A carbon quota distributed information diffusion auction apparatus and method

Abstract

The present application relates to a kind of carbon quota distributed information diffusion auction device and method, the device includes: seller and buyer corresponding agent module, transaction platform module, seller agent module will be submitted to the information of carbon quota provider upload to transaction platform module, and get the auction result from transaction platform module and send carbon quota provider again;Buyer agent module will be submitted to the information of carbon quota demander upload to transaction platform module, and get the auction time and auction result from transaction platform module and send to carbon quota demander again;Transaction platform module carries out integration processing to the information received, by constructing undirected graph, generating DFS tree, and on DFS generation tree, DFDM is carried out auction by being adopted, while managing all intermediary nodes in DFDM, to determine the auction time and auction result information.Compared with prior art, the present application can solve the problem of insufficient liquidity of carbon trading market, improve the income of entire carbon transaction, and also play the role of protecting personal privacy and management, checking data.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon trading data processing, and in particular to a carbon quota distributed information diffusion auction device and method. Background Art

[0002] Carbon trading is a general term for the trading of greenhouse gas emission rights. The basic principle of carbon trading is that one party to a contract pays the other party to obtain greenhouse gas emission reduction credits. The buyer can then use these credits to mitigate the greenhouse effect and achieve their emission reduction goals. The carbon trading market treats carbon emission rights as commodities (carbon quotas), allowing businesses and individuals to freely buy (or sell) required (or excess) emission rights, thereby finding the lowest-cost emission reduction method. Through economic incentives and other measures, the carbon trading market encourages businesses and individuals to adopt energy-saving and emission-reduction measures, promoting the transition to a low-carbon industrial model. This approach not only reduces the risk of global climate change but also creates opportunities for sustainable, low-carbon economic growth.

[0003] Despite the current size of the carbon market, its liquidity is significantly insufficient. This is primarily due to the fact that the primary participants are major emitters, while small and medium-sized enterprises, individual investors, and financial institutions lack the motivation to participate in emission reduction activities, as well as the necessary expertise and capabilities for carbon trading. This lack of motivation and technical knowledge limits their participation in the carbon market, reducing its liquidity. Furthermore, the monitoring, reporting, and verification (MRV) mechanism for carbon emissions urgently needs improvement. The effective and standardized operation of the carbon market relies on authentic carbon emissions data, but the current immaturity of carbon market monitoring technology will affect the quality of carbon emissions data, further reducing the enthusiasm of businesses and individuals.

[0004] In the prior art, Chinese patent CN115249092A (Method for Seeking Optimal Decision-Making Behavior for Multiple Agents in the Electricity Market under a Carbon Trading Market) discloses a method for seeking optimal decision-making behavior for multiple agents in the electricity market under a carbon trading market using multi-objective linear programming. This method employs the concept of linear programming to determine the profit functions and problem model of each party. Using goal programming, the method then obtains the critical carbon price for each party, providing a reference for the agents' actions. However, this solution does not consider the liquidity of carbon market information, namely whether companies will adjust their emission reduction strategies due to lack of knowledge of the unit price of carbon quotas. Furthermore, the reference to the critical carbon price is not conducive to further increasing sellers' profits and is not conducive to improving the social welfare of the transaction.

[0005] Chinese patent CN116720917A (a distributed carbon trading market consensus method) discloses a distributed method for supervising and managing the carbon trading market by using a consensus algorithm and consensus nodes. The method divides the subjects participating in the carbon market into ordinary nodes (buyers and sellers) and consensus nodes (supervisors). In a carbon transaction, more than half of the consensus nodes need to agree before the transaction center can make a final decision, thus effectively ensuring the fairness of carbon trading and the security and authenticity of data, and protecting the privacy of buyers and sellers. However, this method does not consider the information flow, which cannot attract more enterprises and individuals to participate in the transaction; at the same time, the consensus nodes jointly decide, which will inevitably increase the transaction fees of third-party institutions, thus reducing the enthusiasm of buyers and sellers to participate in the transaction.

[0006] Patent CN116777623A (a carbon trading platform based on a blockchain smart contract) discloses a smart contract carbon trading platform based on the encryption characteristics of a blockchain and having a regulatory function. The platform successfully implements a decentralized carbon trading mechanism through the distributed characteristics of the blockchain encryption, improves the privacy and security of data before and after the transaction, and is conducive to the unified regulation of China's carbon market. However, with the introduction of the blockchain, the transaction cost or fee will inevitably increase, which is not conducive to the acceptance of enterprises and individuals; at the same time, this invention also does not consider the market and information flow.

[0007] In summary, the existing technologies related to carbon trading and carbon market have the following shortcomings:

[0008] 1) The existing technologies rarely study the problem of the circulation of the domestic carbon trading market, i.e., how to involve more small and medium-sized enterprises and individuals in carbon trading;

[0009] 2) Some current research on the domestic carbon trading market focuses on the development of optimal emission reduction strategies for each enterprise and the impact of carbon prices on enterprises and individuals, without considering the improvement of the interests of carbon quota providers;

[0010] 3) Another part of the research focuses on the problems of information asymmetry and carbon emission information management in carbon trading, and emphasizes the design of a fair and just trading mechanism, but ignores the problem of privacy leakage of the transaction parties. SUMMARY

[0011] The purpose of the present application is to overcome the shortcomings of the existing technologies and provide a carbon quota distributed information diffusion auction device and method, which can solve the problem of insufficient circulation of the carbon trading market, improve the income of the entire carbon trading, and protect personal privacy and manage and verify data.

[0012] The objectives of the present invention can be achieved by the following technical solutions: a carbon quota distributed information diffusion auction device, comprising a seller agent module, a buyer agent module and a trading platform module, wherein the seller agent module and the buyer agent module are respectively connected to the trading platform module for mutual communication, the seller agent module is connected to the corresponding carbon quota provider for communication, and the buyer agent module is connected to the corresponding carbon quota demander for communication, the seller agent module is used to collect information uploaded by the carbon quota provider, distinguish and integrate it, submit the integrated information to the trading platform module, and obtain auction result information from the trading platform module and then send it to the carbon quota provider;

[0013] The buyer agent module is used to submit the information uploaded by the carbon quota demander to the trading platform module, and obtain the auction time and auction result information from the trading platform module and then send it to the carbon quota demander;

[0014] The trading platform module is used to integrate and process the information submitted by the seller agent module and the buyer agent module by constructing an undirected graph, generating a DFS (Depth-First-Search) spanning tree, and using DFDM (Distributed Fair Diffusion Mechanism) on the DFS spanning tree for auction. At the same time, it manages all intermediary nodes in DFDM to determine the auction time and auction result information.

[0015] A carbon quota distributed information diffusion auction method comprises the following steps:

[0016] S1. The carbon quota provider provides auction information, which is uploaded to the trading platform module through the seller agent module for publication;

[0017] S2. During the auction duration, the carbon quota demander uploads the demand information to the trading platform module through the buyer agent module to join the auction;

[0018] S3. After the auction duration ends, the trading platform module integrates the auction information and demand information;

[0019] S4. The trading platform module applies a distributed fair incentive information diffusion mechanism to execute the auction and determine the auction allocation and payment information;

[0020] S5. Based on the auction allocation and payment information, combined with the feedback information from the seller agent module and the buyer agent module, the transaction platform module integrates the auction results and publishes them.

[0021] Furthermore, the specific process of step S1 is as follows: the carbon quota provider enters the auction through the seller agent module and uploads the number of carbon quotas for auction, the actual unit to which the provider belongs, and the expected transaction time;

[0022] After the trading platform module confirms that the uploaded information is correct, it will release auction information including the carbon quota quantity, provider unit, and duration.

[0023] Furthermore, the specific process of step S2 is as follows: during the auction duration, the carbon quota demander enters the auction through the buyer agent module and uploads his own bid, the referral object, and the real information of the unit to which the demander belongs.

[0024] Furthermore, in step S3, the trading platform module integrates the auction information and the demand information to obtain an undirected graph of the carbon quota auction, and performs a depth traversal algorithm on the undirected graph to generate a DFS spanning tree, thereby finding the strong propagation sequence and weak propagation set of the buyer user.

[0025] Furthermore, the undirected graph of carbon quotas includes:

[0026] Seller agent node S and its first-level transaction object set r s ;

[0027] The buyer agent node set N = {1, 2, 3, ..., n}, the private type of each buyer node i is θ i ={v i , r i}, v i and r i They represent the buyer node i’s psychological valuation of the auction and the neighbor set respectively;

[0028] The set of intermediary nodes C corresponding to the seller and each buyer P ={P S , P1, ..., P i}, where P S Represents the intermediary node corresponding to the seller, P i is the intermediary node corresponding to buyer i.

[0029] Furthermore, the specific process of generating the DFS spanning tree and finding the strong propagation sequence and weak propagation set of the buyer user in step S3 is as follows:

[0030] S31. Run a depth-first traversal algorithm based on the seller agent S and the buyer agent set N, and generate a DFS spanning tree, where the starting node of the DFS spanning tree is the seller agent and the remaining nodes are all buyer agents; the edges of the tree represent the neighbor relationship of each node;

[0031] S32, the buyer agent node calculates its own status and reports it to the corresponding buyer intermediary node. The buyer agent node status is The corresponding values ​​are the parent node set of buyer i, the time when buyer i was first invited, the time when the last buyer in subtree i was invited, the shortest time to reach buyer node i, the buyer node with the highest psychological valuation in subtree i, and the psychological valuation of the buyer node with the highest psychological valuation in subtree i.

[0032] S33, seller intermediary node P s Announce the node ω with the highest psychological evaluation and its status;

[0033] S34, seller intermediary node P s According to the information reported by the buyer, the strong propagation sequence C is compared and screened in turn. ω The buyer node on the , where if and only if the buyer agent node c j When not participating in the auction, ω cannot join the auction. At this time, c j is called a strong propagation buyer, and the strong propagation sequence of ω is defined as C ω , c j ∈C ω ;

[0034] If buyer i is a strong propagation buyer, the corresponding intermediary node P i dt i Report to the seller's intermediary node P s If buyer i is not a strong propagator, he will be penalized for false reporting.

[0035] In this process, each intermediate node P i The information will be verified by comparing the status of all reporting buyer nodes, thereby playing a role in information supervision;

[0036] S35, seller intermediary node P s According to dt i Arrange the key nodes in ascending order to obtain the strong relay sequence C ω And all strong propagation buyer nodes, count two strong propagation nodes c j with c j+1 All buyers on the communication path are regarded as weak communication buyers of the two strong communication buyers, and together constitute the weak communication set

[0037] Furthermore, the specific process of determining the auction allocation information in step S4 is as follows:

[0038] S41, according to the strong propagation sequence C ω , start the transaction from the first node after the seller, that is, the node closest to the seller;

[0039] S42, buyer intermediary node P i Calculate the externality of the current strong propagation buyer node;

[0040] S43, compare the psychological valuation of the next buyer i+1 with the psychological valuation of the current transaction buyer i, if the next buyer i+1 needs to pay except for itself and the weak propagation set When the externality of a buyer is higher than the psychological valuation of the previous buyer, the buyer will replace the previous buyer and join the auction;

[0041] If the next buyer needs to pay except for himself and the weak propagation set When the externality is lower than the psychological valuation of the previous buyer, the current transaction buyer directly wins the auction.

[0042] Furthermore, the payment information of the auction determined in step S4 includes initial payment and re-payment. The initial payment is made to the seller and the strong-propagation buyer. The payment rules for the relevant buyers are divided into three situations:

[0043] (1) For the seller (S), the externality amount paid by the strong communication buyer (strong communication buyer No. 1) with whom the seller has the first transaction in step S41 is the total payment that the seller can obtain in the initial payment stage, which can be expressed as:

[0044]

[0045] (2) For the buyer who finally obtains the item h The amount paid is the externality Expressed as follows:

[0046]

[0047] (3) For non-winner strong communication buyer C j ∈C ω \c h , where '\c h 'Indicates' except c h ', will get a certain amount of communication benefits, the amount = the externalities paid by the next strong communication seller excluding the externalities of itself and the weak communication nodes between the two - Self-paying externalities Expressed as formula:

[0048]

[0049] Therefore, the formula for the buyer node’s initial payment is as follows:

[0050]

[0051] Where, The highest psychological valuation in all nodes except the set * represents the externality of all nodes except the set *; c j represents the jth strong propagation buyer, c h represents the buyer who finally obtains the item; C ω is the strong propagation sequence, is the weak propagation set between c j and c j+1 two strong propagation buyers;

[0052] The payment object of the re-payment is the weak propagation buyer, the strong propagation buyer and the seller, and the relevant buyer re-payment rules are divided into three cases:

[0053] (1) For the strong propagation buyer C j ∈ C ω including the winner, the amount (p' i ) to be paid is The value is equal to the externality of the highest psychological valuation buyer in the weak propagation set except itself The externality of all buyers in the weak propagation set except itself and divided by the sum of the number of weak propagation buyers and the number of sellers 1, which is expressed by the formula:

[0054]

[0055] (2) For the weak propagation buyer The amount (p' i ) to be paid is -R i The value is equal to the externality of the jth strong propagation buyer except itself The externality of all buyers in the weak propagation set except itself and divided by the sum of the number of weak propagation buyers and the number of sellers 1, which is expressed by the formula:

[0056]

[0057] (3) For the seller S, after all the buyers who meet the payment requirements complete the payment, if the total amount of payment is lower than the difference between the payment externality of the jth and j+1th strong propagation buyers, the remaining part is paid by the seller in full;

[0058] The formula for the re-payment of the buyer node is as follows:

[0059]

[0060] Where the propagation incentive is expressed as:

[0061]

[0062] Where, Same as the initial payment phase; Indicates that between c j-1 and c j The buyer with the highest psychological valuation in the weak communication set between two strong communication buyers.

[0063] Furthermore, the specific process of step S5 is: filtering out the seller's income, the final winner, the buyer's information dissemination subsidy incentives and the amount required to be paid by the winner from the allocation information and payment information, and then publishing them to the seller's agent module and the buyer's agent module, and receiving the "confirmation transaction" information from both parties. After receiving the "transaction reached" signal from the two agent modules, the auction result information including the two parties to the transaction and the transaction amount will be announced.

[0064] Compared with the prior art, the present invention has the following advantages:

[0065] Based on the current unstable carbon trading market environment and insufficient information circulation, this invention takes into account the powerful function of social networks in disseminating information and introduces a fair incentive information diffusion auction mechanism based on social networks, so that more small and medium-sized enterprises and individuals can participate in transactions, which can effectively improve the enthusiasm and participation of small and medium-sized enterprises and individuals, and to a certain extent increase the income of the entire carbon trading.

[0066] Different from the currently commonly used first-order sealed auction and negotiation model, the present invention adopts a fair incentive communication auction to introduce more buyers, which can effectively improve the income of each participating communication seller and social welfare on the basis of satisfying incentive compatibility and individual rationality.

[0067] The present invention fully considers the lack of data and privacy leakage in carbon trading, adds intermediary nodes to the mechanism, and the trading platform modules jointly perform the tasks of supervising the entire auction process and managing data, which can reliably maintain fairness and justice in the carbon trading process.

[0068] The present invention utilizes multiple trustworthy intermediary nodes and adopts a distributed mechanism based on intermediary nodes. Compared with the distribution of a single intermediary node, it better solves the situation where the seller has an information advantage in the diffusion auction, ensures that the buyer's privacy is not leaked, and indirectly increases the buyer's enthusiasm for participating in the auction.

[0069] The transaction process of the present invention is all carried out on the network platform, and the payment and distribution of the auction are also presided over by the intermediary node, which greatly facilitates the buyers and sellers and improves the efficiency of auction execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 Schematic diagram of the device structure of the present invention;

[0071] Figure 2This is a diagram of the auction device architecture constructed in the embodiment;

[0072] Figure 3 Schematic diagram of the method flow of the present invention;

[0073] Figure 4 A diagram of social links for the carbon quota market;

[0074] Figure 5 This is a diagram of the information dissemination flow in the carbon quota market;

[0075] Figure 6 Extract schematic diagram for strong propagation sequence;

[0076] Figure 7 Assign a process diagram for DFDM;

[0077] Figure 8 Schematic diagram of the DFS spanning tree generated in the embodiment;

[0078] Explanation of the marks in the figure: 1. Seller agent module, 2. Buyer agent module, 3. Trading platform module. DETAILED DESCRIPTION

[0079] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0080] Example

[0081] like Figure 1 As shown, a carbon quota distributed information diffusion auction device includes a seller agent module 1, a buyer agent module 2 and a trading platform module 3, wherein the seller agent module 1 and the buyer agent module 2 are respectively connected to the trading platform module 3 for mutual communication, the seller agent module 1 is connected to the corresponding carbon quota provider, and the buyer agent module 2 is connected to the corresponding carbon quota demander. The seller agent module 1 is used to collect information uploaded by the carbon quota provider and then distinguish and integrate it, submit the integrated information to the trading platform module 3, and obtain auction result information from the trading platform module 3 and then send it to the carbon quota provider;

[0082] The buyer agent module 2 is used to submit the information uploaded by the carbon quota demander to the trading platform module 3, and obtain the auction time and auction result information from the trading platform module 3 and then send it to the carbon quota demander;

[0083] The trading platform module 3 is used to integrate and process the information submitted by the seller agent module 1 and the buyer agent module 2 by constructing an undirected graph, generating a DFS spanning tree, and using DFDM to conduct auctions on the DFS spanning tree. At the same time, it manages all intermediary nodes in DFDM to determine the auction time and auction result information.

[0084] This embodiment applies the above method to build Figure 2 The auction device overall architecture shown, a total of three parts, respectively, on behalf of the carbon quota providers, sellers agent, on behalf of the carbon quota demand side, the buyer agent and as auctioneer third party certification agency trading platform. Among them, in the transaction, each agent only with a registered user to communicate, both buyers and sellers need to participate in the auction through the agent.

[0085] 1) seller agent mainly by information integration module A1 and results broadcast module A2

[0086] Information integration module A1 main role is to collect carbon quota providers uploaded information, after the integration of the integrated information submitted to the third party certification agency trading platform. For example, a power company using new carbon emission reduction technology, so that its annual carbon quota obtained some surplus. The company will be related information, carbon quota for auction and the expected transaction time input after the information integration module A1 will be the number and time integration and send to the third party platform.

[0087] Results broadcast module A2 for the seller agent, that is, the carbon quota providers provide auction results. For the seller agent, the broadcast results mainly include the transaction price, transaction unit, etc. In addition, the final carbon quota providers determine "transaction" after the results will be reported by broadcast module A2 to the third party platform.

[0088] 2) each carbon quota demand side corresponds to a buyer agent module. Carbon quota demand side 1 corresponding to the buyer agent module B 1 , mainly by information input module B1, auction notification module B2 and results broadcast module B3. Carbon quota demand side n corresponding to the buyer agent module B n , its composition is similar to B 1 , namely information input module B1, auction notification module B2, results broadcast module B3.

[0089] Information input module B1 for receiving the buyer, that is, the carbon quota demand side uploaded information, and submit this information to the third party certification agency trading platform. For example, a company due to the expansion of the industrial scale, resulting in increased demand for carbon quota. The company can be the demand for carbon quota quantity, carbon quota purchase amount after market research as information input, information input module B1 will be the company information, company demand and its purchase amount sent to the third party platform. In addition, through the spread of information to participate in other buyers, users also need to input the basic information of the spread of users in order to obtain the spread of incentives and run the subsequent information diffusion auction algorithm.

[0090] Auction notification module B2 main role has two: one is to meet the carbon quota demand side needs of the auction when the release of the buyer agent to participate; two is when the buyer agent has participated in an auction, the remaining time of the auction is notified.

[0091] Result reporting module B3 is used to provide auction results to the buyer's agent, i.e., the carbon quota demander. For the buyer's agent, this report primarily includes information such as whether the bid was successful, the amount of promotional incentives received, or the required payment. Furthermore, once the carbon quota demander confirms the transaction is complete, the results are reported by reporting module B3 to third-party platform C.

[0092] 3) The third-party certification agency trading platform C consists of an information integration module C1, an information processing module C2, an allocation module C3, a payment module C4, a supervision and management module C5, a timing module C6 and an information broadcast module C7.

[0093] The information integration module C1 organizes and merges the information transmitted by the seller agent and the buyer agent, and corresponds the specific information of each user to each node in the algorithm; the integrated information is then passed to the information processing module C2, which is responsible for constructing this information into an undirected graph through the order of propagation, and implementing a depth traversal algorithm on the graph to generate a depth-first spanning tree (DFS spanning tree), and then find the strong propagation sequence and weak propagation set of the buyer user.

[0094] The allocation module C3 and the payment module C4 together constitute the result module of the entire auction. By adopting a distributed fair incentive information dissemination mechanism on the above-mentioned DFS spanning tree to implement the auction, the allocation and payment calculation of the auction are jointly realized.

[0095] The supervisory management module C5 primarily manages all intermediary nodes in the Distributed Fair Incentive Dissemination Mechanism (DFDM). These intermediary nodes perform two key functions: First, each seller agent and each buyer agent corresponds to a corresponding intermediary node, and information is exchanged only with their respective intermediary nodes, ensuring the privacy of both buyers and sellers. Second, the entire auction process, including information input, is supervised to prevent information leaks and algorithm errors while ensuring data fairness and reliability.

[0096] Timing module C6 ensures that the seller agent, the carbon quota provider, completes the auction on time. When the seller agent's information is consolidated and entered into the trading platform, the module sets a timer and begins counting. When the timer expires, all buyer agents cease inputting information, confirming the auction size.

[0097] The information broadcast module C7 transmits the auction allocation and payment results to the seller's agent and the buyer's agent, and receives "confirmed transaction" information from both parties. After receiving the "transaction completed" signal from both agents, the module records the time, scale, results, and some details of the entire auction, and announces information such as the parties who completed the transaction and the transaction amount.

[0098] In order to achieve the simplicity of transactions and better facilitate both buyers and sellers, all processes of carbon quota auctions are completed on third-party trading platforms; third-party trading platforms can be certified by the state or organization, further ensuring the privacy of both parties involved in the carbon quota auction and the fairness of information throughout the auction.

[0099] Based on the above auction device, a carbon quota distributed information diffusion auction method is implemented, such as Figure 3 As shown, the following steps are included:

[0100] S1. The carbon quota provider provides auction information, which is uploaded to the trading platform module through the seller agent module for publication;

[0101] S2. During the auction duration, the carbon quota demander uploads the demand information to the trading platform module through the buyer agent module to join the auction;

[0102] S3. After the auction duration ends, the trading platform module integrates the auction information and demand information;

[0103] S4. The trading platform module applies a distributed fair incentive information diffusion mechanism to execute the auction and determine the auction allocation and payment information;

[0104] S5. Based on the auction allocation and payment information, combined with the feedback information from the seller agent module and the buyer agent module, the transaction platform module integrates the auction results and publishes them.

[0105] In combination with the auction device architecture constructed in this embodiment, the process of applying the above method is as follows:

[0106] 1. The carbon quota provider uploads the auction information to the third-party trading platform through the information integration module A1 seller agent, and the platform publishes the auction information through the information broadcast module C7.

[0107] 2. The platform starts the timing module C6, which is the countdown of the “time to participate in the auction”. During the countdown period, the carbon quota demander can use the corresponding buyer agent module B n The information input module B1 uploads the demand to the third-party trading platform and disseminates the auction information to other users.

[0108] 3. When the countdown ends, the platform information integration and processing modules A1 and A2 process the collected information and obtain the undirected graph, DFS spanning tree, strong propagation sequence, weak propagation set, etc. of the carbon quota auction in turn.

[0109] 4. The platform internally operates a distributed fair incentive information dissemination mechanism through the allocation module C3, payment module C4 and supervision and management module C5 to obtain the final allocation and payment of the auction.

[0110] V. The platform sends the result to the seller and buyer agent module for confirmation of the transaction through the information dissemination module C7. Finally, the platform publishes the transaction parties and the auction result.

[0111] The detailed steps are as follows:

[0112] S1: Carbon quota provider provides information, and the platform publishes auction information

[0113] The carbon quota provider enters the auction through the seller agent. The main information provided by the seller as the distributed fair incentive information dissemination mechanism is the carbon quota quantity for auction. In addition, the seller also needs to provide the real user's unit and the expected transaction time. After the third-party transaction platform confirms that the information is correct, it publishes the auction information including the carbon quota quantity, provider unit, and duration on the platform website.

[0114] S2: During the auction duration, the carbon quota demander joins the auction and provides information including demand.

[0115] During the auction duration, when the carbon quota demander has certain demand for carbon quota and is interested in the auction, it can enter the auction through the buyer agent. As the buyer of the distributed information dissemination mechanism, the main information it needs to provide is its bid, as well as the introduction objects it invites to join (if there is none, it does not need to be input; if there is more than one object, all can be filled in). In addition, it also needs to provide the real user's unit information. The buyer inputs the above information, which is considered as joining the auction.

[0116] S3: The platform integrates and processes the auction information to prepare for the auction.

[0117] The duration ends, and the new buyer can no longer participate in the auction. The platform processes and models the collected information, and extracts the strong propagation sequence and weak propagation set from all the information.

[0118] For convenience of subsequent description, without loss of generality, it is assumed that there is a certain carbon quota market social chain as Figure 4 The information dissemination flow after sorting is as follows Figure 5 . Figure 4 It represents all buyers participating in the entire auction according to the seller's selling demand and the information dissemination relationship between them.

[0119] Figure 4 , 5In the figure, each node, including sellers and buyers, is represented by a circle, where S represents the seller's agent node and A to M represent the buyer's agent nodes. The numbers next to the buyer's agent nodes represent their respective psychological valuations. The edges between nodes represent the information diffusion relationship between neighbors. Neighbors refer to other companies or individuals that have business relationships, social relationships, etc. with the company or individual and have certain contacts. Figure 5 for Figure 4 The information flow constructed according to a certain search order can more clearly show the propagation relationship between nodes, which facilitates the application of auction algorithms on the graph.

[0120] The auction information after platform integration is as follows:

[0121] (1) Seller agent node S and its first-level transaction object set r s (In step S2, no buyer set of referral targets to be invited to join is entered). Figure 4 、 5 For example, the corresponding nodes are the seller node S, the seller's first-level transaction object set r s ={A, B, C}.

[0122] (2) Buyer agent node set N = {1, 2, 3, ..., n}, where the private type θ of each node i i ={v i , r i} respectively represent their psychological valuation of the auction and the neighbor set (the set of referral objects that the buyer inputs to invite himself to join in step S2). At this time, the set θ={θ1,θ2,...,θ n} represents all buyer private types. Figure 4 、 5 For example, all nodes that are not seller nodes S are buyer nodes, and together they constitute the buyer set N = {A, B, C, ..., M}. The psychological evaluation of each buyer is represented by the number next to it, and the neighbor set is represented by the edges between it and other nodes; for example, the psychological evaluation of node D is v D =5, neighbor set r D ={A, E, F, G}, whose private type θ D ={5,{A,E,F,G}}.

[0123] (3) Assuming that the node with the highest psychological valuation is ω, define the auction information to be available from the buyer agent node c j Propagate to ω if and only if the buyer agent node c j When not participating in the auction, ω cannot join the auction. At this time, c j is called a strong propagation buyer. Define the strong propagation sequence of ω (the set of strong propagation buyers arranged in the order of participating in the auction), and use C ω Indicates that at this time cj ∈C ω .by Figure 5 For example, the buyer node with the highest psychological valuation is M, with a valuation of 13. According to the definition, when the seller S does not initiate the auction and one of the buyers C, L and M does not join the auction, M cannot receive the auction information, that is, it cannot join the auction. According to the definition, M's strong propagation sequence C M ={S, C, L, M}, the strong propagation buyers are C, L, M (represented by red nodes).

[0124] (4) Define the link between two strong communication buyers c j and c j+1 The intermediate buyers are weak propagation buyers, and the set they form is a weak propagation set. Indicates. Figure 5 For example, there are buyer nodes J and I between strong propagation buyers C and L, that is, weak propagation buyers between C and L are J and I (indicated by orange nodes), and the weak propagation set M CL ={J, I}; Since there are no other buyer nodes between the strong propagation buyers L and M, there are no weak propagation buyers and weak propagation sets between L and M.

[0125] (5) The set of intermediary nodes C corresponding to the seller and each buyer P ={P S , P1, ..., P i}, where P S Represents the intermediary node corresponding to the seller, P i is the intermediary node corresponding to buyer i.

[0126] In summary, the auction information after platform integration is shown in Table 1.

[0127] Table 1

[0128]

[0129] Based on the above information, the third-party certification agency trading platform processing steps are as follows Figure 6 As shown:

[0130] S301: Run a depth-first search (DFS) algorithm on the seller agent S and the buyer agent set N, and generate a DFS tree (depth-first search tree). The starting node of the DFS tree is the seller agent, and the remaining nodes are the buyer agents. The edges of the tree represent the neighbor relationships of each node (if two agents are neighbors, they are said to have a neighbor relationship, and an edge is generated between the corresponding two nodes in the DFS tree).

[0131] After running the depth search algorithm (DFS), due to the existence of their respective neighbors, the originally separated seller agent node S and buyer agent node set N are constructed into a DFS spanning tree, in which there is a parent-child relationship between each node (the node that invites other agents to join the auction is called the parent node of the invited agent node, and vice versa), which is conducive to the strong propagation sequence C ω Extraction.

[0132] S302: The buyer agent node calculates its status and reports it.

[0133] Due to the construction of the DFS spanning tree, each node has a more general judgment of whether it is in C ω The above method. At this time, the buyer agent node needs to calculate the status The meanings of these states are shown in Table 2. Then, the buyers will report these states to their respective intermediary nodes P i It should be noted that in this process, the buyer intermediary node P i They will check whether the information of their respective agents is true to ensure the authenticity of the information.

[0134] Table 2

[0135]

[0136] S303: Seller intermediary node P s Announce the node ω with the highest psychological evaluation and its status.

[0137] Seller intermediary node P in the supervision and management module C5 s After finding all buyers’ quotations, we compare and find the node ω with the highest psychological valuation (ω’s psychological valuation is v′ ω ) and inform ω, through its intermediate node P ω Report its partial status dt ω (the time when the user corresponding to the ω node is first invited), and then reports ω to all other buyer agent nodes. Note that the seller intermediary node P s Knowing only the psychological valuations reported by each buyer, the network topology—that is, the set of neighbors of each buyer—is unknown, thus preventing excessive information exposure. After collecting all psychological valuations, the seller intermediary node passes this information to the allocation module C3 and the payment module C4 for subsequent use.

[0138] S304: Seller intermediary node P s Compare and select strong propagation sequence C based on the information reported by buyers ω Buyer node on .

[0139] Through the above steps S302 and S303, the general buyer agent node has known the buyer agent node ω. If it is the key propagation buyer of ω, in order to successfully win the auction or a higher propagation incentive, it needs to inform the corresponding intermediary node P of its willingness to participate in the auction. i , so that subsequent intermediary nodes can accurately include it as a payment object.

[0140] Then, each intermediary node that receives the auction intention can identify whether the buyer belongs to the strong propagation sequence C according to the information reported by the buyer node and the information status of the ω buyer agent node through status comparison, node screening and other steps. ω If buyer i is a strong propagation buyer, then the corresponding intermediary node P i dt i Report to the seller's intermediary node P s If buyer i is not a strong propagation buyer, he will be penalized for false positives. In particular, in this process, each intermediary node P i The information will be verified by comparing the status of all reporting buyer nodes, thereby playing a role in information supervision.

[0141] S305: Seller intermediary node P s According to dt i Arrange the key nodes in ascending order to generate a strong relay sequence.

[0142] Since there is a strict invitation order from sellers to buyer nodes in the strong propagation sequence Cω, it is necessary to sort them according to the time of arrival at each buyer. s According to the time dt of the first invitation of each node i Sort in ascending order, and record the seller as S. The strong propagation sequence after sorting is C ω It is {S, 1, 2, ..., ω}, where 1, 2, etc. represent a buyer agent node.

[0143] Get the strong propagation sequence C ω After counting all strong propagation buyer nodes, count two strong propagation nodes c j with c j+1 All buyers on the communication path (one party can make the other party participate in the auction by information dissemination, and the communication route between the two is called the communication path) are regarded as weak communication buyers of the two strong communication buyers, and together constitute the weak communication set

[0144] S4: The third-party platform applies a distributed fair incentive information diffusion mechanism to execute the auction internally.

[0145] Allocation phase:

[0146] The strong propagation sequence C obtained in the previous step S3 ω , at this time the auction only occurs on this sequence. That is, the seller intermediary node P s The information of the strong propagation sequence (including the position of each node in the sequence and its own psychological evaluation), the weak propagation node and other nodes are passed to the distribution module C3, and then C3 Figure 7 The steps shown determine the allocation of the auction.

[0147] S401: According to the strong propagation sequence C ω , start the transaction from the first node after the seller (the node closest to the seller).

[0148] To ensure the individual rationality of the mechanism, sellers need to provide incentives or subsidies to buyers who spread information to ensure that they spread information. In this case, all strong buyers before the auction winner play a role in information dissemination. Therefore, the distributed fair incentive information dissemination mechanism needs to start the auction from the first strong buyer node.

[0149] S402: The distribution module C3 provides each buyer's intermediary node P i The required buyer node information is provided by the buyer intermediary node P i Calculate the externality of the current strong propagation buyer node.

[0150] In order to ensure the incentive compatibility of the mechanism, the buyer intermediary node P i The externality of the current strong propagation buyer proxy node needs to be calculated, that is, the highest bid among all other network nodes except the selected set. This step can be specifically divided into two parts: ① The intermediary node of the first strong propagation node calculates the externality. At this time, due to insufficient external network information, the allocation module C3 reports the required information (the buyer's psychological valuation of the non-strong propagation sequence) to the intermediary node for calculation; ② The intermediary nodes of subsequent strong propagation nodes calculate the externality only by obtaining the externality information of the previous strong propagation node and the weak propagation information between the two nodes.

[0151] S403: The auction proceeds in sequence or ends.

[0152] According to the size of the psychological valuation of the next buyer i+1 and the psychological valuation of the current transaction buyer i, this step can be divided into two cases A and B.

[0153] S403A: The amount that the next buyer i+1 needs to pay, excluding itself and the weak propagation set When the externality of a buyer is higher than the psychological valuation of the previous buyer, it will replace the previous buyer to join the auction. For example, when the psychological valuation of buyer 2 is higher than the externality of buyer 1, it will replace buyer 1 to join the auction and become the buyer currently allocated carbon quotas.

[0154] S403B: The next buyer needs to pay except for himself and the weak propagation set When the externality is lower than the psychological valuation of the previous buyer, the current transaction buyer directly wins the auction and inputs the information into the payment module C4.

[0155] The allocation process is performed by the allocation module C3 and the intermediate node P i 、P s Execution. Buyer intermediary node P i While acting as an auctioneer, a distributed computing mechanism is also introduced. Without buyer intermediary nodes, each externality calculation requires information from all nodes. However, after the introduction of buyer intermediary nodes, since the previous buyer intermediary node has retained the information from the previous externality calculation after the first calculation, the next buyer intermediary node only needs to obtain information between two strong propagation nodes when performing calculations (that is, only the intermediary nodes corresponding to the strong propagation nodes need to exchange information). This greatly reduces the scope of information acquisition, reduces the duplication of information acquisition work, and also directly reduces the risk of privacy leakage.

[0156] Payment stage:

[0157] The payment process is closely related to the distribution results and can be divided into two stages based on the distribution order: the initial payment and the re-payment stage. The initial payment addresses the majority of the payment and occurs between strong buyers. The re-payment addresses smaller payments and occurs between all buyers, but it effectively ensures buyers' enthusiasm for participating in the mechanism.

[0158] 1) Initial payment stage:

[0159] During the initial payment phase, the main payment recipients are sellers and strong-propagation buyers. The payment rules for relevant buyers are divided into three situations:

[0160] (1) For the seller (S), the externality amount paid by the strong communication buyer (strong communication buyer No. 1) with whom the seller first transacts in step S401 is the total payment that the seller can obtain in the initial payment phase. This can be expressed as:

[0161]

[0162] (2) For the buyer who finally obtains the item (c h The amount paid by ) (who must be one of the strong communication buyers) is its externality Expressed as formula:

[0163]

[0164] (3) For non-winner strong communication buyers (c j ∈C ω\c h , where '\c h 'Indicates' except c h ') will get a certain amount of communication benefits, the amount = the externalities paid by the next strong communication seller excluding the externalities of itself and the weak communication nodes between the two - Self-paying externalities Expressed as formula:

[0165]

[0166] The formula for the buyer node’s initial payment is as follows:

[0167]

[0168] in (* represents any set) represents the highest psychological valuation among all nodes except the * set, that is, the externality except the * set; C j represents the jth strong communication buyer, c h Indicates the buyer who finally obtains the item; C ω is a strong propagation sequence, Between c j and c j+1 A weakly spreading set between two strongly spreading buyers.

[0169] 2) Repayment stage:

[0170] Two-way communication of seller payment externalities and If there is a difference, the difference will be distributed to all buyers who meet the payment requirements during the payment phase. First, determine the number of people who need to pay (Weakly propagated set + the jth strong propagation buyer), and then decide the payment amount based on the difference between the buyers.

[0171] In the re-payment phase, the main payment targets are weak-propagation buyers, strong-propagation buyers, and sellers. The re-payment rules for relevant buyers can be divided into three situations:

[0172] (1) For strong communication buyers including winners (c j ∈C ω ), the amount to be paid (p′ i )for Its value is equal to the externality of the highest psychological valuation buyer in the weak communication set divided by itself and - Externalities of all buyers except themselves and the weakly propagated set and divided by the number of weak propagation buyers The sum of the number of sellers 1 is expressed as:

[0173]

[0174] (2) For weak propagation buyers The amount to be paid (p' i ) is -R i , whose value is equal to the externality between itself and the j-th strong communication buyer - Externalities of all buyers except themselves and the weakly propagated set and divided by the number of weak propagation buyers The sum of the number of sellers 1 is expressed as:

[0175]

[0176] (3) For the seller (S), after all buyers who meet the payment requirements have completed their payments, if the total payment amount is lower than the difference between the payment externalities of the two strong propagation sellers j and j+1, the remaining amount shall be paid in full by the seller.

[0177] The formula for buyer node re-payment is as follows:

[0178]

[0179] The specific expression of propagation incentive is:

[0180]

[0181] Where, c j , Same as the initial payment phase; Indicates that between c j-1 and c j The buyer with the highest psychological valuation in the weak communication set between two strong communication buyers.

[0182] Similar to the allocation process, the payment process is executed by the payment module C4 and the intermediary node. While acting as an "auctioneer," the intermediary node also introduces a distributed computing mechanism. Without a buyer intermediary node, each externality calculation requires information from all nodes. With the introduction of a buyer intermediary node, since the previous buyer intermediary node has already retained the information from the previous externality calculation after the first calculation, the next buyer intermediary node only needs to obtain information between two strong propagation nodes (i.e., only the intermediary nodes corresponding to the strong propagation nodes need to exchange information). This significantly reduces the scope of information acquisition, reduces the duplication of information acquisition work, and directly reduces the risk of privacy leakage.

[0183] S5: The third-party certification agency trading center integrates the auction information and announces the auction results.

[0184] After steps S3 and S4, the carbon quota auction concludes, and all allocation and payment information is stored in the information broadcast module C7. The third-party platform then filters the required information, such as seller revenue, the final winner, the buyer's information dissemination subsidy incentive, and the winner's required payment amount. This information is then disseminated to the respective seller and buyer agents, allowing them to quickly "confirm the transaction." Furthermore, the information broadcast module C7 publicly announces the seller and final winner to ensure fair and equitable auction information.

[0185] The following is a detailed description of the intermediary nodes and the designed auction mechanism:

[0186] 1. The role of intermediary nodes

[0187] Throughout the auction process, this solution utilizes multiple intermediary nodes managed by the supervisory management module C5 and designs a new distributed processing mechanism to address privacy leaks in carbon trading auctions and protect the privacy of both buyers and sellers. Intermediary nodes play a key role in protecting privacy at the following levels:

[0188] In determining the strong propagation buyer sequence and weak propagation buyer set, the seller intermediary node P s Only the psychological valuation of the buyer node is obtained, and the buyer intermediary node P i It obtains information such as the neighbor set of the reporting buyer separately; compared with the traditional centralized mechanism that does not introduce intermediary nodes, this distributed mechanism does not need to obtain the topology of the entire network, which reduces the spread of private information between buyers and greatly reduces the risk of information leakage.

[0189] In the payment and distribution phase, each time the externality is calculated, only a specific buyer intermediary node P is required. i Calculations are performed between nodes and some specific node information is obtained (for example, when determining allocation, it is only necessary to obtain the psychological valuations of two strong communication nodes and the weak communication buyers between them); compared with the traditional centralized mechanism without the introduction of intermediary nodes, this distributed mechanism does not require the psychological valuation information of all buyers for each externality calculation, which effectively prevents the exposure of buyer information; compared with the distributed mechanism of a single intermediary node, multiple intermediary nodes can better prevent the problem of excessive information concentration.

[0190] During the entire auction process, all intermediary nodes will conduct a certain degree of verification of the information each time they receive or obtain node information, ensuring the authenticity of the information and the fairness of the auction.

[0191] 2. Designing an auction mechanism that incorporates individual rationality and incentive compatibility

[0192] 2.1 Individual Rationality

[0193] In the auction mechanism, individual benefits, including those of buyers and sellers, are non-negative. In this scheme, this property ensures that carbon quota demanders actively participate in the auction and disseminate auction information, addressing the low participation of small and medium-sized enterprises and individuals.

[0194] Theorem 1: The distributed fair incentive information dissemination mechanism (DFDM) satisfies individual rationality.

[0195] Proof: After implementing the DFDM mechanism, only the strong and weak propagation buyers may have non-zero returns. The returns of other buyers are all 0. And R i is defined as the re-payment amount, obviously R i ≥0.

[0196] For buyer node i=c j ∈C ω \c h (Non-winner strong communication buyer), according to the payment formula (1-3) (2-1) we can know its final profit Because c j Always precedes in the information transmission sequence with c j+1 , so the subset Externality Set Equivalent to Therefore,

[0197] For buyer node i=c h (Winning buyer), according to the allocation rule, the equivalent amount of the auction item won is defined as From the payment formula (1-2) (2-1), we can know that the final profit at this time is

[0198] For buyer nodes (Weakly propagated buyer), according to the payment formula (2-2), its final profit can be known

[0199] In summary, for the three types of strong or weak communication buyers, their benefits are not less than 0; for other buyers, their benefits are all 0, so the DFDM mechanism satisfies individual rationality.

[0200] 2.2 Incentive Compatibility

[0201] In an auction mechanism, users' actual bids (psychological valuations) represent their dominant strategies, and users who adopt these bids receive non-negative returns. In this scheme, this property ensures that the psychological valuations and neighbors presented by buyer agents are reliable. This eliminates the problem of buyers participating in auctions without disseminating information, addressing the liquidity issues inherent in traditional carbon trading markets.

[0202] Theorem 2: The distributed fair incentive information dissemination mechanism (DFDM) satisfies incentive compatibility.

[0203] Proof: All buyers in the DFDM mechanism are divided into four types: non-winner strong propagation buyers, weak propagation buyers, winners and other buyers. The four types need to be analyzed separately.

[0204] (1) For buyer node i=c j ∈C ω \c h (Non-winner strong spread buyer).

[0205] When its neighbors gather When fixed, since the neighbor set does not change, according to the allocation rule, it cannot falsely report psychological valuation Become a weak communication buyer, that is, it is still a strong communication buyer.

[0206] The final profit can be known from the payment formula (1-3) (2-1) It has nothing to do with its own quotation, that is, the profit does not change; if the node passes the high psychological valuation Become the new winner, according to the definition of re-payment, will not change, but due to the externality paid by the next strong propagation buyer will be greater than the true psychological valuation of the new winner Its final profit will be reduced, that is,

[0207] When its psychological evaluation Fixed, false reporting of neighbor collections When (neighborhood set can only be underreported).

[0208] 1.1 The node is still a non-winner strong propagation buyer. As the number of neighbors of this node decreases, the selection set of the next node to pay the externality may also decrease, that is, The corresponding externality is reduced, i.e. The amount of their own expenditure and repayment amount Unaffected, the final profit may decrease.

[0209] 1.2 The node becomes a weak propagation buyer with non-negative benefits. At this point, its utility becomes Due to reduced revenue It must be non-negative, so the final profit decreases.

[0210] 1.3 The node becomes the new winner. Similar to the situation of false psychological valuation, there is That is, the final profit decreases.

[0211] 1.4 The node becomes a weak or non-strong propagation buyer, and its profit is always 0, and the final profit will also decrease.

[0212] (2) For buyer nodes (Weak spread buyer).

[0213] When its neighbors gather When fixed, since the neighbor set does not change, according to the allocation rule, it cannot falsely report psychological valuation Become a strong propagation buyer, that is, it is still a weak propagation buyer. The final profit can be known from the payment formula (2-2) It has nothing to do with its own quotation, that is, the profit has not changed; if the node passes the high psychological valuation Become the new winner, and its final profit becomes Due to the externality of its expenditure Must be greater than its true psychological valuation Therefore, the final profit will decrease.

[0214] When its psychological evaluation Fixed, false reporting of neighbor collections When (neighborhood set can only be underreported). Due to weak propagation, the final profit of the buyer is It has nothing to do with the objects that the node propagates, that is, no matter how much the number of its neighbors decreases, it will not affect its final benefit.

[0215] (3) For buyer node i=c h (Winner Buyer).

[0216] When its neighbors gather When fixed, the winner’s final income can be obtained according to the proof in Theorem 1: It has nothing to do with its own bid, that is, under the premise that the distribution does not change, no matter how the winner lies about the psychological valuation, the final benefit will not change; if the winner reports a lower bid and becomes a non-winner strong communication buyer, according to the distribution rules, the winner's true psychological valuation must not be lower than the externality paid by the next strong communication node, and its final benefit That is, the final profit is reduced; if the winner reports a lower bid and becomes a weak propagation buyer, according to the allocation rule, the winner's real psychological valuation must not be lower than the externality paid by the next strong propagation node, that is, Therefore, the final profit of the node is That is, the final profit will not increase.

[0217] When its psychological evaluation Fixed, false reporting of neighbor collections When (neighborhood set can only be underreported). According to the allocation rule, the winner’s neighbors do not affect the allocation, that is, the allocation result remains unchanged. According to the payment formula (1-2) (2-1), the winner’s utility is It does not matter what the neighbors are, so the final payoff will not change if the winner lies about his neighbors.

[0218] (4) For other buyers who do not belong to the above three (using c n express).

[0219] When its neighbors gather When fixed, if the buyer is not the child node of the winner, he can change the allocation result by increasing his bid. , it will become a strong propagation buyer, and the payment will be the initial highest valuation on the network. According to the allocation rules, the payment must be greater than its true psychological valuation, so the final profit will be reduced. If the buyer is a child node of the winner, no matter how it bids, the allocation result will not change, and the final profit will still be 0.

[0220] When its psychological evaluation Fixed, false reporting of neighbor collections (Neighborhood set can only be underreported). Since the psychological estimate has not changed, the allocation result will not change, that is, its final benefit is still 0.

[0221] In summary, any buyer node that truthfully reports its private type (psychological valuation and neighbor set) will gain the most benefits, and false reporting will not increase benefits. Therefore, the DFDM mechanism satisfies the incentive compatibility property.

[0222] To demonstrate the effectiveness of this solution, this example uses a specific day's trading on a local exchange within the national carbon quota market as an example. In the power generation industry, for example, in response to low-carbon policies, a power plant upgraded a 600MW thermal generator to a supercritical unit. This increased boiler pressure and temperature, improving power generation efficiency while reducing coal consumption per unit of electricity. After the upgrade, the power plant's annual carbon dioxide emissions decreased by 30%, leaving 600 tons of its allocated carbon quota remaining for the year.

[0223] After pledging the National Certified Emission Reductions (CCER), all 600 tons of carbon quotas met the CCER standards. The company planned to sell all the carbon quotas at one time before the end of the year.

[0224] First, this solution is used for online auction. To maximize profits, the company adopts an online distributed fair incentive information diffusion auction. The specific auction process is as follows:

[0225] S1: The seller provides auction information, which is published on a third-party platform.

[0226] The company entered its information into the official website of an energy exchange (a nationally certified third-party trading platform) and commissioned the exchange to conduct an online information dissemination auction. After verifying the information, the exchange published it on its official website.

[0227] The carbon quota suppliers (sellers) and auction information are shown in Table 3 below:

[0228] Table 3

[0229] Carbon quota suppliers Auction announcement time Auction deadline CCER trading volume Average opening price Power Generation Company S 2023.12.1 2023.12.31 600 tons 79 yuan / ton

[0230] S2: Buyers participate in the auction within the specified time.

[0231] After step S1, users who have carbon quota demand and are able to purchase 600 tons of CCER (carbon quota) will provide their information and join the auction. At the same time, they can also spread the information to users who have more urgent demand and higher bids to benefit from it.

[0232] S3: Third-party platform integrates information.

[0233] At the end of the specified time, the third-party platform first integrates the collected buyer information to obtain the following summary table 4 (the bids of the carbon quota demanders are input by the users themselves after market research, and the neighbors are obtained after sorting out the information input by the buyers, which represents the relationship of buyer information diffusion).

[0234] Table 4

[0235] Carbon quota demanders Bid (unit: Yuan / ton) Neighbor Company A 79 S,D Self-employed B 75 S,D,C Company C 76 S,E,F,G,B Company D 80 H,J,A,B Company E 82 K,C Enterprise F 78 K,L,G,C Self-employed G 81 F,C Enterprise H 80 I,D Enterprise I 79 J,H Enterprise J 78 I,D Company K 83 N,E,F Self-employed L 81 F Self-employed M 80 O,I Enterprise N 84 P,Q,K Company O 79 M Company P 80 Q,N Company Q 79 P,N

[0236] Based on the above integrated information, the third-party platform runs a deep traversal algorithm on the information to obtain the DFS spanning tree as follows Figure 8 As shown in the figure, each node in the DFS spanning tree represents the seller (S) and buyer (A, B, ...) participating in the auction. The edges between nodes represent the neighbor relationship between the two nodes, that is, the invitation-invitee relationship between the two buyers. The number next to the buyer's node represents the buyer's psychological valuation of the auction. In particular, the set of intermediary nodes (represented by blue nodes) is independent of the auction system and serves as the supervisory management module C5, which collects auction information and calculates payment allocation.

[0237] After obtaining the DFS spanning tree, the seller and buyer intermediary node set (supervision and management module C5) determines the strong propagation sequence C according to the position and calculation status of each buyer agent node on the tree shown in Table 2. ω = {S, C, K, N}. In this sequence, excluding the seller S, the remaining buyers C, K, and N are strong propagation buyers (indicated in red in the figure), where N is the buyer with the highest psychological valuation ω. Then the intermediary node determines the weak propagation buyer set M between C and K. ck{E, F, G} (shown in orange in the figure), there is no weak propagation set because there is no buyer node between the proxy nodes K and N.

[0238] S4: The third-party platform determines the auction allocation and payment.

[0239] The supervision management module C5, the allocation module C3, and the payment module C4 jointly determine the allocation and payment of the auction according to the operation mechanism of the distributed fair incentive information diffusion mechanism.

[0240] Allocation process:

[0241] From the foregoing Section 2.4, it is determined that the highest bidder is N, so the allocation of the auction object is only performed on the strong propagation sequence C ω , that is, S, C, K, and N.

[0242] First, the transaction occurs between the seller S and the buyer C. At this time, C "owns these carbon quotas", that is, C pays the amount of money and temporarily owns the auction object, that is, the right to dispose of the carbon quotas. According to the auction rules, it is necessary to continue to determine whether the next strong propagation node K has the qualification to win the auction. After removing the buyer C and all nodes that can be propagated through C from the entire network, the highest psychological valuation is 80 yuan / ton.

[0243] Next, the transaction occurs between the buyer C and the buyer K. After removing the buyer K, all nodes that can be propagated through K, and the weak propagation set M ck from the entire network, the highest psychological valuation is L 81 / ton. This valuation is higher than the psychological valuation of C of 76 yuan / ton, so at this time, according to the auction rules, C will give these carbon quotas to K and obtain the amount of money paid by K.

[0244] Finally, the transaction occurs between the buyer K and the buyer N. After removing the buyer N and all nodes that can be propagated through N from the entire network, the highest psychological valuation is 83 yuan / ton (not higher than the psychological valuation of K of 83 yuan / ton), that is, according to the auction rules, N cannot win the auction object at this time, and the carbon quotas are finally obtained by K.

[0245] Payment process:

[0246] In the allocation process, the buyer N does not participate in the auction, and the final winner is the buyer K, that is, the initial allocation occurs among S, C, and K. According to the initial allocation rule, the seller S obtains 48,000 yuan (at a unit price of 80 yuan / ton, 80*600=48,000); the final winner K needs to pay the externality excluding itself, that is, 49,200 yuan (at a unit price of 82 yuan / ton, 82*600=49,200) and obtains the carbon quotas of 600 tons; the non-winner strong propagation buyer C can obtain a propagation incentive of 49,200 yuan (at a unit price of 82 yuan / ton, 82*600=49,200) excluding the buyer K and the weak propagation set M ckExternalities of the enterprise (81*600=48600) - externalities other than itself (80*600=48000) = 600 yuan.

[0247] Since the externality obtained by buyer C is 48,600 yuan (81 yuan / ton as the unit price, 81*600=48,600), and the externality paid by buyer K is 49,200 yuan (82 yuan / ton as the unit price, 82*600=49,200), the difference between the two is 600 yuan, so this 600 yuan will be paid to the weak propagation set M through the repayment process. ck There are buyers E, F, G, buyer K and seller S in .

[0248] First, determine the number of buyers participating in the repayment phase as four (E, F, G, K). Then, calculate the repayment amounts for each of them in turn. For strong propagation node K, if it does not participate in the auction, the final winner will be E. In this case, the externality price paid by buyer E is now 81 yuan / ton, the externality difference is zero, and the repayment amount is 0 / 4 = 0 yuan. For weak propagation node E, if it does not participate in the auction, the final winner will still be K, but its externality price will become 81 yuan / ton, so the repayment amount is also 0 yuan. For weak propagation nodes F and G, their non-participation in the auction does not affect the final result, so the repayment amount for F and G is 600 / 4 = 150 yuan. Finally, the remaining externality difference is 600-150*2 = 300 yuan, which is fully received by seller S.

[0249] S5: The third-party platform publishes the auction results.

[0250] The final auction results obtained from step S4 are shown in Table 5 below:

[0251] Table 5

[0252] User Information User Type Auction revenue / yuan Auction expenditure / yuan Spread incentives / meta Company S Seller 48300 / / Company C Non-Winning Buyers / / 600 Enterprise F Non-Winning Buyers / / 150 Self-employed G Non-Winning Buyers / / 150 Company K Winner / 49200 /

[0253] The third-party platform transmits the auction results to buyer agent nodes C, F, G, and K, and seller agent nodes via information broadcast module C7. The platform also announces the proceeds or required payment on the corresponding user interface. The auction is concluded when the user clicks "Confirm Transaction." Other buyers' interfaces simply indicate that they did not win the auction, without revealing any other information. After the auction is concluded, the third-party platform uploads and updates the auction details to a Shanghai Stock Exchange database for data storage and reuse.

[0254] Then, we use the traditional auction plan for comparison. If the traditional auction method is used, the buyers participating in the auction will not spread the auction information to other buyers they know in order to maximize their own interests. Therefore, only A, B, and C will receive the notice and participate in the auction. The profit at this time is 79*600=47,400 yuan (A's psychological valuation is the highest, which is 79 yuan / ton).

[0255] Using the distributed fair incentive information dissemination mechanism proposed in this invention, the buyer ultimately earned 48,300 yuan, which is 900 yuan higher than the 47,400 yuan obtained when only A, B, and C participated without dissemination, thus increasing the seller's profit to a certain extent.

[0256] At the same time, because the present invention designs a new distributed mechanism containing intermediary nodes during the auction process, the information required for each calculation of system or node externalities during the allocation and payment process is greatly reduced. Specifically, the original need to know the psychological bids and neighbor information of all nodes is reduced to only the information between some specific nodes (C nodes and K nodes in this case), which effectively reduces the risk of information leakage. Moreover, the expansion of multiple intermediary nodes prevents the information between buyers from being overly concentrated (in this case, information exchange and calculation are only performed between the two intermediary nodes C and K), effectively protecting information security. In addition, the intermediary nodes can also verify the authenticity of the buyer's information during the auction process, playing a role in supervising and managing information while protecting privacy.

[0257] Finally, the results are compared. By comparing the above traditional auction method with the method proposed in this invention, it can be clearly seen that:

[0258] (1) The new auction mechanism proposed in this invention can effectively increase the seller's income; at the same time, in terms of social welfare, it has a more significant improvement effect than the traditional auction method.

[0259] (2) The new mechanism proposed in this invention provides certain incentives to the dissemination nodes, thereby encouraging more individual users and small businesses to participate while improving efficiency, and to a certain extent solves the shortcoming of weak liquidity of carbon quota trading.

[0260] (3) Different from the previous centralized auction mechanism, the present invention introduces a distributed mechanism by adopting multiple intermediary nodes, which effectively protects the privacy of sellers and buyers.

[0261] (4) The intermediary nodes in the present invention also play the role of data supervision and management, and compared with the traditional carbon quota auction mechanism, they can prevent the occurrence of data falsification to a certain extent.

[0262] In summary, this solution introduces a fair incentive information diffusion mechanism to model each enterprise, individual and their neighbors (here, "neighbors" means: other enterprises or individuals that have business relationships, social relationships, etc. with the enterprise or individual, and have certain contacts) as a social network graph, and adopts measures such as dissemination incentives to effectively improve the enthusiasm and participation of small and medium-sized enterprises and individuals, and to a certain extent, can increase the income of the entire carbon trading; in response to the problem of lack of information management system, the present invention, based on the auction mechanism, draws on the form of distributed mechanism design, adds trustworthy intermediary nodes, and at the same time plays a role in protecting personal privacy and managing and verifying data.

Claims

1. A carbon quota distributed information diffusion auction method, applied to a carbon quota distributed information diffusion auction device, characterized in that: The device includes a seller agent module, a buyer agent module and a trading platform module. The seller agent module and the buyer agent module are respectively connected to the trading platform module for mutual communication. The seller agent module is connected to the corresponding carbon quota provider, and the buyer agent module is connected to the corresponding carbon quota demander. The seller agent module is used to collect information uploaded by the carbon quota provider, distinguish and integrate it, submit the integrated information to the trading platform module, and obtain auction result information from the trading platform module and then send it to the carbon quota provider; The buyer agent module is used to submit the information uploaded by the carbon quota demander to the trading platform module, and obtain the auction time and auction result information from the trading platform module and then send it to the carbon quota demander; The trading platform module is used to integrate the information submitted by the seller agent module and the buyer agent module, construct an undirected graph, generate a DFS spanning tree, and use DFDM to conduct auctions on the DFS spanning tree, while managing all intermediary nodes in DFDM to determine the auction time and auction result information; The method comprises the following steps: S1. The carbon quota provider provides auction information, which is uploaded to the trading platform module through the seller agent module for publication; S2. During the auction duration, the carbon quota demander uploads the demand information to the trading platform module through the buyer agent module to join the auction; S3. After the auction duration ends, the trading platform module integrates the auction information and demand information; S4. The trading platform module applies a distributed fair incentive information diffusion mechanism to execute the auction and determine the auction allocation and payment information; S5. Based on the auction allocation and payment information, combined with the feedback information from the seller agent module and the buyer agent module, the trading platform module integrates the auction results and publishes them; In step S3, the trading platform module integrates the auction information and demand information to obtain an undirected graph of the carbon quota auction, and performs a depth traversal algorithm on the undirected graph to generate a DFS spanning tree, thereby finding the strong propagation sequence and weak propagation set of the buyer user; The undirected graph of carbon quotas includes: Seller agent node S and its first-level transaction object set r s ; The buyer agent node set N = {1, 2, 3, ..., n}, the private type of each buyer node i is θ i ={v i , r i }, v i and r i They represent the buyer node i’s psychological valuation of the auction and the neighbor set respectively; The set of intermediary nodes C corresponding to the seller and each buyer P ={P S ,P1,…,P i }, where P S Represents the intermediary node corresponding to the seller, P i is the intermediary node corresponding to buyer i; The specific process of generating the DFS spanning tree and finding the strong propagation sequence and weak propagation set of the buyer user in step S3 is as follows: S31. Run a depth-first traversal algorithm based on the seller agent S and the buyer agent set N, and generate a DFS spanning tree, where the starting node of the DFS spanning tree is the seller agent and the remaining nodes are all buyer agents; the edges of the tree represent the neighbor relationship of each node; S32, the buyer agent node calculates its own status and reports it to the corresponding buyer intermediary node. The buyer agent node status is The corresponding values ​​are the parent node set of buyer i, the time when buyer i was first invited, the time when the last buyer in subtree i was invited, the shortest time to reach buyer node i, the buyer node with the highest psychological valuation in subtree i, and the psychological valuation of the buyer node with the highest psychological valuation in subtree i. S33, seller intermediary node P s Announce the node ω with the highest psychological evaluation and its status; S34, seller intermediary node P s According to the information reported by the buyer, the strong propagation sequence C is compared and screened in turn. ω The buyer node on the , where if and only if the buyer agent node c j When not participating in the auction, ω cannot join the auction. At this time, c j is called a strong propagation buyer, and the strong propagation sequence of ω is defined as C ω , c j ∈C ω ; If buyer i is a strong propagation buyer, the corresponding intermediary node P i dt i Report to the seller's intermediary node P s If buyer i is not a strong propagator, he will be penalized for false reporting. In this process, each intermediate node P i The information will be verified by comparing the status of all reporting buyer nodes, thereby playing a role in information supervision; S35, seller intermediary node P s According to dt i Arrange the key nodes in ascending order to obtain the strong propagation sequence C ω And all strong propagation buyer nodes, count two strong propagation nodes c j with c j+1 All buyers on the communication path are regarded as weak communication buyers of the two strong communication buyers, and together constitute the weak communication set The specific process of determining the auction allocation information in step S4 is as follows: S41, according to the strong propagation sequence C ω , start the transaction from the first node after the seller, that is, the node closest to the seller; S42, buyer intermediary node P i Calculate the externality of the current strong propagation buyer node; S43, compare the psychological valuation of the next buyer i+1 with the psychological valuation of the current transaction buyer i, if the next buyer i+1 needs to pay except for itself and the weak propagation set When the externality of a buyer is higher than the psychological valuation of the previous buyer, the buyer will replace the previous buyer and join the auction; If the next buyer needs to pay except for himself and the weak propagation set When the externality is lower than the psychological valuation of the previous buyer, the current buyer directly wins the auction; The payment information for the auction determined in step S4 includes the initial payment and the re-payment. The initial payment is made to the seller and the strong-propagation buyer. The payment rules for the relevant buyers are divided into three situations: (1) For seller S, the externality amount paid by the strong propagation buyer in the first transaction with him in step S41 is the total payment that the seller can obtain in the initial payment stage, which can be expressed as: (2) For the buyer who finally obtains the item h The amount paid is the externality Expressed as formula: (3) For non-winner strong communication buyers c j ∈C ω \c h , where '\c h 'Indicates' except c h ', will get a certain amount of dissemination benefits, amount (p i ) is equal to the externality paid by the next strong communication seller excluding the externality paid by itself and the weak communication nodes between them. - Self-paying externalities Expressed as formula: Therefore, the formula for the buyer node’s initial payment is as follows: Where, represents the highest psychological valuation among all nodes except the * set, that is, the externality except the * set, * represents any set; c j represents the jth strong communication buyer, c h Indicates the buyer who finally obtains the item; C ω is a strong propagation sequence, Between c j and c j+1 a weakly spreading set between two strongly spreading buyers; The re-payment targets weak-propagation buyers, strong-propagation buyers, and sellers. The re-payment rules for relevant buyers are divided into three situations: (1) For strong communication buyers including winners c j ∈C ω , the amount to be paid (p′ i )for Its value is equal to the externality of the highest psychological valuation buyer in the weak communication set divided by itself and - Externalities of all buyers except themselves and the weakly propagated set and divided by the number of weak propagation buyers The sum of the number of sellers 1 is expressed as: (2) For weak propagation buyers The amount to be paid (p i ′) is -R i , whose value is equal to the externality between itself and the j-th strong communication buyer - Externalities of all buyers except themselves and the weakly propagated set and divided by the number of weak propagation buyers The sum of the number of sellers 1 is expressed as: (3) For seller S, after all buyers who meet the payment requirements have completed their payments, if the total payment amount is lower than the difference between the payment externalities of the two strong propagation sellers j and j+1, the remaining amount shall be paid by the seller in full; The formula for buyer node re-payment is as follows: The propagation incentive is expressed as: Where, c j , Same as the initial payment phase; Indicates that between c j-1 and c j The buyer with the highest psychological valuation in the weak communication set between two strong communication buyers.

2. A carbon quota distributed information diffusion auction method according to claim 1, characterized in that: The specific process of step S1 is as follows: the carbon quota provider enters the auction through the seller agent module and uploads the number of carbon quotas for auction, the actual unit to which the provider belongs, and the expected transaction time; After the trading platform module confirms that the uploaded information is correct, it will release auction information including the carbon quota quantity, provider unit, and duration.

3. A carbon quota distributed information diffusion auction method according to claim 1, characterized in that: The specific process of step S2 is as follows: during the auction duration, the carbon quota demander enters the auction through the buyer agent module and uploads his own bid, the referral object, and the real information of the unit to which the demander belongs.

4. A carbon quota distributed information diffusion auction method according to claim 1, characterized in that: The specific process of step S5 is as follows: the seller's income, the final winner, the buyer's information dissemination subsidy incentives and the amount required to be paid by the winner are screened out from the allocation information and payment information, and then published to the seller's agent module and the buyer's agent module, and "confirmed transaction" information from both parties is received. After receiving the "transaction completed" signal from the two agent modules, the auction result information including the two parties of the transaction and the transaction amount is announced.

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