platform with the help of software, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0151] The specific examples in this embodiment can refer to the examples described in the above embodiments and exemplary implementations, and this embodiment will not be repeated here. Of course, the following is another implementation that can also be envisioned: For example, the total amount of green electricity consumption, the source of green electricity, the consumption time, the energy-saving and emission reduction effects, etc. are part of the green electricity consumption accounting data.

[0152] Storage of accounting data on the chain: Green electricity consumption accounting data involves the participation of multiple parties, especially for the green certificates purchased on the National Renewable Energy Information Management Center platform and the green electricity data for self-use uploaded by users, there is a possibility of fraud, so it is necessary to support the tamper-proof blockchain technology to improve the accuracy of accounting business data from the source. The business data of the green electricity consumption accounting process is put on the chain, which lays the foundation for the reliable traceability process for the subsequent issuance of accounting results, and improves the credibility and authority of the green electricity accounting business.

[0153] Account system on-chain evidence storage: The consumption accounting account is a special electronic account used to record green electricity transaction settlement information, green certificate holding, transfer, write-off and use information, and self-generated and self-used renewable energy. It records the green electricity consumption accounting information of the accounting subject in a true, complete and accurate manner. The consumption accounting account system is put on the chain to provide on-chain consumption accounting and bookkeeping services based on blockchain.

[0154] Issuance of accounting lists on the chain: This service is aimed at green electricity users. By providing enterprises with green electricity consumption accounting lists stamped with State Grid Chain evidence information, it is convenient for enterprises to obtain traceability information on the list and promote green electricity consumption accounting services to gain wider social recognition.

[0155] Trusted traceability verification service: The green electricity consumption accounting list service can effectively support multiple business scenarios such as corporate carbon asset investigation, renewable energy consumption responsibility weight assessment, product carbon footprint, carbon tax deduction management, etc. The list results need to meet the verification work of participants in different business scenarios, so traceability verification work is required. Provide blockchain trusted verification of the issued consumption list for each consumer entity, and trace the green electricity consumption information of the consumption list through the trusted traceability module.

[0156] Green power consumption evaluation and ranking: Based on the consumption accounting list, the green power consumption of accounting entities in the province can be obtained, but there is a lack of green power consumption evaluation and ranking system in different dimensions, which makes it impossible to provide positive feedback to market entities such as enterprises and parks that actively consume green power. Building a green power evaluation and ranking system will help enterprises demonstrate their green development results and play a leading role in demonstration.

[0157] Green electricity consumption label management: At present, electricity-consuming enterprises or parks do not have intuitive carriers to display the overall green electricity consumption, the proportion of green electricity use, etc. Build a green consumption label system to serve all market entities and form effective incentives and guidance for users to actively consume green electricity. The green electricity consumption label is for electricity-consuming enterprises or parks. For enterprises that consume green electricity, their green electricity consumption level will be identified and verified based on the green electricity consumption accounting list, and a blockchain-based certification label will be issued; for users in parks within the province, "Green Park" label certification will be carried out.

[0158] Statistical analysis: This application involves multiple functional pages. By summarizing green electricity consumption accounting data of various dimensions and expanding application scenarios (ranking, identification), it is helpful to fully grasp the status of green electricity consumption accounting business in Zhejiang Province and achieve lean management. It realizes the statistics of green electricity consumption chain data, the issuance and inspection and analysis of green electricity consumption lists / green electricity consumption identification.

[0159] Slave-chain adaptation: Through slave-chain adaptation, convenient and efficient chain services are provided for the application of blockchain technology for business data, unified management of chaining of internal and external network applications is achieved, and the stable and reliable operation of the application system and the State Grid chain is enhanced.

[0160] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0161] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A blockchain-based corporate green electricity consumption ranking evaluation method, characterized in that: include: Acquire first data of a plurality of enterprises in a first region from a preset data source based on a smart contract; Verifying the integrity and consistency of the first data based on a preset logic to obtain a first result; If the first result is qualified, the first data is recorded on the blockchain to obtain second data; Generate a calculation list based on the second data, wherein the calculation list includes: the total amount of green electricity consumed, the source of green electricity, the consumption time, and the energy-saving and emission-reduction effect; The ranking level of the enterprise is calculated based on the accounting list and a first preset algorithm.

2. The method according to claim 1, characterized in that The calculating the ranking level of the enterprise based on the accounting list and the first preset algorithm includes: Based on a weight allocation model, weights are allocated to the total amount of green electricity consumption, the source of green electricity, the consumption time, and the energy conservation and emission reduction effect; The product of the total amount of green electricity consumption, the source of green electricity, the consumption time, the energy-saving and emission-reduction effect and the weights assigned to each of them is used as a calculation factor; A score of the enterprise is obtained based on a sum of the plurality of calculation factors; A ranking level of the enterprise is determined based on the score.

3. The method according to claim 2, characterized in that The weight distribution model includes: A random forest model is used to obtain historical data, wherein the historical data includes: total green electricity consumption, green electricity sources, consumption time, energy conservation and emission reduction effects, and company rankings; and Building multiple decision trees based on the historical data and evaluating the importance of each feature to obtain an importance score; A principal component analysis model is used to obtain a corresponding number of principal components from the importance scores; Sorting based on the variance values ​​of the principal components to obtain a sorting sequence number; Based on the sorting sequence, weights are respectively assigned to the total amount of green electricity consumed, the source of green electricity, the consumption time, and the energy-saving and emission reduction effect.

4. The method according to claim 3, characterized in that The plurality of weights are an arithmetic progression, and the sum of the weights is 1.

5. The method according to claim 1, characterized in that Verifying the integrity of the first data based on a preset logic includes: Each field of the first data is checked item by item.

6. The method according to claim 5, characterized in that Also includes: When the field containing the first data has a null value, the first data is deleted.

7. The method according to claim 1, characterized in that Verifying the consistency of the first data based on a preset logic includes: In the case where the first data includes a plurality of elements, the association between the plurality of elements is verified.

8. The method according to claim 7, characterized in that Also includes: When the correlation is less than a preset threshold, the first data is deleted.

9. The method according to claim 1, characterized in that: When the first result is qualified, recording the first data on the blockchain to obtain second data includes: Acquire the first data of each first node; Packing the first data into blocks based on a hash function; broadcasting the block to a second node in the network of the blockchain; Verifying the validity of the block based on the second node and reaching a consensus through a consensus mechanism; When consensus is reached, the block is added to the blockchain to obtain the second data.

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