WO2022206143A1 - 一种基于区块链的绿色证书交易系统 - Google Patents

一种基于区块链的绿色证书交易系统 Download PDF

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WO2022206143A1
WO2022206143A1 PCT/CN2022/073319 CN2022073319W WO2022206143A1 WO 2022206143 A1 WO2022206143 A1 WO 2022206143A1 CN 2022073319 W CN2022073319 W CN 2022073319W WO 2022206143 A1 WO2022206143 A1 WO 2022206143A1
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transaction
information
node
chain
price
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PCT/CN2022/073319
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English (en)
French (fr)
Inventor
王栋
蒋炜
李达
玄佳兴
李国民
王合建
石欣
李江涛
苏展
周磊
赵丽花
贾帆
Original Assignee
国网区块链科技(北京)有限公司
国网数字科技控股有限公司
国家电网有限公司
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Application filed by 国网区块链科技(北京)有限公司, 国网数字科技控股有限公司, 国家电网有限公司 filed Critical 国网区块链科技(北京)有限公司
Priority to AU2022201721A priority Critical patent/AU2022201721A1/en
Priority to US17/784,627 priority patent/US20240185233A1/en
Priority to JP2022537394A priority patent/JP7337277B2/ja
Priority to KR1020227023492A priority patent/KR20220136998A/ko
Priority to EP22726254.0A priority patent/EP4089616A4/en
Publication of WO2022206143A1 publication Critical patent/WO2022206143A1/zh

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Definitions

  • This application relates to the field of green power certificate trading, and in particular to a blockchain-based green certificate trading system.
  • the green power certificate is an electronic certificate with a unique identification code issued by the state to the power generation enterprises for each MWh of non-water renewable energy on-grid electricity. It is also a policy tool of the renewable energy quota system.
  • the issuance of green certificates requires enterprises to submit relevant materials and be reviewed by various departments. After part of the review is passed, green certificates are issued to enterprises. Enterprises holding green certificates can sell green certificates, and green certificate transactions occur between green certificate buyers and green certificate sellers.
  • the green certificate transactions are carried out by listing and selling on the trading platform, so that the information of the green certificate seller and the green certificate buyer is not equal, that is, the buyer can see the sales information listed by the green certificate seller, and the green certificate seller Before the listing, it is not known what the buyer's desired purchase price is. According to the listed price, the green certificate buyer only wants to buy the green certificate at a lower price, and the seller wants to sell it at a higher price, thus making the transaction volume of successful transactions lower.
  • This application provides a blockchain-based green certificate transaction system, which aims to solve the problem that the total number of successful transactions is small.
  • This application provides a blockchain-based green certificate trading system, including: an on-chain node and an off-chain node; the on-chain node includes: an audit node and an agent node; the agent node supplies power producers and buyers to log in;
  • the audit node is configured to verify the qualification information of the power producer on the proxy node, and send a green certificate to the power producer on the proxy node when the qualification information meets a preset condition ;
  • the off-chain node is used to calculate through the preset model according to the historical transaction price, and obtain the first feature vector used to predict the transaction price of the next transaction;
  • the target feature is mapped to a latent vector; the target feature includes: power generation type, current transaction information and current green certificate information; the latent vector is aggregated by an objective function to obtain a second feature vector; the objective function is to use is a function to ensure the invariance of permutation; the first feature vector and the second feature vector are calculated through a fully connected neural network to obtain the transaction price of the next transaction; the transaction price is used for the electricity The manufacturer and the said purchaser determine the price for reference;
  • the proxy node is configured to, in the case of receiving the sales information of the power producer and the purchase information of the buyer, digitally sign the sales information and the power producer, and, the purchase information and the digital signature of the buyer to the off-chain node;
  • the off-chain node is used to match the sale information and the purchase information, send the matched transaction information to the agent node, and return the sale information and the purchase information in a visual form Show to the front end;
  • the proxy node is further configured to send the successfully matched transaction information to a transaction smart contract; the transaction smart contract performs transaction processing according to the transaction information.
  • the off-chain node is used to calculate through a preset model according to historical transaction prices to obtain a first feature vector for predicting the transaction price of the next transaction, including:
  • the off-chain node is specifically used to obtain the most recent preset transaction price in the historical transaction from the block; input the transaction price into the preset LSTM model, and the LSTM model outputs the corresponding transaction price for each transaction.
  • Hidden vector take the latent vector corresponding to the last transaction price as the first feature vector.
  • the off-chain node is used to calculate the first feature vector and the second feature vector through a fully connected neural network, including:
  • the off-chain node is specifically used for splicing the first feature vector and the second feature vector to obtain a splicing feature vector; by inputting the splicing feature vector into the fully connected neural network to obtain a predicted transaction price .
  • the off-chain node is used to match the sale information and the purchase information, including:
  • the off-chain node is specifically configured to use a two-way auction rule to match the sale information and the purchase information.
  • the number of the proxy node in the blockchain system is set to 0, and the numbers of other nodes in the blockchain system are numbered from 1;
  • the current master node on the chain selects the master node, including:
  • the trigger condition of the original PBFT view replacement is satisfied, or the confirmation information sent by the node with the number 0 is received in the submission stage of the consensus process for k consecutive times, according to the Determine the master node.
  • the master node in the nodes on the chain collects transaction information generated within a preset period of time, and packages the transaction information into a block after verifying the validity of the transaction information.
  • the off-chain node is further configured to return to the front-end in a visual form for display in the case of receiving the green certificate sale information of the power producer and the purchase information of the buyer, so as to display to the front-end.
  • the electricity producer and the buyer provide selling price information and purchase information.
  • the objective function is an AGGREGATE function.
  • the preset model is an LSTM model.
  • the fully connected neural network is an MLP model.
  • the blockchain-based green certificate trading system described in this application includes: on-chain nodes and off-chain nodes; on-chain nodes include: audit nodes and proxy nodes; proxy nodes are used by user node power producers and buyers to log in and use .
  • the off-chain node calculates through the preset model according to the historical transaction price, and obtains the first feature vector for predicting the transaction price of the next transaction, and the first feature vector reflects the historical transaction price. forecast information.
  • the target features of each power producer in the current blockchain platform are mapped as latent vectors; since the target features include: power generation type, current transaction information and current green certificate information, that is, the target features reflect the current environmental information. forecast information.
  • the target function is used to aggregate the latent vectors to obtain the second feature vector, and the first feature vector and the second feature vector are calculated through the fully connected neural network to predict the transaction price of the next transaction.
  • the off-chain node predicts the transaction price of the next transaction according to the historical transaction price and current environment information, thereby ensuring the accuracy of the prediction result.
  • the objective function is used to aggregate the hidden vectors to obtain the second eigenvector, where the objective function is a function used to ensure the invariance of permutation, so that in the process of aggregating the latent vectors, it is possible to eliminate the problems caused by different power producers.
  • the position order of the hidden vectors is different, resulting in different second feature vectors obtained, which further leads to the problem of inaccuracy of the prediction results. Since the blockchain network is a P2P network, the status of each node in the blockchain should be equal.
  • the hidden vector is aggregated by the objective function, and the obtained second feature vector is consistent with the blockchain network.
  • the status of each node should be equal. Therefore, the prediction result is in line with the characteristics of the blockchain, which further ensures the accuracy of the price predicted by this application.
  • the next transaction price predicted by this application can be used for reference by power producers and buyers, thus making it easier to match the sales information and purchase information of power producers and buyers, thereby further improving the transaction success rate.
  • the volume of transactions can be further increased.
  • the present application can increase the transaction volume of the transaction.
  • FIG. 1 is a schematic structural diagram of a blockchain-based green certificate transaction system disclosed in an embodiment of the application.
  • FIG. 2 is a flowchart of a method for a transaction price of a next transaction disclosed in an embodiment of the present application.
  • the issuance and transaction of green certificates are carried out on the blockchain.
  • data sorting and auditing will become easier, improving the issuance of green certificates. efficiency, and at the same time improve the transparency of information. Apart from that, decentralization makes the system more robust without worrying about data loss.
  • Blockchain is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain is a chained data structure that combines data blocks in a sequential manner according to time sequence, and is a cryptographically guaranteed untamperable and unforgeable distributed ledger.
  • Distributed ledger means that transaction accounting is completed by multiple nodes distributed in different places, and each node records a complete account, so they can all participate in monitoring the legality of transactions, and at the same time, they can jointly testify for it.
  • the data stored in the chain structure has good traceability, so it can conveniently collect and organize data, automate the issuance of green certificates, save labor costs, and prevent the occurrence of human errors.
  • the embodiment of the present application introduces an agent node and an off-chain node, wherein the agent node forwards the sales information and digital signature provided by the power producer, as well as the purchase information and digital signature of the buyer, to the off-chain node, Through the matching and management of off-chain nodes, most of the workload of the transaction process is transferred to off-chain nodes, thereby reducing the processing capacity of on-chain nodes, thereby improving real-time performance.
  • the on-chain part records the power generation of each power plant, and the smart contract reviews the power generation of the power plant and other necessary data every month, and automatically issues green certificates to companies that meet the requirements.
  • the transaction of green certificate is also carried out on the chain in the form of smart contracts, so as to record the corresponding transaction information and ensure the disclosure of transaction data.
  • FIG. 1 provides a blockchain-based green certificate transaction system according to an embodiment of the present application, including: on-chain nodes, off-chain nodes, smart contracts and blocks, wherein on-chain nodes may include: audit nodes, proxy nodes and consensus nodes.
  • on-chain nodes may include: audit nodes, proxy nodes and consensus nodes.
  • the proxy node is used by power producers and buyers to log in and use.
  • the proxy node is a proxy server.
  • Figure 1 shows the on-chain part and off-chain part intuitively.
  • the on-chain part includes: green certificate issuance module (corresponding to audit nodes), consensus module (corresponding to consensus nodes), transaction information publishing module (corresponding to proxy nodes) and smart contracts .
  • the green certificate issuance module mainly realizes the function of automatic issuance of green certificates, verifies the qualifications of power plants (corresponding to the power producers in this embodiment) through the form of smart contracts, and issues green certificates to enterprises that meet the conditions, and the issuance records are also used as transactions. Records are collected and packaged by nodes into blocks for storage.
  • the transaction information release module mainly realizes the identity management of both parties in the transaction, which is used for registration, verification, encryption, signature management, authentication and information encryption of transaction users, and identity signature confirmation during the transaction process.
  • the consensus module is used to verify the generated blocks, using the Practical Byzantine Fault Tolerance (PBFT) as the consensus algorithm to determine the master node currently leading the consensus and execute the consensus process.
  • PBFT Practical Byzantine Fault Tolerance
  • the off-chain part can include: a quotation management module (belonging to an off-chain node) and a transaction matching module (belonging to an off-chain node).
  • the quotation management module is used to manage the quotations of the supply and demand sides in the transaction process, and return it to the front-end in a visual form to display to the user, which can be compared to a stock trading scenario.
  • the quotation management module sends the quotation information (corresponding to the sale information and the purchase information in this embodiment) to the transaction matching module, and the transaction matching module sorts and matches the quotation information through certain rules according to the quotation information of both parties to the transaction, and at the same time, the transaction matching module sorts and matches the quotation information through certain rules.
  • Information is encrypted and signed.
  • the power producer will receive an identification (ID i ) that identifies the identity after being reviewed, and the power producer i can join the on-chain system by registering in the agent node with ID i .
  • ID i an identification
  • the block The chain system will assign public key (PK i ), private key (SK i ), wallet address (WA i ), and certificate (Cert i ) to electricity producers.
  • the certificate may include basic information of grid-connected power producers, such as company name, address, installed capacity, smart meter ID (SMID i ) and other information.
  • SMID i smart meter ID
  • the system assigns the public key, private key and wallet address to it, and the buyer can log in to the system with the public key and the set password.
  • the green certificate issuance smart contract on the blockchain runs regularly, and the audit node (the green certificate issuance module in the audit node) issues the green certificate to the power producer by calculating the data of the smart meter of the power plant and other necessary information.
  • a green certificate is issued at 1MWh, and the smart contract issues the green certificate to the wallet address of the corresponding power producer in the form of a transaction.
  • the electricity producer pledges the green certificate that he wants to sell in the bidding transaction smart contract, and marks the sale price, and sends it to the transaction smart contract address and the transaction information publishing module in the proxy node.
  • the sale information (the number of green certificates sold and the sale price) and the digital signature, namely ⁇ sellOrder, sign SKj (MD5(sellOrder))>
  • the off-chain node also known as the bidding server
  • the off-chain node renders the sale information and returns it to the user (seller and buyer) through the front end. If the sale is not successful after a period of time, the smart contract will return the remaining green certificates to the seller (electricity producer).
  • the buyer also referred to as the buyer publishes the purchase information (which can be the purchase price and quantity) in the agent node (for example, in the transaction information publishing module of the agent node), and sends the required currency
  • the electricity producer and the buyer conduct bidding transactions, and specifically adopt a two-way auction mechanism.
  • the two-way auction means that as long as one party accepts the bid of the other party, the two parties can reach a transaction. Then a new round of bidding starts, there can be multiple trading periods, and the trading price is always between the initial bid and the initial ask. Throughout the trading process, price information is public. Continuous two-way auctions do not limit the number and frequency of bids, enabling buyers and sellers to adjust bids in real time, which can better reflect market demand and have higher efficiency.
  • the electricity producer sends sales information to the proxy node (specifically, the transaction information publishing module in the proxy node), and the purchaser sends the sales information to the proxy node (specifically, the transaction information publishing module in the proxy node).
  • Purchase information The proxy node sends the sale information and the digital signature of the electricity producer, and the purchase information and the digital signature of the buyer to the off-chain node. Since the off-chain node (specifically, the quotation management module in the bidding server) renders the sale information and purchase information and returns it to the user through the front end, so that the seller and the buyer can adjust the quotation at any time.
  • the off-chain node (specifically, the price prediction module of the bidding server) is based on the historical transaction price information and the current price of the power producer. information to predict the transaction price of the next transaction.
  • the proxy node sends the information sent by the off-chain node to the smart contract of the bidding transaction in the blockchain. After the smart contract verifies the validity of the transaction information, it will use the currency pledged by both parties and the green certificate to conduct the transaction. If the seller's green certificate fails to be fully sold in this transaction, the smart contract will generate a new order for the remaining green certificate at the same price, and assign a new timestamp to publish; if the buyer is in this transaction If enough green certificates cannot be purchased, the smart contract will generate a new order with the remaining currency and the required number of green certificates, and assign a new timestamp to publish.
  • the master node collects all transactions generated within a period of time, and packages the transactions into a block after verifying the validity of the transactions locally.
  • a block Similar to a Bitcoin block, a block includes a block header and a block body.
  • the block body contains transaction information, which is stored in the form of a Merkle tree.
  • the hash value of the Merkle root is stored in the block header.
  • the block header also includes the hash value, version, timestamp, etc. of the previous block. This embodiment does not use proof of work, so the block header does not need to contain random numbers.
  • Fig. 2 provides a kind of prediction method of transaction transaction price based on the system of the above-mentioned embodiment, may comprise the following steps:
  • the historical transaction price can be obtained from the block. In order to improve the accuracy of the prediction result, in this embodiment, it can be obtained from Obtain the most recent preset transaction price in the history transaction from the block.
  • the acquired transaction price is represented as ⁇ x 1 , x 2 , . . . , x t ⁇ , where x t is the latest t transaction price.
  • the off-chain node performs calculation through a preset model according to the historical transaction price, and the process of obtaining the first feature vector for predicting the transaction price of the next transaction may include the following steps A1 to A2:
  • the long short-term memory neural network (LongShort-TermMemory, LSTM) model is a long short-term memory model.
  • the LSTM model performs an iterative operation on the input transaction price of a preset number of times. Among them, for any price, the specific operation performed by the LSTM model is shown in the following formulas (1) to (6):
  • i, f, g and o represent the input gate, the forgetting gate, the newly added state of the cell and the output gate, respectively
  • c represents the cell state
  • h represents the hidden vector.
  • Wii , bii , Wif , biif , Whf , bhf , Wig, big, Whg, bhg, Wio, bio, Who, and bho represent parameters to be obtained by model training, respectively.
  • ⁇ is the sigmoid function as the activation function
  • h t-1 is the hidden vector at the previous moment (t-1 time)
  • tanh (c t ) is the tanh function, as the activation function, acting on the cell state c at the time t superior.
  • the LSTM model outputs the hidden vector corresponding to each transaction price.
  • the final latent vector is taken as the embedding of the historical periodic change information, and for the convenience of description, it is taken as the first feature vector for predicting the next transaction price.
  • the target features include: power generation type, current transaction information and current green certificate information.
  • the target feature may also include the expected number of green certificates produced and the weather, and may also include other features, as long as the target feature can reflect the current environmental information of the power producer.
  • the target feature of each power producer in the current blockchain system is mapped to a hidden vector, and the principle of mapping to a hidden vector is the same.
  • this embodiment uses any power Take the manufacturer as an example and introduce it.
  • the target feature of the power producer can be mapped into a latent vector through a Multilayer Perceptron (MLP).
  • MLP Multilayer Perceptron
  • the multilayer perceptron model is a fully connected neural network.
  • the trained multi-layer perceptron model can be used to map the target feature of the power producer into a latent vector.
  • the training process can use Training is performed as a loss function.
  • represents the second-order norm
  • ⁇ represents the parameter to be trained.
  • y is the true value; is the model predicted value.
  • the objective function is a function for ensuring permutation invariance.
  • the objective function may specifically be an aggregate function AGGREGATE, an average function avg(), a maximum function max(), an accumulation function sum(), etc.
  • the objective function may also be other functions.
  • the embodiment does not limit the specific content of the objective function, as long as the objective function is a function that can guarantee permutation invariance.
  • the objective function can guarantee the permutation invariance, in this step, regardless of the sorting order of the hidden vectors of all power producers, the prediction result of the transaction price of the next transaction will not be accurate. Therefore, it can ensure the equal status of each node in the P2P network of the blockchain, and then ensure the accuracy of the result of the price prediction based on the second feature vector for the blockchain network.
  • the objective function not only has the above-mentioned accuracy to ensure the prediction result, but also because the objective function does not introduce additional parameters, the model can be simplified to a certain extent and overfitting can be alleviated.
  • S204 Calculate the first feature vector and the second feature vector through a fully connected neural network to obtain the transaction price of the next transaction.
  • the transaction price is used to provide a reference for power producers and buyers to determine prices.
  • the first feature vector and the second feature vector can be spliced to obtain a spliced vector, and then the spliced vector is input into the trained MLP model, and the predicted value of the transaction price of the next transaction is output.
  • the predicted value can be expressed as Among them, arg min means to return the parameter that minimizes the target expression.
  • the consensus node uses PBFT as the consensus mechanism, the consensus node performs the consensus process, and the block confirmed by the consensus can be written into the local blockchain.
  • PBFT when the master node fails, the view is updated according to the view replacement protocol, that is, a new master node is re-elected.
  • the master node undertakes the role of generating blocks and leading the consensus, and should have higher stability and performance.
  • the proxy node of the on-chain system not only acts as a node in the blockchain network, but also is a portal server for other users to log in and is operated by a professional team, so it will have higher stability and performance than the machine in the power producer. Malicious manipulation is also less likely. Considering the above situation, therefore, the embodiment of the present application changes the view replacement strategy.
  • the number of the agent node in the blockchain system on the chain is set to 0, and the other nodes are sequentially numbered 1, 2, ..., N-1.
  • a new master node is selected by the above formula to perform view change; when the current master node is not 0, if the trigger condition of the original PBFT view change is met , or when the master node receives a confirmation message from node 0 in the submission stage of the continuous consensus process for k times, it selects a new master node to perform the view change operation according to the above formula.
  • the master node receives a confirmation message from node 0 in the submission stage of the continuous consensus process for k times, it selects a new master node to perform the view change operation according to the above formula.
  • other operations in the view replacement protocol remain unchanged.
  • the proxy server node can be used as the master node most of the time, which will greatly increase the stability of the system, and at the same time, it will also reduce the burden on the power plant machinery, so that the system can run in an efficient state.
  • the green certificate issuing module, consensus module, and transaction information issuing module may be one or more processors or chips with communication interfaces capable of implementing communication protocols, and may also include memory and memory if necessary. Relevant interfaces, system transmission buses, etc.; the processor or chip executes program-related codes to implement corresponding functions.
  • the quotation management module, transaction matching module, and price prediction module may be one or more processors or chips with communication interfaces capable of implementing communication protocols, and may also include a memory, a display and related interfaces, and a system transmission bus if necessary. etc.; the processor or chip executes program-related codes to implement corresponding functions.

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Abstract

一种基于区块链的绿色证书交易系统，包括：审核节点、代理节点和链下节点；代理节点供电力生产商和购买方使用；审核节点验证代理节点上电力生产商的资格信息，向代理节点上的电力生产商发送绿色证书；链下节点依据历史成交价格和当前环境信息，预测下一次交易的成交价格；代理节点在接收到电力生产商的出售信息与购买方的购买信息的情况下，将出售信息与电力生产商的数字签名，以及，购买信息以及购买方的数字签名发给链下节点，链下节点对出售信息和购买信息进行匹配，将匹配成功的交易信息发送给代理节点；代理节点将匹配成功的交易信息发送给交易智能合约；交易智能合约依据交易信息进行交易处理。该系统可以提高交易成交量。

Description

一种基于区块链的绿色证书交易系统 技术领域
本申请涉及绿色电力证书交易领域，尤其涉及一种基于区块链的绿色证书交易系统。
背景技术
绿色电力证书，简称绿证，是国家对发电企业每兆瓦时非水可再生能源上网电量颁发的具有独特标识代码的电子证书，是非水可再生能源发电量的确认和属性证明以及消费绿色电力的唯一凭证，也是可再生能源配额制的一项政策工具。当前绿证的发放需要企业上报相关材料后由各个部门审核，在部分审核通过后，给企业发放绿证。持有绿证的企业可以出售绿证，绿证购买方与绿证出售方之间，发生绿证交易。
当前，绿证交易大多通过在交易平台挂牌销售的方式进行，使得绿证出售方和绿证购买方的信息不对等，即购买方可以看到绿证出售方挂牌的出售信息，绿证出售方在挂牌前并不知道购买方的欲购买价格是多少。使得绿证购买方依据挂牌价格，只想购买较低价格的绿证，出售方想以较高的价格卖出，从而，使得成功交易的成交量较低。
发明内容
本申请提供了一种基于区块链的绿色证书交易系统，目的在于解决成功交易的总次数较少的问题。
为了实现上述目的，本申请提供了以下技术方案：
本申请提供了一种基于区块链的绿色证书交易系统，包括：链上节点和链下节点；所述链上节点包括：审核节点和代理节点；所述代理节点供电力生产商和购买方进行登录使用；
所述审核节点，用于对所述代理节点上所述电力生产商的资格信息进行验证，在所述资格信息满足预设条件的情况下，向所述代理节点上的电力生产商发送绿色证书；
所述链下节点，用于依据历史成交价格通过预设模型进行计算，得到用于预测下一次交易的成交价格的第一特征向量；分别对当前区块链平台中的每个电力生产商的目标特征映射为隐向量；所述目标特征包括：发电类型、当前的成交信息和当前的绿证信息；对所述隐向量采用目标函数进行聚合，得到第二特征向量；所述目标函数为用于保证置换不变性的函数；通过全连接神经网络对所述第一特征向量与所述第二特征向量进行计算，得到所述下一次交易的成交价格；所述成交价格用于为所述电力生产商和所述购买方确定价格提供参考；
所述代理节点，用于在接收到所述电力生产商的出售信息与所述购买方的购买信息的情况下，将所述出售信息与所述电力生产商的数字签名，以及，所述购买信息以及所述购买方的数字签名发给所述链下节点；
所述链下节点，用于对所述出售信息和所述购买信息进行匹配，将匹配成功的交易信息发送给所述代理节点，并将所述出售信息和所述购买信息以可视化的形式返回给前端进行展示；
所述代理节点，还用于将所述匹配成功的交易信息发送给交易智能合约；所述交易智能合约依据所述交易信息进行交易处理。
可选的，所述链下节点，用于依据历史成交价格通过预设模型进行计算，得到用于预测下一次交易的成交价格的第一特征向量，包括：
所述链下节点，具体用于从区块中获取历史成交中距离当前最近的预设次数成交价格；将所述成交价格输入预设的LSTM模型，所述LSTM模型输出每次成交价格对应的隐向量；将最后一次成交价格对应的隐向量，作为所述第一特征向量。
可选的，所述链下节点，用于通过全连接神经网络对所述第一特征向量与所述第二特征向量进行计算，包括：
所述链下节点，具体用于将所述第一特征向量与所述第二特征向量进行拼接，得到拼接特征向量；通过对所述拼接特征向量输入所述全连接神经网络，得到预测成交价格。
可选的，所述链下节点，用于对所述出售信息和所述购买信息进行匹配，包括：
所述链下节点，具体用于采用双向拍卖规则，对所述出售信息和所述购买信息进行匹配。
可选的，所述代理节点在区块链系统中的编号设置为0，区块链系统中的其他节点的编号从1开始进行编号；
在主节点失效的情况下，所述链上的当前主节点选取主节点的过程，包括：
在所述主节点的编号为0，并且，满足原PBFT视图更换的触发条件的情况下，按照
Figure PCTCN2022073319-appb-000001
确定主节点；
在所述当前主节点的编号不为0，满足原PBFT视图更换的触发条件或在连续k次共识过程的提交阶段收到编号为0的节点发送确认信息的情况下，按照所述
Figure PCTCN2022073319-appb-000002
确定主节点。
可选的，所述链上节点中的主节点收集预设时长内产生的交易信息，在验证所述交易信息的有效性后，打包进一个区块中。
可选的，所述链下节点，还用于在接收到所述电力生产商的绿证出售信息与所述购买方的购买信息的情况下，以可视化的形式返回给前端进行展示，以向所述电力生产商和所述购买方提供售价信息和购买信息。
可选的，所述目标函数为AGGREGATE函数。
可选的，所述预设模型为LSTM模型。
可选的，全连接神经网络为MLP模型。
本申请所述的基于区块链的绿色证书交易系统，包括：链上节点和链下节点；链上节点包括：审核节点和代理节点；代理节点供用户节点电力生产商和购买方进行登录使用。
一方面，在本申请中，电力生产商和购买方将出售信息和购买信息发送给代理节点，代理节点将绿证出售信息和购买信息发送给链下节点，通过链下节点进行匹配，从而，可以一定程度上避免现有技术中由于挂牌销售方式导致的交易双方信息不对等的问题。同时链下节点还以可视化方式返回给前端进行展示，从而，电力生产商和购买方可以依据展示的信息，调整出售信息或购买信息，从而，能更好地反映市场需求，具有更高的效率，进而，可以提高成功交易成交量。
另一方面，在本申请中，链下节点依据历史成交价格通过预设模型进行计算，得到用于预测下一次交易的成交价格的第一特征向量，该第一特征向量反映对历史成交价格的预测信息。分别对当前区块链平台中的每个电力生产商的目标特征映射为隐向量；由于目标特征包括：发电类型、当前的成交信息和当前的绿证信息，即目标特征反映对当前环境信息的预测信息。并对隐向量采用目标函数进行聚合，得到第二特征向量，通过全连接神经网络对第一特征向量与第二特征向量进行计算，预测下一次交易的成交价格。
即本申请中，一方面，链下节点依据历史成交价格和当前环境信息，预测下一次交易的成交价格，从而，保证预测结果的准确性。另一方面，对隐向量采用目标函数进行聚合，得到第二特征向量，其中，目标函数为用于保证置换不变性的函数，进而使得对隐向量进行聚合过程中，排除了由于不同电力生产商的隐向量之间的位置顺序不同，导致得到的第二特征向量不同，进一步导致预测结果不准确性的问题。由于区块链网络是P2P网络，因此，区块链中各个节点的地位应该是平等的，因此，本申请中对隐向量采用目标函数进行聚合，得到的第二特征向量符合区块链网络中各节点地位应该平等的特点，因此，预测结果是符合区块链特性的，进而，进一步保证本申请预测的价格的准确性。
因此，本申请预测的下一次成交价格可以供电力生产商和购买方进行参考，因此，使得电力生产商和购买方的出售信息和购买信息更容易匹配成功，从而，可以进一步提高交易成功率，从而，可以进一步提高交易的成交量。
综上所述，本申请可以提高交易的成交量。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例公开的一种基于区块链的绿证交易系统的结构示意图；
图2为本申请实施例公开的一种下一次交易的成交价格的方法流程图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
在本申请实施例中，将绿证的核发和交易放到区块链上进行，借助区块链数据可追溯和不可篡改的特点，数据的整理和审核将变得更加容易，提高绿证核发效率，同时也会提高信息的透明度。除此之外，去中心化使得系统更加健壮，不必担心数据丢失的问题。
区块链是分布式数据存储、点对点传输、共识机制、加密算法等计算机技术的新型应用模式。区块链是一种按照时间顺序将数据区块以顺序相连的方式组合成的一种链式数据结构，并以密码学方式保证的不可篡改和不可伪造的分布式账本。分布式账本是指交易记账由分布在不同地方的多个节点共同完成，而且每一个节点都记录的是完整的账目，因此它们都可以参与监督交易合法性，同时也可以共同为其作证，从而避免了单一记账人被控制或者被贿赂而记假账的可能性，在本专利中则避免了可再生能源发电厂商与审核 中心勾结以骗取绿证的情况。链式结构存储的数据，具有很好的溯源性，因此可以便利地收集整理数据，使绿证核发实现自动化，节省人工成本，还能够防止人工失误的发生。
由于本申请实施例中，电力生产商和购买方可以依据反馈的出售信息和购买信息，对报价进行调整，因此，需要本申请实施例的区块链系统具有较高的实时性。为了保证实时性，本申请实施例引入代理节点和链下节点，其中，代理节点将电力生产商提供的出售信息和数字签名，以及，购买方的购买信息和数字签名，转发给链下节点，通过链下节点进行匹配和管理，从而，将交易过程的大部分工作量转移到链下节点，从而，减轻链上的节点的处理量，进而，提高实时性。
同时，链上部分记录各个电厂的发电情况，智能合约则每月审核电厂的发电情况以及其他必要的数据，对满足要求的企业自动发放绿证。同时，绿证的交易也通过智能合约的形式在链上进行，以此来记录相应的交易信息，保证交易数据公开。
图1为本申请实施例提供的一种基于区块链的绿色证书交易系统，包括：链上节点、链下节点、智能合约和区块，其中，链上节点可以包括：审核节点、代理节点和共识节点。其中代理节点供电力生产商和购买方进行登录使用。其中，代理节点为代理服务器。
图1直观展示了链上部分和链下部分，其中，链上部分包括：绿证核发模块(对应审核节点)、共识模块(对应共识节点)、交易信息发布模块(对应代理节点)以及智能合约。
其中，绿证核发模块主要实现自动化核发绿证的功能，通过智能合约的形式验证电厂(对应本实施例中的电力生产商)的资格，对满足条件的企业发放绿证，核发记录也作为交易记录由节点收集打包成区块存储。交易信息发布模块主要实现交易双方身份管理，用于注册、验证、加密、签名管理，对交易用户进行身份验证与信息加密及在交易过程中的身份签名确认。共识 模块用于对生成的区块进行验证，使用实用拜占庭算法(Practical Byzantine FaultTolerance，PBFT)作为共识算法，决定当前主导共识的主节点，并执行共识流程。
链下部分可以包括：报价管理模块(属于链下节点)和交易撮合模块(属于链下节点)。
其中，报价管理模块用于对交易过程中供需双方的报价进行管理，并以可视化的形式返回给前端对用户进行展示，可类比股票交易场景。报价管理模块将报价信息(对应本实施例中的出售信息和购买信息)发送给交易撮合模块，交易撮合模块根据交易双方的报价信息，通过一定的规则对报价信息进行排序与匹配，同时对交易信息进行加密与签名处理。
在本申请实施例中，电力生产商经过审核后会收到标识身份的标识(ID i)，电力生产商i凭借ID i在代理节点中注册即可加入链上系统，初次加入时，区块链系统会为电力生产商分配公钥(PK i)、私钥(SK i)、钱包地址(WA i)、证书(Cert i)。其中，证书可以包括入网电力生产商的基本信息，例如公司名称、地址、装机容量、智能电表ID(SMID i)等信息。新加入的电力生产商在分配好以上信息后，通过周围节点下载账本，同步完成后正式成为区块链网络中的节点。购买方在代理节点完成注册后，系统为其分配公钥、私钥和钱包地址，购买方可凭借公钥和设置的密码登录系统。
在本实施例中，区块链上的绿证核发智能合约定期运行，审核节点(审核节点中的绿证核发模块)通过核算电厂智能电表的数据及其他必要信息为电力生产商发放绿证，按照规定1MWh发放一个绿证，智能合约以交易的形式将绿证发放到对应的电力生产商的钱包地址中。每个绿证可表示为REC＝{ID，t，c，m}，其中ID是绿证编号，t是发放时间，c是绿电类型，标识风电、光电等类型，m是一些附加信息，包括所属企业、项目编号等信息。
在本实施例中，电力生产商将想要出售的绿证质押在竞价交易智能合约中，并标明出售价格，发送到交易智能合约地址和代理节点中的交易信息发 布模块，报文可以表示为sellOrder＝<(REC j，rid...)，P ask，t>，其中REC j，rid....为卖家j待出售的ID为rid的绿证，P ask是出售价格，t是挂单时间。同时，将出售信息(出售绿证的数量与出售价格)以及数字签名，即<sellOrder，sign SKj(MD5(sellOrder))>，发送给链下节点(又可称为竞价服务器)，链下节点(例如，竞价服务器中的报价管理模块)将出售信息渲染后通过前端返回给用户(出售方和购买方)。如果一段时间后没能全部出售成功，智能合约将退回剩余的绿证给出售方(电力生产商)。
在本实施例中，购买方(又可称为买家)在代理节点(例如，在代理节点的交易信息发布模块)中发布购买信息(可以为购买价格和数量)，并将所需的货币质押在竞价交易智能合约中，即将buyOrder＝<P bid，d，Coin，t>发送到交易智能合约地址和代理节点(具体的，可以为代理节点中的交易信息发布模块)，其中P bid为购买单价，d为购买数量，Coin＝P bid*d为质押到合约中的货币数量，y为挂单时间。代理节点将购买信息以及数字签名，即buyOrder＝<buyOrder，sign SKi(MD5(buyOrder))>，发送给链下节点(竞价服务器)，链下节点(竞价服务器上的报价管理模块)将购买信息渲染后通过前端返回给用户(出售方和购买方)。如果一段时间后没能购买到足够数量的绿证，智能合约将退回剩余的货币给购买方。
在本实施例中，电力生产商和购买方之间通过竞价交易，具体的采用双向拍卖机制。其中，双向拍卖指：只要一方中有人接受另一方的叫价，两者便可以达成交易。然后再开始新一轮的叫价，可以有多个交易期，交易价格总是介于初始出价和初始要价之间。在整个交易过程中，价格信息是公开的。连续双向拍卖则不限制叫价的次数和频率，使得买卖双方能够实时调整报价，能更好地反映市场需求，具有更高的效率。
具体的，在本实施例中，电力生产商向代理节点(具体可以为代理节点中的交易信息发布模块)发送出售信息，购买方向代理节点(具体可以为代理节点中的交易信息发布模块)发送购买信息。代理节点将出售信息和电力生产商的数字签名，以及，购买信息以及购买方的数字签名发送给链下节点。 由于链下节点(具体可以为竞价服务器中的报价管理模块)将出售信息和购买信息渲染后通过前端返回给用户，使得出售方与购买方之间可以随时调整报价。
为了使得电力生产商和购买方的报价更合理，进一步提高交易成功率，在本实施例中，链下节点(具体可以为竞价服务器的价格预测模块)依据历史成交价格信息和电力生产商的当前信息，对下一次交易的成交价格进行预测。
其中，具体的预测过程在图2对应的实施例进行介绍，这里不再赘述。
链下节点(具体可以为竞价服务器中的交易撮合模块)对出售信息和购买信息进行匹配，对于成功匹配的交易，以双方的订单和数字签名即matchedOrder＝<sellOrder，sign SKj(MD5(sellOrder))，buyOrder，sign SKi(MD5(buyOrder))>进行表示，并将匹配成功的交易信息(<matchedOrder，sign SKas(MD5(matchedOrder)>)发送给代理节点，其中SK as是链下节点(具体可以为竞价服务器)的私钥，链下节点(具体可以为竞价服务器)将<matchedOrder，sign SKas(MD5(matchedOrder)>发送给代理服务器后更新交易信息。
代理节点将链下节点发送的信息发送给区块链中的竞价交易智能合约，智能合约验证交易信息的有效性之后会使用双方质押的货币和绿证进行交易。如果出售方绿证在本次交易中没能完全出售，智能合约会将剩余的绿证以相同的价格生成一个新的订单，并赋予新的时间戳进行发布；如果购买方在本次交易中没能购买到足够的绿证，智能合约会将剩余的货币和所需绿证数量生成一个新的订单，并赋予新的时间戳进行发布。
在本实施例中，主节点收集一段时间内产生的所有交易，本地验证交易的有效性后打包进一个区块中。与比特币区块类似，一个区块包括区块头和区块体，区块体中是交易信息，以Merkle树的形式存储，Merkle根的哈希值保存在区块头中，除此之外，区块头还包括前一区块哈希值、版本、时间戳等，本实施例不使用工作量证明，因此区块头中不必包含随机数。
图2为基于上述实施例的系统提供一种交易成交价格的预测方法，可以包括以下步骤：
S201、依据历史成交价格通过预设模型进行计算，得到用于预测下一次交易的成交价格的第一特征向量。
在本实施例中，由于区块中保存有历史的交易信息，因此，在本步骤中，可以从区块中获取历史成交价格，为了提高预测结果的准确性，在本实施例中，可以从区块中获取历史成交中距离当前最近的预设次数成交价格。
作为示例，在本实施例中，将获取的成交价格表示为{x 1，x 2，...，x t}，其中x t为最近t次的成交价格。
在本步骤中，链下节点依据历史成交价格通过预设模型进行计算，得到用于预测下一次交易的成交价格的第一特征向量的过程可以包括以下步骤A1～步骤A2：
A1、将获取的成交价格输入预设的LSTM模型。
在本实施例中，长短期记忆神经网络(LongShort-TermMemory，LSTM)模型为长短期记忆模型。
在本实施例中，LSTM模型对输入的预设次数的成交价格进行迭代运算。其中，任意一个价格，LSTM模型执行的具体的运算，如下公式(1)～公式(6)所示：
i t＝σ(W iix t+b ii+W iih t-1+b ii)     (1)
f t＝σ(W ifx t+b if+W hfh t-1+b hf)      (2)
g t＝tanh(W igx t+b ig+W hgh t-1+b hg)    (3)
o t＝σ(W iox t+b io+W hoh t-1+b ho)     (4)
c t＝f t⊙c t-1+i t⊙g t      (5)
h t＝o t⊙tanh(c t)      (6)
式中，i、f、g和o分别表示输入门、遗忘门、细胞新增加的状态和输出门，c表示细胞状态，h表示隐向量。 W ii、b ii、W if、b if、W hf、b hf、W ig、b ig、W hg、b hg、W io、b io、W ho和b ho分别表示模型训练要得到的参数。其中，σ是sigmoid函数，作为激活函数；h t-1是上一时刻(t-1时刻)的隐向量；tanh(c t)是tanh函数，作为激活函数，作用在t时刻的细胞状态c上。
通过上述公式(1)～(6)，LSTM模型输出每次成交价格对应的隐向量。
A2、将最后一次成交价格对应的隐向量，作为第一特征向量。
在本实施例中，取最终的隐向量作为历史周期性变化信息的嵌入，为了描述方便，作为用于预测下一次成交价格的第一特征向量。
S202、分别对当前区块链平台中的每个电力生产商的目标特征映射为隐向量。
在本实施例中，目标特征包括：发电类型、当前的成交信息和当前的绿证信息。当然，在实际中，目标特征还可以包括预计生产绿证的数量和天气，还可以包括其他特征，只要目标特征可以反映电力生产商的当前环境信息即可。
在本实施例中，对当前区块链系统中的每个电力生产商的目标特征，分别映射为隐向量，其中，映射为隐向量的原理相同，为了方便介绍，本实施例以任意一个电力生产商为例，进行介绍。
在本步骤中，可以通过多层感知机模型(Multilayer Perceptron，MLP)将电力生产商的目标特征映射为隐向量。在本实施例中，多层感知机模型是一种全连接神经网络。
其中，在本实施例中，可以采用训练后的多层感知机模型，将电力生产商的目标特征映射为隐向量。其中，训练过程可以使用
Figure PCTCN2022073319-appb-000003
作为损失函数进行训练，损失函数中|| ||表示二阶范数，θ表示待训练参数。其中，y是真实值；
Figure PCTCN2022073319-appb-000004
是模型预测值。
S203、对全部电力生产商的隐向量采用目标函数进行聚合，得到第二特征向量。
在本实施例中，目标函数为用于保证置换不变性的函数。
在本实施例中，目标函数具体可以为聚合函数AGGREGATE、平均函数avg()、最大函数max()、累加函数sum()等函数，当然，在实际中，目标函数还可以为其他函数，本实施例不对目标函数的具体内容作限定，只要目标函数是可以保证置换不变性的函数即可。
在本实施例中，由于目标函数可以保证置换不变性，因此，在本步骤中，无论全部电力生产商的隐向量的排序顺序如何，都不会对下一次交易的成交价格的预测结果的准确性造成影响，因此，可以保证区块链这个P2P网络中，各节点地位平等的需求，进而，保证基于第二特征向量进行价格预测的结果的对区块链网络的准确性。
在本实施例中，目标函数不仅具有上述可以保证预测结果的准确性，同时，由于目标函数没有引入额外的参数，一定程度上可以简化模型，缓和过拟合。
S204、通过全连接神经网络对第一特征向量与第二特征向量进行计算，得到下一次交易的成交价格。
在本实施例中，成交价格用于为电力生产商和购买方确定价格提供参考。
在本步骤中，可以将第一特征向量与第二特征向量进行拼接，得到拼接向量，然后，将拼接向量输入训练后的MLP模型中，输出下一次交易的成交价格的预测值。该预测值可以表示为
Figure PCTCN2022073319-appb-000005
其中，arg min表示返回使目标式最小的参数。
在本实施例中，共识节点使用PBFT作为共识机制，由共识节点执行共识过程，共识确认后的区块即可写入本地区块链上。在PBFT中，当主节点失效时根据视图更换协议更新视图，即重新选出新的主节点。主节点承担着生成区块和主导共识的作用，理应具有更高的稳定性和性能。链上系统的代理节点不仅作为区块链网络中的节点，而且是其他用户登录的门户服务器并由专业团队运营，因此会比电力生产商中的机器具有更高的稳定性和性能，同时被恶意操纵的可能性也更小。考虑到上述情况，因此，本申请实施例更改视图更换的策略。
具体的，将代理节点在链上区块链系统的编号设为0，其他节点依次编号为1，2，...，N-1。产生主节点的方式变为：
Figure PCTCN2022073319-appb-000006
其中，mod表示模运算，v∈N是新视图的编号且v＝v pre+1，其中，v pre表示上一视图的编号，
Figure PCTCN2022073319-appb-000007
是向上取整操作，p表示新的主节点。当前主节点为0时，若满足原PBFT视图更换的触发条件时，则由上式选出新的主节点进行视图更换；当前主节点不为0时，若满足原PBFT视图更换的触发条件时，或者主节点在连续k次共识过程的提交阶段收到0节点发来的确认消息时，按照上式选出新的主节点进行视图更换操作。在本实施例中，除了选取主节点的方式变化之外，视图更换协议中的其他操作均不变。
从上述选取主节点的方式，可以将代理服务器节点充当大部分时候的主节点，这将大大增加系统的稳定性，同时这也减轻电厂机器的负担，从而，能够让系统运行在高效状态。
在本申请的实施例中，所述绿证核发模块、共识模块、交易信息发布模块分别可以是具有通信接口能够实现通信协议的一个或多个处理器或者芯片，如有需要还可以包括存储器及相关的接口、系统传输总线等；所述处理器或者芯片执行程序相关的代码实现相应的功能。所述报价管理模块、交易撮合模块、价格预测模块分别可以是具有通信接口能够实现通信协议的一个或多个处理器或者芯片，如有需要还可以包括存储器、显示器及相关的接口、系统传输总线等；所述处理器或者芯片执行程序相关的代码实现相应的功能。
本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其它实施例的不同之处，各个实施例之间相同或相似部分互相参见即可。
对所公开的实施例的上述说明，本说明书中各实施例中记载的特征可以相互替换或者组合，使本领域专业技术人员能够实现或使用本申请。
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用本申请。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本申请的精神或范围的情况下， 在其它实施例中实现。因此，本申请将不会被限制于本文所示的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。

Claims (9)

  1. 一种基于区块链的绿色证书交易系统，其特征在于，包括：链上节点和链下节点；所述链上节点包括：审核节点和代理节点；所述代理节点供电力生产商和购买方进行登录使用；
    所述审核节点，用于对所述代理节点上所述电力生产商的资格信息进行验证，在所述资格信息满足预设条件的情况下，向所述代理节点上的电力生产商发送绿色证书；
    所述链下节点，用于依据历史成交价格通过预设模型进行计算，得到用于预测下一次交易的成交价格的第一特征向量；分别对当前区块链平台中的每个电力生产商的目标特征映射为隐向量；所述目标特征包括：发电类型、当前的成交信息和当前的绿证信息；对所述隐向量采用目标函数进行聚合，得到第二特征向量；所述目标函数为用于保证置换不变性的函数；通过全连接神经网络对所述第一特征向量与所述第二特征向量进行计算，得到所述下一次交易的成交价格；所述成交价格用于为所述电力生产商和所述购买方确定价格提供参考；
    所述代理节点，用于在接收到所述电力生产商的出售信息与所述购买方的购买信息的情况下，将所述出售信息与所述电力生产商的数字签名，以及，所述购买信息以及所述购买方的数字签名发给所述链下节点；
    所述链下节点，用于对所述出售信息和所述购买信息进行匹配，将匹配成功的交易信息发送给所述代理节点，并将所述出售信息和所述购买信息以可视化的形式返回给前端进行展示；
    所述代理节点，还用于将所述匹配成功的交易信息发送给交易智能合约；所述交易智能合约依据所述交易信息进行交易处理；
    所述链下节点，用于通过全连接神经网络对所述第一特征向量与所述第二特征向量进行计算，包括：
    所述链下节点，具体用于将所述第一特征向量与所述第二特征向量进行拼接，得到拼接特征向量；通过对所述拼接特征向量输入所述全连接神经网络，得到预测成交价格。
  2. 根据权利要求1所述的系统，其特征在于，所述链下节点，用于依据历史成交价格通过预设模型进行计算，得到用于预测下一次交易的成交价格的第一特征向量，包括：
    所述链下节点，具体用于从区块中获取历史成交中距离当前最近的预设次数成交价格；将所述成交价格输入预设的LSTM模型，所述LSTM模型输出每次成交价格对应的隐向量；将最后一次成交价格对应的隐向量，作为所述第一特征向量。
  3. 根据权利要求1所述的系统，其特征在于，所述链下节点，用于对所述出售信息和所述购买信息进行匹配，包括：
    所述链下节点，具体用于采用双向拍卖规则，对所述出售信息和所述购买信息进行匹配。
  4. 根据权利要求1所述的系统，其特征在于，所述代理节点在区块链系统中的编号设置为0，区块链系统中的其他节点的编号从1开始进行编号；
    在主节点失效的情况下，所述链上的当前主节点选取主节点的过程，包括：
    在所述主节点的编号为0，并且，满足原PBFT视图更换的触发条件的情况下，按照
    Figure PCTCN2022073319-appb-100001
    确定主节点；
    在所述当前主节点的编号不为0，满足原PBFT视图更换的触发条件或在连续k次共识过程的提交阶段收到编号为0的节点发送确认信息的情况下，按照所述
    Figure PCTCN2022073319-appb-100002
    确定主节点。
  5. 根据权利要求1所述的系统，其特征在于，所述链上节点中的主节点收集预设时长内产生的交易信息，在验证所述交易信息的有效性后，打包进一个区块中。
  6. 根据权利要求1所述的系统，其特征在于，所述链下节点，还用于在接收到所述电力生产商的绿证出售信息与所述购买方的购买信息的情况下，以可视化的形式返回给前端进行展示，以向所述电力生产商和所述购买方提供售价信息和购买信息。
  7. 根据权利要求1所述的系统，其特征在于，所述目标函数为AGGREGATE函数。
  8. 根据权利要求1所述的系统，其特征在于，所述预设模型为LSTM模型。
  9. 根据权利要求1所述的系统，其特征在于，全连接神经网络为MLP模型。
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