Transaction knowledge graph generation method and device, electronic equipment and storage medium

Abstract

The invention provides a transaction knowledge graph generation method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring a transaction data set of a target object, and determining a first object set and a second object set corresponding to each type according to the transaction data set; a core node corresponding to the target object is determined, each first transaction object corresponds to a first node and is connected with the core node and the first node, and a second transaction object corresponds to a second node and is connected with the core node and the second node; and determining node information of the first node and node information of the second node based on the transaction data to obtain a transaction knowledge graph of the target object. Therefore, according to the scheme, the transaction data is displayed in the form of the knowledge graph, the transaction object can be taken as a center, and fund transactions related to the transaction object can be continuously expanded, so that a complete fund transaction link can be displayed.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method, device, electronic device and storage medium for generating a transaction knowledge graph. Background Art

[0002] Anti-money laundering involves a large number of financial transactions. Generally, such large amounts of data are displayed in the form of tables or lists, and the links of financial transactions cannot be fully displayed. Summary of the invention

[0003] The present disclosure provides a method, device, electronic device and storage medium for generating a transaction knowledge graph, so as to solve the problem that the capital chain cannot be fully displayed.

[0004] To this end, one purpose of the present disclosure is to propose a method for generating a transaction knowledge graph.

[0005] The second objective of the present disclosure is to propose a device for generating a transaction knowledge graph.

[0006] A third objective of the present disclosure is to provide an electronic device.

[0007] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0008] A fifth object of the present disclosure is to provide a computer program product.

[0009] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes a method for generating a transaction knowledge graph, including: obtaining a transaction data set of a target object, and determining a global transaction object set that has a transaction relationship with the target object based on the transaction data set; determining a first object set and a second object set corresponding to each type from the global transaction object set based on the transaction type, the first object set including N first transaction objects, and the second object set including M second transaction objects, where N and M are positive integers, and M≥2; determining a core node corresponding to the target object, each first transaction object corresponding to a first node, and connecting the core node and the first node, and determining that the M second transaction objects correspond to a second node, and connecting the core node and the second node; determining the node information of the first node based on the transaction data corresponding to the first transaction object, and determining the node information of the second node based on the transaction data of the M second transaction objects, so as to obtain the transaction knowledge graph of the target object.

[0010] According to one embodiment of the present disclosure, determining the first object set and the second object set corresponding to each type from the global transaction object set according to the transaction type includes: determining the transaction volume of the transaction object according to the transaction data of the transaction object; grouping the transaction objects in the global transaction object set according to the type of the transaction object to obtain a local transaction object set of each type; for each transaction type, screening the transaction objects according to the transaction volume within the local transaction object set of the transaction type to obtain the first transaction object and the second transaction object of the transaction type, so as to obtain the first object set and the second object set, wherein the transaction volume of the first transaction object is greater than the transaction volume of the second transaction object.

[0011] According to one embodiment of the present disclosure, determining the node information of the second node based on the transaction data of the M second transaction objects includes: acquiring the transaction volume of the second transaction object from the transaction data of the second transaction object; determining the total transaction volume of the second node based on the transaction volume of the second transaction object; and determining the node information of the second node based on the total transaction volume and the transaction volume of the second transaction object.

[0012] According to one embodiment of the present disclosure, the method also includes: determining the transaction direction between the target object and the transaction object based on the transaction data; determining the direction of the connection edge between the core node and the first node based on the transaction direction between the target object and the first transaction object; or, aggregating the transaction directions corresponding to the M second transaction objects to obtain the aggregated transaction direction corresponding to the second node, and determining the direction of the connection edge between the core node and the second node based on the aggregated transaction direction.

[0013] According to one embodiment of the present disclosure, it is characterized in that after obtaining the transaction knowledge graph of the target object, it also includes: receiving a target instruction, and according to the target instruction, determining the target node to be processed from the transaction knowledge graph, and performing operations related to the target instruction on the target node to update the transaction knowledge graph.

[0014] According to one embodiment of the present disclosure, the method also includes: storing historical update operations of the transaction knowledge graph in a queue; selecting and monitoring the historical update operations, and in response to monitoring that a first historical update operation is selected, obtaining historical update data corresponding to the first historical update operation from the queue; and updating the transaction knowledge graph according to the historical update data.

[0015] According to one embodiment of the present disclosure, the method further includes: caching the state of the transaction knowledge graph, and switching the state of the transaction knowledge graph according to a state switching instruction.

[0016] According to an embodiment of the present disclosure, it is characterized in that the method also includes: receiving a node expansion instruction, and determining a first target node from the transaction knowledge graph according to the node expansion instruction; judging whether the first target node is the second node; in response to the first target node being the second node, determining expansion information of the second node according to node information of the second node, and displaying the expansion information; in response to the first target node being the first node, displaying node information corresponding to the first node.

[0017] According to one embodiment of the present disclosure, the method of determining the expansion information of the second node based on the node information of the second node and displaying the expansion information includes: generating a first node corresponding to each of the M second transaction objects; determining the node information of the first node corresponding to the second transaction object based on the node information of the second node; determining the direction of the connecting edge between the first node corresponding to the second transaction object and the core node based on the transaction direction of the second transaction object and the target object, and deleting the corresponding connecting edge between the second node and the second node to obtain the expansion information of the second node, and splicing it into the transaction knowledge graph.

[0018] According to one embodiment of the present disclosure, after obtaining the transaction knowledge graph of the target object, it also includes: receiving a node aggregation instruction, and determining at least two second target nodes from the transaction knowledge graph according to the node aggregation instruction; in response to the at least two second target nodes corresponding to the same transaction type, aggregating the at least two second target nodes to generate the second node, and determining the node information and corresponding connection edges of the second node based on the node information and connection edges of the at least two second target nodes; deleting the at least two second target nodes, and connecting the second nodes corresponding to the at least two second target nodes to the core node to update the transaction knowledge graph.

[0019] According to one embodiment of the present disclosure, the method also includes: in response to the at least two second target nodes corresponding to different transaction types, dividing the at least two second target nodes according to the transaction types to obtain second target nodes of the same transaction type, and executing the aggregation process corresponding to the node aggregation instruction on the second target nodes of the same transaction type.

[0020] According to one embodiment of the present disclosure, it is characterized in that the method also includes: receiving a node hiding instruction, and determining at least one third target node from the transaction knowledge graph according to the node hiding instruction; hiding the third target node and the connecting edge corresponding to the third target node.

[0021] To achieve the above-mentioned purpose, the second aspect of the present disclosure proposes a device for generating a transaction knowledge graph, including: a first determination module, used to obtain a transaction data set of a target object, and determine a global transaction object set that has a transaction relationship with the target object based on the transaction data set; a second determination module, used to determine a first object set and a second object set corresponding to each type from the global transaction object set according to the transaction type, the first object set includes N first transaction objects, and the second object set includes M second transaction objects, where N and M are positive integers, and M≥2; a connection module, used to determine the core node corresponding to the target object, each first transaction object corresponds to a first node, and connects the core node and the first node, and determines that the M second transaction objects correspond to a second node, and connects the core node and the second node; a third determination module, used to determine the node information of the first node based on the transaction data corresponding to the first transaction object, and determine the node information of the second node based on the transaction data of the M second transaction objects, so as to obtain the transaction knowledge graph of the target object.

[0022] According to one embodiment of the present disclosure, the second determination module is further used to: determine the transaction volume of the transaction object based on the transaction data of the transaction object; group the transaction objects in the global transaction object set according to the type of the transaction object to obtain a local transaction object set of each type; for each transaction type, filter the transaction objects according to the transaction volume within the local transaction object set of the transaction type to obtain a first transaction object and a second transaction object of the transaction type, so as to obtain the first object set and the second object set, wherein the transaction volume of the first transaction object is greater than the transaction volume of the second transaction object.

[0023] According to one embodiment of the present disclosure, the third determination module is further used to: obtain the transaction volume of the second transaction object from the transaction data of the second transaction object; determine the total transaction volume of the second node based on the transaction volume of the second transaction object; and determine the node information of the second node based on the total transaction volume and the transaction volume of the second transaction object.

[0024] According to one embodiment of the present disclosure, the connection module is also used to: determine the transaction direction between the target object and the transaction object based on the transaction data; determine the direction of the connection edge between the core node and the first node based on the transaction direction between the target object and the first transaction object; or, aggregate the transaction directions corresponding to the M second transaction objects to obtain the aggregated transaction direction corresponding to the second node, and determine the direction of the connection edge between the core node and the second node based on the aggregated transaction direction.

[0025] According to one embodiment of the present disclosure, it is characterized in that the third determination module is also used to: receive a target instruction, and according to the target instruction, determine the target node to be processed from the transaction knowledge graph, and perform operations related to the target instruction on the target node to update the transaction knowledge graph.

[0026] According to one embodiment of the present disclosure, the third determination module is also used to: store the historical update operations of the transaction knowledge graph in a queue; select and monitor the historical update operations, and in response to monitoring that a first historical update operation is selected, obtain historical update data corresponding to the first historical update operation from the queue; and update the transaction knowledge graph according to the historical update data.

[0027] According to one embodiment of the present disclosure, the third determination module is further used to: cache the state of the transaction knowledge graph, and switch the state of the transaction knowledge graph according to a state switching instruction.

[0028] According to one embodiment of the present disclosure, it is characterized in that the third determination module is also used to: receive a node expansion instruction, and determine a first target node from the transaction knowledge graph according to the node expansion instruction; determine whether the first target node is the second node; in response to the first target node being the second node, determine the expansion information of the second node according to the node information of the second node, and display the expansion information; in response to the first target node being the first node, display the node information corresponding to the first node.

[0029] According to one embodiment of the present disclosure, the third determination module is also used to: generate a first node corresponding to each of the M second transaction objects; determine the node information of the first node corresponding to the second transaction object based on the node information of the second node; determine the direction of the connecting edge between the first node corresponding to the second transaction object and the core node based on the transaction direction of the second transaction object and the target object, and delete the corresponding connecting edge between the second node and the second node to obtain the expanded information of the second node, and splice it into the transaction knowledge graph.

[0030] According to one embodiment of the present disclosure, the third determination module is also used to: receive a node aggregation instruction, and determine at least two second target nodes from the transaction knowledge graph according to the node aggregation instruction; in response to the at least two second target nodes corresponding to the same transaction type, aggregate the at least two second target nodes to generate the second node, and determine the node information and corresponding connection edges of the second node based on the node information and connection edges of the at least two second target nodes; delete the at least two second target nodes, and connect the second nodes corresponding to the at least two second target nodes to the core node to update the transaction knowledge graph.

[0031] According to one embodiment of the present disclosure, the third determination module is also used to: in response to the at least two second target nodes corresponding to different transaction types, divide the at least two second target nodes according to the transaction types to obtain second target nodes of the same transaction type, and execute the aggregation process corresponding to the node aggregation instruction on the second target nodes of the same transaction type.

[0032] According to one embodiment of the present disclosure, it is characterized in that the third determination module is also used to: receive a node hiding instruction, and determine at least one third target node from the transaction knowledge graph according to the node hiding instruction; and hide the third target node and the connection edge corresponding to the third target node.

[0033] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the method for generating a transaction knowledge graph as described in the first aspect embodiment of the present disclosure.

[0034] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the method for generating a transaction knowledge graph as described in the first aspect embodiment of the present disclosure.

[0035] To achieve the above-mentioned purpose, the fifth aspect of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the method for generating a transaction knowledge graph as described in the first aspect of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0037] Figure 1 A flowchart of a method for generating a transaction knowledge graph provided in an embodiment of the present disclosure;

[0038] Figure 2 A flowchart of another method for generating a transaction knowledge graph provided in an embodiment of the present disclosure;

[0039] Figure 3 A flowchart of another method for generating a transaction knowledge graph provided in an embodiment of the present disclosure;

[0040] Figure 4 A flowchart of a process of expanding nodes in a transaction knowledge graph in a method for generating a transaction knowledge graph provided in an embodiment of the present disclosure;

[0041] Figure 5 A flowchart of a process for aggregating nodes in a transaction knowledge graph in a method for generating a transaction knowledge graph provided in an embodiment of the present disclosure;

[0042] Figure 6 An example diagram of a process for updating a transaction knowledge graph provided in an embodiment of the present disclosure;

[0043] Figure 7 A structural example diagram of a transaction knowledge graph generation device provided in an embodiment of the present disclosure;

[0044] Figure 8 This is a diagram showing an example structure of an electronic device provided in an embodiment of the present disclosure.

[0045] The above drawings show clear embodiments of the present disclosure, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present disclosure in any way, but to illustrate the concepts of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0046] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0047] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0048] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application are in compliance with the provisions of relevant laws and regulations.

[0049] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards.

[0050] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the relevant content of the solution.

[0051] Figure 1 This is a flowchart of a method for generating a transaction knowledge graph provided in an embodiment of the present disclosure. Figure 1 As shown, the method for generating the transaction knowledge graph includes:

[0052] S101, obtaining a transaction data set of a target object, and determining a global transaction object set having a transaction relationship with the target object according to the transaction data set.

[0053] It should be noted that the execution subject of the method for generating a transaction knowledge graph provided in the embodiment of the present disclosure is an electronic device, which may be a terminal device. Optionally, the terminal device may be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device may be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a personal digital assistant (PDA), etc., and the non-mobile electronic device may be a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine or a self-service machine, etc. The embodiment of the present disclosure does not make specific limitations.

[0054] In some implementations, an individual or an organization can be used as a target object to determine the transaction knowledge graph of the target object. Optionally, the transaction data of the target object in different transaction institutions can be obtained, and a transaction data set can be formed based on the transaction data. The transaction data set includes but is not limited to: transaction time, transaction parties, transaction varieties, transaction quantities, transaction prices, transaction types, etc.

[0055] Furthermore, by parsing the transaction data in the transaction data set, one or more transaction objects having a transaction relationship with the target object can be determined, and the transaction objects can be aggregated to obtain a global transaction object set, that is, the global transaction object set includes all transaction objects having a transaction relationship with the target object.

[0056] S102, according to the transaction type, determine a first object set and a second object set corresponding to each type from the global transaction object set, the first object set includes N first transaction objects, and the second object set includes M second transaction objects, where N and M are positive integers, and M≥2.

[0057] In some implementations, the transaction data set includes transaction objects and transaction object types. By querying the transaction data set, the transaction type of each transaction object can be determined, and the transaction objects in the global transaction object set can be classified according to the transaction type to obtain a local transaction object set corresponding to each transaction type. For example, the transaction type can be a public transaction or a private transaction.

[0058] Further, the first object set and the second object set may be determined according to the transaction amount of each transaction object in the local transaction object set. Optionally, the transaction objects may be sorted based on the transaction amount, and the first object set and the second object set may be determined according to the sorting result. Optionally, the transaction objects may be sorted in descending order of transaction amount.

[0059] For example, the top three transaction objects in the sorting result may be used as the first transaction objects in the first object set, and the remaining transaction objects in the sorting result may be the second transaction objects in the second object set.

[0060] S103, determining a core node corresponding to the target object, each first transaction object corresponds to a first node, and connecting the core node and the first node, and determining that M second transaction objects correspond to one second node, and connecting the core node and the second node.

[0061] In some implementations, for the initialized knowledge graph, any node in the knowledge graph can be selected as the core node corresponding to the target object, thereby determining the first node corresponding to each first transaction object in the first object set and the second node corresponding to M second transaction objects in the second object set.

[0062] Furthermore, the transaction direction between the target object and the first transaction object and the second transaction object is determined, and then the directed edge connecting the core node and the first node, as well as the directed edge connecting the core node and the second node are determined according to the transaction direction. Then, the core node and the first node can be connected, and the core node and the second node can be connected according to the directed edges, so as to obtain the initial knowledge graph of the target object.

[0063] Optionally, the transaction direction between the target object and the first transaction object, and the transaction direction between the target object and the second transaction object may be determined from the transaction data set.

[0064] S104, determining the node information of the first node based on the transaction data corresponding to the first transaction object, and determining the node information of the second node based on the transaction data of the M second transaction objects, so as to obtain a transaction knowledge graph of the target object.

[0065] In some implementations, the transaction data corresponding to each first transaction object is determined from the transaction data set, and the transaction data is used as the node information of the first node, where the node information may include the transaction amount. The transaction data of M second transaction objects is determined from the transaction data set, and the transaction data is used as the node information of the second node, where the node information may include the total transaction amount of the M second transaction objects.

[0066] Furthermore, the transaction knowledge graph of the target object can be obtained by adding the node information of the first node and the node information of the second node to the initial knowledge graph of the target object. Optionally, the transaction knowledge graph of the target object can be drawn based on a graph visualization framework.

[0067] In the method for generating a transaction knowledge graph provided in an embodiment of the present disclosure, a transaction data set of a target transaction object is determined, and a global transaction object set having a transaction relationship with the target transaction object is determined based on the transaction data set. By dividing the global transaction object set into transaction types, a first object set and a second object set corresponding to each type can be determined. Further, the core node corresponding to the target transaction object is determined, and each first transaction object corresponds to a first node, M second transaction objects correspond to a second node, and the core node, the first node and the second node are connected, and the node information can be added to obtain the transaction knowledge graph of the target object. By displaying transaction data in the form of a knowledge graph, it is possible to realize a transaction object-centered approach, and continuously expand the related capital transactions, so as to display a complete capital transaction chain, which helps to gain a deep insight into the transaction relationship and facilitates risk management for regulators.

[0068] Figure 2 This is a flowchart of a method for generating a transaction knowledge graph provided in an embodiment of the present disclosure. Figure 2 As shown, the method for generating the transaction knowledge graph includes:

[0069] S201, obtaining a transaction data set of a target object, and determining a global transaction object set having a transaction relationship with the target object according to the transaction data set.

[0070] The relevant contents of step S201 can be found in the above embodiment and will not be described again here.

[0071] S202, determining the transaction volume of the transaction object according to the transaction data of the transaction object.

[0072] S203, grouping the transaction objects in the global transaction object set according to the types of the transaction objects to obtain a local transaction object set of each type.

[0073] In some implementations, the transaction volume of the transaction object and the type of the transaction object can be determined from the transaction data set. The transaction volume refers to the transaction amount of the transaction object. Optionally, by determining the type of the transaction object, the transaction objects in the global transaction object set are grouped into multiple local transaction object sets according to the type of the transaction object. The number of local transaction object sets is the same as the number of types of transaction objects, that is, transaction objects of the same type are grouped into the same local transaction object set.

[0074] S204, for each transaction type, filter the transaction objects according to the transaction volume in the local transaction object set of the transaction type to obtain a first transaction object and a second transaction object of the transaction type, so as to obtain a first object set and a second object set, wherein the transaction volume of the first transaction object is greater than the transaction volume of the second transaction object.

[0075] Optionally, the transaction objects may be filtered by setting a filtering condition. For example, the filtering condition is that the transaction objects with the top three transaction volumes are the first transaction objects, and the remaining transaction objects are the second transaction objects. In other words, the transaction objects in the local transaction object set may be sorted according to the size of their transaction volumes to filter the transaction objects and obtain the first object set and the second object set.

[0076] For example, assume that the transaction type is a public transaction, and the transaction objects and transaction volumes in the corresponding local transaction object set are: object A, transaction volume 50,000; object B, transaction volume 100,000; object C, transaction volume 1,000; object D, transaction volume 15,000; object E, transaction volume 200,000; object F, transaction volume 8,000. Sort them from large to small in terms of transaction volume as: object E, object B, object A, object D, object F, object C, and take the top three transaction objects as the first transaction objects, and the remaining transaction objects as the second transaction objects. Then the first object set includes: object E, object B, object A, and the second object set includes: object D, object F, and object C.

[0077] S205, determining a core node corresponding to the target object, each first transaction object corresponds to a first node, and connecting the core node and the first node, and determining that M second transaction objects correspond to one second node, and connecting the core node and the second node.

[0078] In some implementations, in the process of connecting the first transaction object, the second transaction object, and the target object, the direction of the connection edge can be determined according to the transaction direction between different transaction objects, so as to intuitively display the flow direction of funds, goods or information between the transaction objects, help clarify the flow and path of the transaction activities, and facilitate tracking of the transaction process.

[0079] Optionally, the transaction direction between the target object and the transaction object may be determined based on the transaction data. By determining the transaction direction between the target object and the first transaction object, the direction of the connection edge between the core node and the first node may be determined based on the transaction direction between the target object and the first transaction object.

[0080] Optionally, for the second transaction object, the transaction directions corresponding to the M second transaction objects are aggregated to obtain the aggregated transaction direction corresponding to the second node, and the direction of the connection edge between the core node and the second node is determined according to the aggregated transaction direction.

[0081] Furthermore, the core node and the first node may be connected, and the core node and the second node may be connected according to the direction of the connecting edge between the core node and the first node, and the direction of the connecting edge between the core node and the second node.

[0082] S206, determining the node information of the first node based on the transaction data corresponding to the first transaction object, and determining the node information of the second node based on the transaction data of the M second transaction objects, so as to obtain a transaction knowledge graph of the target object.

[0083] In some implementations, node information can be determined based on the transaction volume of the transaction object to intuitively present transaction data in the transaction knowledge graph and improve the efficiency of information processing. Optionally, the transaction volume of each first transaction object is determined and the transaction volume is used as the node information of the first node corresponding to the first transaction object.

[0084] In some implementations, for the second transaction object, the total transaction volume of the second transaction object may also be used as the node information of the second node. That is, by obtaining the transaction volume of the second transaction object from the transaction data of the second transaction object, and determining the total transaction volume of the second node according to the transaction volume of the second transaction object, the node information of the second node may be determined according to the total transaction volume and the transaction volume of the second transaction object.

[0085] In the method for generating a transaction knowledge graph provided in an embodiment of the present disclosure, a transaction data set of a target transaction object is determined, and a global transaction object set having a transaction relationship with the target transaction object is determined based on the transaction data set. By dividing the global transaction object set into transaction types, and based on the transaction volume of the transaction object, a first object set and a second object set corresponding to each type can be determined. Further, the core node corresponding to the target transaction object is determined, and each first transaction object corresponds to a first node, M second transaction objects correspond to a second node, and the core node, the first node and the second node are connected, and then the node information can be added to obtain the transaction knowledge graph of the target object. By displaying transaction data in the form of a knowledge graph, it is possible to realize a transaction object-centered approach, and continuously expand the related capital transactions, so as to display a complete capital transaction chain, which helps to gain a deep insight into the transaction relationship and facilitates risk management for regulators.

[0086] Figure 3 This is a flowchart of a method for generating a transaction knowledge graph provided in an embodiment of the present disclosure. Figure 3 As shown, the method for generating the transaction knowledge graph includes:

[0087] S301, obtaining a transaction data set of a target object, and determining a global transaction object set having a transaction relationship with the target object according to the transaction data set.

[0088] S302, according to the transaction type, determine a first object set and a second object set corresponding to each type from the global transaction object set, the first object set includes N first transaction objects, and the second object set includes M second transaction objects, where N and M are positive integers, and M≥2.

[0089] S303, determining a core node corresponding to the target object, each first transaction object corresponds to a first node, and connecting the core node and the first node, and determining that M second transaction objects correspond to one second node, and connecting the core node and the second node.

[0090] S304, determining the node information of the first node based on the transaction data corresponding to the first transaction object, and determining the node information of the second node based on the transaction data of the M second transaction objects, so as to obtain a transaction knowledge graph of the target object.

[0091] The relevant contents of step S301 to step S304 can be found in the above embodiment and will not be repeated here.

[0092] S305, receiving a target instruction, and according to the target instruction, determining a target node to be processed from the transaction knowledge graph, and performing operations related to the target instruction on the target node to update the transaction knowledge graph.

[0093] In some implementations, after constructing the transaction knowledge graph of the target object, the nodes in the transaction knowledge graph can be updated, such as expansion, aggregation, deletion, etc. In other words, the target node in the transaction knowledge graph can be determined according to the target instruction, so as to execute the operation of the target instruction on the target node and update the transaction knowledge graph, thereby improving the practicality and value of the knowledge graph and improving the analysis efficiency.

[0094] In some implementations, after updating the transaction knowledge graph, the update operation can be regarded as a historical update operation, and the historical update operation of the transaction knowledge graph is stored in a queue. By selecting and monitoring the historical update operation, in response to the first historical update operation being selected, the historical update data corresponding to the first historical update operation is obtained from the queue, and the transaction knowledge graph can be updated according to the historical update data, thereby realizing the timely update of the transaction knowledge graph and improving the efficiency of data update.

[0095] In some implementations, after updating the transaction knowledge graph, the states before and after the update can be cached, that is, by caching the state of the transaction knowledge graph and switching the state of the transaction knowledge graph according to the state switching instruction. When the state of the knowledge graph needs to be accessed or queried, the system can directly obtain it from the cache without recalculation, which greatly reduces the time for data access and improves the response speed of the system.

[0096] In the method for generating a transaction knowledge graph provided in an embodiment of the present disclosure, a transaction data set of a target transaction object is determined, and a global transaction object set having a transaction relationship with the target transaction object is determined based on the transaction data set. By dividing the global transaction object set into transaction types, a first object set and a second object set corresponding to each type can be determined. Further, the core node corresponding to the target transaction object is determined, and each first transaction object corresponds to a first node, M second transaction objects correspond to a second node, and the core node, the first node and the second node are connected, and the node information can be added to obtain the transaction knowledge graph of the target object. By displaying transaction data in the form of a knowledge graph, it is possible to realize a transaction object-centered approach, and continuously expand the related capital transactions, so as to display a complete capital transaction chain, which helps to gain a deep insight into the transaction relationship and facilitates risk management for regulators.

[0097] Based on the above embodiments, the disclosed embodiments can explain the process of expanding nodes in the transaction knowledge graph, such as Figure 4 As shown in the figure, the process of expanding the nodes in the transaction knowledge graph includes:

[0098] S401, receiving a node expansion instruction, and determining a first target node from a transaction knowledge graph according to the node expansion instruction.

[0099] In some implementations, when it is determined that an expansion operation needs to be performed on a node in the transaction knowledge graph, the target instruction is a node expansion instruction. By parsing the node expansion instruction, the node indicated to be expanded in the node expansion instruction can be obtained, and the node is determined to be the first target node from the transaction knowledge graph.

[0100] Optionally, the first target node may be determined according to the node identifier by obtaining the node identifier corresponding to the node indicated to be expanded in the node expansion instruction, matching the node in the transaction knowledge graph according to the node identifier, and taking the successfully matched node as the first target node.

[0101] S402: Determine whether the first target node is the second node.

[0102] S403: In response to the first target node being the second node, determining expansion information of the second node according to the node information of the second node, and displaying the expansion information.

[0103] In some implementations, if the node is an aggregate node, the node can be expanded. That is, by determining whether the first target node is an aggregated second node, the first target node is expanded according to the node expansion instruction. By expanding the first target node, the data items it contains can be made more intuitive and easy to access, which helps to improve the efficiency of data access.

[0104] In some implementations, if the first target node is determined to be the second node, the node of each second transaction object corresponding to the second node may be displayed, that is, the first nodes corresponding to M second transaction objects are generated.

[0105] Further, the node information of the first node corresponding to the second transaction object is determined according to the node information of the second node. Optionally, the transaction volume of the second transaction object can be determined from the node information as the node information of the first node.

[0106] Furthermore, the transaction direction of the second transaction object and the target object is determined, and based on the transaction direction of the second transaction object and the target object, the direction of the connecting edge between the first node corresponding to the second transaction object and the core node is determined, and the corresponding connecting edge between the second node and the second node is deleted to obtain the expanded information of the second node, which is spliced ​​in the transaction knowledge graph to display the expanded information.

[0107] Optionally, the expansion information of the second node may be displayed in a floating manner near the second node. For example, an expansion list is generated according to the expansion information and is displayed in a floating manner in the transaction knowledge graph.

[0108] S404, in response to the first target node being the first node, displaying node information corresponding to the first node.

[0109] Optionally, if it is determined that the first target node is a non-aggregated first node, the node information corresponding to the first node may be directly displayed.

[0110] In the method for generating a transaction knowledge graph provided in an embodiment of the present disclosure, the first target node which is the second node is expanded according to the node expansion instruction to display the expansion information of the node in the transaction knowledge graph. This allows users to intuitively see the expansion information of the nodes in the transaction knowledge graph, helps to clearly understand the overall situation of the transaction, and enhances the transparency of the transaction information.

[0111] Based on the above embodiments, the embodiments of the present disclosure can explain the process of aggregating nodes in the transaction knowledge graph, such as Figure 5As shown in Figure 2, the process of aggregating nodes in the transaction knowledge graph includes:

[0112] S501, receiving a node aggregation instruction, and determining at least two second target nodes from the transaction knowledge graph according to the node aggregation instruction.

[0113] In some implementations, when it is determined that an aggregation operation needs to be performed on nodes in the transaction knowledge graph, the target instruction is a node aggregation instruction. By parsing the node aggregation instruction, the node indicated for aggregation in the node aggregation instruction can be obtained, and the node is determined as the second target node from the transaction knowledge graph.

[0114] Optionally, the second target node may be determined based on the node identifier by obtaining the node identifier corresponding to the node indicated to be aggregated in the node aggregation instruction, matching is performed in the transaction knowledge graph based on the node identifier, and the successfully matched node is used as the second target node.

[0115] S502, in response to the fact that the transaction types corresponding to at least two second target nodes are the same, at least two second target nodes are aggregated to generate a second node, and node information and corresponding connection edges of the second node are determined based on the node information and connection edges of the at least two second target nodes.

[0116] In some implementations, by determining whether the transaction types of the second target nodes are the same, at least two second target nodes with the same transaction type can be aggregated to obtain a second node. Further, by obtaining the node information of the second target node and determining the transaction direction of the second target node, the transaction volume and connection edges corresponding to the second target node can be determined, and then the node information and connection edges of the aggregated second node can be determined based on the transaction volume and connection edges corresponding to the second target node.

[0117] Optionally, the total transaction volume may be calculated based on the transaction volume corresponding to the second target node, and the total transaction volume and the transaction volume corresponding to the second target node may be used as the node information of the aggregated second node.

[0118] In some implementations, in response to the different transaction types corresponding to at least two second target nodes, the at least two second target nodes are divided according to the transaction type to obtain second target nodes of the same transaction type, and an aggregation process corresponding to the node aggregation instruction is executed on the second target nodes of the same transaction type. By aggregating nodes of the same transaction type, the transaction knowledge graph can be made more concise and clear, which helps relevant parties understand and analyze transaction data more quickly.

[0119] S503, delete at least two second target nodes, and connect the second nodes corresponding to the at least two second target nodes to the core node to update the transaction knowledge graph.

[0120] In some implementations, after the second target nodes are aggregated, at least two second target nodes may be deleted, and the second node after the second target nodes are aggregated may be connected to the core node to update the transaction knowledge graph.

[0121] In some implementations, in order to simplify the transaction knowledge graph, nodes in the transaction knowledge graph may be hidden. By receiving a node hiding instruction and determining at least one third target node from the transaction knowledge graph according to the node hiding instruction, the third target node and the connection edge corresponding to the third target node may be hidden.

[0122] In the method for generating a transaction knowledge graph provided by the embodiment of the present disclosure, according to the node aggregation instruction, the second target node to be aggregated is determined, and according to the transaction type of the second target node, the second target node is aggregated to obtain the second node, and the node information and connection edge of the second node are determined, so as to connect the second node with the core node according to the node information and the connection edge to form a transaction knowledge graph. By aggregating the second target nodes of the same transaction type, the structure of the transaction knowledge graph can be simplified, making the information easier to understand, and also helping to simplify the query process and improve query efficiency.

[0123] Figure 6 The flowchart for updating the transaction knowledge graph is shown. The target object is determined, and the global transaction object set having a transaction relationship with the target object is determined based on the transaction data of the target object, and the first object set and the second object set are determined from the global transaction object set based on the transaction type. Further, the core node corresponding to the target object is determined, and each first transaction object corresponds to a first node, M second transaction objects correspond to a second node, and the core node and the first node are connected, and the core node and the second node are connected. Further, the node information of the first node and the node information of the second node are determined, and the transaction knowledge graph of the target object can be obtained based on the node information.

[0124] Furthermore, according to the target instruction, operations related to the target instruction are performed on the target nodes determined to be processed in the transaction knowledge graph to update the transaction knowledge graph.

[0125] If the target instruction is a node expansion instruction, the first target node is determined from the transaction knowledge graph according to the node expansion instruction, and it is determined whether the first target node is the aggregated second node. If the first target node is the second node, the expansion information of the second node is determined according to the node information of the second node, and the expansion information is displayed; if the first target node is the first node, the node information corresponding to the first node is displayed.

[0126] If the target instruction is a node aggregation instruction, at least two second target nodes are determined from the transaction knowledge graph according to the node aggregation instruction, and the aggregation process corresponding to the node aggregation instruction is executed on the second target nodes of the same transaction type.

[0127] If the target instruction is a node hiding instruction, and according to the node hiding instruction, at least one third target node is determined from the transaction knowledge graph, and the third target node and the connection edge corresponding to the third target node are hidden.

[0128] When a state switching instruction of the transaction knowledge graph is received, the state of the transaction knowledge graph can be switched according to the state switching instruction. For example, the state switching instruction is the previous step, the next step, or the restoration of the initial state.

[0129] Corresponding to the methods for generating transaction knowledge graphs provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides a device for generating a transaction knowledge graph. Since the device for generating a transaction knowledge graph provided in the embodiment of the present disclosure corresponds to the methods for generating transaction knowledge graphs provided in the above-mentioned embodiments, the implementation methods of the above-mentioned methods for generating transaction knowledge graphs are also applicable to the device for generating transaction knowledge graphs provided in the embodiments of the present disclosure, and will not be described in detail in the following embodiments.

[0130] Figure 7 This is a diagram showing an example of the structure of a device for generating a transaction knowledge graph provided in an embodiment of the present disclosure. Figure 7 As shown, the device for generating the transaction knowledge graph includes: a first determination module 701, a second determination module 702, a connection module 703 and a third determination module 704.

[0131] The first determination module 701 is used to obtain a transaction data set of a target object, and determine a global transaction object set having a transaction relationship with the target object according to the transaction data set;

[0132] A second determination module 702 is used to determine, according to the transaction type, a first object set and a second object set corresponding to each type from the global transaction object set, wherein the first object set includes N first transaction objects, and the second object set includes M second transaction objects, where N and M are positive integers, and M≥2;

[0133] A connection module 703 is used to determine the core node corresponding to the target object, each first transaction object corresponds to a first node, and connect the core node and the first node, and determine that the M second transaction objects correspond to a second node, and connect the core node and the second node;

[0134] The third determination module 704 is used to determine the node information of the first node based on the transaction data corresponding to the first transaction object, and to determine the node information of the second node based on the transaction data of the M second transaction objects, so as to obtain the transaction knowledge graph of the target object.

[0135] According to one embodiment of the present disclosure, the second determination module 702 is further used to: determine the transaction volume of the transaction object based on the transaction data of the transaction object; group the transaction objects in the global transaction object set according to the type of the transaction object to obtain a local transaction object set of each type; for each transaction type, filter the transaction objects according to the transaction volume within the local transaction object set of the transaction type to obtain a first transaction object and a second transaction object of the transaction type, so as to obtain the first object set and the second object set, wherein the transaction volume of the first transaction object is greater than the transaction volume of the second transaction object.

[0136] According to one embodiment of the present disclosure, the third determination module 704 is further used to: obtain the transaction volume of the second transaction object from the transaction data of the second transaction object; determine the total transaction volume of the second node based on the transaction volume of the second transaction object; and determine the node information of the second node based on the total transaction volume and the transaction volume of the second transaction object.

[0137] According to one embodiment of the present disclosure, the connection module 703 is also used to: determine the transaction direction between the target object and the transaction object based on the transaction data; determine the direction of the connection edge between the core node and the first node based on the transaction direction between the target object and the first transaction object; or, aggregate the transaction directions corresponding to the M second transaction objects to obtain the aggregated transaction direction corresponding to the second node, and determine the direction of the connection edge between the core node and the second node based on the aggregated transaction direction.

[0138] According to one embodiment of the present disclosure, it is characterized in that the third determination module 704 is also used to: receive a target instruction, and according to the target instruction, determine the target node to be processed from the transaction knowledge graph, and perform operations related to the target instruction on the target node to update the transaction knowledge graph.

[0139] According to one embodiment of the present disclosure, the third determination module 704 is further used to: store the historical update operations of the transaction knowledge graph in a queue; select and monitor the historical update operations, and in response to monitoring that a first historical update operation is selected, obtain historical update data corresponding to the first historical update operation from the queue; and update the transaction knowledge graph according to the historical update data.

[0140] According to one embodiment of the present disclosure, the third determination module 704 is further used to: cache the state of the transaction knowledge graph, and switch the state of the transaction knowledge graph according to a state switching instruction.

[0141] According to an embodiment of the present disclosure, it is characterized in that the third determination module 704 is also used to: receive a node expansion instruction, and determine a first target node from the transaction knowledge graph according to the node expansion instruction; determine whether the first target node is the second node; in response to the first target node being the second node, determine the expansion information of the second node according to the node information of the second node, and display the expansion information; in response to the first target node being the first node, display the node information corresponding to the first node.

[0142] According to one embodiment of the present disclosure, the third determination module 704 is also used to: generate a first node corresponding to each of the M second transaction objects; determine the node information of the first node corresponding to the second transaction object based on the node information of the second node; determine the direction of the connecting edge between the first node corresponding to the second transaction object and the core node based on the transaction direction of the second transaction object and the target object, and delete the corresponding connecting edge between the second node and the second node to obtain the expanded information of the second node, and splice it into the transaction knowledge graph.

[0143] According to one embodiment of the present disclosure, the third determination module 704 is also used to: receive a node aggregation instruction, and determine at least two second target nodes from the transaction knowledge graph according to the node aggregation instruction; in response to the at least two second target nodes corresponding to the same transaction type, aggregate the at least two second target nodes to generate the second node, and determine the node information and corresponding connection edges of the second node based on the node information and connection edges of the at least two second target nodes; delete the at least two second target nodes, and connect the second nodes corresponding to the at least two second target nodes to the core node to update the transaction knowledge graph.

[0144] According to one embodiment of the present disclosure, the third determination module 704 is also used to: in response to the at least two second target nodes corresponding to different transaction types, divide the at least two second target nodes according to the transaction types to obtain second target nodes of the same transaction type, and execute the aggregation process corresponding to the node aggregation instruction on the second target nodes of the same transaction type.

[0145] According to an embodiment of the present disclosure, it is characterized in that the third determination module 704 is also used to: receive a node hiding instruction, and determine at least one third target node from the transaction knowledge graph according to the node hiding instruction; and hide the third target node and the connection edge corresponding to the third target node.

[0146] In the device for generating the transaction knowledge graph provided by the embodiment of the present disclosure, the transaction data set of the target transaction object is determined, and the global transaction object set that has a transaction relationship with the target transaction object is determined based on the transaction data set. By dividing the global transaction object set into transaction types, the first object set and the second object set corresponding to each type can be determined. Further, the core node corresponding to the target transaction object is determined, and each first transaction object corresponds to a first node, M second transaction objects correspond to a second node, and the core node, the first node and the second node are connected, and then the node information can be added to obtain the transaction knowledge graph of the target object. By displaying transaction data in the form of a knowledge graph, it is possible to realize a transaction object-centered approach, and continuously expand the related capital transactions, so as to display a complete capital transaction chain, which helps to gain a deep insight into the transaction relationship and facilitates risk management for regulators.

[0147] Figure 8 This is a diagram showing an example of the structure of an electronic device provided in an embodiment of the present disclosure. Figure 8 As shown, the electronic device 800 may include: a transceiver 801 , a processor 802 , and a memory 803 .

[0148] The processor 802 executes the computer execution instructions stored in the memory, so that the processor 802 executes the scheme in the above embodiment. The processor 802 can be a general-purpose processor, including a central processing unit CPU, a network processor (network processor, NP), etc.; it can also be a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0149] The memory 803 is connected to the processor 802 via a system bus and implements communication therebetween. The memory 803 is used to store computer program instructions.

[0150] The transceiver 801 may be used to obtain tasks to be executed and configuration information of the tasks to be executed.

[0151] The system bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The transceiver is used to realize the communication between the database access device and other computers (such as clients, read-write libraries, and read-only libraries). The memory may include random access memory (RAM) and may also include non-volatile memory.

[0152] The electronic device provided in the embodiments of the present disclosure may be the terminal device of the above embodiments.

[0153] The disclosed embodiment also provides a chip for running instructions, which is used to execute the technical solution of the method for generating a transaction knowledge graph in the above embodiment.

[0154] The disclosed embodiment also provides a computer-readable storage medium, in which computer instructions are stored. When the computer instructions are executed on a computer, the computer executes the technical solution of the method for generating a transaction knowledge graph in the above embodiment.

[0155] The embodiments of the present disclosure also provide a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When at least one processor executes the computer program, it can implement the technical solution of the method for generating a transaction knowledge graph in the above embodiments.

[0156] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0157] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for generating a transaction knowledge graph, characterized in that: include: Acquire a transaction data set of a target object, and determine a global transaction object set having a transaction relationship with the target object based on the transaction data set; According to the transaction type, determine a first object set and a second object set corresponding to each type from the global transaction object set, wherein the first object set includes N first transaction objects, and the second object set includes M second transaction objects, wherein N and M are positive integers, and M≥2; Determine a core node corresponding to the target object, each first transaction object corresponds to a first node, and connect the core node and the first node, and determine that the M second transaction objects correspond to a second node, and connect the core node and the second node; Based on the transaction data corresponding to the first transaction object, the node information of the first node is determined, and based on the transaction data of the M second transaction objects, the node information of the second node is determined to obtain the transaction knowledge graph of the target object.

2. The method according to claim 1, characterized in that The determining, according to the transaction type, from the global transaction object set, a first object set and a second object set corresponding to each type, comprises: Determining the transaction volume of the transaction object according to the transaction data of the transaction object; According to the types of transaction objects, the transaction objects in the global transaction object set are grouped to obtain a local transaction object set of each type; For each transaction type, transaction objects are screened according to the transaction volume within a local transaction object set of the transaction type to obtain a first transaction object and a second transaction object of the transaction type, so as to obtain the first object set and the second object set, wherein the transaction volume of the first transaction object is greater than the transaction volume of the second transaction object.

3. The method according to claim 1, characterized in that The determining the node information of the second node based on the transaction data of the M second transaction objects includes: Acquire the transaction volume of the second transaction object from the transaction data of the second transaction object; determining a total transaction volume of the second node according to the transaction volume of the second transaction object; The node information of the second node is determined according to the total transaction volume and the transaction volume of the second transaction object.

4. The method according to claim 1, characterized in that: The method further comprises: Determining a transaction direction between the target object and the transaction object according to the transaction data; Determine the direction of the edge connecting the core node and the first node according to the transaction direction between the target object and the first transaction object; or The transaction directions corresponding to the M second transaction objects are aggregated to obtain an aggregated transaction direction corresponding to the second node, and the direction of the connection edge between the core node and the second node is determined according to the aggregated transaction direction.

5. The method according to claim 1, characterized in that: After obtaining the transaction knowledge graph of the target object, the method further includes: Receive a target instruction, and according to the target instruction, determine a target node to be processed from the transaction knowledge graph, and perform operations related to the target instruction on the target node to update the transaction knowledge graph.

6. The method according to claim 5, characterized in that The method further comprises: The historical update operations of the transaction knowledge graph are stored in a queue; Selecting and monitoring the historical update operation, and in response to monitoring that a first historical update operation is selected, obtaining historical update data corresponding to the first historical update operation from the queue; The transaction knowledge graph is updated according to the historical update data.

7. The method according to claim 5, characterized in that The method further comprises: The state of the transaction knowledge graph is cached, and the state of the transaction knowledge graph is switched according to the state switching instruction.

8. The method according to any one of claims 6 to 7, characterized in that: The method further comprises: receiving a node expansion instruction, and determining a first target node from the transaction knowledge graph according to the node expansion instruction; Determining whether the first target node is the second node; In response to the first target node being the second node, determining expansion information of the second node according to node information of the second node, and displaying the expansion information; In response to the first target node being the first node, node information corresponding to the first node is displayed.

9. The method according to claim 8, characterized in that The determining, according to the node information of the second node, expansion information of the second node and displaying the expansion information includes: Generate a first node corresponding to each of the M second transaction objects; Determining the node information of the first node corresponding to the second transaction object according to the node information of the second node; According to the transaction direction of the second transaction object and the target object, the direction of the connecting edge between the first node corresponding to the second transaction object and the core node is determined, and the connecting edge corresponding to the second node and the second node is deleted to obtain the expansion information of the second node, which is spliced ​​into the transaction knowledge graph.

10. The method according to any one of claims 5 to 7, characterized in that: After obtaining the transaction knowledge graph of the target object, the method further includes: receiving a node aggregation instruction, and determining at least two second target nodes from the transaction knowledge graph according to the node aggregation instruction; In response to the transaction types corresponding to the at least two second target nodes being the same, aggregating the at least two second target nodes to generate the second node, and determining the node information and the corresponding connection edge of the second node according to the node information and the connection edge of the at least two second target nodes; The at least two second target nodes are deleted, and the second nodes corresponding to the at least two second target nodes are connected to the core node to update the transaction knowledge graph.

11. The method according to claim 10, characterized in that The method further comprises: In response to the different transaction types corresponding to the at least two second target nodes, the at least two second target nodes are divided according to the transaction types to obtain second target nodes of the same transaction type, and the aggregation process corresponding to the node aggregation instruction is executed on the second target nodes of the same transaction type.

12. The method according to any one of claims 5 to 7, characterized in that: The method further comprises: receiving a node hiding instruction, and determining at least one third target node from the transaction knowledge graph according to the node hiding instruction; The third target node and the connection edge corresponding to the third target node are hidden.

13. A device for generating a transaction knowledge graph, characterized in that: include: A first determination module is used to obtain a transaction data set of a target object, and determine a global transaction object set having a transaction relationship with the target object based on the transaction data set; A second determination module is used to determine, according to the transaction type, a first object set and a second object set corresponding to each type from the global transaction object set, wherein the first object set includes N first transaction objects, and the second object set includes M second transaction objects, wherein N and M are positive integers, and M≥2; A connection module, used to determine the core node corresponding to the target object, each first transaction object corresponds to a first node, and connect the core node and the first node, and determine that the M second transaction objects correspond to a second node, and connect the core node and the second node; The third determination module is used to determine the node information of the first node based on the transaction data corresponding to the first transaction object, and to determine the node information of the second node based on the transaction data of the M second transaction objects, so as to obtain the transaction knowledge graph of the target object.

14. The device according to claim 13, characterized in that The second determining module is further used for: Determining the transaction volume of the transaction object according to the transaction data of the transaction object; According to the types of transaction objects, the transaction objects in the global transaction object set are grouped to obtain a local transaction object set of each type; For each transaction type, transaction objects are screened according to the transaction volume within a local transaction object set of the transaction type to obtain a first transaction object and a second transaction object of the transaction type, so as to obtain the first object set and the second object set, wherein the transaction volume of the first transaction object is greater than the transaction volume of the second transaction object.

15. The device according to claim 13, characterized in that The third determining module is further used for: Acquire the transaction volume of the second transaction object from the transaction data of the second transaction object; determining a total transaction volume of the second node according to the transaction volume of the second transaction object; The node information of the second node is determined according to the total transaction volume and the transaction volume of the second transaction object.

16. The device according to claim 13, characterized in that The connection module is also used for: Determining a transaction direction between the target object and the transaction object according to the transaction data; Determine the direction of the edge connecting the core node and the first node according to the transaction direction between the target object and the first transaction object; or The transaction directions corresponding to the M second transaction objects are aggregated to obtain an aggregated transaction direction corresponding to the second node, and the direction of the connection edge between the core node and the second node is determined according to the aggregated transaction direction.

17. The device according to claim 13, characterized in that The third determining module is further used for: Receive a target instruction, and according to the target instruction, determine a target node to be processed from the transaction knowledge graph, and perform operations related to the target instruction on the target node to update the transaction knowledge graph.

18. The device according to claim 17, characterized in that The third determining module is further used for: The historical update operations of the transaction knowledge graph are stored in a queue; Selecting and monitoring the historical update operation, and in response to monitoring that a first historical update operation is selected, obtaining historical update data corresponding to the first historical update operation from the queue; The transaction knowledge graph is updated according to the historical update data.

19. The device according to claim 17, characterized in that The third determining module is further used for: The state of the transaction knowledge graph is cached, and the state of the transaction knowledge graph is switched according to the state switching instruction.

20. The device according to any one of claims 18-19, characterized in that The third determining module is further used for: receiving a node expansion instruction, and determining a first target node from the transaction knowledge graph according to the node expansion instruction; Determining whether the first target node is the second node; In response to the first target node being the second node, determining expansion information of the second node according to node information of the second node, and displaying the expansion information; In response to the first target node being the first node, node information corresponding to the first node is displayed.

21. The device according to claim 20, characterized in that The third determining module is further used for: Generate a first node corresponding to each of the M second transaction objects; Determining the node information of the first node corresponding to the second transaction object according to the node information of the second node; According to the transaction direction of the second transaction object and the target object, the direction of the connecting edge between the first node corresponding to the second transaction object and the core node is determined, and the connecting edge corresponding to the second node and the second node is deleted to obtain the expansion information of the second node, which is spliced ​​into the transaction knowledge graph.

22. The device according to any one of claims 17 to 19, characterized in that The third determining module is further used for: receiving a node aggregation instruction, and determining at least two second target nodes from the transaction knowledge graph according to the node aggregation instruction; In response to the transaction types corresponding to the at least two second target nodes being the same, aggregating the at least two second target nodes to generate the second node, and determining the node information and the corresponding connection edge of the second node according to the node information and the connection edge of the at least two second target nodes; The at least two second target nodes are deleted, and the second nodes corresponding to the at least two second target nodes are connected to the core node to update the transaction knowledge graph.

23. The device according to claim 22, characterized in that The third determining module is further used for: In response to the different transaction types corresponding to the at least two second target nodes, the at least two second target nodes are divided according to the transaction types to obtain second target nodes of the same transaction type, and the aggregation process corresponding to the node aggregation instruction is executed on the second target nodes of the same transaction type.

24. The device according to any one of claims 17 to 19, characterized in that The third determining module is further used for: receiving a node hiding instruction, and determining at least one third target node from the transaction knowledge graph according to the node hiding instruction; The third target node and the connection edge corresponding to the third target node are hidden.

25. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 12.

26. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 12 when executed by a processor.

27. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 12 when being executed by a processor.

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