Carbon asset transaction matching method and device and computer equipment

Abstract

The application relates to the technical field of carbon asset transaction, and discloses a carbon asset transaction matching method, a carbon asset transaction matching device and computer equipment. The method comprises the following steps: obtaining target carbon emission data of a target transaction object corresponding to a target node; performing data processing on the target carbon emission data to generate a first transaction request corresponding to the target transaction object; sending the first transaction request to each other node of a node network, and obtaining a second transaction request corresponding to other transaction objects sent by each other node; matching the first transaction request and each second transaction request according to a preset price matching rule to obtain a matching result; and performing carbon asset transaction between the target transaction object and the other transaction objects by using the matching result to generate a transaction result. Through implementation of the technical scheme, the automatic matching and efficient matching of carbon asset transaction can be realized based on a decentralized node network.

Description

Technical Field

[0001] This invention relates to the field of carbon asset trading technology, specifically to a carbon asset trading matching method, apparatus, and computer equipment. Background Technology

[0002] With the increasing severity of global climate change, carbon emission management has become a key measure in addressing climate change. As a market-based means of emission reduction, carbon asset trading has been promoted and implemented in many regions. Currently, the operation of regional carbon markets mainly relies on centralized trading platforms. Under this model, all carbon emission data and trading requests from all trading parties need to be uploaded to a central server for unified storage, processing, matching, and settlement.

[0003] However, while centralized architectures are easier to manage, their trading efficiency is easily constrained by the performance bottleneck of the central server, especially when handling high-concurrency requests, where system latency becomes significant, impacting market liquidity. Furthermore, due to the high degree of centralization, there is a risk of single point of failure; if the central server malfunctions, the entire trading system may be paralyzed. Summary of the Invention

[0004] In view of this, the present invention provides a carbon asset trading matching method, apparatus and computer equipment to solve the problems of low efficiency and single point of failure risk in centralized carbon asset trading platforms when handling high concurrency requests.

[0005] In a first aspect, the present invention provides a carbon asset trading matching method, applied to a target node in a node network, the node network including nodes created for each trading object, comprising: acquiring target carbon emission data of the target trading object corresponding to the target node; processing the target carbon emission data to generate a first trading request corresponding to the target trading object; sending the first trading request to each other node in the node network, and acquiring second trading requests corresponding to other trading objects sent by each other node; matching the first trading request with each second trading request according to a preset price matching rule to obtain a matching result; and using the matching result to conduct carbon asset trading between the target trading object and other trading objects to generate a trading result.

[0006] The carbon asset trading matching method provided in this invention enables the target node to autonomously acquire and process the carbon emission data of local trading objects to generate trading requests, exchange request information with other nodes, and then automatically match and execute transactions according to preset rules. This achieves decentralized and automated processing of the entire carbon asset trading process, effectively improving trading efficiency and reducing system risks caused by reliance on central nodes.

[0007] In one optional implementation, data processing is performed on the target carbon emission data to generate a first transaction request corresponding to the target trading object, including: obtaining the carbon emission index corresponding to the target trading object; comparing the target carbon emission data with the carbon emission index, and determining the transaction type corresponding to the target trading object based on the comparison result; determining the transaction quantity and expected transaction value corresponding to the transaction type; and generating the first transaction request corresponding to the target trading object using the transaction type, transaction quantity, and expected transaction value.

[0008] The carbon asset trading matching method provided in this invention generates trading types by automatically comparing carbon emission data with preset indicators, quantifies the trading quantity and expected value, and constructs structured trading requests, thereby achieving objectivity, standardization and executability in the generation of trading demands.

[0009] In one optional implementation, generating a first transaction request corresponding to a target trading object using the transaction type, transaction quantity, and expected transaction value includes: obtaining the current total carbon asset transaction volume and the current average carbon asset transaction value corresponding to multiple trading objects; determining a first ratio between the transaction quantity and the current total carbon asset transaction volume, and determining a second ratio between the expected transaction value and the current average carbon asset transaction value; determining the priority corresponding to the target trading object using the first ratio and the second ratio; and generating a first transaction request corresponding to the target trading object using the transaction type, transaction quantity, expected transaction value, and priority.

[0010] The carbon asset trading matching method provided in this invention introduces a priority calculation mechanism based on real-time market dynamics, combined with a dual evaluation of trading volume ratio and price rationality, so that the generated trading requests have market adaptability and ranking value, significantly improving the efficiency and fairness of subsequent matching processes.

[0011] In one optional implementation, sending the first transaction request to each other node in the node network includes: obtaining the current network transmission rate of the target node and the delay parameter data between the target node and each other node; comparing the delay parameter data with a first preset threshold, adjusting the current network transmission rate based on the first comparison result to obtain the target transmission rate between the target node and each other node; and encrypting and sending the first transaction request corresponding to the target node to each other node according to each target transmission rate.

[0012] The carbon asset trading matching method provided in this invention optimizes the efficiency and security of request transmission between decentralized nodes while ensuring data integrity by dynamically monitoring network latency and adaptively adjusting the transmission rate.

[0013] In one optional implementation, the delay parameter data is compared with a first preset threshold, and the current network transmission rate is adjusted based on the first comparison result to obtain the target transmission rate between the target node and all other nodes. This includes: when the first comparison result indicates that the delay parameter data is greater than the first preset threshold, the current network transmission rate is reduced by a first step length to obtain the target transmission rate between the target node and all other nodes; the first step length is determined based on the ratio of the first preset threshold to the delay parameter data; when the first comparison result indicates that the delay parameter data is less than or equal to the first preset threshold, the current network transmission rate is increased by a second step length to obtain the target transmission rate between the target node and all other nodes; the second step length is determined based on the ratio of the delay parameter data to the first preset threshold.

[0014] The carbon asset trading matching method provided in this invention achieves fine-grained and flexible control of transmission rate through dynamic ratio calculation, which avoids the risk of data loss during network congestion and maximizes the use of bandwidth resources during low-latency periods, significantly improving the stability and efficiency of decentralized network transmission.

[0015] In one optional implementation, the first transaction request and each of the second transaction requests are matched according to a preset price matching rule to obtain a matching result, including: extracting the first transaction data corresponding to the target node from the first transaction request; extracting the second transaction data corresponding to other nodes from the second transaction request for any other node; determining the matching degree between the target node and each of the other nodes using the first transaction data and each of the second transaction data; and matching the first transaction request and each of the second transaction requests according to the matching degree to obtain a matching result.

[0016] The carbon asset trading matching method provided in this invention extracts quantitative data from both parties to the transaction and calculates the precise matching degree between nodes, thereby achieving automated and objective matching of carbon asset transactions in a decentralized environment, significantly improving matching accuracy and execution efficiency.

[0017] In one optional implementation, the first transaction data includes a first transaction type and a first expected transaction value corresponding to the target node, and the second transaction data includes a second transaction type and a second expected transaction value corresponding to other nodes. Determining the matching degree between the target node and each of the other nodes using the first transaction data and each of the second transaction data includes: matching the first transaction type and each of the second transaction types to determine the transaction type matching result between the target node and each of the other nodes; determining the difference between the first expected transaction value and each of the second expected transaction values; and using the transaction type matching result and the difference to determine the matching degree between the target node and each of the other nodes.

[0018] The carbon asset trading matching method provided in this invention combines the screening of matching transaction types with the quantification of expected value differences to construct a matching degree evaluation model that takes into account both transaction feasibility and economic rationality, effectively improving the transaction rate and quality of the matching results.

[0019] In one optional implementation, matching the first transaction request with each of the second transaction requests according to the matching degree to obtain a matching result includes: determining whether the matching degree is greater than a second preset threshold; if the matching degree is greater than the second preset threshold, then matching any other node with a matching degree greater than the second preset threshold with the target node to obtain a matching result.

[0020] The carbon asset trading matching method provided in this invention performs hard screening of node trading requests by setting a matching degree threshold, and only performs matching on nodes with high matching degree, which effectively reduces invalid matching operations and improves the trading success rate and resource utilization.

[0021] Secondly, the present invention provides a carbon asset trading matching device, applied to a target node in a node network, the node network including nodes created for each trading object; comprising: an acquisition module for acquiring target carbon emission data of the target trading object corresponding to the target node; a processing module for processing the target carbon emission data to generate a first trading request corresponding to the target trading object; a sending module for sending the first trading request to each other node in the node network and acquiring second trading requests corresponding to other trading objects sent by each other node; a matching module for matching the first trading request with each second trading request according to a preset price matching rule to obtain a matching result; and a trading module for using the matching result to conduct carbon asset trading between the target trading object and other trading objects to generate a trading result.

[0022] Thirdly, the present invention provides a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the carbon asset trading matching method of the first aspect or any corresponding embodiment described above.

[0023] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the carbon asset trading matching method of the first aspect or any corresponding embodiment described above.

[0024] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the carbon asset trading matching method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0025] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating the carbon asset trading matching method according to an embodiment of the present invention;

[0027] Figure 2 This is a flowchart illustrating another carbon asset trading matching method according to an embodiment of the present invention;

[0028] Figure 3 This is a flowchart illustrating another carbon asset trading matching method according to an embodiment of the present invention;

[0029] Figure 4 This is a flowchart illustrating another carbon asset trading matching method according to an embodiment of the present invention;

[0030] Figure 5 This is a structural block diagram of a carbon asset trading matching device according to an embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Currently, regional carbon market trading primarily relies on centralized trading platforms. In this model, all carbon asset trading information is centrally stored and processed, with the trading platform responsible for collecting, matching, and settling transaction data. Traditional centralized platforms typically have a data center where companies upload their carbon emission data and trading requests for centralized processing by a central server.

[0034] However, traditional centralized platforms are prone to performance bottlenecks when handling large-scale transaction requests, leading to data transmission and processing delays that affect the speed of transaction matching and market liquidity. Secondly, centralized systems are susceptible to single points of failure. If the central server fails, is attacked, or undergoes maintenance or upgrades, the entire carbon market trading system may be affected. Finally, data security and privacy issues are significant. Enterprises' sensitive data is centrally stored on the platform, posing a risk of leakage and misuse, thus causing considerable concern about data security and privacy protection. These problems stem from the limitations of centralized architecture, which struggles to meet the high concurrency, high efficiency, and security requirements of a large-scale carbon trading market.

[0035] In view of this, the technical solution of this invention constructs a carbon asset trading matching system based on edge computing and a decentralized architecture, pushing data acquisition, processing, and matching functions down to distributed edge nodes, and utilizing blockchain technology to ensure data security and consistency. The target node acquires local carbon emission data and preprocesses it through edge computing to generate a first trading request; then, a dynamic rate adjustment mechanism broadcasts the request to other nodes, ensuring privacy and eliminating the risk of single points of failure; finally, based on preset price matching rules, the matching degree of the request is calculated, and nodes with high matching degrees are matched to generate trading results, improving market liquidity and price rationality, and achieving decentralized, automated, and highly reliable operation of carbon asset trading.

[0036] According to an embodiment of the present invention, a carbon asset trading matching method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] This embodiment provides a carbon asset trading matching method, which can be used for target nodes in a node network, the node network including nodes created for each trading object; Figure 1 This is a flowchart of a carbon asset trading matching method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps.

[0038] Step S101: Obtain the target carbon emission data of the target trading object corresponding to the target node.

[0039] A node network refers to a decentralized transaction node network built on blockchain technology, composed of edge computing nodes for various transaction objects. A target node is an edge computing node created within the node network for a specific transaction object. A target transaction object refers to the carbon asset trading participant corresponding to the target node, such as a company or institution. Target carbon emission data refers to the pre-processed real-time carbon emission data of the target transaction object. Specifically, target nodes are deployed within the target transaction object, collecting raw data in real time through carbon emission monitoring devices distributed within the target transaction object. This data can include daily and hourly carbon emissions, energy consumption data, etc. The collection frequency can be set by a preset frequency parameter F (times / hour), which the target transaction object adjusts according to its own production characteristics and carbon emission stability. For example, a high-carbon-emission enterprise can set F=12 (i.e., collecting data every 5 minutes) to ensure the real-time nature and relevance of the data. The collected raw data {D} raw The data undergoes preprocessing, including cleaning to remove outliers and noise, followed by normalization to map the data to the [0, 1] interval. The formula is as follows:

[0040]

[0041] Among them, D normalized For the data type cleaned by the normalization formula, D min D is the minimum value of this data type. max This is the maximum value for this data type.

[0042] Step S102: Process the target carbon emission data to generate the first transaction request corresponding to the target trading object.

[0043] The first transaction request refers to the carbon asset transaction request generated by the target node after processing the target carbon emission data. Specifically, after obtaining the raw carbon emission data, the target node analyzes it and calculates the carbon asset transaction needs according to preset business rules, thus forming the first transaction request.

[0044] Step S103: Send the first transaction request to each other node in the node network, and obtain the second transaction request corresponding to other transaction objects sent by each other node.

[0045] Other nodes refer to edge computing nodes in the node network other than the target node. Other trading partners refer to the carbon asset trading participants corresponding to each other node, forming trading counterparties with the target trading partner. The second trading request refers to the carbon asset trading request generated by other nodes for other trading partners, with the same format as the first trading request. Specifically, after the target node generates the first trading request, it uses the peer-to-peer communication mechanism of the decentralized node network built on blockchain to broadcast the first trading request to all other nodes in the network. At the same time, the target node also continuously listens to the network, receiving the second trading requests broadcast by other nodes, representing their respective corresponding trading partners, thereby realizing the sharing of trading requests among all nodes.

[0046] Step S104: Match the first transaction request with each of the second transaction requests according to the preset price matching rules to obtain the matching result.

[0047] Price matching rules are the criteria used to determine whether transaction requests are matched. The matching result refers to the tradable transaction pairs and related parameters determined after matching the first and second transaction requests. Specifically, the target node matches the first transaction request with the second transaction requests issued by other nodes. Price matching rules are based on the carbon asset parameters involved in the transaction requests from both parties. For example, when a target trading party wants to sell a certain amount of carbon assets, and other trading parties have a buying demand, the price, quantity, priority, and other parameters of both parties are compared and matched according to preset matching rules. If the matching conditions are met, a matching process is performed, and a matching result is generated, clearly identifying the tradable trading parties and transaction parameters.

[0048] Step S105: Use the matching results to conduct carbon asset transactions between the target trading object and other trading objects, and generate trading results.

[0049] The transaction result refers to the final state generated after a carbon asset transaction is executed based on the matching results. Specifically, after the matching results are confirmed, the transaction is automatically executed through smart contract technology. The target node and other nodes update their local carbon asset ledgers, recording the transfer of carbon assets, and synchronize the transaction information to all nodes across the blockchain network to ensure data consistency and immutability. Finally, a transaction result containing the asset transfer status and ledger update records is generated, completing the entire carbon asset transaction process.

[0050] The carbon asset trading matching method provided in this invention enables the target node to autonomously acquire and process the carbon emission data of local trading objects to generate trading requests, exchange request information with other nodes, and then automatically match and execute transactions according to preset rules. This achieves decentralized and automated processing of the entire carbon asset trading process, effectively improving trading efficiency and reducing system risks caused by reliance on central nodes.

[0051] This embodiment provides a carbon asset trading matching method, which can be used for target nodes in a node network, the node network including nodes created for each trading object; Figure 2 This is a flowchart of a carbon asset trading matching method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps.

[0052] Step S201: Obtain the target carbon emission data of the target trading object corresponding to the target node. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0053] Step S202: Process the target carbon emission data to generate the first transaction request corresponding to the target trading object.

[0054] Specifically, step S202 includes:

[0055] Step S2021: Obtain the carbon emission index corresponding to the target trading object.

[0056] Carbon emission quotas refer to the upper limit or benchmark value of carbon emissions allocated or set for a target trading entity, used to measure whether its actual carbon emissions exceed the limit. Specifically, the carbon emission quotas of the target trading entity are pre-allocated by the carbon market management agency or relevant regulatory authorities (such as carbon allowances for enterprises) and stored in the target node's local database or related system. The target node obtains the carbon emission quotas of the target trading entity by reading the locally stored quota data or by connecting with an external management system, using it as a benchmark to determine trading demand.

[0057] Step S2022: Compare the target carbon emission data with the carbon emission indicators, and determine the transaction type corresponding to the target trading object based on the comparison results.

[0058] The transaction type refers to the direction of carbon asset trading determined based on the comparison between the actual carbon emissions of the target trading entity and the carbon emission quota, including buying or selling. Specifically, the target node compares the target carbon emission data with the carbon emission quota. If the actual emissions corresponding to the target carbon emission data exceed the quota, it indicates that the target trading entity needs to replenish carbon assets, and the transaction type is determined to be buying; if the actual emissions are lower than the quota, it indicates that there are excess carbon allowances, and the transaction type is determined to be selling.

[0059] Step S2023: Determine the number of transactions and the expected transaction value corresponding to the transaction type.

[0060] The trading quantity refers to the amount of carbon assets that the target trading entity intends to buy or sell in a carbon asset transaction. The expected trading value refers to the price that the target trading entity expects to pay in a carbon asset transaction. Specifically, the trading quantity is determined by the difference between the target carbon emission data and the carbon emission quota. For example, if the carbon emission quota is 1000 tons and the actual emission is 1200 tons, then the purchase quantity is 200 tons; if the actual emission is 800 tons, then the sale quantity is 200 tons. The expected trading value can be set by the target trading entity based on the current average market price and its own needs. When buying, the expected trading value should not be higher than the average market price to reduce costs; when selling, the expected trading value should not be lower than the average market price to obtain profit.

[0061] Step S2024: Using the transaction type, transaction quantity, and expected transaction value, generate the first transaction request corresponding to the target transaction object.

[0062] Encapsulate the transaction type, transaction quantity, and expected transaction value into a standard format transaction request data set {R = (T type ,Q,P)}。 Where, T type Here, Q represents the transaction type, P represents the transaction quantity, and Q represents the expected transaction value. The first generated transaction request will be further sorted by the target node's priority function to ensure that reasonable transaction requests enter the matching process first.

[0063] In some optional implementations, step S2024 above includes:

[0064] Step a1: Obtain the total current carbon asset trading volume and the average current carbon asset trading value corresponding to multiple trading objects.

[0065] The current total carbon asset trading volume refers to the total amount of carbon asset transactions by all trading entities in the node network during the current period, denoted as Q. total The current average trading value of carbon assets refers to the weighted average price of carbon asset transactions within the current period, denoted as P. market Specifically, a market snapshot can be generated at a fixed period (e.g., hourly), and the total carbon asset trading volume of all trading objects in the blockchain network can be statistically analyzed to obtain the current total carbon asset trading volume Q. total Simultaneously, the weighted average price of transactions within this period is calculated to form the current average transaction value P of the carbon asset. market This information is then broadcast to all nodes in the node network via the blockchain. Target nodes can obtain the current total carbon asset trading volume and the current average carbon asset trading value from their local cache or the blockchain network, ensuring real-time performance and consistency.

[0066] Step a2: Determine the first ratio between the number of transactions and the current total trading volume of carbon assets, and determine the second ratio between the expected trading value and the current average trading value of carbon assets.

[0067] The first ratio refers to the ratio of the trading quantity Q of the target trading entity to the current total carbon asset trading volume Q. total The second ratio refers to the ratio of the expected trading value P to the current average trading value P of carbon assets. market The ratio. Specifically, it is the ratio of the number of transactions Q of the target trading entity to the total current carbon asset trading volume Q. total The first ratio is obtained. Divide the expected transaction value P of the target transaction object by the current average transaction value P of carbon assets. market The second ratio is obtained. When the transaction type is buy, the second ratio is... P buy The first ratio is the expected transaction value at the time of purchase; when the transaction type is sell, the second ratio is... P sell This represents the expected transaction value when selling.

[0068] Step a3: Use the first ratio and the second ratio to determine the priority of the target transaction object.

[0069] Priority refers to the order in which transaction requests are processed, calculated using a weighted coefficient based on a first ratio and a second ratio. Specifically, the priority of a target transaction object is calculated using a preset priority function, combining the first and second ratios, as shown in the following formula:

[0070]

[0071] Where ω1 and ω2 are weighting coefficients, and PriceScore is the weighting coefficient when a buy request is made, if P... buy ≥P market ,but When a sell request is made, if P sell ≤P market ,but Otherwise, the value is 0. This function combines the proportion of transaction size and the reasonableness of the price to generate a priority value; the higher the value, the higher the priority of the transaction request.

[0072] Step a4: Using the transaction type, transaction quantity, expected transaction value, and priority, generate the first transaction request corresponding to the target transaction object.

[0073] The transaction type, transaction quantity Q, expected transaction value P, and calculated priority are encapsulated into a standard format transaction request data set. The specific format is {R=(T type The priority parameter (Q, P, Priority) is embedded in the request to ensure that the request is broadcast and matched in order of priority within the node network.

[0074] In the above implementation, by introducing a priority calculation mechanism based on real-time market dynamics and combining a dual evaluation of transaction volume ratio and price rationality, the generated transaction requests have market adaptability and ranking value, significantly improving the efficiency and fairness of subsequent matching processes.

[0075] Step S203: Send the first transaction request to all other nodes in the node network, and obtain the second transaction requests corresponding to other transaction objects sent by each other node. For details, please refer to [link to details]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0076] Step S204: According to the preset price matching rules, the first transaction request is matched with each of the second transaction requests to obtain the matching result. For details, please refer to [link to details]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0077] Step S205: Utilizing the matching results, carbon asset transactions are conducted between the target trading entity and other trading entities, generating trading results. For details, please refer to [link to details]. Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0078] The carbon asset trading matching method provided in this invention generates trading types by automatically comparing carbon emission data with preset indicators, quantifies the trading quantity and expected value, and constructs structured trading requests, thereby achieving objectivity, standardization and executability in the generation of trading demands.

[0079] This embodiment provides a carbon asset trading matching method, which can be used for target nodes in a node network, the node network including nodes created for each trading object; Figure 3 This is a flowchart of a carbon asset trading matching method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps.

[0080] Step S301: Obtain the target carbon emission data of the target trading object corresponding to the target node. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0081] Step S302: Process the target carbon emission data to generate a first transaction request corresponding to the target trading object. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0082] Step S303: Send the first transaction request to each other node in the node network, and obtain the second transaction request corresponding to other transaction objects sent by each other node.

[0083] Specifically, step S303 includes:

[0084] Step S3031: Obtain the current network transmission rate of the target node, as well as the delay parameter data between the target node and each other node.

[0085] The current network transmission rate refers to the real-time data transmission rate of the target node when sending the first transaction request, measured in bytes per second, and denoted as V. current Delay parameter data refers to the real-time communication delay between the target node and other nodes, measured in milliseconds and denoted as τ. Specifically, the target node obtains the current network transmission rate through a real-time monitoring module, which is determined by the real-time statistical data transmission volume. Simultaneously, the target node periodically sends probe packets to other nodes, calculating the round-trip time of each probe packet to obtain the delay parameter data with each other node. The target node can also use a linear regression algorithm to predict future network conditions, dynamically adjusting the delay parameters and coefficients to ensure the real-time performance and accuracy of the data.

[0086] Step S3032: Compare the delay parameter data with the first preset threshold, and adjust the current network transmission rate based on the first comparison result to obtain the target transmission rate between the target node and each other node.

[0087] The first preset threshold refers to the preset delay judgment threshold, in milliseconds, denoted as τ. threshold The first comparison result refers to the comparison between the delay parameter data and the first preset threshold. The target transmission rate refers to the transmission rate adjusted according to the first comparison result, denoted as V. new Specifically, the real-time latency parameter data is compared with a first preset threshold. If the latency parameter data is greater than the first preset threshold, it indicates that the network latency is too high, and the current network transmission rate is reduced to prioritize data integrity; if the latency parameter data is less than the first preset threshold, the rate is gradually increased to increase throughput.

[0088] In some optional implementations, step S3032 above includes:

[0089] Step b1: When the delay parameter data represented by the first comparison result is greater than the first preset threshold, the current network transmission rate is reduced according to the first step length to obtain the target transmission rate between the target node and each other node; the first step length is determined according to the ratio of the first preset threshold to the delay parameter data.

[0090] When the delay parameter data τ is greater than the first preset threshold τ threshold At that time, the length of the first step is determined based on the ratio of the delay parameter data to the first preset threshold, and combined with the formula. Calculate the target transmission rate.

[0091] Step b2: When the delay parameter data represented by the first comparison result is less than or equal to the first preset threshold, the current network transmission rate is increased according to the second step length to obtain the target transmission rate between the target node and each other node; the second step length is determined based on the ratio of the delay parameter data to the first preset threshold.

[0092] When the delay parameter data τ is less than the first preset threshold τ threshold At that time, the second step length is determined based on the ratio of the first preset threshold to the delay parameter data, and combined with the formula. Calculate the target transmission rate.

[0093] In the above implementation, the transmission rate is precisely and flexibly controlled through dynamic ratio calculation, which avoids the risk of data loss during network congestion and maximizes the use of bandwidth resources during low-latency periods, significantly improving the stability and efficiency of decentralized network transmission.

[0094] Step S3033: According to the respective target transmission rates, the first transaction request corresponding to the target node is encrypted and sent to each of the other nodes.

[0095] The target node encapsulates and encrypts the first transaction request using an encrypted communication protocol (such as TLS / SSL) based on the calculated target transmission rate, ensuring the security of data transmission. Nodes then broadcast the encrypted transaction request packet to other nodes according to the adaptively adjusted target transmission rate through the peer-to-peer communication mechanism of the blockchain network.

[0096] Step S3034: Obtain the second transaction request corresponding to other transaction objects sent by other nodes.

[0097] After other nodes generate the encrypted second transaction request using the method described above, they broadcast it to the entire blockchain network. Upon receiving the broadcast encrypted request packet, the target node decrypts it using its local key, extracting information such as the transaction type, quantity, and price, and stores this information in its local cache. Simultaneously, the target node can verify the legitimacy of the request, such as through the blockchain consensus mechanism, ensuring that the received second transaction request originates from a valid node and has not been tampered with, thus providing reliable data for subsequent matching processes.

[0098] Step S304: According to the preset price matching rules, the first transaction request is matched with each of the second transaction requests to obtain the matching result. For details, please refer to [link to details]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0099] Step S305: Utilizing the matching results, carbon asset transactions are conducted between the target trading entity and other trading entities, generating trading results. For details, please refer to [link to details]. Figure 2Step S205 of the illustrated embodiment will not be described again here.

[0100] The carbon asset trading matching method provided in this invention optimizes the efficiency and security of request transmission between decentralized nodes while ensuring data integrity by dynamically monitoring network latency and adaptively adjusting the transmission rate.

[0101] This embodiment provides a carbon asset trading matching method, which can be used for target nodes in a node network, the node network including nodes created for each trading object; Figure 4 This is a flowchart of a carbon asset trading matching method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps.

[0102] Step S401: Obtain the target carbon emission data of the target trading object corresponding to the target node. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0103] Step S402: Process the target carbon emission data to generate a first transaction request corresponding to the target trading object. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0104] Step S403: Send the first transaction request to all other nodes in the node network, and obtain the second transaction requests corresponding to other transaction objects sent by each other node. For details, please refer to [link to details]. Figure 3 Step S303 of the illustrated embodiment will not be described again here.

[0105] Step S404: Match the first transaction request with each of the second transaction requests according to the preset price matching rules to obtain the matching result.

[0106] Specifically, step S404 includes:

[0107] Step S4041: Extract the first transaction data corresponding to the target node from the first transaction request; for any other node, extract the second transaction data corresponding to the other node from the second transaction request.

[0108] The first transaction data refers to the core transaction parameters extracted from the first transaction request generated by the target node. The second transaction data refers to the core transaction parameters extracted from the second transaction request sent by other nodes. Specifically, both the first and second transaction requests are standard format data sets (e.g., {R = (T... type When extracting data, we directly parse the first transaction data in the first transaction request and the second transaction data in the second transaction request.

[0109] Step S4042: Using the first transaction data and each of the second transaction data, determine the matching degree between the target node and each of the other nodes.

[0110] Matching degree is a quantitative metric used to measure whether the transaction requests of a target node match those of other nodes. Specifically, using a preset matching degree function, the matching degree between the target node and each other node is calculated by combining the first transaction data and each of the second transaction data.

[0111] In some optional implementations, the first transaction data includes a first transaction type and a first expected transaction value corresponding to the target node, and the second transaction data includes a second transaction type and a second expected transaction value corresponding to other nodes; the above step S4042 includes:

[0112] Step c1: Match the first transaction type with each of the second transaction types to determine the transaction type matching results between the target node and each of the other nodes.

[0113] The first transaction type (buy / sell at the target node) must be complementary to the second transaction type of other nodes. Specifically, if the first transaction type is buy and the second transaction type is sell, the transaction types match and the result is recorded as 1; if both are buy or sell, they do not match and the result is recorded as 0.

[0114] Step c2: Determine the difference between the first expected transaction value and each of the second expected transaction values, and use the transaction type matching results and the difference to determine the matching degree between the target node and each of the other nodes.

[0115] Calculate the absolute difference between the first expected transaction value and the second expected transaction value |P bry -P sell |, divided by the current market average trading value P market This yields the relative price deviation; subtracting this deviation from 1 and multiplying it by the weighting coefficient β, we obtain the matching result with the transaction type. The results of multiplying by the weighting coefficient α are summed, which is the predefined matching degree formula:

[0116] Here, α and β are preset weights used to adjust the degree of influence of transaction type and price deviation on the matching degree.

[0117] In the above implementation, by combining the screening of transaction type matching with the quantification of expected value difference, a matching degree evaluation model that takes into account both transaction feasibility and economic rationality is constructed, which effectively improves the transaction rate and quality of the matching results.

[0118] Step S4043: Match the first transaction request with each of the second transaction requests according to the matching degree to obtain the matching result.

[0119] Using the extracted transaction data, the matching degree between the target node and each other node is calculated. The matching degree is measured based on key parameters such as the number of transactions and the expected transaction value. Based on the calculated matching degree, the first transaction request is matched with the second transaction request to obtain the matching result.

[0120] In some optional implementations, step S4043 above includes:

[0121] Step d1: Determine whether the matching degree is greater than the second preset threshold.

[0122] The calculated matching score is compared with a pre-set second threshold. If the matching score is greater than the threshold, it means that the matching degree of the transaction request is high enough and can enter the matching process; if it is less than or equal to the threshold, it is considered that the matching degree is insufficient and matching will not be performed for the time being.

[0123] Step d2: If the matching degree is greater than the second preset threshold, then match the transaction between any other node with a matching degree greater than the second preset threshold and the target node to obtain the matching result.

[0124] When the matching degree is greater than the second preset threshold θ, it indicates that the target node and the other nodes have a high degree of compatibility in terms of transaction type and expected transaction value, and a transaction can be matched. Specifically, when the target node calculates the matching degree with each other node one by one, if the matching degree of a currently detected other node is already greater than the second preset threshold, the subsequent matching degree calculation is immediately interrupted, and a transaction is directly matched between the target node and the matching node that meets the threshold, generating a matching result that only contains the transaction details of this pair of nodes.

[0125] Optionally, when initiating the matching process, the target node first sorts all second transaction requests in descending order based on the priority values ​​carried in all received second transaction requests, placing the highest priority request at the head of the detection queue. Then, a chained matching phase begins. The target node prioritizes the highest priority second transaction request from the sorted queue, calculates its matching degree with the first transaction request in real time; if the matching degree is greater than a second preset threshold, it immediately performs a transaction matching with this high-priority node, generates a matching result containing only the transaction details of that node, and terminates the process. If the matching degree does not reach the threshold, it automatically moves to the second highest priority second transaction request in the queue, recalculates the matching degree, and performs the same matching judgment. If the matching is successful, the result is output and the process terminates; if the matching fails, it continues to detect the remaining nodes in descending order of priority. This process continues until the first node with a matching degree meets the threshold and the matching is successful.

[0126] In the above implementation, by setting a matching degree threshold, node transaction requests are hard-screened, and matching is only performed on nodes with high matching degree, which effectively reduces invalid matching operations and improves transaction success rate and resource utilization.

[0127] Step S405: Utilizing the matching results, carbon asset transactions are conducted between the target trading entity and other trading entities, generating trading results. For details, please refer to [link to details]. Figure 3 Step S305 of the illustrated embodiment will not be described again here.

[0128] The carbon asset trading matching method provided in this invention extracts quantitative data from both parties to the transaction and calculates the precise matching degree between nodes, thereby achieving automated and objective matching of carbon asset transactions in a decentralized environment, significantly improving matching accuracy and execution efficiency.

[0129] This embodiment also provides a carbon asset trading matching device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0130] This embodiment provides a carbon asset trading matching device, applied to target nodes in a node network, the node network including nodes created for each trading object; such as Figure 5 As shown, it includes:

[0131] The acquisition module 501 is used to acquire the target carbon emission data of the target trading object corresponding to the target node;

[0132] Processing module 502 is used to process the target carbon emission data and generate the first transaction request corresponding to the target trading object;

[0133] The sending module 503 is used to send the first transaction request to each other node in the node network and to obtain the second transaction request corresponding to other transaction objects sent by each other node;

[0134] The matching module 504 is used to match the first transaction request with each of the second transaction requests according to the preset price matching rules, and obtain the matching result.

[0135] The trading module 505 is used to conduct carbon asset transactions between the target trading object and other trading objects using the matching results, and to generate trading results.

[0136] In some alternative implementations, the processing module 502 includes:

[0137] The first acquisition submodule is used to acquire the carbon emission indicators corresponding to the target trading object;

[0138] The comparison submodule is used to compare the target carbon emission data with the carbon emission index and determine the transaction type corresponding to the target trading object based on the comparison results.

[0139] The first determination submodule is used to determine the number of transactions and the expected transaction value corresponding to the transaction type;

[0140] The generation submodule is used to generate the first transaction request corresponding to the target transaction object by using the transaction type, transaction quantity, and expected transaction value.

[0141] In some alternative implementations, the generation submodule includes:

[0142] The acquisition unit is used to acquire the total current carbon asset trading volume and the current average carbon asset trading value corresponding to multiple trading objects.

[0143] The first determining unit is used to determine a first ratio between the trading quantity and the current total trading volume of carbon assets, and to determine a second ratio between the expected trading value and the current average trading value of carbon assets.

[0144] The second determining unit is used to determine the priority of the target transaction object using the first ratio and the second ratio;

[0145] The generation unit is used to generate the first transaction request corresponding to the target transaction object by utilizing the transaction type, transaction quantity, expected transaction value, and priority.

[0146] In some alternative implementations, the sending module 503 includes:

[0147] The second acquisition submodule is used to acquire the current network transmission rate of the target node, as well as the delay parameter data between the target node and each other node;

[0148] The comparison submodule is used to compare the delay parameter data with a first preset threshold, and adjust the current network transmission rate based on the first comparison result to obtain the target transmission rate between the target node and each other node.

[0149] The sending submodule is used to encrypt and send the first transaction request corresponding to the target node to each of the other nodes according to the respective target transmission rates.

[0150] In some optional implementations, the comparison submodule includes:

[0151] The first comparison unit is used to reduce the current network transmission rate by a first step length when the first comparison result indicates that the delay parameter data is greater than the first preset threshold, so as to obtain the target transmission rate between the target node and each other node; the first step length is determined according to the ratio of the first preset threshold to the delay parameter data.

[0152] The second comparison unit is used to increase the current network transmission rate by a second step size when the delay parameter data represented by the first comparison result is less than or equal to the first preset threshold, so as to obtain the target transmission rate between the target node and each other node; the second step size is determined based on the ratio of the delay parameter data to the first preset threshold.

[0153] In some alternative implementations, the matching module 504 includes:

[0154] The extraction submodule is used to extract the first transaction data corresponding to the target node from the first transaction request; and for any other node, to extract the second transaction data corresponding to the other node from the second transaction request.

[0155] The second determination submodule is used to determine the matching degree between the target node and each other node using the first transaction data and each of the second transaction data.

[0156] The matching submodule is used to match the first transaction request with each of the second transaction requests according to the matching degree, and obtain the matching result.

[0157] In some optional implementations, the second determining submodule includes:

[0158] The matching unit is used to match the first transaction type with each of the second transaction types to determine the transaction type matching results between the target node and each of the other nodes;

[0159] The third determining unit is used to determine the difference between the first expected transaction value and each of the second expected transaction values, and to determine the matching degree between the target node and each of the other nodes using the transaction type matching results and the difference.

[0160] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0161] In this embodiment, the carbon asset trading matching device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0162] The carbon asset trading matching device provided in this embodiment of the invention generates trading requests by autonomously acquiring and processing the carbon emission data of local trading objects through target nodes, exchanging request information with other nodes, and then automatically matching and executing transactions according to preset rules. This realizes decentralized and automated processing of the entire carbon asset trading process, effectively improving trading efficiency and reducing system risks caused by reliance on central nodes.

[0163] This invention also provides a computer device having the above-described features. Figure 5 The carbon asset trading matching device shown.

[0164] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 6 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0165] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0166] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0167] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0168] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0169] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0170] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0171] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0172] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A carbon asset trading matching method, characterized in that, The method is applied to target nodes in a node network, the node network comprising nodes created for each transaction object; the method includes: Obtain the target carbon emission data of the target trading object corresponding to the target node; The method of processing the target carbon emission data to generate a first transaction request corresponding to the target trading object includes: obtaining the carbon emission index corresponding to the target trading object; comparing the target carbon emission data with the carbon emission index, and determining the transaction type corresponding to the target trading object based on the comparison result; determining the transaction quantity and expected transaction value corresponding to the transaction type; and generating the first transaction request corresponding to the target trading object using the transaction type, the transaction quantity, and the expected transaction value, including: obtaining the current total carbon asset transaction volume and the current average carbon asset transaction value corresponding to multiple trading objects; determining a first ratio between the transaction quantity and the current total carbon asset transaction volume, and determining a second ratio between the expected transaction value and the current average carbon asset transaction value; determining the priority corresponding to the target trading object using the first ratio and the second ratio; and generating the first transaction request corresponding to the target trading object using the transaction type, the transaction quantity, the expected transaction value, and the priority. The first transaction request is sent to each other node in the node network, and second transaction requests corresponding to other transaction objects sent by each of the other nodes are obtained; wherein, sending the first transaction request to each other node in the node network includes: obtaining the current network transmission rate of the target node, and the latency parameter data between the target node and each of the other nodes; comparing the latency parameter data with a first preset threshold, and adjusting the current network transmission rate based on the first comparison result to obtain the target transmission rate between the target node and each of the other nodes, including: when the first comparison result indicates that the latency parameter data is greater than the first preset threshold, The current network transmission rate is reduced according to the first step length to obtain the target transmission rate between the target node and each of the other nodes; the first step length is determined based on the ratio of the first preset threshold to the delay parameter data; when the first comparison result indicates that the delay parameter data is less than or equal to the first preset threshold, the current network transmission rate is increased according to the second step length to obtain the target transmission rate between the target node and each of the other nodes; the second step length is determined based on the ratio of the delay parameter data to the first preset threshold; according to each of the target transmission rates, the first transaction request corresponding to the target node is encrypted and sent to each of the other nodes respectively. According to the preset price matching rules, the first transaction request and each of the second transaction requests are matched to obtain the matching result; The matching results are used to conduct carbon asset transactions between the target trading entity and the other trading entities, generating transaction results.

2. The method according to claim 1, characterized in that, The step of matching the first transaction request with each of the second transaction requests according to a preset price matching rule to obtain a matching result includes: Extract first transaction data corresponding to the target node from the first transaction request; for any other node, extract second transaction data corresponding to the other node from the second transaction request; Using the first transaction data and each of the second transaction data, the matching degree between the target node and each of the other nodes is determined; The first transaction request and each of the second transaction requests are matched according to the matching degree to obtain the matching result.

3. The method according to claim 2, characterized in that, The first transaction data includes a first transaction type and a first expected transaction value corresponding to the target node, and the second transaction data includes a second transaction type and a second expected transaction value corresponding to the other nodes; The step of determining the matching degree between the target node and each of the other nodes using the first transaction data and each of the second transaction data includes: The first transaction type and each of the second transaction types are matched to determine the transaction type matching results between the target node and each of the other nodes; Determine the difference between the first expected transaction value and each of the second expected transaction values, and use the transaction type matching result and the difference to determine the matching degree between the target node and each of the other nodes.

4. The method according to claim 2, characterized in that, The step of matching the first transaction request with each of the second transaction requests according to the matching degree to obtain the matching result includes: Determine whether the matching degree is greater than a second preset threshold; If the matching degree is greater than the second preset threshold, then a transaction matching is performed between any other node with a matching degree greater than the second preset threshold and the target node to obtain the matching result.

5. A carbon asset trading matching device, characterized in that, The apparatus is applied to a target node in a node network, the node network comprising nodes created for each transaction object; the apparatus includes: The acquisition module is used to acquire the target carbon emission data of the target trading object corresponding to the target node; The processing module is used to process the target carbon emission data and generate a first transaction request corresponding to the target trading object; The sending module is used to send the first transaction request to each other node in the node network, and to obtain the second transaction request corresponding to other transaction objects sent by each of the other nodes; The matching module is used to match the first transaction request with each of the second transaction requests according to a preset price matching rule, and obtain the matching result; The trading module is used to conduct carbon asset transactions between the target trading object and the other trading objects using the matching results, and generate trading results; The processing module includes: The first acquisition submodule is used to acquire the carbon emission index corresponding to the target trading object; The comparison submodule is used to compare the target carbon emission data with the carbon emission index, and determine the transaction type corresponding to the target trading object based on the comparison result. The first determination submodule is used to determine the number of transactions and the expected transaction value corresponding to the transaction type; A generation submodule is used to generate a first transaction request corresponding to the target transaction object using the transaction type, the transaction quantity, and the expected transaction value; The generation submodule includes: The acquisition unit is used to acquire the total current carbon asset trading volume and the current average carbon asset trading value corresponding to multiple trading objects. The first determining unit is configured to determine a first ratio between the transaction quantity and the current total carbon asset transaction volume, and to determine a second ratio between the expected transaction value and the current average carbon asset transaction value; The second determining unit is used to determine the priority corresponding to the target transaction object using the first ratio and the second ratio; The generation unit is used to generate a first transaction request corresponding to the target transaction object using the transaction type, the transaction quantity, the expected transaction value, and the priority. The sending module includes: The second acquisition submodule is used to acquire the current network transmission rate of the target node, as well as the delay parameter data between the target node and each of the other nodes; The comparison submodule is used to compare the delay parameter data with a first preset threshold, and adjust the current network transmission rate based on the first comparison result to obtain the target transmission rate between the target node and each of the other nodes. The sending submodule is used to encrypt and send the first transaction request corresponding to the target node to each of the other nodes according to the respective target transmission rates; The comparison submodule includes: The first comparison unit is used to reduce the current network transmission rate by a first step length when the first comparison result indicates that the delay parameter data is greater than the first preset threshold, so as to obtain the target transmission rate between the target node and each of the other nodes; the first step length is determined based on the ratio of the first preset threshold to the delay parameter data. The second comparison unit is used to increase the current network transmission rate by a second step size when the first comparison result indicates that the delay parameter data is less than or equal to the first preset threshold, so as to obtain the target transmission rate between the target node and each of the other nodes; the second step size is determined based on the ratio of the delay parameter data to the first preset threshold.

6. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the carbon asset trading matching method according to any one of claims 1 to 4.

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