A multi-agent data transaction method for a data element market

Abstract

The application relates to a multi-agent data transaction method for a data element market. The method first constructs a transaction blockchain network in the data element market through attribute parameters of data suppliers, data traders and data demanders in the data element market, and selects core data traders. Secondly, a revenue function of the data suppliers, an efficiency function of the data traders and an utility function of the data demanders are respectively constructed based on the attribute parameters of the agents and the core data traders, and the overall efficiency of the agents in the transaction blockchain network is maximized based on a Q learning algorithm to generate adjusted offers of the data traders. Then, the adjusted offers are submitted to the transaction blockchain network, and the transaction is triggered through an intelligent contract. Finally, layered payment is started after the intelligent contract verifies the compliance of the transaction, and the transaction is completed. The method realizes mathematical quantification and balanced solution of multi-party interest game, and improves the flexibility, fairness and resource allocation efficiency of data transaction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-agent data transaction, in particular to a multi-agent data transaction method for a data element market. BACKGROUND

[0002] The existing multi-agent data transaction method attempts to introduce blockchain technology to realize decentralized transaction, but has the following shortcomings: the node role division is single, and the differentiated needs of data suppliers, data merchants and demanders are not fully considered; there is a lack of quantitative benefit distribution model, making it difficult to balance the short-term income and long-term cooperation of all parties; the dynamic strategy generation mechanism relies on preset rules and cannot adapt to market changes.

[0003] The distributed ledger, smart contract and consensus mechanism of blockchain technology provide a new paradigm for data transaction. Its decentralized nature can realize the transparency of data right and circulation, the automatic execution of smart contract can reduce transaction costs, and the consensus mechanism can ensure data tamper-proof. However, the existing blockchain data transaction platform focuses on evidence storage and settlement, lacks a dynamic income distribution mechanism, and has not effectively solved the complex needs of data value ambiguity and multi-party game. SUMMARY

[0004] Therefore, it is necessary to provide a multi-agent data transaction method for a data element market, which comprises:

[0005] S1: setting attribute parameters for data suppliers, data merchants and data demanders in the data element market; constructing a transaction blockchain network in the data element market based on the attribute parameters, and selecting core data merchants;

[0006] S2: constructing a data supplier's income function, a data merchant's benefit function and a data demander's utility function based on the attribute parameters of each agent and the core data merchants, maximizing the overall benefit of each agent in the transaction blockchain network based on the Q-learning algorithm, generating an adjusted price of the data merchant, and submitting the adjusted price to the transaction blockchain network to trigger the transaction through a smart contract;

[0007] S3: starting layered payment after the smart contract verifies the compliance of the transaction, and completing the transaction.

[0008] Beneficial effects: the method first constructs a transaction blockchain network in the data element market through the attribute parameters of the data suppliers, data traders and data demanders in the data element market, and selects a core data trader; secondly, based on the attribute parameters of each subject and the core data trader, a revenue function of the data supplier, an efficiency function of the data trader and an utility function of the data demander are constructed, and the overall efficiency of each subject in the transaction blockchain network is maximized based on the Q learning algorithm to generate an adjusted offer of the data trader; then, the adjusted offer is submitted to the transaction blockchain network, and the transaction is triggered by the smart contract; finally, the hierarchical payment is started after the smart contract verifies the compliance of the transaction, and the transaction is completed; the method realizes the mathematical quantification and balanced solution of the multi-party interest game, realizes the real-time optimization and closed-loop feedback of the transaction strategy, improves the flexibility, fairness and resource allocation efficiency of the data transaction, and provides a new solution for building a trusted and efficient data transaction market. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating any creative labor.

[0010] Figure 1 The flowchart of the multi-subject data transaction method for the data element market in the embodiments of the present application. DETAILED DESCRIPTION

[0011] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below. In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the spirit of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0012] In addition, the terms "first", "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise specifically limited.

[0013] As shown in Figure 1 The present embodiment provides a multi-subject data transaction method for a data element market, which comprises:

[0014] S1: Set attribute parameters for data suppliers, data vendors, and data demanders in the data element market; construct a transaction blockchain network in the data element market based on the attribute parameters, and select core data vendors.

[0015] In this embodiment,

[0016] The data provider's attribute parameters include data quality. Storage costs ( For storage unit price, For the first i The amount of data from each data provider (for storage time) and transmission capacity ( For the first i Bandwidth of each data provider For the first i (Transmission distance of each data provider).

[0017] Data vendor attributes include reputation score. (Reputation score is the historical transaction success rate) T (Time period), processing delay ( For the first j The service speed of each data provider For the first j (Request delivery rate of individual data providers).

[0018] The attribute parameters of the data requester include budget limit and timeliness requirements.

[0019] Furthermore, constructing a transaction blockchain network in the data element market based on attribute parameters includes:

[0020] The data suppliers, data vendors, and data demanders in the data element market are each taken as nodes, and the attribute parameters of the data suppliers, data vendors, and data demanders are taken as the attributes of the corresponding nodes.

[0021] Based on the data quality of the data supplier connected to any data provider, as well as the corresponding data provider's reputation score and processing latency, the node weight of the corresponding data provider is calculated using the following formula:

[0022] ;

[0023] in, Indicates the first j The node weight corresponding to each data provider; Indicates the first j The reputation score of a data provider; Indicates the relationship with the firstj A collection of data providers connected to a data vendor; Indicates the relationship with the first j The first data provider connection i Data quality of each data supplier; Indicates the first j Processing delays for individual data providers;

[0024] Iterate through all data providers and calculate the node weights corresponding to each data provider;

[0025] A transaction blockchain network is constructed based on all nodes, the attributes of each node, and the node weights corresponding to each data provider.

[0026] Furthermore, the selection of core data providers includes: determining core data providers based on the node weights corresponding to the data providers and the transmission capabilities between the data providers and data suppliers, using the following formula:

[0027] ;

[0028] in, Indicates a core data provider; Represents a set of data vendors; Indicates the first j The node weight corresponding to each data provider; Indicates the first j The transmission capacity between a data provider and the data suppliers it connects to.

[0029] S2: Based on the attribute parameters of each entity and the core data provider, construct the revenue function of the data supplier, the benefit function of the data provider, and the utility function of the data demander. Based on the Q-learning algorithm, maximize the overall benefit of each entity in the transaction blockchain network, generate the adjusted price of the data provider, and submit the adjusted price to the transaction blockchain network to trigger the transaction through smart contract.

[0030] In this embodiment, in order to maximize the long-term interests of the data trading market alliance, a benefit function system for data suppliers, data providers, and data demanders is constructed based on the node data and network structure in the trading blockchain network. This system provides a mathematical basis for the dynamic trading strategy optimization of the Q-learning algorithm by quantifying the interests of all parties, ensuring that the strategy generation conforms to the global optimal goal.

[0031] Specifically, the revenue function for the data provider is expressed as:

[0032] ;

[0033] ;

[0034] in, Indicates the firsti a revenue function of a data provider; denotes the number of data merchants; denotes the number of data providers; i a benchmark transaction price between the j th data provider and the th data merchant; j a data quality of the i th data provider connected to the th data merchant; denotes a quality competition factor, denotes a set of data providers; a data quality of the j th data provider connected to the l th data merchant; denotes a transmission capacity between the j th data merchant and the i th data provider connected to the th data merchant; j denotes a transmission capacity between the l th data merchant and the th data provider connected to the i th data merchant; j a reward price based on transmission capacity between the th data provider and the i th data merchant; denotes a storage cost of the th data provider;

[0035] denotes a reward coefficient.

[0036] ;

[0037] ;

[0038] s.t. ;

[0039] wherein, a revenue function of the j th data merchant; denotes the number of data demanders; denotes a price offer of the j th data merchant to the k th data demander; denotes a timeliness penalty function; denotes an actual transmission time between the j th data demander and the k th data merchant; denotes a timeliness requirement of the k th data demander; denotes the number of data providers; denotes the storage cost of the kth data provider; i denotes the storage cost of the kth data provider; denotes the data quality of the kth data provider connected to the kth data broker; j denotes the data quality of the kth data provider connected to the kth data broker; i denotes the data quality of the kth data provider connected to the kth data broker; denotes the reputation value of the kth data broker; j denotes the reputation value of the kth data broker; denotes the reputation adjustment factor; denotes the upper limit of the kth data demander's budget for a single transaction; denotes the demand matching degree response function; denotes the data quality provided by the kth data broker to the kth data demander; j denotes the data quality provided by the kth data broker to the kth data demander; k denotes the risk aversion coefficient; denotes the offer of the kth data broker to the 1st data demander; denotes the offer of the kth data broker to the 1st data demander; j denotes the offer of the kth data broker to the 1st data demander; denotes the offer of the kth data broker to the 1st data demander; j denotes the offer of the kth data broker to the 1st data demander; denotes the offer of the kth data broker to the 1st data demander; s.t. denotes constrained to; denotes the variance; denotes the quality sensitivity coefficient; denotes the benchmark data quality provided by the kth data broker to the kth data demander; j denotes the benchmark data quality provided by the kth data broker to the kth data demander; k denotes the benchmark data quality provided by the kth data broker to the kth data demander.

[0040] Data quality is calculated through three dimensions, including:

[0041] Completeness: performing completeness detection on the data set submitted by the data provider; denotes the completeness detection result of the kth data provider; denotes the completeness detection result of the kth data provider; i denotes the number of data in the data set; denotes the indicator function, which is 1 when the condition is met, otherwise 0; denotes the kth data in the data set of the kth data provider; denotes empty; i f denotes empty;

[0042] Timeliness: denotes the timeliness of the kth data provider; i denotes the timeliness of the kth data provider; ​​​​​​​​is a time unit; is a data type coefficient; represents the time of response; represents the time of request start;

[0043] information entropy: , , , ; wherein, represents the information entropy of the i th data provider; represents the original information entropy of the i th data provider; represents the maximum possible information entropy; represents the number of intervals for dividing data; represents the probability of falling into the b th interval; represents the total number of records;

[0044] Multi-dimensional quality fusion ;

[0045] The exponential parameter determination adopts the entropy weight method to objectively assign weights, first constructs an evaluation matrix , calculates the index information entropy: , , and then determines the weight , to obtain: the first index , the second index , and the third index ; wherein, represents the index information entropy of the m th index; represents the number of data providers; represents the data value of the i th data provider on the m th index after standardization; represents the data value of the i th data provider on the m th index.

[0046] The utility function of the data demander is represented as:

[0047] ;

[0048] s.t. ;

[0049] wherein, represents the utility function of the k th data demander; represents the number of data traders; represents the data value of thej The data provider provided the first k Data quality for each data requester; Indicates the quality sensitivity index; Indicates a reputation discount factor; Indicates the first j The reputation score of a data provider; This represents the price elasticity coefficient; Indicates the first j The data provider gave the first k Quotations from data requesters; This represents the upper limit of the budget for a single transaction for the k-th data requester; This indicates an indicator function; its value is 1 if the condition is met, and 0 otherwise. Indicates the first j The data vendor and the first k The actual transmission time between data requesters; Indicates the first k The timeliness requirements of each data requester; st indicates a constraint.

[0050] Furthermore, based on the aforementioned benefit function system, and using the Q-learning algorithm to maximize the overall benefit of each entity in the transaction blockchain network, the adjusted quotes generated by the data provider include:

[0051] Step 1: Define the state space, and represent the states as follows:

[0052] ;

[0053] ;

[0054] ;

[0055] ;

[0056] ;

[0057] in, express t The state at any given moment; express t Time-varying quality decay at any given moment; express t The average market price at any given time; express t Average reputation score on online data at any given time; express t The average remaining budget of the demand side at any given moment; Indicates the number of data providers; Indicates the relationship with the first j The first data provider connectioni Data quality of each data supplier; Indicates the mass decay coefficient. ; Indicates the number of data vendors; express t Time of the first j A data provider's quote; express t Time of the first j The reputation score of a data provider; Indicates the number of data requesters; This represents the upper limit of the budget for a single transaction for the k-th data requester; Indicates the first j The data provider gave the first k Quotations from data requesters;

[0058] Step 2: Design the action space for the data provider. The actions of the data provider are represented as follows:

[0059] ;

[0060] in, express t Time of the first j The actions of individual data vendors; express t Time of the first j Price adjustments by individual data providers; express t Time of the first j The bandwidth allocation ratio of each data provider; express t Time of the first j Each data provider's service quality commitment threshold;

[0061] t The service quality commitment thresholds at any given time include: t Data integrity commitment threshold at any given time This represents the minimum requirement for data field integrity. t upper limit of response latency at any moment The unit is seconds, representing the maximum tolerable time from accepting a request to completing delivery; t Data freshness threshold at any given time The unit is seconds, which represents the maximum tolerable interval between the time the data was generated and the time of the transaction.

[0062] Step 3: Based on the Q function, and using ε- A greedy strategy selects actions, and the action selection is represented as follows:

[0063] ;

[0064] in, Represents the Q function; Represents the set of actions to be selected; This represents the selection probability, which decays with each training epoch. The decay formula is: , This represents the selection probability before decay. Indicates the decay rate;

[0065] Step 4: Update the Q function, and update the formula as follows:

[0066] ;

[0067] ;

[0068] in, express t The action chosen at any given moment; Indicates the learning rate. ; express t The reward function at each time step; Indicates the discount factor; express t The state at time +1; express t Time of the first j The benefit function of a data vendor; express t -1 moment j The benefit function of a data vendor; Indicates the penalty coefficient. ; express t -1 moment j A data provider's quote; This represents the updated set of candidate actions; in this embodiment, the learning rate can be dynamically adjusted using the following formula: ;

[0069] Step 5: Repeat steps 3-4 until the Q function converges, find the action with the largest Q value, and add the price adjustment amount of the action with the largest Q value to the data provider's quote at the previous moment to obtain the data provider's adjusted quote.

[0070] The dynamic trading strategy is optimized based on the interests of the three parties (the revenue function of the data supplier, the benefit function of the data provider, and the utility function of the data demander), aiming to maximize overall efficiency. This optimization is guided by a game theory equilibrium mechanism. A Q-learning model is constructed using market states and actions, utilizing… ε- The greedy strategy balances exploration and utilization by updating the strategy based on changes in returns and price fluctuations, forming a dynamic optimization closed loop.

[0071] S3: After the smart contract verifies the compliance of the transaction, it initiates tiered payment and completes the transaction.

[0072] Specifically, the steps include:

[0073] The transaction is represented as: ;in, Indicates the first j Data vendors Indicates the first k One data demander, Indicates the first j The data provider gave the first k The adjusted quote from each data requester. Indicates the first j The data provider provided the first k Data quality for each data requester Indicates the first j The data vendor and the first k The actual transmission time between data requesters;

[0074] The smart contract determines whether a transaction meets the constraints. If it does, it distributes the profits according to the tiered payment model and completes the transaction.

[0075] The constraints are as follows:

[0076] st ;

[0077] Where st represents the constraint; Indicates the first j The data provider gave the first k Quotations from data requesters; Indicates the first k The minimum data quality required by each data requester; This represents the upper limit of the budget for a single transaction for the k-th data requester; Indicates the first k The timeliness requirements of each data requester; Indicates the first k The total budget of each data requester.

[0078] Furthermore, the transaction process is as follows:

[0079] The total amount paid by the data requester is:

[0080] ;

[0081] in, Indicates the first j The data provider gave the first k The total quote from the data requester, i.e. the first data requester...k The total amount paid by the data demander to the data merchant; j represents the adjusted offer of the j data merchant to the k data demander; represents the quality premium coefficient, ; represents the timeliness reward amount; represents the data quality provided by the j data merchant to the k data demander; represents the minimum data quality required by the k data demander; represents the actual transmission time between the j data merchant and the k data demander; represents the timeliness requirement of the k data demander.

[0082] The revenue of the data merchant is:

[0083] ;

[0084] wherein, represents the revenue of the j data merchant; represents the commission ratio of the j data merchant, , , are the weights of the data integrity commitment threshold , the response delay threshold , and the data freshness threshold , respectively; represents the delay penalty rate.

[0085] The revenue of the data supplier is:

[0086] ;

[0087] wherein, represents the revenue of the i data supplier; represents the number of data merchants; represents the data quality of the j data supplier connected to the i data merchant; represents the quality competition factor; represents the set of data suppliers; represents the data quality of the j data supplier connected to the​l Data quality representation from each data supplier; Indicates the first j The data provider and its connection i Transmission capabilities between data providers; Indicates the first j The data provider and its connection p Transmission capabilities between data providers; This indicates the number of data providers.

[0088] In this embodiment, the step further includes:

[0089] Based on the total number of historical transactions, the number of successful transactions in this round, and the reputation score of the data provider updated with the number of transactions in this round, the update formula is as follows:

[0090] ;

[0091] in, Indicates the first j The updated reputation value of each data provider; Indicates the first j Total number of historical transactions for each data provider; Indicates the first j The historical reputation value of each data provider; This is a function indicating whether a transaction was successful; the value is 1 if the transaction was successful, and 0 otherwise. Indicates the first j The number of new transactions by each data provider in this round of deals;

[0092] The data quality of the data provider is updated based on historical data quality and the number of times the data is accessed. The update formula is as follows:

[0093] ;

[0094] in, Indicates the first i The quality of updated data from each data provider; Indicates the first i Data quality before updates from each data provider; Indicates the natural decay coefficient; Indicates a time interval; Indicates the first k One data demander; Indicates the number of times the data has been accessed; Indicates variance; Indicates the first j The data provider gave the first k The adjusted quote from each data requester;

[0095] The updated reputation value and data quality feedback are fed back to step S2 for calculating the reward function and updating the Q value, thereby optimizing the next round of transaction strategy decision. The blockchain ensures state synchronization and tamper-proof transaction, forming an adaptive closed loop of "strategy generation → transaction execution → state feedback → model iteration", and finally realizing long-term revenue maximization and market equilibrium.

[0096] The multi-agent data transaction method for the data element market provided by the embodiment has the following beneficial effects:

[0097] The method first constructs a transaction blockchain network in the data element market by attribute parameters of data suppliers, data merchants and data demanders in the data element market, and selects a core data merchant; secondly, based on the attribute parameters of each agent and the core data merchant, a revenue function of the data supplier, an efficiency function of the data merchant and an utility function of the data demander are constructed, and the overall efficiency of each agent in the transaction blockchain network is maximized based on the Q learning algorithm to generate an adjusted price of the data merchant; then, the adjusted price is submitted to the transaction blockchain network, and the transaction is triggered by the smart contract; finally, after the smart contract verifies the compliance of the transaction, hierarchical payment is started to complete the transaction; the method realizes mathematical quantification and balanced solution of multi-party interest game, realizes real-time optimization and closed loop feedback of transaction strategy, improves the flexibility, fairness and resource allocation efficiency of data transaction, and provides a new solution for building a trusted and efficient data transaction market.

[0098] The technical features of the above-described embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described, but as long as the combinations of the technical features do not contradict, they should be considered within the scope of the present application.

[0099] The above-described embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the application. It should be noted that for ordinary skilled persons in the art, some modifications and improvements can be made without departing from the concept of the present application, and these are within the protection scope of the present application. Therefore, the patent protection scope of the present application should be subject to the appended claims.

Claims

1. A multi-entity data trading method for the data element market, characterized in that, include: S1: Set attribute parameters for data suppliers, data vendors, and data demanders in the data element market; Construct a transaction blockchain network in the data element market based on attribute parameters, and select core data providers; Building transaction blockchain networks in the data element market based on attribute parameters includes: The data suppliers, data vendors, and data demanders in the data element market are each taken as nodes, and the attribute parameters of the data suppliers, data vendors, and data demanders are taken as the attributes of the corresponding nodes. Based on the data quality of the data supplier connected to any data provider, as well as the corresponding data provider's reputation score and processing latency, the node weight of the corresponding data provider is calculated using the following formula: ； in, Indicates the first j The node weight corresponding to each data provider; Indicates the first j The reputation value of a data provider; Indicates the relationship with the first j A collection of data providers connected to a data vendor; Indicates the relationship with the first j The first data provider connection i Data quality of each data supplier; Indicates the first j Processing delays for individual data providers; Iterate through all data providers and calculate the node weights corresponding to each data provider; A transaction blockchain network is constructed based on all nodes, the attributes of each node, and the node weights corresponding to each data provider. S2: Based on the attribute parameters of each subject and the core data provider, construct the revenue function of the data supplier, the benefit function of the data provider, and the utility function of the data demander respectively. Based on the Q-learning algorithm, maximize the overall benefit of each subject in the transaction blockchain network, generate the adjusted price of the data provider, and submit the adjusted price to the transaction blockchain network to trigger the transaction through smart contract. The revenue function for the data provider is expressed as: ； ； in, Indicates the first i The revenue function of a data supplier; Indicates the number of data vendors; Indicates the first i The data supplier and the first j The benchmark transaction price for a data provider; Indicates the relationship with the first j The first data provider connection i Data quality of each data supplier; This represents the quality competitiveness factor. ; Represents a set of data providers; Indicates the relationship with the first j The first data provider connection l Data quality representation from each data supplier; Indicates the first j The data provider and its connection i Transmission capabilities between data providers; Indicates the first j The data provider and its connection l Transmission capabilities between data providers; Indicates the first i The data supplier and the first j Incentive pricing based on transmission capacity among data providers; Indicates the first i Storage costs for each data provider; Indicates the reward coefficient; The benefit function of a data provider is expressed as: ； ； s.t. ; in, Indicates the first j The benefit function of a data vendor; Indicates the number of data requesters; Indicates the first j The data provider gave the first k Quotations from data requesters; This represents a time-sensitive penalty function; Indicates the first j The data vendor and the first k The actual transmission time between data requesters; Indicates the first k The timeliness requirements of each data requester; Indicates the number of data providers; Indicates the first i Storage costs for each data provider; Indicates the relationship with the first j The first data provider connection i Data quality of each data supplier; Indicates the first j The reputation value of a data provider; Indicates the credit adjustment factor; This represents the upper limit of the budget for a single transaction for the k-th data requester; This represents the demand matching response function; Indicates the first j The data provider provided the first k Data quality for each data requester; Indicates the risk aversion coefficient; Indicates the first j The first data provider gives a quote to the first data requester; Indicates the first j The data provider gave the first Quotations from data requesters; st indicates constraints; Indicates variance; This represents the quality sensitivity coefficient; Indicates the first j The data provider provided the first k The baseline data quality for each data requester; The utility function of the data demander is expressed as: ； s.t. ; in, Indicates the first k The utility function of each data demander; Indicates the number of data vendors; Indicates the first j The data provider provided the first k Data quality for each data requester; Indicates the quality sensitivity index; Indicates a reputation discount factor; Indicates the first j The reputation value of a data provider; This represents the price elasticity coefficient; Indicates the first j The data provider gave the first k Quotations from data requesters; This represents the upper limit of the budget for a single transaction for the k-th data requester; This indicates an indicator function; its value is 1 if the condition is met, and 0 otherwise. Indicates the first j The data vendor and the first k The actual transmission time between data requesters; Indicates the first k The timeliness requirement of each data requester; st indicates a constraint; S3: After the smart contract verifies the compliance of the transaction, it initiates tiered payment and completes the transaction.

2. The multi-entity data trading method for data element markets according to claim 1, characterized in that, The attributes of data suppliers include data quality, storage costs, and transmission capacity; the attributes of data providers include reputation value and processing latency; and the attributes of data demanders include budget limits and timeliness requirements.

3. The multi-entity data trading method for data element markets according to claim 1, characterized in that, The selection of core data providers includes: determining core data providers based on the node weights corresponding to the data providers and the transmission capabilities between the data providers and data suppliers, using the following formula: ； in, Indicates a core data provider; Represents a set of data vendors; Indicates the first j The node weight corresponding to each data provider; Indicates the first j The transmission capacity between a data provider and the data suppliers it connects to.

4. The multi-entity data trading method for data element markets according to claim 1, characterized in that, Based on the Q-learning algorithm, to maximize the overall benefit of all participants in the transaction blockchain network, the data provider's adjusted quote includes: Step 1: Define the state space, and represent the states as follows: ； ； ； ； ； in, express t The state at any given moment; express t Time-varying quality decay at any given moment; express t The average market price at any given time; express t Average reputation score on online data at any given time; express t The average remaining budget of the demand side at any given moment; Indicates the number of data providers; Indicates the relationship with the first j The first data provider connection i Data quality of each data supplier; Indicates the mass decay coefficient. ; Indicates the number of data vendors; express t Time of the first j A data provider's quote; express t Time of the first j The reputation value of a data provider; Indicates the number of data requesters; This represents the upper limit of the budget for a single transaction for the k-th data requester; Indicates the first j The data provider gave the first k Quotations from data requesters; Step 2: Design the action space for the data provider. The actions of the data provider are represented as follows: ； in, express t Time of the first j The actions of individual data vendors; express t Time of the first j Price adjustments by individual data providers; express t Time of the first j The bandwidth allocation ratio of each data provider; express t Time of the first j Each data provider's service quality commitment threshold; Step 3: Based on the Q function, and using ε- A greedy strategy selects actions, and the action selection is represented as follows: ； in, Indicates the probability of selection; Represents the Q function; Represents the set of actions to be selected; Step 4: Update the Q function, and update the formula as follows: ； ； in, express t The action chosen at any given moment; Indicates the learning rate; express t The reward function at each time step; Indicates the discount factor; express t The state at time +1; express t Time of the first j The benefit function of a data vendor; express t -1 moment j The benefit function of a data vendor; Indicates the penalty coefficient. ; express t -1 moment j A data provider's quote; This represents the updated set of candidate actions; Step 5: Repeat steps 3-4 until the Q function converges, find the action with the largest Q value, and add the price adjustment amount of the action with the largest Q value to the data provider's quote at the previous moment to obtain the data provider's adjusted quote.

5. The multi-entity data trading method for data element markets according to claim 1, characterized in that, S3 include: The transaction is represented as: ;in, Indicates the first j Data vendors Indicates the first k One data demander, Indicates the first j The data provider gave the first k The adjusted quote from each data requester. Indicates the first j The data provider provided the first k Data quality for each data requester Indicates the first j The data vendor and the first k The actual transmission time between data requesters; The smart contract determines whether a transaction meets the constraints. If it does, it distributes the profits according to the tiered payment model and completes the transaction. The constraints are as follows: s.t. ; Where st represents the constraint; Indicates the first j The data provider gave the first k Quotations from data requesters; Indicates the first k The minimum data quality required by each data requester; This represents the upper limit of the budget for a single transaction for the k-th data requester; Indicates the first k The timeliness requirements of each data requester; Indicates the first k The total budget of each data requester.

6. The multi-entity data trading method for data factor markets according to claim 1, characterized in that, S3 also includes: Based on the total number of historical transactions, the number of successful transactions in this round, and the reputation score of the data provider updated with the number of transactions in this round, the update formula is as follows: ； in, Indicates the first j The updated reputation value of each data provider; Indicates the first j Total number of historical transactions for each data provider; Indicates the first j The historical reputation value of each data provider; This is a function indicating whether a transaction was successful; the value is 1 if the transaction was successful, and 0 otherwise. Indicates the first j The number of new transactions by each data provider in this round of deals; The data quality of the data provider is updated based on historical data quality and the number of times the data is accessed. The update formula is as follows: ； in, Indicates the first i The quality of updated data from each data provider; Indicates the first i Data quality before updates from each data provider; Indicates the natural decay coefficient; Indicates a time interval; Indicates the first k One data demander; Indicates the number of times the data has been accessed; Indicates variance; Indicates the first j The data provider gave the first k The adjusted quote from each data requester; The updated reputation value and data quality are fed back to step S2 to calculate the reward function and update the Q value, thereby optimizing the next round of trading strategy decisions.

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