Associated account quota control method and device, electronic equipment and storage medium

Abstract

The invention discloses an associated account quota control method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a registered account sequence to be subjected to limit control; wherein the registration account sequence comprises a plurality of target registration accounts and target registration information corresponding to each target registration account; performing binary tree modeling on the registered account sequence based on the target registration information to obtain at least one target binary tree; wherein the target binary tree represents association information between the target registered accounts; and performing limit control on the target registered account according to the at least one target binary tree. Based on the technical scheme of the invention, the association information among the multiple registered accounts can be identified by constructing the binary tree so as to achieve the effect of quota control during transaction of the registered accounts, so that severe behaviors such as registration of multiple accounts by the same user to set the quota are avoided, accurate management and control of the account quota are realized, and a good transaction environment is ensured.

Description

Technical Field

[0001] This invention relates to the field of computer application technology, and in particular to a method, apparatus, electronic device, and storage medium for controlling the limit of linked accounts. Background Technology

[0002] Currently, the widespread adoption of digital finance has significantly improved the efficiency of financial services, but at the same time, it has also brought new financial risks. For example, the malicious act of a single user registering multiple accounts to obtain credit limits poses a significant financial risk.

[0003] In conventional financial system risk identification scenarios, the focus is often on the attributes of a single node, such as the transaction behavior of a certain account, while the relationship between accounts is often ignored. As a result, the accuracy of account limit control is often low, making it difficult to control the malicious behavior of the same user registering multiple accounts to obtain credit limits. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for controlling the limit of associated accounts, in order to solve the current technical problem of the difficulty in accurately controlling the limit of accounts.

[0005] According to one aspect of the present invention, a method for controlling the credit limit of associated accounts is provided, the method comprising:

[0006] Obtain a sequence of registered accounts to be subject to quota control; wherein, the sequence of registered accounts includes multiple target registered accounts and target registration information corresponding to each target registered account;

[0007] Based on the target registration information, a binary tree model is performed on the sequence of registered accounts to obtain at least one target binary tree; wherein, the target binary tree represents the association information between the target registered accounts;

[0008] The target registered account is subject to quota control based on at least one of the target binary trees.

[0009] According to another aspect of the present invention, a linked account limit control device is provided, the device comprising:

[0010] The account acquisition module is used to acquire a sequence of registered accounts to be subject to quota control; wherein, the sequence of registered accounts includes multiple target registered accounts and target registration information corresponding to each target registered account;

[0011] The binary tree modeling module is used to perform binary tree modeling on the registered account sequence based on the target registration information to obtain at least one target binary tree; wherein, the target binary tree represents the association information between the target registered accounts;

[0012] A quota control module is used to control the quota of the target registered account according to at least one of the target binary trees.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] 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 associated account limit control method according to any embodiment of the present invention.

[0017] 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 associated account limit control method according to any embodiment of the present invention.

[0018] The technical solution of this invention involves obtaining a sequence of registered accounts to be subject to credit limit control. This sequence includes multiple target registered accounts and target registration information corresponding to each target account. A binary tree model is then performed on the registered account sequence based on the target registration information to obtain at least one target binary tree. This target binary tree represents the association information between the target registered accounts. Credit limit control is then applied to the target registered accounts based on at least one target binary tree. This invention, by constructing a binary tree to identify the association information between multiple registered accounts and thus control credit limits during transactions, avoids malicious behaviors such as the same user registering multiple accounts to obtain credit limits, achieving precise control of account credit limits and ensuring a healthy transaction environment.

[0019] 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

[0020] 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.

[0021] Figure 1This is a flowchart of a method for controlling the limit of associated accounts according to Embodiment 1 of the present invention;

[0022] Figure 2 This is an example diagram of a binary tree provided according to an embodiment of the present invention;

[0023] Figure 3 This is a flowchart of a method for controlling the limit of associated accounts according to Embodiment 2 of the present invention;

[0024] Figure 4 This is a flowchart of a binary tree modeling method provided by an embodiment of the present invention;

[0025] Figure 5 This is an overall execution flowchart of a quota control method provided by an embodiment of the present invention;

[0026] Figure 6 This is an example diagram of a first limit control method provided by an embodiment of the present invention;

[0027] Figure 7 This is a schematic diagram of the structure of a linked account limit control device according to Embodiment 3 of the present invention;

[0028] Figure 8 This is a schematic diagram of the structure of an electronic device that implements the associated account limit control method of this invention. Detailed Implementation

[0029] 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.

[0030] 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 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.

[0031] Example 1

[0032] Figure 1 This is a flowchart illustrating a method for controlling the credit limit of linked accounts, as provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where the malicious behavior of a single user registering multiple accounts to obtain credit limits needs to be controlled. This method can be executed by a linked account credit limit control device, which can be implemented in hardware and / or software and can be configured in a computer. Figure 1 As shown, the method includes:

[0033] S110. Obtain the sequence of registered accounts to be subject to quota control; wherein, the sequence of registered accounts includes multiple target registered accounts and target registration information corresponding to each target registered account.

[0034] In this invention, the registered account sequence can be understood as a sequence of registered accounts. Registered accounts can be registered accounts of the target application. In this embodiment, the target application can be preset according to scenario requirements, and no specific limitation is made here. The target application can have transaction functionality. The target registered account in this embodiment can be understood as a registered account to be subject to quota control, i.e., the aforementioned registered account. The target registration information can be understood as the registration information of the target registered account. The target registration information can characterize the identity of the registered account. In this embodiment, the registration information can be preset according to scenario requirements, and no specific limitation is made here. The order of the target registered accounts in the registered account sequence can be related to the registration time of the target registered accounts. For example, the registered account sequence includes account A, account B, account C, and account D.

[0035] S120. Based on the target registration information, perform binary tree modeling on the registered account sequence to obtain at least one target binary tree; wherein, the target binary tree represents the association information between the target registered accounts.

[0036] The association information can characterize the degree of association between registered accounts. In this embodiment of the invention, the same binary tree corresponds to at least one node, one node represents one registered account, and registered accounts on the same binary tree have identity associations.

[0037] S130. Limit control is applied to the target registered account based on at least one of the target binary trees.

[0038] Based on the above embodiments, account limit control can be performed by calculating outliers corresponding to the binary tree. Optionally, the step of controlling the limit of the target registered account based on at least one of the target binary trees includes:

[0039] For each target binary tree, determine the associated outlier value corresponding to the target binary tree;

[0040] If the associated anomaly value exceeds the associated anomaly threshold, the quota control is applied to at least one of the target registered accounts corresponding to the target binary tree.

[0041] The associated anomaly value characterizes the degree of association anomaly among registered accounts on the target binary tree. In this embodiment, the associated anomaly value can be determined based on the depth and number of nodes of the binary tree. The specific associated anomaly value is related to the actual depth and number of nodes of the binary tree, and is not specifically limited here. For example, the associated anomaly value can be 1, 0.5, or 0. The associated anomaly threshold can be understood as a threshold corresponding to the associated anomaly value. In this embodiment, the associated anomaly threshold can be preset according to scenario requirements, and is not specifically limited here. Preferably, the associated anomaly value can be 0.

[0042] Based on the above embodiments, the method achieves the effect of determining the degree of abnormal association between registered accounts based on a binary tree and controlling the corresponding quota accordingly.

[0043] Based on the above embodiments, the degree of association anomaly between at least one registered account corresponding to a node in the binary tree can be determined based on the actual depth and actual number of nodes in the binary tree. Optionally, determining the association anomaly value corresponding to the target binary tree includes:

[0044] Determine the total number of nodes corresponding to the target binary tree and the target depth corresponding to the target binary tree, and determine the first weight corresponding to the total number of nodes, the first weight corresponding to the target depth, and the creation time of the target binary tree;

[0045] The associated outlier value corresponding to the target binary tree is determined based on the total number of nodes, the first weight, the target depth, the second weight, and the creation time.

[0046] The total number of nodes can be understood as the total number of nodes in the target binary tree. For example... Figure 2 As shown, Figure 2 This is an example diagram of a binary tree provided according to an embodiment of the present invention. Figure 2 The binary tree shown has a total of 3 nodes. The target depth can be understood as the depth of the target binary tree. In simpler terms, the target depth can also be described as the height of the target binary tree. Figure 2 The target depth of the binary tree shown can be 2.

[0047] The first weight can be understood as a pre-set calculation weight for the total number of nodes. The second weight can be understood as a pre-set calculation weight for the target depth. In this embodiment of the invention, the first weight and the second weight can be pre-set according to scenario requirements, and are not specifically limited here. The first weight and the second weight can be the same or different.

[0048] The creation time can be understood as the total time required to create the target binary tree. Specifically, the time interval between starting to create the root node and completing the creation of the last leaf node is taken as the creation time of the target binary tree. In this invention, the creation time of different target binary trees can be different or the same.

[0049] Specifically, the determination of the associated outlier value corresponding to the target binary tree based on the total number of nodes, the first weight, the target depth, the second weight, and the creation time may include:

[0050] When there is only one node in the binary tree, 0 is directly used as the associated outlier value;

[0051] When a node in a binary tree has a lookup value greater than 1, the associated outlier can be calculated using the following formula:

[0052] V = w1V1 + w2V2;

[0053]

[0054] Wherein, V represents the associated outlier; w1 is the first weight; V1 is the first outlier; w2 is the second weight; V2 is the second outlier; I represents the total number of nodes; D represents the target depth; and T represents the creation time.

[0055] Based on the above embodiments, the effect of calculating associated outliers using a binary tree is achieved.

[0056] The technical solution of this invention involves obtaining a sequence of registered accounts to be subject to credit limit control. This sequence includes multiple target registered accounts and target registration information corresponding to each target account. A binary tree model is then performed on the registered account sequence based on the target registration information to obtain at least one target binary tree. This target binary tree represents the association information between the target registered accounts. Credit limit control is then applied to the target registered accounts based on at least one target binary tree. This invention effectively controls credit limits for transactions by identifying the association information between multiple registered accounts through binary tree construction, thus preventing malicious behaviors such as the same user registering multiple accounts to obtain credit limits. This achieves precise control of account credit limits and ensures a healthy transaction environment.

[0057] Example 2

[0058] Figure 3 This is a flowchart of a method for controlling the limit of associated accounts provided in Embodiment 2 of the present invention. This embodiment focuses on refining at least one target binary tree obtained by modeling the registered account sequence based on the target registration information as described in the previous embodiments. For example... Figure 3 As shown, the method includes:

[0059] S210. Obtain the sequence of registered accounts to be subject to quota control.

[0060] S220. Take the first target registered account in the registered account sequence as the root node and construct a first binary tree.

[0061] like Figure 2 As shown in the diagram, node A is the root node. The first binary tree can be understood as the first binary tree created.

[0062] S230. Iteratively execute the operation of determining the first processing account and the second processing account in the registered account sequence, determining the target association information between the first processing account and the second processing account, and updating the first binary tree with the second processing account as a child node when the target association information satisfies the target association condition, to obtain the updated first binary tree; otherwise, construct the second binary tree with the second processing account as the root node to obtain at least one target binary tree.

[0063] The second processing account can be understood as a registered account for node establishment. The first processing account can be understood as a registered account for which node establishment has been completed. In this embodiment of the invention, the first processing account can be one or more. Taking the registered account sequence including account A, account B, account C, and account D as an example, if the second processing account is account B, the first processing account is account A; if the second processing account is account C, the first processing account is account A and account B; if the second processing account is account D, the first processing account is account A, account B, and account C.

[0064] The target association information can characterize the degree of association between the second processing account and the account established by the completed node. Correspondingly, the target association condition can be that there is an association between the second processing account and the first processing account. During the iterative loop, the first binary tree can be understood as a binary tree that has already been created. The second binary tree can be understood as a newly created binary tree. The target binary tree can be understood as a binary tree created based on the registered account sequence.

[0065] Based on the above embodiments, the degree of association between registered accounts can be determined based on the registration information of the registered accounts. Optionally, the target registration information includes first registration information and second registration information, wherein the account identity representation capability of the first registration information is higher than that of the second registration information; determining the target association information between the first processing account and the second processing account includes:

[0066] Based on the first registration information, a first association information between the first processing account and the second processing account is determined; and based on the second registration information, a second association information between the first processing account and the second processing account is determined.

[0067] The first association information and the second association information are used as the target association information.

[0068] In this embodiment, both the first registration information and the second registration information are information entered by the user when registering an account. In this embodiment, the registration information is obtained under reasonable and legal conditions. The registration information can be preset according to scenario requirements and is not specifically limited here. Correspondingly, the account association representation capability of the first association information is higher than that of the second association information. Optionally, the first association information can represent the degree of association between accounts under the first registration information, specifically it can be a value representing the degree of account association, i.e., a first association value. The second association information can represent the degree of association between accounts under the second registration information, specifically it can also be a value representing the degree of account association, i.e., a second association value. The first association information and the second association information can be different or the same. The target association information can be understood as a collective term for the first association information and the second association information. Further, in this embodiment, the first processing account can include at least one sub-registered account, and the second processing account can be a single registered account. There is a target association information between the second processing account and each of the sub-registered accounts; that is, there is a first association value and a second association value between the second processing account and each of the sub-registered accounts. That is, the first association information includes at least one first association value, and the second association information includes at least one second association value.

[0069] Based on the above embodiments, registration information is classified, and the relationship between accounts is analyzed from multiple dimensions of registration information to determine the effectiveness of the association information.

[0070] Based on the above embodiments, the node corresponding to the registered account can be inserted into the already created binary tree in the following manner. Optionally, the target association condition includes a first association condition and a second association condition; when the target association information satisfies the target association condition, updating the first binary tree by treating the second processing account as a child node includes:

[0071] If the first association information satisfies the first association condition, the second processing account is used as a child node on the first side to update the first binary tree of the target.

[0072] If the first association information does not meet the first association condition and the second association information meets the second association condition, the second processing account is used as a child node on the second side to update the first binary tree of the target.

[0073] The first association condition can be that among the multiple first association values ​​included in the first association information, there is a first association value that exceeds a first association threshold. The second association condition can be that among the multiple second association values ​​included in the second association information, there is a second association value that exceeds a second association threshold. The first side and the second side are different sides of the binary tree. In simple terms, the first side is the left side of the binary tree, and the second side is the right side of the binary tree.

[0074] More specifically, the first binary tree containing the registered account whose first association value exceeds the first association threshold is used as the target's first binary tree; the second processing account is inserted as a left leaf node into the target's first binary tree; otherwise, the first binary tree containing the registered account whose second association value exceeds the second association threshold is used as the target's first binary tree; the second processing account is inserted as a right leaf node into the target's first binary tree. In this embodiment of the invention, both the first association threshold and the second association threshold can be preset according to scenario requirements, and are not specifically limited here. The first association threshold and the second association threshold can be the same or different.

[0075] Based on the above embodiments, a binary tree model representing the association information between the target registered accounts was implemented.

[0076] The following describes an optional modeling method for binary trees. Before proceeding, the names used are standardized. Specifically, the following strong identity information feature set represents the first registration information; the weak identity information feature set represents the second registration information; strong identification correlation represents the first association information; and weak identification correlation represents the second association information.

[0077] 1. Account Relevance Calculation. L represents weak user profile features, and H represents strong user profile features. The relevance between accounts is calculated based on these two types of user profile feature sets. The relevance includes weak identification relevance J(L) and strong identification relevance J(H). Specifically, the weak identity information feature set X(L) = (xL1, xL2, ..., xLn) and the strong identity information feature set X(H) = (xH1, xH2, ..., xHn) of registered account X are determined, and the weak identity information feature set Y(L) = (yL1, yL2, ..., yLn) and the strong identity information feature set Y(H) = (yH1, yH2, ..., yHn) of registered account Y are determined. The relevance is determined using the Jaccard similarity coefficient. The formula is as follows:

[0078]

[0079] Among them, J XY (L) indicates a weak identifying association between account X and account Y; J XY (H) indicates a strong identifying association between account X and account Y.

[0080] 2. Binary Tree Modeling. Utilizing the structured relationships and computability of binary trees, the account association degree J is used as a model. XY To represent account relationships within a binary tree based on inheritance dependencies, a binary tree model is constructed. For example... Figure 4 As shown, Figure 4 This is a flowchart of a binary tree modeling method according to an embodiment of the present invention. Specifically, the first account is taken as the root node of the binary tree, and each node stores the strong identity information feature set and the weak identity information feature set of the corresponding account. If a new account is opened, the account association degree between the new account and the registered accounts is calculated. If the strong identification association degree is greater than the threshold α, the newly opened account node is inserted into the binary tree as the left child node of the root node; if the strong identification association degree is less than the threshold α, but the weak identification association degree is greater than the threshold β, the newly opened account node is inserted into the binary tree as the right child node of the root node. The following describes a method for... Figure 4 To further explain, the first account is A, which is the root node of the binary tree, storing feature sets A(L) and A(H). The left subtree: After opening account A, a new account B is opened with changed identity features, forming a new user profile, storing feature sets B(L) and B(H). The strong account association J between accounts A and B is calculated. AB (H), if J ABIf (H) > α, it indicates that accounts A and B have a strong correlation and a high degree of identity overlap. In this case, node B is inserted into the binary tree as the left child of the root node. Right subtree: After opening account A, a new account C is opened with changed identity features, forming a new user profile, and feature sets C(L) and C(H) are stored. The strong account correlation J between accounts A and C is calculated. AC (H), if J AC If (H) < α, it indicates that the strong correlation between accounts A and C is not high. Then, calculate the weak account correlation J between A and C. AC (L), if J AC If (L) > β, it indicates that A and C have a weak correlation. In this case, node C is inserted into the binary tree as the right child of the root node. Accounts B and C become the root nodes of the left and right subtrees, respectively. New nodes with strong and weak correlations are inserted according to the above rules to form a complete binary tree model of related accounts. If accounts D, E, F, or G with strong or weak correlations to B or C are opened, they are inserted into the binary tree in sequence, increasing the number of nodes and the height of the binary tree.

[0081] S240. Limit control is applied to the target registered account based on at least one of the target binary trees.

[0082] Based on the above embodiments, various quota control methods can be used to control the quota of registered accounts based on the actual situation of associated outliers. Optionally, the quota control for at least one target registered account corresponding to the target binary tree includes:

[0083] If the first associated information meets the target control conditions, the first quota control method is used to control the quota of at least one target registered account corresponding to the target binary tree; otherwise, the second quota control method is used to control the quota of at least one target registered account corresponding to the target binary tree.

[0084] The first and second quota control methods are two different quota control methods. The target control condition can be a first association value between nodes as the target value, and the target value can be 1.

[0085] The following combination Figure 5 The specific methods for controlling credit limits are explained. Figure 5 This is an overall execution flowchart of a quota control method provided by an embodiment of the present invention.

[0086] 1. The first limit control method directly links to limit inheritance. When calculating the account association degree between binary tree nodes, if the strong identification association degree J(H) is 1, it means that the two accounts are completely identical and belong to the same user. Their account limits should be inheritable throughout their lifecycle. In this case, the child node account inherits the remaining limit of its parent node account. The remaining limit of the parent node account is E. t (A) If the initial limit for opening a sub-node account is E0(B), then E0(B) = E t (A). With Figure 6 For example, Figure 6 This is an example diagram of a first limit control method provided by an embodiment of the present invention. That is, if a user cancels account A and then opens account B with the same information, the limit of account B at the time of opening will inherit the remaining limit of account A.

[0087] 2. Second limit control method: Tiered limit control during transactions. The higher the abnormal behavior risk value V, the more frequently the user opens accounts repeatedly, and the greater the risk. To provide risk control functions that match user identification, a three-level abnormal behavior risk threshold V is set. L V M and V H The thresholds increase sequentially, corresponding to low, medium, and high risks. Once a user's abnormal behavior risk value V reaches the threshold, tiered limit control is implemented, and the account limit L is adjusted.

[0088]

[0089] Where L0 > L1 > L2 > L3. L0 represents the basic limit under risk-free account conditions. When the abnormal behavior risk value V exceeds the low threshold V... L When L is adjusted to a Class I limit L1; when the abnormal behavior risk value V exceeds the medium threshold V M When L is adjusted to the second-class limit L2; when the abnormal behavior risk value V exceeds the high threshold V H At this time, the account may have a higher risk, so the L limit is adjusted to the third-class limit L3, which can be the lowest usable limit.

[0090] The technical solution of this invention involves determining a first processing account in the registered account sequence, using the first processing account as the root node to construct a first binary tree; iteratively executing the process of determining the first processing account and its corresponding second processing account in the registered account sequence, determining target association information between the first processing account and the second processing account, and updating the first binary tree by using the second processing account as a child node if the target association information satisfies the target association condition, thus obtaining an updated first binary tree; otherwise, constructing a second binary tree by using the second processing account as the root node, resulting in at least one target binary tree. This invention implements binary tree modeling representing the association information between the target registered accounts.

[0091] Regarding the technical solution of this invention, on the one hand, the preprocessing requirements for data are relatively low. It does not require massive amounts of feature data for modeling or complex feature engineering. Limited feature data can be used to calculate account correlation. Furthermore, it can be adjusted and optimized according to different business needs and data characteristics. For example, for accounts with limited registration information, a binary tree model can be built using only the account name or email address, demonstrating strong adaptability. On the other hand, this invention offers high backtracking capability. Based on a binary tree structure, it provides clear hierarchical and path relationships between accounts, facilitating recursive and backtracking operations. Risk identification and control results can be verified through the model's structure and calculation process, which is beneficial for subsequent supervision.

[0092] Example 3

[0093] Figure 7 This is a schematic diagram of a linked account limit control device provided in Embodiment 3 of the present invention. Figure 7 As shown, the device includes: an account acquisition module 310, a binary tree modeling module 320, and a quota control module 330.

[0094] Among them, the account acquisition module 310 is used to acquire the sequence of registered accounts to be subject to quota control;

[0095] The registered account sequence includes multiple target registered accounts and target registration information corresponding to each target registered account; the binary tree modeling module 320 is used to perform binary tree modeling on the registered account sequence based on the target registration information to obtain at least one target binary tree; wherein the target binary tree represents the association information between the target registered accounts; the quota control module 330 is used to perform quota control on the target registered accounts according to at least one target binary tree.

[0096] The technical solution of this invention involves obtaining a sequence of registered accounts to be subject to credit limit control. This sequence includes multiple target registered accounts and target registration information corresponding to each target account. A binary tree model is then performed on the registered account sequence based on the target registration information to obtain at least one target binary tree. This target binary tree represents the association information between the target registered accounts. Credit limit control is then applied to the target registered accounts based on at least one target binary tree. This invention, by constructing a binary tree to identify the association information between multiple registered accounts and thus control credit limits during transactions, avoids malicious behaviors such as the same user registering multiple accounts to obtain credit limits, achieving precise control of account credit limits and ensuring a healthy transaction environment.

[0097] Optionally, the binary tree modeling module 320 includes: a binary tree modeling unit and an iteration execution unit;

[0098] The binary tree modeling unit is used to construct a first binary tree by taking the first target registered account in the registered account sequence as the root node.

[0099] The iterative execution unit is used to iteratively execute the operation of determining the first processing account and the second processing account in the registered account sequence, determining the target association information between the first processing account and the second processing account, and updating the first binary tree with the second processing account as a child node when the target association information satisfies the target association condition, so as to obtain the updated first binary tree; otherwise, the operation of constructing the second binary tree with the second processing account as the root node is performed to obtain at least one target binary tree.

[0100] Optionally, the target registration information includes first registration information and second registration information, wherein the account identity representation capability of the first registration information is higher than that of the second registration information; the iterative execution unit includes: an association information determination subunit, configured to determine first association information between the first processing account and the second processing account based on the first registration information; and to determine second association information between the first processing account and the second processing account based on the second registration information;

[0101] The first association information and the second association information are used as the target association information.

[0102] Optionally, the target association condition includes a first association condition and a second association condition; the iterative execution unit includes: a first update subunit and a second update subunit;

[0103] The first update subunit is used to update the first binary tree of the target by taking the second processing account as a child node of the first side when the first association information satisfies the first association condition.

[0104] The second update subunit is used to update the first binary tree of the target by treating the second processing account as a child node on the second side when the first association information does not meet the first association condition and the second association information meets the second association condition.

[0105] Optionally, the quota control module 330 includes: an outlier determination unit and a quota control unit;

[0106] The outlier determination unit is used to determine the associated outlier value corresponding to each target binary tree.

[0107] The quota control unit is used to control the quota of at least one target registered account corresponding to the target binary tree when the associated abnormal value exceeds the associated abnormal threshold.

[0108] Optionally, the outlier determination unit is specifically used for:

[0109] Determine the total number of nodes corresponding to the target binary tree and the target depth corresponding to the target binary tree, and determine the first weight corresponding to the total number of nodes, the first weight corresponding to the target depth, and the creation time of the target binary tree;

[0110] The associated outlier value corresponding to the target binary tree is determined based on the total number of nodes, the first weight, the target depth, the second weight, and the creation time.

[0111] Optionally, the credit limit control unit is specifically used for:

[0112] If the first associated information meets the target control conditions, the first quota control method is used to control the quota of at least one target registered account corresponding to the target binary tree; otherwise, the second quota control method is used to control the quota of at least one target registered account corresponding to the target binary tree.

[0113] The associated account limit control device provided in this embodiment of the invention can execute the associated account limit control method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0114] Example 4

[0115] Figure 8A schematic diagram of an electronic device 10 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.

[0116] like Figure 8 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0117] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0118] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 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 11 performs the various methods and processes described above, such as the associated account limit control method.

[0119] In some embodiments, the associated account limit control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the associated account limit control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the associated account limit control method by any other suitable means (e.g., by means of firmware).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 controlling the credit limit of linked accounts, characterized in that, include: Obtain a sequence of registered accounts to be subject to quota control; wherein, the sequence of registered accounts includes multiple target registered accounts and target registration information corresponding to each target registered account; Based on the target registration information, a binary tree model is performed on the sequence of registered accounts to obtain at least one target binary tree; wherein, the target binary tree represents the association information between the target registered accounts; The target registered account is subject to quota control based on at least one of the target binary trees.

2. The method according to claim 1, characterized in that, The step of modeling the registered account sequence into a binary tree based on the target registration information to obtain at least one target binary tree includes: Using the first target registered account in the registered account sequence as the root node, construct a first binary tree; Iteratively execute the process of determining the first processing account and the second processing account in the registered account sequence, determining the target association information between the first processing account and the second processing account, and updating the first binary tree with the second processing account as a child node if the target association information satisfies the target association condition, to obtain the updated first binary tree; otherwise, construct the second binary tree with the second processing account as the root node to obtain at least one target binary tree.

3. The method according to claim 2, characterized in that, The target registration information includes first registration information and second registration information, wherein the account identity representation capability of the first registration information is higher than that of the second registration information; Determining the target association information between the first processing account and the second processing account includes: Based on the first registration information, a first association information between the first processing account and the second processing account is determined; and based on the second registration information, a second association information between the first processing account and the second processing account is determined. The first association information and the second association information are used as the target association information.

4. The method according to claim 3, characterized in that, The target association conditions include a first association condition and a second association condition; the step of updating the first binary tree by treating the second processing account as a child node when the target association information satisfies the target association conditions includes: If the first association information satisfies the first association condition, the second processing account is used as a child node on the first side to update the first binary tree of the target. If the first association information does not meet the first association condition and the second association information meets the second association condition, the second processing account is used as a child node on the second side to update the first binary tree of the target.

5. The method according to claim 1, characterized in that, The step of controlling the credit limit of the target registered account based on at least one of the target binary trees includes: For each target binary tree, determine the associated outlier value corresponding to the target binary tree; If the associated anomaly value exceeds the associated anomaly threshold, the quota control is applied to at least one of the target registered accounts corresponding to the target binary tree.

6. The method according to claim 5, characterized in that, Determining the associated outlier value corresponding to the target binary tree includes: Determine the total number of nodes corresponding to the target binary tree and the target depth corresponding to the target binary tree, and determine the first weight corresponding to the total number of nodes, the first weight corresponding to the target depth, and the creation time of the target binary tree; The associated outlier value corresponding to the target binary tree is determined based on the total number of nodes, the first weight, the target depth, the second weight, and the creation time.

7. The method according to claims 3 and 5, characterized in that, The step of controlling the credit limit for at least one target registered account corresponding to the target binary tree includes: If the first associated information meets the target control conditions, the first quota control method is used to control the quota of at least one target registered account corresponding to the target binary tree; otherwise, the second quota control method is used to control the quota of at least one target registered account corresponding to the target binary tree.

8. A device for controlling the limit of linked accounts, characterized in that, include: The account acquisition module is used to acquire a sequence of registered accounts to be subject to quota control; wherein, the sequence of registered accounts includes multiple target registered accounts and target registration information corresponding to each target registered account; The binary tree modeling module is used to perform binary tree modeling on the registered account sequence based on the target registration information to obtain at least one target binary tree; wherein, the target binary tree represents the association information between the target registered accounts; A quota control module is used to control the quota of the target registered account according to at least one of the target binary trees.

9. 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 associated account limit control method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the associated account limit control method according to any one of claims 1-7.

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