Inverter transaction identification method, device and equipment, storage medium and product

Abstract

The invention discloses an inverse quantity transaction identification method and device, equipment, a storage medium and a product. The method can be applied to the field of financial science and technology. The transaction data to be identified comprises a plurality of financial transactions; converting the to-be-identified transaction data into a to-be-identified transaction sequence according to the transaction characteristics of the financial transactions; determining a target sequence combination strategy of the to-be-identified transaction sequence; and according to the target sequence combination strategy, determining the inverse transaction in the to-be-identified transaction data. According to the technical scheme of the embodiment of the invention, the recognition efficiency and recognition accuracy of the inverse transaction are improved, and the application scene of the inverse transaction recognition is enriched.

Description

Technical Field

[0001] This invention relates to the field of financial technology, and in particular to a method, apparatus, device, storage medium, and product for identifying reverse transaction volume. Background Technology

[0002] Reverse volume trading primarily manifests as transactions between financial institutions involving similar prices and amounts, resulting in a market characterized by relatively small price fluctuations but high trading volume. Reverse volume trading is not genuine trading volume but rather a form of artificial liquidity; it not only fails to increase market depth but also disrupts normal market order. Therefore, identifying reverse volume trading is crucial.

[0003] Current methods for identifying reverse trading typically employ a sliding window-like approach to transaction analysis. For example, all data within a day is iterated over, and each pair of data is compared sequentially to determine whether their sum is 0 or less than a fixed value.

[0004] Existing reverse transaction identification methods are only applicable to one-to-one matching between two transactions. They cannot query multiple transactions across all transaction data to determine if reverse transaction risk exists. Furthermore, one-to-one matching between two transactions is inefficient, and the identification process relies on manual transactions, resulting in low identification accuracy. Summary of the Invention

[0005] This invention provides a method, apparatus, device, storage medium, and product for identifying reselling transactions, in order to improve the efficiency and accuracy of identifying reselling transactions and to expand the applicable scenarios for identifying reselling transactions.

[0006] According to one aspect of the present invention, a method for identifying reverse transaction volume is provided, the method comprising:

[0007] Obtain transaction data to be identified; the transaction data to be identified includes several financial transactions;

[0008] Based on the transaction characteristics of each financial transaction, the transaction data to be identified is transformed into a transaction sequence to be identified;

[0009] Determine the target sequence combination strategy for the transaction sequence to be identified;

[0010] Based on the target sequence combination strategy, the inverse transactions in the transaction data to be identified are determined.

[0011] According to another aspect of the present invention, a reverse transaction identification device is provided, the device comprising:

[0012] The transaction acquisition module is used to acquire transaction data to be identified; the transaction data to be identified includes several financial transactions.

[0013] The transaction sequence conversion module is used to convert the transaction data to be identified into a transaction sequence to be identified based on the transaction characteristics of each financial transaction.

[0014] The combination strategy determination module is used to determine the target sequence combination strategy for the transaction sequence to be identified;

[0015] The reverse transaction identification module is used to identify reverse transactions in the transaction data to be identified based on the target sequence combination strategy.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the reverse transaction identification method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the reverse transaction identification method according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the front-end and back-end communication request and response method described in any embodiment of the present invention.

[0022] The technical solution of this invention acquires transaction data to be identified, transforms the transaction data into a transaction sequence based on the transaction characteristics of each financial transaction in the transaction data, determines a target sequence combination strategy for the transaction sequence, and identifies inverse transactions in the transaction data based on the target sequence combination strategy. This technical solution transforms the inverse transaction problem into a function decision problem to obtain the target sequence combination strategy, thereby determining inverse transactions based on the combination strategy. This method can identify several inverse transactions in a batch of transactions, not just two, enriching the identification scenarios for inverse transactions. Furthermore, it eliminates the need for transaction-by-transaction adaptation and traversal, improving the identification efficiency of inverse transactions. The identification process requires no manual intervention, avoiding the high error rate caused by manual identification and improving the accuracy of inverse transaction identification.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of a reverse transaction identification method provided in Embodiment 1 of the present invention;

[0026] Figure 2 This is a flowchart of a reverse transaction identification method provided in Embodiment 2 of the present invention;

[0027] Figure 3 This is a flowchart of a reverse transaction identification method provided in Embodiment 3 of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of a reverse transaction identification device provided in Embodiment 4 of the present invention;

[0029] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the reverse transaction identification method of this invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Example 1

[0033] Figure 1 This is a flowchart of a method for identifying reverse trading in an embodiment of the present invention. This embodiment is applicable to the automatic identification of reverse trading behavior during financial transactions. The method can be executed by a reverse trading identification device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0034] S110. Obtain the transaction data to be identified; the transaction data to be identified includes several financial transactions.

[0035] S120. Based on the transaction characteristics of each financial transaction, the transaction data to be identified is transformed into a transaction sequence to be identified.

[0036] S130. Determine the target sequence combination strategy for the transaction sequence to be identified.

[0037] S140. Based on the target sequence combination strategy, identify the inverse transactions in the transaction data to be identified.

[0038] It should be noted that reverse trading refers to transactions between financial institutions that are similar in price and amount. The determination of reverse trading involves identifying a sequence of transactions where the sum of the amounts is 0 or less than a fixed value. For example, given the sequence [12,34,41,-25,33,-9,43,-12], where positive numbers represent buy transactions and negative numbers represent sell transactions, we can determine that there is a reverse trading risk between the first and eighth transactions since the transaction amounts of the first transaction (12) and the eighth transaction (-12) are equal to 0. Similarly, there is a reverse trading risk between the second transaction (34), the fourth transaction (-25), and the sixth transaction (-9).

[0039] The transaction data to be identified refers to the data that needs to be analyzed for inverse transaction volume. This data contains several financial transactions. Typically, in financial transaction scenarios, the number of financial transactions contained in the transaction data to be identified is large. This data includes both buy and sell transactions.

[0040] Based on the transaction characteristics of financial transactions, the transaction data to be identified is transformed into a transaction sequence to be identified. The transaction characteristics refer to whether the transaction is a buy or sell transaction. Each financial transaction is transformed according to its transaction amount and characteristics to obtain the transaction sequence to be identified. For example, the transformed transaction sequence is [12,34,41,-25,33,-9,43,-12]. Taking the first transaction as an example, "12" represents the transaction amount, a positive number indicates a buy transaction, and "-25" indicates a sell transaction.

[0041] A target sequence combination strategy can be generated based on the momentum annealing algorithm to identify the transaction sequence. The transaction sequence to be identified is then transformed according to a preset data format to obtain a representation in the following data format:

[0042] [p1*x1,p2*x2,…p i *x i …,p n *x n ]

[0043] Where, p i p represents the i-th transaction in the transaction sequence to be identified. i The sign of the value indicates whether to buy or sell; a positive number represents buying, and a negative number represents selling; where x i The value of x can be 0 or 1, and the sequence combination strategy is [x1, x2, ... x i …,x n The target sequence combination strategy is a sequence combination strategy that satisfies the judgment conditions of reverse volume trading.

[0044] Because x i The value of n can be 0 or 1. Therefore, different sequence combination strategies can be obtained. Taking an n value of 8 as an example, the generated sequence combination strategies can be [0,0,0,0,0,0,0,0], [1,1,1,1,1,1,1,1,1], [0,1,0,0,1,0,0,0], or [0,0,0,0,0,0,1,1], etc. Based on the above data format [p1*x1,p2*x2,…p i *x i …,p n *x n The following mathematical function can be established to represent this:

[0045] f(x) = (p1x1 + p2x2 + ... + p i x i +…+p n x n ) 2

[0046] Among them, the condition for judging inverse volume transactions can be that when f(x) is 0, x i When the value is 1, the corresponding transaction p i This is known as reverse trading. Therefore, the sequence combination strategy [x1, x2, ... x] that satisfies the condition that (x) takes the value of 0 is... i …,x n This refers to the target sequence combination strategy.

[0047] Taking the transaction sequence to be identified as [12,34,41,-25,33,-9,43,-12] as an example, several sets of sequence combination strategies are randomly generated, such as [1,1,1,1,1,1,1,1,1], [0,1,0,0,1,0,0,0] or [0,0,0,0,0,0,1,1], etc., and substituted into the above function f(x), we obtain the combinations that make f(x) equal to 0, namely [0,0,0,0,0,0,0,0,0], [1,0,0,0,0,0,0,1] and [0,1,0,1,0,1,0,0]. Since the combination [0,0,0,0,0,0,0,0] does not satisfy the existence of x i The condition is that the value of is 1. Therefore, [1,0,0,0,0,0,0,1] and [0,1,0,1,0,1,0,0] are used as the target sequence combination strategy for the transaction sequence [12,34,41,-25,33,-9,43,-12] to be identified.

[0048] Based on the target sequence combination strategy, the inverse transactions in the transaction data to be identified are determined. Continuing the previous example, based on the target sequence combination strategy [1,0,0,0,0,0,0,1], the inverse transactions in the transaction data to be identified are identified as the 1st and 12th transactions; based on the target sequence combination strategy [0,1,0,1,0,1,0,0], the inverse transactions in the transaction data to be identified are identified as the 2nd, 4th, and 6th transactions.

[0049] The technical solution of this invention acquires transaction data to be identified, transforms the transaction data into a transaction sequence based on the transaction characteristics of each financial transaction in the transaction data, determines a target sequence combination strategy for the transaction sequence, and identifies inverse transactions in the transaction data based on the target sequence combination strategy. This technical solution transforms the inverse transaction problem into a function decision problem to obtain the target sequence combination strategy, thereby determining inverse transactions based on the combination strategy. This method can identify several inverse transactions in a batch of transactions, not just two, enriching the identification scenarios for inverse transactions. Furthermore, it eliminates the need for transaction-by-transaction adaptation and traversal, improving the identification efficiency of inverse transactions. The identification process requires no manual intervention, avoiding the high error rate caused by manual identification and improving the accuracy of inverse transaction identification.

[0050] Example 2

[0051] Figure 2 This is a flowchart of a reverse transaction identification method provided in Embodiment 2 of the present invention. This embodiment is an optimization and improvement based on the above technical solutions.

[0052] Furthermore, the step "determining the target sequence combination strategy for the transaction sequence to be identified" is refined to "if the current iteration period is not the first iteration and the current cycle period is not the first cycle, then obtain the historical combination strategy of the previous cycle period under the current iteration period; the historical combination strategy includes a first historical strategy and a second historical strategy; generate a first current strategy based on the first historical strategy, and generate a second current strategy based on the second historical strategy; generate a first target strategy based on the transaction sequence to be identified based on the first current strategy and the first historical strategy, and generate a second target strategy based on the transaction sequence to be identified based on the second current strategy and the second historical strategy; if the current iteration period meets the preset iteration end condition and the current cycle period meets the preset cycle end condition, then determine the target sequence combination strategy for the transaction sequence to be identified based on the first target strategy and the second target strategy." This improves the method for determining the target sequence combination strategy for the transaction sequence to be identified.

[0053] It should be noted that for parts not described in detail in the embodiments of the present invention, please refer to the descriptions in other embodiments. For example... Figure 2 As shown, the method includes the following specific steps:

[0054] S210. Obtain the transaction data to be identified; the transaction data to be identified includes several financial transactions.

[0055] S220. Based on the transaction characteristics of each financial transaction, the transaction data to be identified is transformed into a transaction sequence to be identified.

[0056] S230. If the current iteration period is not the first iteration and the current cycle period is not the first cycle, then obtain the historical combination strategy of the previous cycle period under the current iteration period; the historical combination strategy includes the first historical strategy and the second historical strategy.

[0057] S240. Generate a first current strategy based on the first historical strategy, and generate a second current strategy based on the second historical strategy.

[0058] S250. Based on the first current strategy and the first historical strategy, generate a first target strategy based on the transaction sequence to be identified, and generate a second target strategy based on the second current strategy and the second historical strategy based on the transaction sequence to be identified.

[0059] S260. If the current iteration period meets the preset iteration end condition and the current cycle period meets the preset cycle end condition, then the target sequence combination strategy of the transaction sequence to be identified is determined according to the first target strategy and the second target strategy.

[0060] S270. Based on the target sequence combination strategy, identify the inverse transactions in the transaction data to be identified.

[0061] The iteration cycle and the cycle period can be preset by relevant technical personnel according to actual needs. For example, the iteration cycle can be set to 100 times and the cycle period can be set to 50 times.

[0062] In any iteration cycle, the entire loop cycle must be executed. For example, for the 20th iteration, the loop cycle needs to be executed 50 times in this iteration cycle.

[0063] Taking any iteration cycle as an example, and considering that the iteration cycle is not the first iteration, and the cycle under the iteration cycle is not the first cycle, we will use the iteration cycle and the cycle as the current iteration cycle and the current cycle as examples to illustrate.

[0064] Obtain the historical combination strategy from the previous iteration cycle under the current iteration cycle; where the historical combination strategy includes the first historical strategy and the second historical strategy. For example, if the current iteration cycle is the 20th iteration and the current cycle cycle is the 10th cycle, then the previous cycle cycle is the 9th cycle cycle under the 20th iteration cycle.

[0065] Both the first and second historical strategies are possible combinations of the transaction sequences to be identified determined in the previous cycle, such as [1,0,0,0,0,0,0,0,1]. Based on a preset neighborhood function, a perturbation can be randomly generated to obtain the first current strategy of the first historical strategy. For example, the neighborhood function can be the randn() function, which can generate several different combinations of the first historical strategy. For example, if the first historical strategy is [1,0,0,0,0,0,0,1], then the first current strategy randomly generated based on the preset neighborhood function can be [1,1,0,0,0,0,0,1]. The second current strategy is generated in the same way, based on the preset neighborhood function and the second historical strategy. For example, if the second historical strategy is [1,0,1,0,0,0,0,1], then the first current strategy randomly generated based on the preset neighborhood function can be [1,0,0,0,1,0,0,1].

[0066] In an optional embodiment, a first target strategy is generated based on a transaction sequence to be identified, according to a first current strategy and a first historical strategy; and a second target strategy is generated based on a transaction sequence to be identified, according to a second current strategy and a second historical strategy, including:

[0067] Step a1: Obtain the objective function value of the first historical strategy and the objective function value of the second historical strategy.

[0068] The objective function values ​​for the first and second historical strategies are calculated based on a preset optimization function in the previous iteration cycle. The calculation method for the objective function value is the same for any iteration cycle. Steps a21 and a22 will be explained in detail using the objective function value at the current iteration number as an example.

[0069] Step a21: Based on the first current strategy and the transaction sequence to be identified, determine the objective function value of the first current strategy based on the objective function value of the second historical strategy; determine the first target strategy based on the objective function value of the first current strategy and the objective function value of the first historical strategy.

[0070] For example, let's set the current loop cycle as the Nth iteration, and the preset objective optimization function is expressed as follows:

[0071] F N (σ N )=(p1σ1+p1σ2+…+p1σ n ) 2 +ηF N-1 (τ N-1 );

[0072] Among them, F N (σN ) represents the objective function value of the first current strategy in the Nth iteration; σ1, σ2, ..., σ n Let p1, p2, ..., p be the first current policy. n The sequence of transactions to be identified; η represents the momentum coupling parameter, which can be preset by relevant technical personnel according to actual needs, with a value range of [0,1]. Preferably, η can be set to 0.2. F N-1 (τ N-1 ) represents the objective function value of the second historical strategy in the (N-1)th cycle, i.e. the previous cycle.

[0073] The first target strategy can be determined based on the objective function value of the first current strategy and the objective function value of the first historical strategy. Optionally, determining the first target strategy based on the objective function value of the first current strategy and the objective function value of the first historical strategy includes: if the objective function value of the first current strategy is less than the objective function value of the first historical strategy, then the first current strategy is determined as the first target strategy.

[0074] For example, if the first current policy σ N The objective function value is X1, and the first historical strategy σ is obtained. N-1 The objective function value is X2. When X1 < X2, the first current policy is determined as the first objective policy.

[0075] The above technical solution achieves accurate determination of the first target strategy in each iteration and cycle by using the objective function value to iteratively update the new and old strategies. When the objective function value of the first current strategy is less than that of the first historical strategy, it can be considered that the first current strategy is more accurate than the first historical strategy relative to the transaction sequence to be identified. Therefore, the strategy replacement is carried out by direct replacement, which improves the efficiency and accuracy of strategy update iteration.

[0076] Optionally, if the objective function value of the first current strategy is not less than the objective function value of the first historical strategy, then the probability value of the first current strategy and the probability value of the first historical strategy are determined based on the objective function value of the first current strategy, the objective function value of the first historical strategy, the period value of the current iteration period, and the period value of the current loop period; and the first target strategy is determined based on the probability value of the first current strategy and the probability value of the first historical strategy.

[0077] When determining that the objective function value of the first current strategy is not less than the objective function value of the first historical strategy, the probability values ​​of the first current strategy and the first historical strategy can be determined based on a preset probability determination function, according to the objective function value of the first current strategy, the objective function value of the first historical strategy, the period value of the current iteration cycle, and the period value of the current loop cycle.

[0078] For example, the probability determination function P σ The data is expressed in the following form:

[0079]

[0080] Among them, F σ (N) represents the objective function value of the first current policy; F σ (N-1) represents the objective function value of the first historical strategy; k represents the period value of the current cycle, for example, if the current cycle is the 30th cycle, then the value of k is 30; T represents the period value of the current iteration cycle, for example, if the current iteration cycle is the 50th iteration, then the value of T is 50. P σ This represents the probability value of the first current policy. 1-P σ This represents the probability value of the first historical strategy.

[0081] If the probability value of the first current strategy is greater than the probability value of the first historical strategy, then the first current strategy is determined as the first target strategy; if the probability value of the first current strategy is not greater than the probability value of the first historical strategy, then the first historical strategy is determined as the first target strategy.

[0082] The above technical solution achieves accurate determination of the first target strategy by determining that the objective function value of the first current strategy is not less than the objective function value of the first historical strategy, determining the probability value of the first current strategy and the probability value of the first historical strategy using a preset probability determination function, and selecting the first target strategy based on the probability value. This further improves the accuracy of determining the target sequence combination strategy of the transaction sequence to be identified.

[0083] Step a22: Based on the second current strategy and the transaction sequence to be identified, determine the objective function value of the second current strategy based on the objective function value of the first historical strategy; determine the second target strategy based on the objective function value of the second current strategy and the objective function value of the second historical strategy.

[0084] For example, let's set the current loop cycle as the Nth iteration, and the preset objective optimization function is expressed as follows:

[0085] F N (τ N )=(p1τ1+p1τ2+…+p1τ n )2 +ηF N-1 (σ N-1 );

[0086] Among them, F N (τ N ) represents the objective function value of the second current strategy in the current Nth iteration; τ1, τ2, ..., τ n Let p1, p2, ..., p be the second current policy. n The transaction sequence to be identified; η represents the momentum coupling parameter, which can be preset by relevant technical personnel according to actual needs, with a value range of [0,1]. Preferably, η can be set to 0.2. F N-1 (σ N-1 ) represents the objective function value of the first historical strategy in the (N-1)th cycle, i.e., the previous cycle.

[0087] If the objective function value of the second current policy is less than the objective function value of the second historical policy, then the second current policy is determined as the second objective policy.

[0088] If the objective function value of the second current strategy is not less than the objective function value of the second historical strategy, then the probability value of the second current strategy and the probability value of the second historical strategy are determined based on the objective function value of the second current strategy, the objective function value of the second historical strategy, the period value of the current iteration cycle, and the period value of the current loop cycle; and the second target strategy is determined based on the probability value of the second current strategy and the probability value of the second historical strategy.

[0089] When determining that the objective function value of the second current strategy is not less than the objective function value of the second historical strategy, the probability value of the second current strategy and the probability value of the second historical strategy can be determined based on a preset probability determination function, according to the objective function value of the second current strategy, the objective function value of the second historical strategy, the period value of the current iteration cycle, and the period value of the current loop cycle.

[0090] For example, the probability determination function P τ The data is expressed in the following form:

[0091]

[0092] Among them, F τ (N) represents the objective function value of the second current policy; F τ (N-1) represents the objective function value of the second historical strategy; k represents the period value of the current cycle, for example, if the current cycle is the 30th cycle, then the value of k is 30; T represents the period value of the current iteration cycle, for example, if the current iteration cycle is the 50th iteration, then the value of T is 50. P τ This represents the probability value of the second current policy. 1-Pτ This represents the probability value of the second historical strategy.

[0093] If the probability value of the second current policy is greater than the probability value of the second historical policy, then the second current policy is determined as the second target policy; if the probability value of the second current policy is not greater than the probability value of the second historical policy, then the second historical policy is determined as the second target policy.

[0094] The above technical solution obtains the objective function values ​​of the first and second historical strategies. Based on the first current strategy and the transaction sequence to be identified, and using the objective function value of the second historical strategy, it determines the objective function value of the first current strategy. Then, based on the objective function values ​​of the first current strategy and the first historical strategy, it determines the first target strategy. In determining the first target strategy, it comprehensively considers both the first and second historical strategies and iteratively updates the strategy based on the objective functions corresponding to the current and historical strategies, thereby improving the accuracy of determining the first target strategy for each iteration and cycle. Similarly, the second target strategy is iteratively updated based on the objective functions of the historical and current strategies, further improving the accuracy of determining the second target strategy for each iteration and cycle, and ultimately enhancing the accuracy of determining the target sequence combination strategy for the transaction sequence to be identified.

[0095] If the current iteration period meets the preset iteration termination condition and the current cycle period meets the preset cycle termination condition, then the target sequence combination strategy for the transaction sequence to be identified is determined according to the first target strategy and the second target strategy. Specifically, the cycle termination condition can be that the current iteration period is the last iteration period and the current cycle period is the last cycle period. For example, if the set iteration period is 100, the set cycle period is 50, the current iteration period is the 100th iteration, and the current cycle period is the 50th cycle, then the cycle termination condition is satisfied.

[0096] When a preset loop termination condition is met, either the first target strategy or the second target strategy can be determined as the target sequence combination strategy for the transaction sequence to be identified. To further improve the accuracy of determining the target sequence combination strategy, in an optional embodiment, determining the target sequence combination strategy for the transaction sequence to be identified based on the first target strategy and the second target strategy includes: determining the target similarity between the first target strategy and the second target strategy; if the target similarity is greater than a preset similarity threshold, then the first target strategy or the second target strategy is determined as the target sequence combination strategy for the transaction sequence to be identified.

[0097] For example, the target similarity between the strings of the first target strategy and the strings of the second target strategy can be determined based on a preset string matching algorithm, such as the edit distance algorithm or the maximum common subsequence algorithm. If the target similarity is greater than a preset similarity threshold, both target strategies can be considered sufficiently accurate and can both be used as target sequence combination strategies. In this case, the first target strategy or the second target strategy is determined as the target sequence combination strategy for the transaction sequence to be identified. The similarity threshold can be preset by relevant technical personnel according to actual needs; for example, the similarity threshold can be set to 90%.

[0098] For example, if both the first and second target strategies are [1,0,0,0,1,0,0,1], then the target similarity is 100%, and [1,0,0,0,1,0,0,1] is determined as the target sequence combination strategy for the transaction sequence to be identified.

[0099] The above technical solution improves the accuracy of determining the target sequence combination strategy by determining the target similarity between the first target strategy and the second target strategy, and when the target similarity is greater than a preset similarity threshold, determining the first target strategy or the second target strategy as the target sequence combination strategy of the transaction sequence to be identified.

[0100] If the target similarity is not greater than a preset similarity threshold, it can be considered that there is a certain deviation between the two strategies, and the global optimum has not been found; it may be a local optimum. In this case, the iteration count and loop count can be adjusted to continue the loop until the condition is met. For example, the iteration count and loop count can be increased until the target similarity between the first and second target strategies is greater than the preset similarity threshold.

[0101] If the current cycle is the first cycle, two initial policies are randomly generated, namely the first initial policy and the second initial policy. Let the first initial policy be σ. Ι The second initial strategy is τ Ι The objective function values ​​corresponding to the first and second initial strategies are determined as follows:

[0102] F Ι (σ Ι )=(p1σ1+p1σ2+…+p1σ n ) 2 ;

[0103] F Ι (τ Ι )=(p1τ1+p1τ2+…+p1τ n ) 2 ;

[0104] If the current iteration cycle does not meet the preset iteration termination condition and the current loop cycle does not meet the preset loop termination condition, then continue to execute the above steps until the preset iteration termination condition is met, then end the loop, and obtain the first target strategy and the second target strategy of the last loop cycle under the last iteration cycle.

[0105] This embodiment's technical solution generates a first current strategy based on a first historical strategy, and a second current strategy based on a second historical strategy. It then generates a first target strategy based on the transaction sequence to be identified, using the first current strategy and the first historical strategy, and finally generates a second target strategy based on the transaction sequence to be identified, using the second current strategy and the second historical strategy. This achieves the determination of the first and second target strategies for any iteration period and any number of iterations. The strategy determination process incorporates historical strategies from previous iteration periods and continuously optimizes the strategy during each iteration update to obtain the optimal target strategy for the current iteration period and current number of iterations. This improves the accuracy of determining the target sequence combination strategy for the transaction sequence to be identified, thereby improving the accuracy of identifying inverse transactions in the transaction data to be identified.

[0106] Example 3

[0107] Figure 3 This is a flowchart of a method for identifying reverse transaction volume according to Embodiment 3 of the present invention. This embodiment provides a preferred example based on the above embodiments.

[0108] like Figure 3 As shown, the method includes the following specific steps:

[0109] S1. Obtain the transaction sequence to be identified, set the number of iterations and the number of cycles in each iteration period, and set the initial combination strategy, which includes the first initial strategy and the second initial strategy.

[0110] In the momentum annealing algorithm, the number of iterations is equivalent to the set temperature, which is related to the convergence of the optimization problem. As the number of iterations increases, the temperature T gradually decreases, thus allowing the combinatorial convergence. The role of the neighborhood function is to partially change the combinatorial strategy based on a random number. For example, given the existing combinatorial strategy w is [0,1,0,1,0,1,0,0], by randomly changing the value of a position in the vector with a random number, the current combinatorial w is changed, resulting in the changed combinatorial strategy [1,1,0,1,0,1,0,0].

[0111] Suppose that the first initial strategy is denoted as σ1 and the second initial strategy is denoted as τ1.

[0112] S2. Randomly generate perturbation quantities using a preset domain function to obtain two new combination strategies.

[0113] S3. Determine the objective function values ​​corresponding to the two new combination strategies.

[0114] The first initial policy σ1 is randomly generated based on a combination of perturbations to obtain the first current policy σ2. Similarly, the second initial policy τ1 is randomly generated based on a combination of perturbations to obtain the second current policy τ2. The objective function value of the first current policy σ2 is determined as follows:

[0115] F2(σ2)=(p1σ 21 +p1σ 22 +…+p1σ 2n ) 2 +ηF1(τ1);

[0116] Since τ1 is the initial strategy, then F1(τ1) = (p1τ) 11 +p1τ 12 +…+p1τ 1n ) 2 Where p1, p2, ..., p n τ represents the transaction sequence to be identified; η represents the momentum coupling parameter, which can be preset by relevant technical personnel according to actual needs, with a value range of [0,1]. Preferably, η can be set to 0.2. 11 ,τ 12 ,…,τ 1n For the second initial strategy; σ 21 ,σ 22 ,…,σ 2n This is the first current strategy.

[0117] The method for determining the objective function value of the second current policy τ2 is similar. The method for determining the objective function value of the second current policy τ2 is as follows:

[0118] F2(τ2)=(p1τ 21 +p1τ 22 +…+p1τ 2n ) 2 +ηF1(σ1);

[0119] Since σ1 is the initial policy, then F1(σ1) = (p1σ) 11 +p1σ 12 +…+p1σ 1n ) 2 Where p1, p2, ..., p n The sequence of transactions to be identified is denoted as η; η represents the momentum coupling parameter, which can be preset by relevant technical personnel according to actual needs, with a value range of [0,1]. Preferably, η can be set to 0.2. 11 ,σ 12 ,…,σ1n The first initial strategy; τ 21 ,τ 22 ,…,τ 2n This is the second current strategy.

[0120] S4. Compare the objective function values ​​of the old and new strategies, and update and iterate the old and new strategies accordingly.

[0121] If the objective function value of the first current policy σ2 is less than the objective function value of the first initial policy σ1, then the first current policy σ2 is adopted as the current optimal policy, and the first initial policy σ1 is discarded; if the objective function value of the first current policy σ2 is not less than the objective function value of the first initial policy σ1, then the first current policy σ2 is adopted as the current optimal policy, and the first initial policy σ1 is discarded. The probability determines the current optimal strategy; if based on If the probability value of the first current policy σ2 is greater than that of the first initial policy σ1, then the first initial policy σ1 is discarded and the first current policy σ2 is adopted as the current optimal policy; otherwise, the first initial policy σ1 is still adopted as the current optimal policy.

[0122] If the objective function value of the second current policy τ2 is less than the objective function value of the second initial policy τ1, then the second current policy τ2 is adopted as the current optimal policy, and the second initial policy τ1 is discarded; if the objective function value of the second current policy τ2 is not less than the objective function value of the second initial policy τ1, then the second current policy τ2 is adopted as the current optimal policy, and the second initial policy τ1 is discarded. The probability determines the current optimal strategy; if based on If the probability value of the second current policy τ2 is greater than that of the second initial policy τ1, then the second initial policy τ1 is discarded and the second current policy τ2 is adopted as the current optimal policy; otherwise, the second initial policy τ1 is still adopted as the current optimal policy.

[0123] S5. Repeat steps 2, 3 and 4 above for a preset number of iterations k times. The optimal strategy combination obtained is regarded as the optimal solution under the current iteration cycle.

[0124] S6. Repeat steps 2, 3, 4, and 5 above until the number of iterations reaches a set threshold, to obtain the optimal combination strategy σ. opti and τ opti .

[0125] S7. If two optimal combination strategies σ opti and τ opti If the two optimal combination strategies are the same, the algorithm is considered to have found the global optimum. If the two optimal combination strategies are different and significantly different, the iteration can be continued by adjusting the number of loops and iterations until the two optimal combination strategies σ are found. opti and τ opti Same or similar.

[0126] The information collected in this invention is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and public order and good morals are not violated. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0127] Example 4

[0128] Figure 4 This is a schematic diagram of a reverse trading identification device provided in Embodiment 4 of the present invention. The reverse trading identification device provided in this embodiment of the present invention is applicable to automatically identifying reverse trading behavior during financial transactions. This reverse trading identification device can be implemented in hardware and / or software, such as… Figure 4 As shown, the device includes: a transaction acquisition module 401, a transaction sequence conversion module 402, a combination strategy determination module 403, and a reverse transaction identification module 404. Among them,

[0129] The transaction acquisition module 401 is used to acquire transaction data to be identified; the transaction data to be identified includes several financial transactions.

[0130] The transaction sequence conversion module 402 is used to convert the transaction data to be identified into a transaction sequence to be identified based on the transaction characteristics of each financial transaction.

[0131] The combination strategy determination module 403 is used to determine the target sequence combination strategy of the transaction sequence to be identified.

[0132] The reverse transaction identification module 404 is used to identify reverse transactions in the transaction data to be identified based on the target sequence combination strategy.

[0133] The technical solution of this invention acquires transaction data to be identified, transforms the transaction data into a transaction sequence based on the transaction characteristics of each financial transaction in the transaction data, determines a target sequence combination strategy for the transaction sequence, and identifies inverse transactions in the transaction data based on the target sequence combination strategy. This technical solution transforms the inverse transaction problem into a function decision problem to obtain the target sequence combination strategy, thereby determining inverse transactions based on the combination strategy. This method can identify several inverse transactions in a batch of transactions, not just two, enriching the identification scenarios for inverse transactions. Furthermore, it eliminates the need for transaction-by-transaction adaptation and traversal, improving the identification efficiency of inverse transactions. The identification process requires no manual intervention, avoiding the high error rate caused by manual identification and improving the accuracy of inverse transaction identification.

[0134] Optionally, the combination strategy determination module 403 includes:

[0135] The historical combination strategy acquisition unit is used to acquire the historical combination strategy of the previous cycle under the current iteration cycle if the current iteration cycle is not the first iteration and the current loop cycle is not the first loop; the historical combination strategy includes a first historical strategy and a second historical strategy.

[0136] The current strategy generation unit is configured to generate a first current strategy based on the first historical strategy, and to generate a second current strategy based on the second historical strategy;

[0137] The target strategy generation unit is configured to generate a first target strategy based on the transaction sequence to be identified, according to the first current strategy and the first historical strategy; and to generate a second target strategy based on the transaction sequence to be identified, according to the second current strategy and the second historical strategy.

[0138] The combination strategy determination unit is used to determine the target sequence combination strategy of the transaction sequence to be identified based on the first target strategy and the second target strategy if the current iteration period meets the preset iteration end condition and the current loop period meets the preset loop end condition.

[0139] Optionally, the target policy generation unit includes:

[0140] The function value acquisition subunit is used to acquire the objective function value of the first historical strategy and the objective function value of the second historical strategy.

[0141] The first function value determination subunit is used to determine the objective function value of the first current strategy based on the objective function value of the second historical strategy, according to the first current strategy and the transaction sequence to be identified.

[0142] The first strategy determination subunit is configured to determine a first target strategy based on the objective function value of the first current strategy and the objective function value of the first historical strategy; and,

[0143] The second function value determination subunit is used to determine the objective function value of the second current strategy based on the objective function value of the first historical strategy, according to the second current strategy and the transaction sequence to be identified.

[0144] The second function value determination subunit is used to determine the second target strategy based on the target function value of the second current strategy and the target function value of the second historical strategy.

[0145] Optionally, the first strategy determines the sub-unit, specifically used for:

[0146] If the objective function value of the first current strategy is less than the objective function value of the first historical strategy, then the first current strategy is determined as the first objective strategy.

[0147] Optionally, the first strategy for determining the sub-unit is also used for:

[0148] If the objective function value of the first current strategy is not less than the objective function value of the first historical strategy, then the probability value of the first current strategy and the probability value of the first historical strategy are determined based on the objective function value of the first current strategy, the objective function value of the first historical strategy, the period value of the current iteration cycle, and the period value of the current loop cycle.

[0149] The first target strategy is determined based on the probability value of the first current strategy and the probability value of the first historical strategy.

[0150] Optionally, a combination strategy determination unit is used specifically for:

[0151] Determine the target similarity between the first target strategy and the second target strategy;

[0152] If the target similarity is greater than a preset similarity threshold, then the first target strategy or the second target strategy is determined as the target sequence combination strategy of the transaction sequence to be identified.

[0153] The reverse transaction identification device provided in this embodiment of the invention can execute the reverse transaction identification method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0154] Example 5

[0155] Figure 5 A schematic diagram of an electronic device 50 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0156] like Figure 5As shown, the electronic device 50 includes at least one processor 51 and a memory, such as a read-only memory (ROM) 52 and a random access memory (RAM) 53, communicatively connected to the at least one processor 51. The memory stores computer programs executable by the at least one processor. The processor 51 can perform various appropriate actions and processes based on the computer program stored in the ROM 52 or loaded into the RAM 53 from storage unit 58. The RAM 53 can also store various programs and data required for the operation of the electronic device 50. The processor 51, ROM 52, and RAM 53 are interconnected via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0157] Multiple components in electronic device 50 are connected to I / O interface 55, including: input unit 56, such as keyboard, mouse, etc.; output unit 57, such as various types of monitors, speakers, etc.; storage unit 58, such as disk, optical disk, etc.; and communication unit 59, such as network card, modem, wireless transceiver, etc. Communication unit 59 allows electronic device 50 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0158] Processor 51 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 51 performs the various methods and processes described above, such as the reverse transaction identification method.

[0159] In some embodiments, the reverse transaction identification method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 58. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 50 via ROM 52 and / or communication unit 59. When the computer program is loaded into RAM 53 and executed by processor 51, one or more steps of the reverse transaction identification method described above may be performed. Alternatively, in other embodiments, processor 51 may be configured to perform the reverse transaction identification method by any other suitable means (e.g., by means of firmware).

[0160] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0161] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0162] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0163] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0164] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0165] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0166] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0167] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for identifying reverse volume transactions, characterized in that, include: Obtain transaction data to be identified; the transaction data to be identified includes several financial transactions; Based on the transaction characteristics of each financial transaction, the transaction data to be identified is transformed into a transaction sequence to be identified; Determine the target sequence combination strategy for the transaction sequence to be identified; Based on the target sequence combination strategy, the inverse transactions in the transaction data to be identified are determined.

2. The method according to claim 1, characterized in that, The strategy for determining the target sequence combination of the transaction sequence to be identified includes: If the current iteration period is not the first iteration and the current cycle period is not the first cycle, then obtain the historical combination strategy of the previous cycle period under the current iteration period; the historical combination strategy includes the first historical strategy and the second historical strategy. A first current strategy is generated based on the first historical strategy, and a second current strategy is generated based on the second historical strategy; Based on the first current strategy and the first historical strategy, a first target strategy is generated based on the transaction sequence to be identified; and based on the second current strategy and the second historical strategy, a second target strategy is generated based on the transaction sequence to be identified. If the current iteration period meets the preset iteration end condition and the current loop period meets the preset loop end condition, then the target sequence combination strategy of the transaction sequence to be identified is determined according to the first target strategy and the second target strategy.

3. The method according to claim 2, characterized in that, The step of generating a first target strategy based on the transaction sequence to be identified, according to the first current strategy and the first historical strategy, and generating a second target strategy based on the transaction sequence to be identified, according to the second current strategy and the second historical strategy, includes: Obtain the objective function value of the first historical strategy and the objective function value of the second historical strategy; Based on the first current strategy and the transaction sequence to be identified, the objective function value of the first current strategy is determined based on the objective function value of the second historical strategy. Based on the objective function value of the first current strategy and the objective function value of the first historical strategy, a first target strategy is determined; and, Based on the second current strategy and the transaction sequence to be identified, the objective function value of the second current strategy is determined based on the objective function value of the first historical strategy. The second target strategy is determined based on the objective function value of the second current strategy and the objective function value of the second historical strategy.

4. The method according to claim 3, characterized in that, The step of determining the first target strategy based on the objective function value of the first current strategy and the objective function value of the first historical strategy includes: If the objective function value of the first current strategy is less than the objective function value of the first historical strategy, then the first current strategy is determined as the first objective strategy.

5. The method according to claim 4, characterized in that, The method further includes: If the objective function value of the first current strategy is not less than the objective function value of the first historical strategy, then the probability value of the first current strategy and the probability value of the first historical strategy are determined based on the objective function value of the first current strategy, the objective function value of the first historical strategy, the period value of the current iteration cycle, and the period value of the current loop cycle. The first target strategy is determined based on the probability value of the first current strategy and the probability value of the first historical strategy.

6. The method according to claim 2, characterized in that, The step of determining the target sequence combination strategy for the transaction sequence to be identified based on the first target strategy and the second target strategy includes: Determine the target similarity between the first target strategy and the second target strategy; If the target similarity is greater than a preset similarity threshold, then the first target strategy or the second target strategy is determined as the target sequence combination strategy of the transaction sequence to be identified.

7. A reverse transaction identification device, characterized in that, include: The transaction acquisition module is used to acquire transaction data to be identified; the transaction data to be identified includes several financial transactions. The transaction sequence conversion module is used to convert the transaction data to be identified into a transaction sequence to be identified based on the transaction characteristics of each financial transaction. The combination strategy determination module is used to determine the target sequence combination strategy for the transaction sequence to be identified; The reverse transaction identification module is used to identify reverse transactions in the transaction data to be identified based on the target sequence combination strategy.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the reverse transaction identification method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the reverse transaction identification method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the reverse transaction identification method according to any one of claims 1-6.

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