Transaction clearing method and device based on influence factor tensor decomposition and asynchronous consensus, and medium

Abstract

This invention discloses a transaction clearing method, device, and medium based on influence factor tensor decomposition and asynchronous consensus. The method includes: constructing an influence factor tensor decomposition model for order transactions; deconstructing the transaction order into multi-dimensional high-order tensors; calculating the contribution value of each participant in the high-order tensors using a Shapley value contribution metric algorithm; constructing an asynchronous consensus network model; and performing transaction clearing and settlement on the transaction order based on the asynchronous consensus network model and the contribution values ​​to obtain the clearing result. This method can dynamically calculate the contribution of each participant in each transaction order to the success of the transaction, and then distribute rewards according to the contribution.

Description

Technical Field

[0001] This application belongs to the fields of information security and e-commerce technology, specifically referring to a transaction clearing method, device and medium based on influence factor tensor decomposition and asynchronous consensus. Background Technology

[0002] In existing distributed systems, within a homogeneous consortium, all nodes are "data-sharing nodes." The challenge lies in quantifying node contributions and distributing rewards based on qualitative indicators (such as cooperative spirit and corporate strength). Traditional methods rely on static weight allocation based on subjective evaluation, a "review-allocation" model. Fuzzy analytic hierarchy process (FAHP) is a typical subjective weighting method, depending on expert scoring. Its output (contribution rate) is static, evaluation-based weights, reflecting a node's "overall capability" or "deserved share," rather than its dynamic contribution in a single transaction.

[0003] Most existing revenue distribution systems target alliances with homogeneous participants, offering a comprehensive and subjective evaluation of overall contribution over a period (e.g., using FAHP). These methods fail to address the unique challenges of multi-level, fixed-role heterogeneous business networks (e.g., platform-general agent-channel-sales), including: Immediacy Challenge: Revenue distribution must be settled and distributed immediately after each independent business transaction, rather than periodically aggregated, to achieve rapid cash flow turnover and precise management. Objectivity Challenge: Revenue distribution must be based on objectively verifiable business actions occurring within the transaction (e.g., 'who paid for the goods', 'who signed the contract'), rather than subjective assessments of the participants' overall capabilities. Multidimensional Deconstruction Challenge: A transaction is the result of multi-dimensional efforts (market, business, capital, technology, etc.), requiring a mechanism to automatically deconstruct the total transaction value, reasonably map it to various contribution dimensions, and then calculate each party's contribution in each dimension. The challenge of modeling complex relationships is to depict the collaborative yet competitive relationship between different parties in a specific transaction model across different contribution dimensions (for example, the funding work is usually fully undertaken by one party rather than shared by multiple parties).

[0004] Therefore, traditional static weight allocation methods based on subjective evaluation cannot, in a highly heterogeneous, fixed-role, and clearly defined multi-level distribution network (such as platforms, general agents, channels, and sales), quantify the multi-dimensional and specific behavioral contributions of each participant in each specific business transaction in real time, automatically, and fairly, and complete the clearing. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this application provides a transaction clearing method, device, and medium based on influence factor tensor decomposition and asynchronous consensus, which can dynamically calculate the "contribution" of each participant in each transaction order to the success of the transaction, and then distribute the revenue according to the contribution.

[0006] This invention provides a transaction clearing method based on influence factor tensor decomposition and asynchronous consensus, the method comprising: Construct a tensor decomposition model of the influencing factors of order transactions; Deconstruct transaction orders into high-order tensors with multiple dimensions; The contribution metric algorithm based on Shapley value is used to calculate the contribution value of each participant in the higher-order tensor; Construct an asynchronous consensus network model; Based on the asynchronous consensus network model and the contribution value, the transaction order is cleared and settled, and the clearing result is obtained.

[0007] Furthermore, according to the transaction clearing method based on impact factor tensor decomposition and asynchronous consensus provided in this application, the construction of the impact factor tensor decomposition model for order transactions includes: Define a higher-order tensor T(m,p,f) for transaction order O, wherein the higher-order tensor T(m,p,f) is a third-order tensor; The higher-order tensor T(m,p,f) includes a pattern dimension set M={m1,…,mm}, which is defined as a set of business scenarios in which transactions occur. The pattern dimension set M={m1,…,mm} includes multiple sub-patterns m, and each sub-pattern m is pre-set with a basic profit allocation weight vector W_m, and satisfies ΣW_m=1. The higher-order tensor T(m,p,f) includes a participant dimension set P={p1,...,pp}, which is defined as the set of all participants related to this transaction order. The participant dimension set P={p1,...,pp} includes multiple sub-participants p. The higher-order tensor T(m,p,f) includes an influencing factor dimension set F={f1,…,ff}, which is defined as the factors that affect the success of a transaction. The influencing factor dimension set F={f1,…,ff} includes multiple sub-influencing factors f, each of which has a pre-set weight w_f and satisfies Σw_f=1. The higher-order tensor T(m,p,f) is between [0,1] and is defined as the degree of contribution made by sub-participant p to sub-influence factor f under sub-mode m.

[0008] Furthermore, according to the transaction clearing method based on impact factor tensor decomposition and asynchronous consensus provided in this application, the value of the higher-order tensor T(m,p,f) is automatically assigned according to preset objective business rules or obtained by self-learning from historical data in system logs.

[0009] Furthermore, according to the transaction clearing method based on impact factor tensor decomposition and asynchronous consensus provided in this application, the contribution metric algorithm based on Shapley value calculates the contribution value of each participant in the higher-order tensor, including: Calculate the total value of the patterns in the higher-order tensor T(m,p,f) as V(m) = Order_Amount * W_m; Calculate the value distribution V(m,f) of the sub-influence factor f in the higher-order tensor T(m,p,f), and distribute the total value V(m) of the calculated model to each sub-influence factor f according to the weight of the sub-influence factor f; where V(m,f)=V(m)*w_f; Calculate the direct value of the contribution of the sub-participant p on the sub-influence factor f in the higher-order tensor T(m,p,f): Contribution(p,f) = T(m,p,f) * V(m,f); The Shapley value algorithm is used to calculate the Shapley value φ(p) for each sub-participant p for all participant dimension sets N.

[0010] Furthermore, according to the transaction clearing method based on impact factor tensor decomposition and asynchronous consensus provided in this application, the Shapley value φ(p) of each sub-participant p is calculated as follows: φ(p)=Σ{S N\{p}}[|S|!(|N|-|S|-1)! / |N|!]*(v(S∪{p})-v(S)); Where N is the set of all parties involved in this order, that is, a subset N of the party dimension set P. P; S is the set of sub-alliances, which is any subset of N, S N; p represents the sub-participants, and v(S) is the total value that alliance S can obtain; Where v(S)=Σ_{f}[MAX_{p∈S}(T(m,p,f))]*V(m,f) represents the value of the alliance S on a certain sub-influence factor f, which is determined by the member of the alliance S that creates the highest direct value on that sub-influence factor f.

[0011] Furthermore, according to the transaction clearing method based on influence factor tensor decomposition and asynchronous consensus provided in this application, the construction of the asynchronous consensus network model includes: The asynchronous consensus network model consists of several consensus nodes; wherein, the consensus nodes are divided into: The proposal node is responsible for receiving the clearing calculation result R and packaging it into a proposal broadcast; Verification nodes are responsible for verifying and voting on proposals; The proposal node and the verification node can be converted into each other; The states maintained by the verification node include: A local copy of the ledger stores the clearing records for which consensus has been reached; The pending proposal pool stores proposals that have been received but for which consensus has not yet been reached. View number, an identifier for the current consensus round, used to handle master node failures; The sequence number is assigned to each consensus proposal in a monotonically increasing sequence to guarantee total order.

[0012] Furthermore, according to the transaction clearing method based on influence factor tensor decomposition and asynchronous consensus provided in this application, the step of clearing and settling the transaction order based on the asynchronous consensus network model and the contribution value and obtaining the clearing result includes: Broadcast and verify the settlement proposal; Consensus nodes collect votes and submit them locally; If a consensus node fails to collect enough votes within a preset time, the current view's proposing node is suspected of being faulty, and a view protocol change is initiated.

[0013] Furthermore, according to the transaction clearing method based on impact factor tensor decomposition and asynchronous consensus provided in this application, the step of broadcasting and verifying the clearing proposal includes: The proposal node packages the clearing result R, transaction hash H_tx, tensor digest D_T, current view v, and assigned sequence number s into a message PREPARE(v,s,H_tx,D_T,R), attaches its digital signature Sig_proposer, and broadcasts it to all verification nodes. Upon receiving the message PREPARE(v,s,H_tx,D_T,R), each verification node i independently performs a rigorous verification: Format check: Signature valid, view v consistent with local; Business logic verification: The node obtains basic transaction information from a trusted source based on the transaction hash H_tx, recalculates the tensor digest D_T' according to the published rules, and verifies that D_T' == D_T; Conflict checking: Ensures that there are no other results with the same value (H_tx,s) in the local ledger, thus avoiding double-spending; If the verification is successful, the node stores the PREPARE(v,s,H_tx,D_T,R) message into the pending proposal pool and performs the consensus node's vote collection and local submission steps. The consensus node performs vote collection and local submission, including: Voting: The verification node generates a voting message VOTE(v,s,H_tx,i), signs it, and broadcasts it to all other consensus nodes; Collection and Judgment: Each validator node collects the voting messages VOTE(v,s,H_tx,i) sent by other validators. Local Commit: After the voting message VOTE(v,s,H_tx,i) reaches the quorum, the validator node marks (s,H_tx,D_T,R) as "committed" and writes it to the temporary storage area of ​​the local ledger copy; the clearing result R is regarded as "finalized" on the validator node and can be used for subsequent business. Network-wide finality broadcast: The validator then broadcasts a public message COMMIT(v,s,H_tx); when other consensus nodes receive the specified number of public messages COMMIT(v,s,H_tx), they move the corresponding record from the temporary storage area to the persistent storage area.

[0014] On the other hand, this application also provides a transaction clearing terminal device based on influence factor tensor decomposition and asynchronous consensus, including a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0015] On the other hand, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the methods described above.

[0016] The beneficial effects of this invention are as follows: This application provides a transaction clearing method, device, and medium based on influence factor tensor decomposition and asynchronous consensus. The method constructs an influence factor tensor decomposition model for order transactions, structuring each transaction order as a high-order tensor containing multiple dimensions. Each participant in the high-order tensor has an influence factor vector in different dimensions. Then, using the Shapley value algorithm, the marginal contribution of each participant in the "cooperative alliance" formed by this transaction is scientifically calculated, thereby quantifying their deserved benefits and ensuring fairness. Simultaneously, a lightweight asynchronous consensus network is designed to ensure that all participants reach a final consensus on the clearing result of the same transaction. This achieves the ability to deconstruct the value of a single transaction in real time, objectively, and automatically, and to achieve fair and accurate clearing in complex business relationship networks. Attached Figure Description

[0017] The technical solution and other beneficial effects of this application will become apparent from the following detailed description of specific embodiments in conjunction with the accompanying drawings.

[0018] Figure 1 This is a flowchart illustrating a transaction clearing method based on influence factor tensor decomposition and asynchronous consensus provided in an embodiment of the present invention.

[0019] Figure 2 This is a system architecture diagram of a transaction clearing method based on influence factor tensor decomposition and asynchronous consensus provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0022] The following disclosure provides many different embodiments or examples for implementing different structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. In addition, various specific examples of processes and materials are provided in this application, but those skilled in the art will recognize the application of other processes and / or the use of other materials.

[0023] The embodiments of this application will now be further described in conjunction with the accompanying drawings and specific implementation details.

[0024] Example 1: Figure 1 This is a flowchart illustrating a transaction clearing method based on influence factor tensor decomposition and asynchronous consensus provided in an embodiment of the present invention. Figure 2 This is a system architecture diagram of a transaction clearing method based on influence factor tensor decomposition and asynchronous consensus provided in an embodiment of the present invention.

[0025] like Figure 1 , Figure 2 As shown, the transaction clearing method based on influence factor tensor decomposition and asynchronous consensus provided in this embodiment includes: Construct a tensor decomposition model of the influencing factors of order transactions; Deconstruct transaction orders into high-order tensors with multiple dimensions; The contribution metric algorithm based on Shapley value is used to calculate the contribution value of each participant in the higher-order tensor; Construct an asynchronous consensus network model; Based on the asynchronous consensus network model and the contribution value, the transaction order is cleared and settled, and the clearing result is obtained.

[0026] Specifically, in this embodiment, constructing the tensor decomposition model of the influencing factors of order transactions includes: Define a higher-order tensor T(m,p,f) for transaction order O, wherein the higher-order tensor T(m,p,f) is a third-order tensor; wherein, the higher-order tensor T(m,p,f) can also be extended to a higher-order tensor according to actual needs. In this embodiment, a third-order tensor is used as an example for explanation.

[0027] In this embodiment, the higher-order tensor T(m,p,f) is described using three dimensions, which are: Mode Dimension (M): Represents the business scenario in which the transaction occurs. For example: m1 = standard direct sales, m2 = project registration sales, m3 = channel recharge deduction sales.

[0028] Participant dimension (Party, P): The set of all parties that may be involved in this transaction {p1:Platform,p2:Distributor,p3:Partner,p4:Sales,...}.

[0029] Factor dimension (F): Define a set of factors that affect the success of a transaction, such as: F1=marketing promotion, F2=pre-sales support, F3=price negotiation, F4=contract signing, F5=funding advance.

[0030] The higher-order tensor T(m,p,f) includes a pattern dimension set M={m1,…,mm}, which is defined as a set of business scenarios in which transactions occur. The pattern dimension set M={m1,…,mm} includes multiple sub-patterns m, and each sub-pattern m is pre-set with a basic profit allocation weight vector W_m, and satisfies ΣW_m=1. The higher-order tensor T(m,p,f) includes a participant dimension set P={p1,...,pp}, which is defined as the set of all participants related to this transaction order. The participant dimension set P={p1,...,pp} includes multiple sub-participants p. The higher-order tensor T(m,p,f) includes an influencing factor dimension set F={f1,…,ff}, which is defined as the factors that affect the success of a transaction. The influencing factor dimension set F={f1,…,ff} includes multiple sub-influencing factors f, each of which has a pre-set weight w_f and satisfies Σw_f=1. The higher-order tensor T(m,p,f) is between [0,1] and is defined as the contribution of sub-participant p to sub-influence factor f under sub-mode m. The value of the higher-order tensor T(m,p,f) is automatically assigned according to pre-set objective business rules or learned from historical data in system logs. Specifically, for example, a transaction order is a sale achieved through "project filing (m2)," where the general agent (p2) is responsible for price negotiation (f3) and advance payment (f5), and the channel (p3) is responsible for market promotion (f1) and contract signing (f4). Then, some values ​​in the tensor are: T(m2,p2,f3)=0.9, T(m2,p2,f5)=1.0, T(m2,p3,f1)=0.8, T(m2,p3,f4)=0.7, and the rest are 0.

[0031] According to the order transaction influencing factor tensor decomposition model provided in this embodiment, the specific modeling parameters are set as follows: Order transaction parameters: Transaction order O refers to a successfully paid transaction record and is the input source for the clearing process.

[0032] Key attributes of transaction order O include: Order_ID: A unique identifier for the order; Order_Amount: Total order amount (e.g., 1000.00 USD); Product_Info: Information about the purchased products, which may affect the profit-sharing model; Buyer_ID: Buyer identifier (e.g., end customer Yeastar ID); Seller_ID: Seller identifier (e.g., Partner ID for direct order placement); Order_Amount (Order Amount): The net transaction amount of the order, which is the source of the total value pool for clearing calculations; its data type is set to floating point with currency unit; the order amount is the base for all profit sharing calculations.

[0033] Relevant parameters of the higher-order tensor T(m,p,f): A higher-order tensor T(m,p,f) is a high-dimensional data structure used to quantify the contributions of each participant in different dimensions.

[0034] In this embodiment, the higher-order tensor T(m,p,f) is a third-order tensor whose elements T(m, p, f) quantify the contribution of a specific participant to a specific influence factor under a specific mode.

[0035] Its data structure is set as a third-order tensor with dimensions |M| × |P| × |F|.

[0036] Its element range is: T(m, p, f) ∈ [0, 1]. 0 indicates no contribution, and 1 indicates complete dominance. It is usually automatically filled by the system based on preset rules, historical collaboration data, or machine learning models.

[0037] Where m is the pattern index of the sub-pattern, and M is the set of pattern dimensions, representing the business model or scenario dimension in which the transaction occurs.

[0038] Values ​​and examples: M = {m1, m2, m3, ...} m1 = standard_direct_sale (Standard Direct Sales) m2 = project_registered_sale (Project registered sales) m3 = balance_deduction_sale (Sales with balance deduction) Different models correspond to different basic profit-sharing rules and sets of participants.

[0039] The relevant parameters of the basic profit allocation weight vector W_m are: The basic profit allocation weight vector W_m is a vector related to the mode dimension M, defining the proportion of basic revenue reserved by the platform under this mode. Its data structure is a vector [W_{m1}, W_{m2}, W_{m3}, ...].

[0040] Value and example: W_{m2} = 0.70. This means that in the project reporting mode, 70% of the order amount will go into the clearing pool (the remaining 30% may be the platform's hard costs or profits).

[0041] The purpose of the basic profit distribution weight vector W_m is to determine the total value available for profit sharing.

[0042] p is the index of the sub-participant; P is the participant dimension set, representing the set of all business participants that may be related to this order; Values ​​and examples: P = {p1, p2, p3, p4, ...} p1 = platform (platform provider) p2 = distributor (general agent) p3 = partner (channel partner) p4 = sales (individual salesperson) p5 = technical_support (Technical Support Team) The role of the participant dimension set P is: the set of recipients of the proceeds.

[0043] f is the index of the sub-influence factor; F is the set of influence factor dimensions, representing the set of dimensions of key behaviors or resource inputs that affect the completion of the transaction.

[0044] Its possible values ​​and examples: F = {f1, f2, f3, f4, f5, ...} f1 = marketing_promotion (Marketing Promotion) f2 = presales_support (Pre-sales support) f3 = price_negotiation (price negotiation) f4 = contract_signing (contract signing) f5 = capital_advance (capital advance payment) f6 = customer_success (Customer success / maintenance) The purpose of the influencing factor dimension set F is to decouple transaction value to different contribution dimensions, thereby achieving refined measurement.

[0045] The weight w_f of the impact factor: The influence factor weight w_f represents the relative importance of the factor to the success of the transaction.

[0046] Its constraint is: Σ_{f ∈F} w_f = 1.

[0047] Values ​​and examples: w_{f1}=0.2, w_{f3}=0.3, w_{f5}=0.25...

[0048] The role of the weight w_f of the impact factors is to allocate the total model value V(m) to each impact factor according to its importance.

[0049] The order transaction influencing factor tensor decomposition model provided in this embodiment describes in detail the construction process of the higher-order tensor T(m,p,f) of the order transaction based on its objectivity and the logic of dynamically constructing the model.

[0050] The objective data source and automatic assignment rules for its higher-order tensor T(m,p,f) are as follows: The value of each element T(m,p,f) in the higher-order tensor T(m,p,f) is assigned from objective business system logs or predefined rules related to the transaction order O, completely eliminating subjective evaluation. The system maintains a "factor-event" mapping rule base, and Table 1 below provides a specific example:

[0051] Table 1. The underscore "_" indicates that the dimension can take any value in this context, or that its specific value is not important.

[0052] The above assignments are automatically completed by the system at the moment the transaction order O is successfully paid, by querying relevant data sources, without any manual intervention. The resulting high-order tensor T(m,p,f) is a digital mirror of the transaction facts, possessing objectivity, real-time performance, and auditability.

[0053] Meanwhile, in this embodiment, to further accurately simulate commercial reality, this method applies competitive normalization constraints to the column vectors of the higher-order tensor T(m,p,f) in transaction order O under the same influence factor f: For any pattern m and influence factor f, the following condition must be met: Σ_{p∈P}T(m,p,f)=1.

[0054] This constraint simulates a scenario where "the total amount of a specific task (impact factor) is 1 (100%), and it is completed competitively by all participating parties." For example, the task of "funding advance" can be completed either by A (1.0), by B (1.0), or by A and B jointly according to a certain proportion (e.g., A contributes 70% and B contributes 30%), but the total is 1. This forces the system to allocate contributions based on the most accurate objective evidence, avoiding the ambiguity that multiple nodes can simultaneously obtain high "cooperation awareness" scores.

[0055] The specific implementation of this constraint is as follows: when multiple objective events point to the same factor (which rarely occurs, such as joint advance payment), the system calls a "contribution conflict resolution submodule" to automatically normalize the initial assignment according to preset rules (such as payment ratio, contractual agreement).

[0056] Therefore, in this embodiment, the deconstruction of transaction orders into multi-dimensional high-order tensors represents an objective mapping of specific behaviors within a single transaction. For example, a deconstructed high-order tensor T(project reporting mode, general agent, fund advance) = 1.0 represents a factual record indicating that "in this transaction, the general agent undertook 100% of the fund advance work." This is dynamic, transaction-bound, and objective. The deconstruction of transaction orders into multi-dimensional high-order tensors T(m,p,f) in this embodiment is not a simple "weight from 0 to 1," but a high-order data structure that deconstructs a transaction into a three-dimensional space of mode × participants × influencing factors. Each element T(m,p,f) quantifies a specific, traceable business behavior (such as fund advance F5), and its value is derived from objective business rules or system logs (e.g., who paid for the goods, the system automatically marks T(_,payer_id,F5) = 1.0), rather than subjective evaluation.

[0057] like Figure 1 As shown, in this embodiment, the contribution metric algorithm based on Shapley value calculates the contribution value of each participant in the higher-order tensor. Its goal is to calculate the "fair" contribution φ(p) of each participant p to the total revenue of this transaction. The specific method steps include: Calculate the total value V(m) = Order_Amount * W_m in the higher-order tensor T(m,p,f); where W_m is the platform retention ratio under this mode, that is, each sub-mode m is pre-set with a basic profit distribution weight vector W_m.

[0058] Calculate the value distribution V(m,f) of the sub-influence factor f in the higher-order tensor T(m,p,f), and distribute the total value V(m) of the calculated model to each sub-influence factor f according to the weight of the sub-influence factor f; where V(m,f)=V(m)*w_f; Calculate the direct value of the contribution of the sub-participant p on the sub-influence factor f in the higher-order tensor T(m,p,f): Contribution(p,f) = T(m,p,f) * V(m,f); The Shapley value algorithm is used to calculate the Shapley value φ(p) for each sub-participant p for all participant dimension sets P.

[0059] The Shapley value φ(p) of each sub-participant p is calculated using the following formula: φ(p)=Σ{S N\{p}}[|S|!(|N|-|S|-1)! / |N|!]*(v(S∪{p})-v(S)); Where N is the set of all parties involved in this order, that is, a subset N of the party dimension set P. P; S is the set of sub-alliances, which is any subset of N, S N; p represents the sub-participants, and v(S) is the total value that alliance S can obtain; Where v(S)=Σ_{f}[MAX_{p∈S}(T(m,p,f))]*V(m,f) represents the value of the alliance S on a certain sub-influence factor f, which is determined by the member of the alliance S that creates the highest direct value on that sub-influence factor f.

[0060] Specifically, in this embodiment, the MAX operation in the total value v(S) that alliance S can obtain means that, for alliance S and the influencing factor f, the value creation capability of the alliance on this factor is determined by the single member within the alliance who contributes the most to this factor. This accurately models the "complementary strengths" or "barrel effect" in business—the strength of a team in a certain aspect depends on its strongest member. Combined with the tensor normalization constraint: since the higher-order tensor T(m,p,f) has been normalized, the result of the MAX operation directly reflects the alliance's "maximum share of the work." V(m,f) is the total value of the work. Therefore, [MAX(...)]×V(m,f) intuitively represents the value that alliance S can "claim" in this work dimension. The summation Σ means that the total value v(S) of alliance S is the sum of the values ​​it can claim across all work dimensions (F). This simulates the total value that an alliance can achieve by integrating the strongest aspects of its members.

[0061] The Shapley value algorithm perfectly solves the fairness issues of "contribution aggregation" and "marginal contribution". For example, even if both sales (p4) and channel (p3) are involved in customer communication, the Shapley value will scientifically evaluate the incremental value brought by each person, avoiding the unfairness of simply allocating proportionally.

[0062] Specifically, in this embodiment, the parameters calculated by the contribution metric algorithm based on the Shapley value include: In a higher-order tensor T(m,p,f), the total value V(m) of a pattern represents the total value available for profit sharing under pattern m. Its calculation formula, as shown above, is V(m) = Order_Amount × W_m. A concrete example is: with an order amount of $1000 and W_m = 0.7, then V(m) = $700.

[0063] The value distribution V(m,f) of the sub-impact factor f represents the value allocated to the sub-impact factor f under pattern m. Its calculation formula, as shown above, is V(m, f) = V(m) × w_f. A specific example is: V(m) = $700, w_{f3} = 0.3, then V(m, f3) = $210.

[0064] The direct value created by sub-participant p on the sub-impact factor f, Contribution(p,f), represents the direct value created by sub-participant p on the sub-impact factor f. Its calculation formula, as shown above, is Contribution(p, f) = T(m, p, f) × V(m, f). A specific example is: T(m, p2, f3) = 0.9, V(m, f3) = $210, then Contribution(p2, f3) = $189.

[0065] The parameters for calculating the Shapley value include: The set N of all participating parties represents the set of all participating parties related to this order, that is, a subset N of the participating party dimension set P. P. The set of all participating alliances N is the "large alliance" in the Shapley value calculation.

[0066] The sub-association set S represents any subset of N (including the empty set and N itself). N. The set of sub-alliances S is calculated in the Shapley value formula by traversing all possible sub-alliances to determine the marginal contribution.

[0067] The alliance characteristic function v(S) represents the total value that the alliance can obtain from the transaction without cooperating with other parties, given an input subset S of participating parties. In this method, the total value characteristic function of alliance S is v(S) = Σ_f [ MAX_{p∈S} ( T(m, p, f) ) ] × V(m, f). In the above formula for the total value characteristic function of alliance S, the value that alliance S can obtain for each influencing factor f is determined by the member in the alliance who contributes the most to that factor (since contributions can be reused, the maximum value is taken). Then the values ​​of all factors are summed. A specific example is: if S = {p2, p3}, for factor f3, T(m, p2, f3) = 0.9, T(m, p3, f3) = 0.4, then the value contribution of the alliance on f3 is MAX(0.9, 0.4) × V(m, f3) = 0.9 × V(m, f3).

[0068] The Shapley value φ(p) of participant p represents the Shapley value of each sub-participant p, i.e., its fair share. In this method, the formula for calculating the Shapley value φ(p) is: φ(p)=Σ_{S N\{p}}[|S|!(|N|-|S|-1)! / |N|!]*(v(S∪{p})-v(S)); In the above formula for calculating the Shapley value φ(p): Σ_{S N \ {p}} represents the summation of all possible unions S that do not contain p.

[0069] [ |S|! × (|N| - |S| - 1)! / |N|! ] is the weighting coefficient, representing the probability of the union S occurring (all permutations are equally likely).

[0070] [v(S ∪{p}) - v(S)] represents the marginal contribution brought about by participant p joining the alliance S.

[0071] The purpose of the above formula for calculating the Shapley value φ(p) is: φ(p) is the calculated amount that is fairly distributed to participant p.

[0072] The cardinality |S| represents the number of participants in the alliance S. The cardinality |S| is used to calculate the factorial weight in the Shapley value formula.

[0073] The total coalition cardinality |N| represents the number of participants in the coalition N. The total coalition cardinality |N| is used to calculate the factorial weight in the Shapley formula. In this embodiment, the specific steps of the provided Shapley value algorithm include: Given the characteristic function v(S) of the total value of the entire alliance set N and the sub-alliance set S, the Shapley value φ(p) of each participant p is calculated using the following algorithm: Initialization: For all participants p∈N, set φ(p)=0.

[0074] Generate permutations: Generate a set Π(N) of all possible permutations π of set N. There are |N|! permutations in total.

[0075] Iterating through the permutations (outer loop): For each permutation π = (p_π1, p_π2, ..., p_π|N|) ∈ Π(N): a. Initialize the alliance: Let the current alliance S = .

[0076] b. Traverse the elements in the permutation (inner loop): in order from k=1 to |N|: i. Let the current participant p = p_πk.

[0077] ii. Calculate the marginal contribution of participant p joining the alliance S: mc = v(S∪{p}) - v(S).

[0078] iii. Add the marginal contribution to the Shapley value of p: φ(p) += mc.

[0079] iv. Add p to the alliance: S = S∪{p}.

[0080] Averaging: After traversing all permutations, divide the cumulative contribution of each participant by the total number of permutations: φ(p) = φ(p) / |N|!.

[0081] And by combining the marginal contribution of the characteristic function v(S) of the total value of the alliance S of the higher-order tensor T(m,p,f), it can be efficiently calculated: Since the total value characteristic function v(S) = Σ_f [ MAX_{p∈S} ( T(m,p, f) ) ] × V(m, f) in this method is decomposable, the marginal contribution calculation can be optimized: For factor f, a variable current_max_f is maintained during the traversal and permutation, representing the maximum value of the current alliance S on factor f.

[0082] When a new participant p_new joins, the following calculation is performed: new_max_f = max(current_max_f, T(m, p_new, f)).

[0083] Then the marginal contribution of p_new to factor f is: (new_max_f - current_max_f) * V(m, f).

[0084] Update current_max_f = new_max_f.

[0085] In this way, the time complexity for each marginal contribution calculation is reduced from O(|F| * |S|) to O(|F|). The time complexity for the entire Shapley value approximation is O(M * |N| * |F|), thus improving computational efficiency. In this embodiment, constructing the asynchronous consensus network model includes: Configure the various parameters in the asynchronous consensus network model, including: Parameters in a consensus network: The clearing result R represents the clearing scheme to be agreed upon, calculated by the contribution metric engine. The data structure of the clearing result R is a set { (p1, φ1), (p2, φ2), ..., (pk, φk)}, where φi is the Shapley value of participant pi.

[0086] The transaction hash H_tx (Tx_Hash) represents the cryptographic hash value (such as SHA-256) of the original transaction order O. The transaction hash H_tx serves as a unique identifier and tamper-proof anchor for the clearing proposal, strongly binding the clearing result R to the original transaction.

[0087] A higher-order tensor digest, D_T (Digest(T)), represents a Merkle root hash or similar digest of the higher-order tensor T of the original transaction order O. Consensus nodes do not need to store the complete tensor; they only need to verify the higher-order tensor digest D_T to ensure the consistency of the input data used for computation.

[0088] The node signature, Signature_Ni(R), represents the digital signature of consensus node Ni on the combined message of the clearing result R, Tx_Hash, and Digest(T). The node signature Signature_Ni(R) indicates that the node has verified and approved this clearing scheme, serving as evidence of consensus.

[0089] The consensus threshold (Threshold) represents the minimum number or proportion of signatures required for a consensus network to achieve finality. In this method, the consensus threshold (Threshold) > 2 / 3 × Total_Nodes (more than two-thirds of the total number of nodes). The consensus threshold (Threshold) ensures the security and liveness of the system, preventing a few node failures or malicious actions from affecting the final result.

[0090] Parameters in the clearing ledger: A Ledger entry (Ledger_Entry) represents the smallest record unit written into the clearing ledger D1. The Ledger entry (Ledger_Entry) immutably records the origin of each profit sharing transaction.

[0091] System-level parameters: The settlement strategy function `Settlement_Policy(p)` ​​represents the settlement strategy of participant `p`, defining how funds in its clearing ledger are converted into actual cash flow. Strategy types include Immediate, Periodic, Offset, and Hybrid. The `Settlement_Policy(p)` ​​function connects clearing calculations with fund execution, enabling flexible financial management.

[0092] The asynchronous consensus network model consists of several consensus nodes; wherein, the consensus nodes are divided into: The proposal node is responsible for receiving the clearing calculation result R and packaging it into a proposal broadcast; Verification nodes are responsible for verifying and voting on proposals; wherein the number of verification nodes K ≥ 4, and the number of verification nodes K = 3f + 1, where f is the tolerable number of faulty or malicious nodes.

[0093] The proposal node and the verification node can be converted into each other; The states maintained by the verification node include: The local ledger copy Ledger_i stores the clearing records that have reached a consensus; The Pool_pending pool stores proposals that have been received but for which consensus has not yet been reached. View ID (View_v) is the identifier of the current consensus round and is used to handle master node failures. Sequence number Seq_s is assigned to each consensus proposal in a monotonically increasing sequence, guaranteeing total order.

[0094] The process of clearing and settling the transaction orders based on the asynchronous consensus network model and the contribution value, and obtaining the clearing results, includes: The process involves broadcasting and verifying a settlement proposal. The settlement proposal is generated by computation engine B1, which produces the settlement result R = {(P1, φ1), (P2, φ2), ...}. The proposing node B2 then broadcasts this result, along with the transaction hash and a digest of tensor T, to all nodes in the consensus network C. Asynchronous verification is performed by each consensus node independently verifying: a) the authenticity of the transaction; b) whether the assignment of tensor T conforms to predefined rules; and c) whether the Shapley value calculation is correct. After successful verification, the node signs the result.

[0095] Specifically, the broadcasting and verification of the clearing proposal includes: The proposal node packages the clearing result R, transaction hash H_tx, tensor digest D_T, current view v, and assigned sequence number s into a message PREPARE(v,s,H_tx,D_T,R), attaches its digital signature Sig_proposer, and broadcasts it to all verification nodes. Upon receiving the message PREPARE(v,s,H_tx,D_T,R), each verification node i independently performs a rigorous verification: Format check: Signature valid, view v consistent with local; Business logic verification: Nodes obtain basic transaction information from a trusted source based on the transaction hash H_tx, recalculate the tensor digest D_T' according to publicly available rules, and verify that D_T' == D_T; this step ensures that all nodes work based on the same, correct input data, and not just transmit hashes; Conflict checking: Ensures that there are no other results with the same value (H_tx,s) in the local ledger, thus avoiding double-spending; If the verification is successful, the node stores the PREPARE(v,s,H_tx,D_T,R) message into the pending proposal pool and performs the consensus node voting collection and local submission steps.

[0096] Consensus nodes collect votes and submit them locally. Final confirmation of the vote collection is achieved when more than two-thirds of the consensus nodes sign the same result R; the clearing scheme is then considered "final." After final confirmation, the clearing result is recorded in the ledger, written to the immutable clearing ledger D1. Each participant has their own ledger view and can only see records relevant to them. This mechanism ensures that even if individual nodes (such as servers in a certain region) fail or act maliciously, a correct consensus can be reached as long as a majority of honest nodes are online. Once the clearing result is on the blockchain, it is irrefutable, providing the ultimate basis for reconciliation and auditing.

[0097] The consensus node performs vote collection and local submission, including: Voting: The verification node i generates a voting message VOTE(v,s,H_tx,i) and signs it, then broadcasts it to all other consensus nodes; Collection and Judgment: Each validator node i collects voting messages VOTE(v,s,H_tx,i) sent by other validator nodes; when validator node i receives valid VOTE messages for (v, s, H_tx) from at least 2f different nodes (including itself), the "quorum" is reached.

[0098] Local Commit: After the voting message VOTE(v,s,H_tx,i) reaches the quorum, the validator node i marks (s,H_tx,D_T,R) as "committed" and writes it to the staging area of ​​the local ledger copy; the clearing result R is regarded as "finalized" on the validator node and can be used for subsequent business (such as updating the profit sharing account). Network-wide final broadcast: Verification node i then broadcasts a public message COMMIT(v,s,H_tx); when other consensus nodes receive a specified number (in this embodiment, the specified number is set to 2f+1) of public messages COMMIT(v,s,H_tx), they move the corresponding record from the temporary storage area to the persistent storage area.

[0099] If a consensus node fails to collect enough votes within a preset time, the current view's proposing node is suspected of being faulty, and a view protocol change is initiated.

[0100] The view protocol change processing includes: The node increments the local view number v+1 and broadcasts the VIEW_CHANGE(v+1, i) message.

[0101] Once the master node of the new view receives 2f valid VIEW_CHANGE messages, it initiates a new consensus round and may rebroadcast pending, valid old proposals.

[0102] In summary, the asynchronous consensus network model provided in this embodiment is based on tensor digest recalculation verification according to public rules, incorporating business logic consistency into the core of consensus and preventing malicious nodes from submitting computation results that violate the rules. Furthermore, this asynchronous consensus network model does not require all nodes to synchronize; as long as 2f+1 nodes submit locally, the result takes effect, making it suitable for distributed business environments. It also provides optimizations for the clearing scenario; the design of the sequence number 's' prevents replay attacks on clearing instructions; and the view change protocol ensures high availability.

[0103] The asynchronous consensus network model provided in this embodiment aims to achieve authoritative consensus on complex clearing calculation results. This asynchronous consensus network model broadcasts not only transaction hashes, but more importantly, the "clearing result R" and the "Digest(T) of the calculation basis." Consensus nodes verify the correctness of the clearing logic, not just the existence of the data. This is to address the trust issue among multiple stakeholders and is part of the clearing process.

[0104] This application provides a transaction clearing method, device, and medium based on influence factor tensor decomposition and asynchronous consensus. The method constructs an influence factor tensor decomposition model for order transactions, structuring each transaction order as a high-order tensor containing multiple dimensions. Each participant in the high-order tensor has an influence factor vector in different dimensions. Then, the Shapley value algorithm is used to scientifically calculate the marginal contribution of each participant in the "cooperative alliance" formed by this transaction, thereby quantifying their deserved benefits and ensuring fairness. Simultaneously, a lightweight asynchronous consensus network is designed to ensure that all participants reach a final consensus on the clearing result of the same transaction. This achieves the ability to deconstruct the value of a single transaction in real time, objectively, and automatically, and to achieve fair and accurate clearing in complex business relationship networks.

[0105] The transaction clearing method based on impact factor tensor decomposition and asynchronous consensus proposed in this embodiment realizes a paradigm shift from "cash flow" to "value contribution." Traditional solutions track how money is distributed, while this method tracks who creates value. By quantifying the contribution of each dimension through a tensor model and then using Shapley value for scientific allocation, the profit-sharing logic is elevated from "rule-based" to "contribution-based," making it fairer, more reasonable, and easier for all parties to accept.

[0106] This method fundamentally improves the handling of complexity. The high-order tensor can accommodate almost infinitely complex business dimensions (such as temporary promotions, technical support levels, and KPI completion rates). The system can adapt to business changes by adjusting the values ​​and factor weights within the tensor without rewriting the profit-sharing logic, making it highly scalable.

[0107] This method achieves fairness and incentive compatibility by introducing game theory. The Shapley value is a recognized fair allocation solution in cooperative game theory. Its mathematical properties guarantee that the allocation scheme satisfies the requirements of efficiency, symmetry, dummy elements, and additivity, mathematically eliminating the problems of "free-riding" and "underestimation of contribution," making the profit-sharing system itself a powerful incentive mechanism.

[0108] This method provides financial-grade security and trustworthiness. The introduction of an asynchronous consensus ledger provides finality and tamper-proofness for the clearing results. This solves the biggest trust problem in B2B multi-level marketing, allowing all participants to independently verify the source and calculation process of every penny they receive.

[0109] This method also incorporates deep interdisciplinary technologies, creatively integrating tensor algebra, cooperative game theory, and distributed consensus—three theories from different fields—to construct a completely new theoretical foundation for the clearing system, which has significant academic and practical value.

[0110] Example 2: This embodiment also provides a transaction clearing terminal based on influence factor tensor decomposition and asynchronous consensus. The terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the embodiments of the method described in Embodiment 1 of the present invention.

[0111] Furthermore, as an executable solution, the transaction clearing terminal based on influence factor tensor decomposition and asynchronous consensus can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The transaction clearing terminal based on influence factor tensor decomposition and asynchronous consensus may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above-described structure of the transaction clearing terminal based on influence factor tensor decomposition and asynchronous consensus is merely an example and does not constitute a limitation on the transaction clearing terminal based on influence factor tensor decomposition and asynchronous consensus. It may include more or fewer components than described above, or combine certain components, or use different components. For example, the transaction clearing terminal based on influence factor tensor decomposition and asynchronous consensus may also include input / output devices, network access devices, buses, etc., which are not limited in this embodiment of the present invention.

[0112] Furthermore, as an executable solution, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the transaction clearing terminal based on influence factor tensor decomposition and asynchronous consensus, connecting all parts of the transaction clearing terminal using various interfaces and lines.

[0113] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the transaction clearing terminal based on influence factor tensor decomposition and asynchronous consensus by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0114] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in the embodiments of the present invention.

[0115] If the module / unit integrated into the transaction clearing terminal based on influence factor tensor decomposition and asynchronous consensus is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc.

[0116] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the present invention. Finally, it should be noted that in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0117] The foregoing has provided a detailed description of a transaction clearing method, device, and medium based on influence factor tensor decomposition and asynchronous consensus provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the technical solutions and core ideas of this application. Those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A transaction clearing method based on influence factor tensor decomposition and asynchronous consensus, characterized in that, The method includes: Construct a tensor decomposition model of the influencing factors of order transactions; Deconstruct transaction orders into high-order tensors with multiple dimensions; The contribution metric algorithm based on Shapley value is used to calculate the contribution value of each participant in the higher-order tensor; Construct an asynchronous consensus network model; Based on the asynchronous consensus network model and the contribution value, the transaction order is cleared and settled, and the clearing result is obtained.

2. The transaction clearing method based on influence factor tensor decomposition and asynchronous consensus according to claim 1, characterized in that, The construction of the tensor decomposition model of the influencing factors of order transactions includes: Define a higher-order tensor T(m,p,f) for transaction order O, wherein the higher-order tensor T(m,p,f) is a third-order tensor; The higher-order tensor T(m,p,f) includes a pattern dimension set M={m1,…,mm}, which is defined as a set of business scenarios in which transactions occur. The pattern dimension set M={m1,…,mm} includes multiple sub-patterns m, and each sub-pattern m is pre-set with a basic profit allocation weight vector W_m, and satisfies ΣW_m=1. The higher-order tensor T(m,p,f) includes a participant dimension set P={p1,...,pp}, which is defined as the set of all participants related to this transaction order. The participant dimension set P={p1,...,pp} includes multiple sub-participants p. The higher-order tensor T(m,p,f) includes an influencing factor dimension set F={f1,…,ff}, which is defined as the factors that affect the success of a transaction. The influencing factor dimension set F={f1,…,ff} includes multiple sub-influencing factors f, each of which has a pre-set weight w_f and satisfies Σw_f=1. The higher-order tensor T(m,p,f) is between [0,1] and is defined as the degree of contribution made by sub-participant p to sub-influence factor f under sub-mode m.

3. The transaction clearing method based on influence factor tensor decomposition and asynchronous consensus according to claim 2, characterized in that, The value of the higher-order tensor T(m,p,f) is automatically assigned according to pre-set objective business rules or obtained by self-learning from historical data in the system log.

4. The transaction clearing method based on influence factor tensor decomposition and asynchronous consensus according to claim 2, characterized in that, The contribution metric algorithm based on Shapley values ​​calculates the contribution value of each participant in the higher-order tensor, including: Calculate the total value of the patterns in the higher-order tensor T(m,p,f) as V(m) = Order_Amount * W_m; Calculate the value distribution V(m,f) of the neutron impact factor f, and distribute the total value V(m) of the calculated model to each sub-impact factor f according to the weight of the sub-impact factor f; where V(m,f)=V(m)*w_f; Calculate the direct value of the contribution of the sub-participant p on the sub-influence factor f in the higher-order tensor T(m,p,f): Contribution(p,f) = T(m,p,f) * V(m,f); The Shapley value algorithm is used to calculate the Shapley value φ(p) for each sub-participant p for all participant dimension sets P.

5. The transaction clearing method based on influence factor tensor decomposition and asynchronous consensus according to claim 4, characterized in that, The formula for calculating the Shapley value φ(p) of each sub-participant p is: φ(p)=Σ{S N\{p}}[|S|!(|N|-|S|-1)! / |N|!]*(v(S∪{p})-v(S)); Where N is the set of all parties involved in this order, that is, a subset N of the party dimension set P. P; S is the set of sub-alliances, which is any subset of N, S N; p represents the sub-participants, and v(S) is the total value that alliance S can obtain; Where v(S)=Σ_{f}[MAX_{p∈S}(T(m,p,f))]*V(m,f) represents the value of the alliance S on a certain sub-influence factor f, which is determined by the member of the alliance S that creates the highest direct value on that sub-influence factor f.

6. The transaction clearing method based on influence factor tensor decomposition and asynchronous consensus according to claim 4, characterized in that, The construction of the asynchronous consensus network model includes: The asynchronous consensus network model consists of several consensus nodes; wherein, the consensus nodes are divided into: The proposal node is responsible for receiving the clearing calculation result R and packaging it into a proposal broadcast; Verification nodes are responsible for verifying and voting on proposals; The proposal node and the verification node can be converted into each other; The states maintained by the verification node include: A local copy of the ledger stores the clearing records for which consensus has been reached; The pending proposal pool stores proposals that have been received but for which consensus has not yet been reached. View number, an identifier for the current consensus round, used to handle master node failures; The sequence number is assigned to each consensus proposal in a monotonically increasing sequence to guarantee total order.

7. The transaction clearing method based on influence factor tensor decomposition and asynchronous consensus according to claim 6, characterized in that, The process of clearing and settling the transaction orders based on the asynchronous consensus network model and the contribution value, and obtaining the clearing results, includes: Broadcast and verify the settlement proposal; Consensus nodes collect votes and submit them locally; If a consensus node fails to collect enough votes within a preset time, the current view's proposing node is suspected of being faulty, and a view protocol change is initiated.

8. The transaction clearing method based on influence factor tensor decomposition and asynchronous consensus according to claim 7, characterized in that, The process of broadcasting and verifying the settlement proposal includes: The proposal node packages the clearing result R, transaction hash H_tx, tensor digest D_T, current view v, and assigned sequence number s into a message PREPARE(v,s,H_tx,D_T,R), attaches its digital signature Sig_proposer, and broadcasts it to all verification nodes. Upon receiving the message PREPARE(v,s,H_tx,D_T,R), each verification node i independently performs a rigorous verification: Format check: Signature valid, view v consistent with local; Business logic verification: The node obtains basic transaction information from a trusted source based on the transaction hash H_tx, recalculates the tensor digest D_T' according to the published rules, and verifies that D_T' == D_T; Conflict checking: Ensures that there are no other results with the same value (H_tx,s) in the local ledger, thus avoiding double-spending; If the verification is successful, the node stores the PREPARE(v,s,H_tx,D_T,R) message into the pending proposal pool and performs the consensus node's vote collection and local submission steps. The consensus node performs vote collection and local submission, including: Voting: The verification node generates a voting message VOTE(v,s,H_tx,i), signs it, and broadcasts it to all other consensus nodes; Collection and Judgment: Each validator node collects the voting messages VOTE(v,s,H_tx,i) sent by other validators. Local Commit: After the voting message VOTE(v,s,H_tx,i) reaches the quorum, the validator node marks (s,H_tx,D_T,R) as "committed" and writes it to the temporary storage area of ​​the local ledger copy; the clearing result R is regarded as "finalized" on the validator node and can be used for subsequent business. Network-wide finality broadcast: The validator then broadcasts a public message COMMIT(v,s,H_tx); when other consensus nodes receive the specified number of public messages COMMIT(v,s,H_tx), they move the corresponding record from the temporary storage area to the persistent storage area.

9. A transaction clearing terminal device based on influence factor tensor decomposition and asynchronous consensus, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 8.

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