Data processing method, data processing device, and electronic device

Abstract

The application provides a data processing method, a data processing device and an electronic device. The method comprises the following steps: obtaining the data of a user, wherein the data is the current account detailed transaction data of the user; determining whether the data of the user meets the judgment condition of a first processing node; adding the first processing node into a processing task in the case that the data of the user meets the judgment condition of the first processing node; repeating the steps of obtaining the data, determining whether the data meets the judgment condition and adding the first processing node into the processing task at least once until a predetermined condition is met, and the establishment of the processing task is completed, wherein the predetermined condition is judging whether the data of all users meets the judgment condition of the first processing node; and executing the processing task according to the adding sequence of the first processing node in the processing task. The method solves the problem of high code complexity in the data processing of the existing technology for the income and expenditure analysis.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a data processing method, a data processing apparatus, a computer-readable storage medium, and an electronic device. Background Technology

[0002] In the financial sector, income and expenditure analysis is a crucial component, often employing hard-coding for real-time analysis. However, for complex financial applications, hard-coding presents several challenges. It generates excessive if / else statements, exponentially increasing code complexity and volume, and making application troubleshooting, upgrades, and maintenance more difficult.

[0003] Therefore, there is an urgent need for a method to reduce the code complexity when processing income and expenditure analysis data. Summary of the Invention

[0004] The main objective of this application is to provide a data processing method, a data processing apparatus, a computer-readable storage medium, and an electronic device to at least solve the problem of high code complexity in income and expenditure analysis in the prior art.

[0005] To achieve the above objectives, according to one aspect of this application, a data processing method is provided, comprising: an acquisition step, acquiring user data, wherein the data is the user's current account transaction details; a determination step, determining whether the user data meets the judgment conditions of a first processing node, wherein the first processing node is used to process the data having multiple transaction initiation channels, and the judgment condition is that the data is income-type data or expenditure-type data; an execution step, wherein if the user data meets the judgment conditions of the first processing node, the first processing node is added to a processing task; repeating the acquisition step, the determination step, and the execution step at least once until a predetermined condition is met, thereby completing the establishment of the processing task, wherein the predetermined condition is determining whether all the user data meets the judgment conditions of the first processing node; and executing the processing task according to the order in which the first processing nodes are added in the processing task.

[0006] Optionally, after performing the steps, the method further includes: adding a second processing node to the processing task after all the first processing nodes have been added, wherein the second processing node is used to process the data having one of the transaction initiation channels.

[0007] Optionally, before the determining step, generating the first processing node includes: initializing parameters within a first operator, wherein the first operator is used to generate a processing algorithm in the first processing node, and the parameters are used to characterize the attributes of the user's data; and establishing pre-processing tasks based on the same parameters, wherein each pre-processing task includes multiple processing nodes, and each processing node of the pre-processing task is the first processing node.

[0008] Optionally, before the determination step, the second processing node is generated, including: establishing a first node according to a second operator, the second operator being used to generate the processing algorithm in the second processing node; a processing step, obtaining the left child node and right child node of the first node according to the binary classification result of the first node; repeating the determination step multiple times to form a binary tree; traversing the binary tree to determine the last node in the leaf node layer of the binary tree as the second processing node.

[0009] Optionally, the first node is used to determine the income / expenditure type of the data, the left child node of the first node is used to determine the income type of the data, the right child node of the first node is used to determine the expenditure type of the data, the left child node of the left child node of the first node is used to determine whether the data is interest, the right child node of the left child node of the first node is used to determine the digest code of the data, the left child node of the right child node of the first node is used to determine whether the data is a NetsUnion transaction or a UnionPay transaction, and the right child node of the right child node of the first node is used to determine not to add it to the second processing node.

[0010] Optionally, after executing the processing task, the method further includes: calling an income and expenditure analysis task according to a first interface, wherein the first interface is a subclass interface, and the income and expenditure analysis task is a task for performing income and expenditure analysis processing on the data; executing the income and expenditure analysis task to obtain the income and expenditure analysis results of the user's data.

[0011] Optionally, after executing the processing task, the method further includes: upon completion of adding all the second processing nodes, invoking an income and expenditure analysis task according to the second interface, wherein the second interface is a parent interface, and the income and expenditure analysis task is to perform income and expenditure analysis processing on the data; executing the income and expenditure analysis task to obtain the income and expenditure analysis results of the user's data.

[0012] To achieve the above objectives, according to one aspect of this application, a data processing apparatus is provided, comprising: an acquisition unit for acquiring user data, wherein the data is the user's current account transaction details; a determination unit for determining whether the user data meets the judgment conditions of a first processing node, wherein the first processing node is used to process the data having multiple transaction initiation channels, and the judgment conditions are income-type data or expenditure-type data; a first execution unit for executing a step of adding the first processing node to a processing task if the user data meets the judgment conditions of the first processing node; a processing unit for repeating the acquisition step, the determination step, and the execution step until a predetermined condition is met to complete the establishment of the processing task, wherein the predetermined condition is determining whether all the user data meets the judgment conditions of the first processing node; and a second execution unit for executing the processing task according to the order in which the first processing nodes were added in the processing task.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0014] According to another aspect of this application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to perform any of the methods described by the computer program.

[0015] Applying the technical solution of this application, the data processing method includes: first, acquiring user data and determining whether the user data meets the judgment conditions of the first processing node; then, if the user data meets the judgment conditions of the first processing node, adding the first processing node to the processing task; repeating the acquisition step, the determination step, and the execution step at least once until the predetermined conditions are met, thus completing the establishment of the processing task; finally, executing the processing task according to the order in which the first processing nodes were added. By constructing a chain-like processing task, the first processing node that needs to be executed can be directly identified, making the processing chain clearer, simplifying the processing flow, and thus reducing the code complexity in the data processing process. Attached Figure Description

[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1A hardware structure block diagram of a mobile terminal performing a data processing method according to an embodiment of this application is shown;

[0018] Figure 2 A schematic flowchart of a data processing method according to an embodiment of this application is shown;

[0019] Figure 3 The diagram illustrates an overall flow chart of a data processing method according to an embodiment of this application.

[0020] Figure 4 A schematic diagram of a processing flow for a second node according to an embodiment of this application is shown;

[0021] Figure 5 A structural block diagram of a data processing apparatus provided according to an embodiment of this application is shown.

[0022] The above figures include the following reference numerals:

[0023] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0024] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 for the embodiments of this application 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.

[0027] As described in the background section, the data processing for income and expenditure analysis in the prior art involves high code complexity. To solve the above-mentioned technical problems, embodiments of this application provide a data processing method, a data processing apparatus, a computer-readable storage medium, and an electronic device.

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0029] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a data processing method according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0030] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the device information display method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0031] This embodiment provides a data processing method that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] Figure 2 This is a flowchart of a data processing method according to an embodiment of this application. For example... Figure 2 As shown, the method includes the following steps:

[0033] Step S101, obtaining the user's data, which is the user's current account transaction details.

[0034] Specifically, Flink streaming processing can be used to retrieve users' current account transaction details from the Kafka message queue in real time. Apache Flink is a framework and distributed processing engine used for stateful computation on both unbounded and bounded data streams.

[0035] Step S102, Determine step, determine whether the data of the above user meets the judgment condition of the first processing node, wherein the first processing node is used to process the above data with multiple transaction initiation channels, and the judgment condition is that the above data is income type data or expenditure type data.

[0036] Specifically, the data types of the aforementioned users may be income or expenditure types. Different data types require different processing methods from different processing nodes. Therefore, it is necessary to determine whether the data type matches the processing type of the first processing node. In the financial field, data with multiple transaction initiation channels is a special type of transaction data that requires processing.

[0037] Step S103: Execute the following step: If the user's data meets the judgment conditions of the first processing node, add the first processing node to the processing task.

[0038] Specifically, if the data type successfully corresponds to the processing data type of the first node, then the first processing node is added to the processing task, and the processing task has at least one first processing node.

[0039] Step S104: Repeat the above acquisition step, determination step and execution step at least once until the predetermined condition is met and the above processing task is established. The predetermined condition is to determine whether the data of all the above users meets the judgment condition of the first processing node.

[0040] Specifically, by repeatedly performing the above steps, the number of first nodes in the processing task is increased until the number of first nodes in the processing task reaches a threshold. According to the order in which the first processing nodes are added, a chain-like processing task can be formed.

[0041] Step S105: Execute the above processing task according to the order in which the first processing node in the above processing task is added.

[0042] Specifically, by executing tasks according to the order in which the first processing node is added, a chain-like analysis task can be constructed, which can process income and expenditure details data more clearly and efficiently.

[0043] Applying the technical solution of this application, the data processing method includes: first, acquiring user data and determining whether the user data meets the judgment conditions of the first processing node; then, if the user data meets the judgment conditions of the first processing node, adding the first processing node to the processing task; repeating the acquisition step, the determination step, and the execution step at least once until the predetermined conditions are met, thus completing the establishment of the processing task; finally, executing the processing task according to the order in which the first processing nodes were added. By constructing a chain-like processing task, the first processing node that needs to be executed can be directly identified, making the processing chain clearer, simplifying the processing flow, and thus reducing the code complexity in the data processing process.

[0044] In one embodiment of this application, in addition to steps S101 to S105, after step S103, a step S106 is added: after all the first processing nodes have been added, a second processing node is added to the processing task. This second processing node is used to process the data that has one of the aforementioned transaction initiation channels. In the financial field, transaction data with one of the aforementioned transaction initiation channels is actually a type of normally processed transaction data. Therefore, in addition to processing specially processed transaction data, normally processed transaction data also needs to be processed. Using the above method, special transaction data can be processed first, and after processing the special transaction data, normal transaction data processing can be performed, further clarifying the processing chain.

[0045] In another embodiment of this application, based on steps S101 to S105, step S102 is further refined, including: step S1021, initializing the parameters within the first operator, wherein the first operator is used to generate the processing algorithm in the first processing node, and the parameters are used to characterize the attributes of the user's data; step S1022, establishing pre-processing tasks according to the same parameters, wherein each pre-processing task includes multiple processing nodes, and each processing node of the pre-processing task is a first processing node. The first operator is used to construct chained income and expenditure analysis logic, which can specify the methods for initializing task information, constructing chained computing nodes, and controlling node execution. Each first processing node is abstracted as an Indicator class. Furthermore, the first operator is also used to execute asynchronous database queries.

[0046] To further reduce the conditional judgments in data processing, in one embodiment of this application, based on the above-mentioned step S106, step S106 is further refined, including: step S1061, establishing a first node according to the second operator, the second operator being used to generate the processing algorithm in the second processing node; step S1062, processing step, obtaining the left child node and right child node of the first node according to the binary classification result of the first node; step S1063, repeating the above determination step multiple times to form a binary tree; step S1064, traversing the binary tree to determine the last node in the leaf node layer of the binary tree as the second processing node. In the above steps, the second operator first constructs the process first node, i.e., sets the attributes of the processing task, and then constructs the left and right subtrees of the current node according to different conditions. After the current node is executed, the attribute assignment in the stream data determines which left or right subtree the next node will execute. The abstract class inherited by each task processing class, after construction, will call the method to run the process to perform the income and expenditure analysis process. After traversing the binary tree, a second processing node is obtained, which is the last node of the first processing node. This method effectively reduces conditional checks and makes the link processing more efficient.

[0047] In another embodiment of this application, based on the above-described step S1061, step S1061 is further refined, such as... Figure 3 As shown, the first node is used to determine the income and expenditure type of the data; the left child node of the first node is used to determine the income type of the data; the right child node of the first node is used to determine the expenditure type of the data; the left child node of the left child node of the first node is used to determine whether the data is interest; the right child node of the left child node of the first node is used to determine the digest code of the data; the left child node of the right child node of the first node is used to determine whether the data is a NetsUnion transaction or a UnionPay transaction; and the right child node of the right child node of the first node is used to determine whether to add the data to the second processing node. Figure 3This example illustrates a binary tree approach. The first node of the binary tree determines the data type. If the data is income, the left subtree is executed; if it's expenditure, the right subtree is executed. After determining the data type as income, it checks if the income is interest. If it is interest, the left subtree continues; otherwise, the right subtree is executed to determine the specific income type based on the data digest. For example, the digest could indicate rent or mortgage payments. After determining the data type as expenditure, the specific expenditure type is determined, or the expenditure may be excluded from the income / expenditure analysis. The expenditure type can be further determined using UnionPay or NetsUnion. Then, the specific category is matched using UnionPay merchant type matching or NetsUnion industry classification matching. Finally, a unique second processing node is obtained. The binary tree type is not limited to this; leaf node layers can be added as needed.

[0048] To further achieve rapid invocation, in another embodiment of this application, in addition to steps S101 to S105 above, after step S103, the following steps are included: Step S1031, invoking the income and expenditure analysis task according to the first interface, wherein the first interface is a subclass interface, and the income and expenditure analysis task is a task to perform income and expenditure analysis processing on the above data; Step S1032, executing the above income and expenditure analysis task to obtain the income and expenditure analysis results of the user's data. In the process of designing the first operator, processing sub-nodes can be stored. Each first processing node must implement the subclass interface, which specifically performs the income and expenditure analysis.

[0049] In another embodiment of this application, based on steps S101 to S105 described above, the method further includes the following steps after step S103: Step S1033, after all the second processing nodes have been added, calling the income and expenditure analysis task according to the second interface, wherein the second interface is a parent interface, and the income and expenditure analysis task is to perform income and expenditure analysis processing on the data; Step S1034, executing the income and expenditure analysis task to obtain the income and expenditure analysis results of the user's data. In the above steps, each task processing class must implement the parent interface, which can define the specific income and expenditure analysis logic to be executed.

[0050] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the data processing method of this application will be described in detail below with reference to specific embodiments.

[0051] This embodiment relates to a specific data processing method, such as... Figure 4 As shown, it includes the following steps:

[0052] Step S1: Input streaming data, initialize task parameters, execute specific construction logic, process common parameters of nodes, and build the processing chain;

[0053] Step S2: Determine whether the current first processing node should be added to the income and expenditure chain;

[0054] Step S3: Determine whether to end the construction of the income and expenditure chain. If yes, execute the income and expenditure analysis process.

[0055] Step S4: Determine if there is a next processing node. If yes, add the new node; otherwise, add the second processing node to execute the income and expenditure analysis process.

[0056] This application also provides a data processing apparatus. It should be noted that the data processing apparatus of this application can be used to execute the data processing method provided in this application. This apparatus is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0057] The data processing apparatus provided in the embodiments of this application will be described below.

[0058] Figure 5 This is a schematic diagram of a data processing apparatus according to an embodiment of this application. Figure 5 As shown, the device includes:

[0059] Acquisition unit 10 is used to acquire user data, which is the current account transaction data of the user.

[0060] Specifically, Flink streaming processing can be used to retrieve users' current account transaction details from the Kafka message queue in real time. Apache Flink is a framework and distributed processing engine used for stateful computation on both unbounded and bounded data streams.

[0061] The determining unit 20 is used to determine whether the data of the above-mentioned user meets the judgment conditions of the first processing node, wherein the first processing node is used to process the above-mentioned data with multiple transaction initiation channels, and the judgment conditions are data of income type or data of expenditure type.

[0062] Specifically, the data types of the aforementioned users may be income or expenditure types. Different data types require different processing methods from different processing nodes. Therefore, it is necessary to determine whether the data type matches the processing type of the first processing node. In the financial field, data with multiple transaction initiation channels is a special type of transaction data that requires processing.

[0063] The first execution unit 30 is used to execute the steps, and when the user's data meets the judgment conditions of the first processing node, the first processing node is added to the processing task.

[0064] Specifically, if the data type successfully corresponds to the processing data type of the first node, then the first processing node is added to the processing task, and the processing task has at least one first processing node.

[0065] The processing unit 40 is used to repeat the above acquisition step, determination step and execution step until a predetermined condition is met and the above processing task is completed. The predetermined condition is to determine whether the data of all the above users meets the judgment condition of the first processing node.

[0066] Specifically, by repeatedly performing the above steps, the number of first nodes in the processing task is increased until the number of first nodes in the processing task reaches a threshold. According to the order in which the first processing nodes are added, a chain-like processing task can be formed.

[0067] The second execution unit 50 is used to execute the processing task according to the order in which the first processing node is added in the processing task.

[0068] Specifically, by executing tasks according to the order in which the first processing node is added, a chain-like analysis task can be constructed, which can process income and expenditure details data more clearly and efficiently.

[0069] Applying the technical solution of this application, the data processing method includes: an acquisition unit for acquiring user data; a determination unit for determining whether the user data meets the judgment conditions of a first processing node; a first execution unit for adding the first processing node to the processing task if the user data meets the judgment conditions of the first processing node; a processing unit for repeating the acquisition step, the determination step, and the execution step at least once until predetermined conditions are met, thus completing the establishment of the processing task; and a second execution unit for executing the processing task according to the order in which the first processing nodes are added. By constructing a chain-like processing task, the first processing node that needs to be executed can be directly identified, making the processing chain clearer, simplifying the processing flow, and thus reducing the code complexity in the data processing process.

[0070] In one embodiment of this application, in addition to the aforementioned acquisition unit, determination unit, first execution unit, processing unit, and second execution unit, a joining unit is further included after the first execution unit. The joining unit is used to add a second processing node to the processing task after all the aforementioned first processing nodes have been added. The second processing node is used to process the data having one of the aforementioned transaction initiation channels. In the financial field, transaction data having one of the aforementioned transaction initiation channels is actually a type of normally processed transaction data. Therefore, in addition to processing specially processed transaction data, normally processed transaction data must also be processed. Through the above method, special transaction data can be processed first, and after processing the special transaction data, normal transaction data processing can be performed, further clarifying the processing chain.

[0071] In another embodiment of this application, based on the aforementioned acquisition unit, determination unit, first execution unit, processing unit, and second execution unit, the determination unit is further refined to include an initialization module and a first establishment module. The initialization module initializes the parameters within a first operator, which generates the processing algorithm in the first processing node. The parameters characterize the attributes of the user's data. The first establishment module establishes pre-processing tasks based on the same parameters. Each pre-processing task includes multiple processing nodes, and each processing node in the pre-processing task is a first processing node. The first operator constructs chained revenue and expenditure analysis logic, specifying methods for initializing task information, constructing chained computation nodes, and controlling node execution. Each first processing node is abstracted as an Indicator class. Furthermore, the first operator also performs asynchronous database queries.

[0072] To further reduce the conditional judgments in data processing, in one embodiment of this application, the above-mentioned joining unit is further refined, including a second establishment module, a first processing module, a second processing module, and a determination module. The second establishment module is used to establish a first node based on a second operator, which generates the processing algorithm in the second processing node. The first processing module is used for processing steps, obtaining the left and right child nodes of the first node based on the binary classification result of the first node. The second processing module is used to repeat the determination step multiple times to form a binary tree. The determination module is used to traverse the binary tree and determine the last node in the leaf node layer of the binary tree as the second processing node. In the above steps, the second operator first constructs the process first node, i.e., sets the attributes of the processing task, and then constructs the left and right subtrees of the current node according to different conditions. After the current node is executed, the attribute assignments within the stream data determine which left or right subtree the next node will execute. Each task processing class inherits an abstract class, and after construction, it calls a method to run the process for income and expenditure analysis. After traversing the binary tree, a second processing node is obtained, which is the last node of the first processing node. This method effectively reduces conditional checks and makes the link processing more efficient.

[0073] In another embodiment of this application, based on the aforementioned second establishment module, the second establishment module is further refined, such as... Figure 3 As shown, the first node is used to determine the income and expenditure type of the data; the left child node of the first node is used to determine the income type of the data; the right child node of the first node is used to determine the expenditure type of the data; the left child node of the left child node of the first node is used to determine whether the data is interest; the right child node of the left child node of the first node is used to determine the digest code of the data; the left child node of the right child node of the first node is used to determine whether the data is a NetsUnion transaction or a UnionPay transaction; and the right child node of the right child node of the first node is used to determine whether to add the data to the second processing node. Figure 3This example illustrates a binary tree approach for data type determination. The first node of the binary tree determines the data type. If the data is income, the left subtree is executed; if it's expenditure, the right subtree is executed. After determining the data type as income, it checks if the income is interest. If it is interest, the left subtree continues; otherwise, the right subtree is executed. The data digest code is used to determine the specific income type. For example, the digest code can be used to determine if the income is rent or mortgage. After determining the data type as expenditure, the specific expenditure type is determined, or it may be excluded from the income / expenditure analysis. The expenditure type can be further determined using UnionPay or NetsUnion. The specific category is then matched based on UnionPay merchant type or NetsUnion industry classification. Finally, a unique second processing node is obtained. The binary tree type is not limited to this; leaf node layers can be added as needed.

[0074] To further achieve rapid invocation, in another embodiment of this application, in addition to the aforementioned acquisition unit, determination unit, first execution unit, processing unit, and second execution unit, a first invocation module and a first execution module are further included after the first execution unit. The invocation module is used to invoke the income and expenditure analysis task according to a first interface, where the first interface is a subclass interface, and the income and expenditure analysis task is a task that performs income and expenditure analysis processing on the aforementioned data. The execution module is used to execute the income and expenditure analysis task to obtain the income and expenditure analysis results of the user's data. During the design of the first operator, processing sub-nodes can be stored. Each first processing node must implement the subclass interface, which specifically performs the income and expenditure analysis.

[0075] In another embodiment of this application, in addition to the aforementioned acquisition unit, determination unit, first execution unit, processing unit, and second execution unit, a second invocation module and a second execution module are further included after the first execution unit. The second invocation module is used to invoke a revenue and expenditure analysis task according to a second interface after all the aforementioned second processing nodes have been added. The second interface is a parent interface, and the revenue and expenditure analysis task is to perform revenue and expenditure analysis processing on the aforementioned data. The second execution module is used to execute the revenue and expenditure analysis task to obtain the revenue and expenditure analysis results of the user's data. In the above steps, each task processing class must implement the parent interface, which can define the specific revenue and expenditure analysis logic.

[0076] The aforementioned data processing device includes a processor and a memory. The acquisition unit, determination unit, first execution unit, processing unit, and second execution unit are all stored as program units in the memory. The processor executes the program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0077] A processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and data processing is performed by adjusting kernel parameters.

[0078] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0079] This invention provides a computer-readable storage medium including a stored program, wherein the program, when running, controls the device containing the computer-readable storage medium to execute the data processing method.

[0080] Specifically, the data processing methods include:

[0081] Step S101, obtaining the user's data, which is the user's current account transaction details.

[0082] Specifically, Flink streaming processing can be used to retrieve users' current account transaction details from the Kafka message queue in real time. Apache Flink is a framework and distributed processing engine used for stateful computation on both unbounded and bounded data streams.

[0083] Step S102, Determine step, determine whether the data of the above user meets the judgment condition of the first processing node, wherein the first processing node is used to process the above data with multiple transaction initiation channels, and the judgment condition is that the above data is income type data or expenditure type data.

[0084] Specifically, the data types of the aforementioned users may be income or expenditure types. Different data types require different processing methods from different processing nodes. Therefore, it is necessary to determine whether the data type matches the processing type of the first processing node. In the financial field, data with multiple transaction initiation channels is a special type of transaction data that requires processing.

[0085] Step S103: Execute the following step: If the user's data meets the judgment conditions of the first processing node, add the first processing node to the processing task.

[0086] Specifically, if the data type successfully corresponds to the processing data type of the first node, then the first processing node is added to the processing task, and the processing task has at least one first processing node.

[0087] Step S104: Repeat the above acquisition step, determination step and execution step at least once until the predetermined condition is met and the above processing task is established. The predetermined condition is to determine whether the data of all the above users meets the judgment condition of the first processing node.

[0088] Specifically, by repeatedly performing the above steps, the number of first nodes in the processing task is increased until the number of first nodes in the processing task reaches a threshold. According to the order in which the first processing nodes are added, a chain-like processing task can be formed.

[0089] Step S105: Execute the above processing task according to the order in which the first processing node in the above processing task is added.

[0090] Specifically, by executing tasks according to the order in which the first processing node is added, a chain-like analysis task can be constructed, which can process income and expenditure details data more clearly and efficiently.

[0091] Optionally, after performing the steps, the method further includes: adding a second processing node to the processing task after all the first processing nodes have been added, wherein the second processing node is used to process the data having one of the transaction initiation channels.

[0092] Optionally, before the above determination step, generating the first processing node includes: initializing the parameters in the first operator, wherein the first operator is used to generate the processing algorithm in the first processing node, and the parameters are used to characterize the attributes of the user's data; and establishing pre-processing tasks according to the same parameters, wherein the pre-processing tasks include multiple processing nodes, and each processing node of the pre-processing tasks is the first processing node.

[0093] Optionally, before the above determination step, the second processing node is generated, including: establishing a first node according to a second operator, wherein the second operator is used to generate the processing algorithm in the second processing node; the processing step is to obtain the left child node and the right child node of the first node according to the binary classification result of the first node; repeating the above determination step multiple times to form a binary tree; traversing the binary tree to determine the last node in the leaf node layer of the binary tree as the second processing node.

[0094] Optionally, the first node is used to determine the income / expenditure type of the data, the left child node of the first node is used to determine the income type of the data, the right child node of the first node is used to determine the expenditure type of the data, the left child node of the left child node of the first node is used to determine whether the data is interest, the right child node of the left child node of the first node is used to determine the digest code of the data, the left child node of the right child node of the first node is used to determine whether the data is a NetsUnion transaction or a UnionPay transaction, and the right child node of the right child node of the first node is used to determine whether to add the data to the second processing node.

[0095] Optionally, after performing the above processing task, the method further includes: calling an income and expenditure analysis task according to a first interface, wherein the first interface is a subclass interface, and the income and expenditure analysis task is a task for performing income and expenditure analysis processing on the above data; and executing the income and expenditure analysis task to obtain the income and expenditure analysis results of the user's data.

[0096] Optionally, after performing the above processing tasks, the method further includes: after all the above second processing nodes have been added, calling the income and expenditure analysis task according to the second interface, wherein the above second interface is a parent interface, and the above income and expenditure analysis task is to perform income and expenditure analysis processing on the above data; executing the above income and expenditure analysis task to obtain the income and expenditure analysis results of the above user's data.

[0097] This invention provides a processor for running a program, wherein the program executes the data processing method described above.

[0098] Specifically, the data processing methods include:

[0099] Step S101, obtaining the user's data, which is the user's current account transaction details.

[0100] Specifically, Flink streaming processing can be used to retrieve users' current account transaction details from the Kafka message queue in real time. Apache Flink is a framework and distributed processing engine used for stateful computation on both unbounded and bounded data streams.

[0101] Step S102, Determine step, determine whether the data of the above user meets the judgment condition of the first processing node, wherein the first processing node is used to process the above data with multiple transaction initiation channels, and the judgment condition is that the above data is income type data or expenditure type data.

[0102] Specifically, the data types of the aforementioned users may be income or expenditure types. Different data types require different processing methods from different processing nodes. Therefore, it is necessary to determine whether the data type matches the processing type of the first processing node. In the financial field, data with multiple transaction initiation channels is a special type of transaction data that requires processing.

[0103] Step S103: Execute the following step: If the user's data meets the judgment conditions of the first processing node, add the first processing node to the processing task.

[0104] Specifically, if the data type successfully corresponds to the processing data type of the first node, then the first processing node is added to the processing task, and the processing task has at least one first processing node.

[0105] Step S104: Repeat the above acquisition step, determination step and execution step at least once until the predetermined condition is met and the above processing task is established. The predetermined condition is to determine whether the data of all the above users meets the judgment condition of the first processing node.

[0106] Specifically, by repeatedly performing the above steps, the number of first nodes in the processing task is increased until the number of first nodes in the processing task reaches a threshold. According to the order in which the first processing nodes are added, a chain-like processing task can be formed.

[0107] Step S105: Execute the above processing task according to the order in which the first processing node in the above processing task is added.

[0108] Specifically, by executing tasks according to the order in which the first processing node is added, a chain-like analysis task can be constructed, which can process income and expenditure details data more clearly and efficiently.

[0109] Optionally, after performing the steps, the method further includes: adding a second processing node to the processing task after all the first processing nodes have been added, wherein the second processing node is used to process the data having one of the transaction initiation channels.

[0110] Optionally, before the above determination step, generating the first processing node includes: initializing the parameters in the first operator, wherein the first operator is used to generate the processing algorithm in the first processing node, and the parameters are used to characterize the attributes of the user's data; and establishing pre-processing tasks according to the same parameters, wherein the pre-processing tasks include multiple processing nodes, and each processing node of the pre-processing tasks is the first processing node.

[0111] Optionally, before the above determination step, the second processing node is generated, including: establishing a first node according to a second operator, wherein the second operator is used to generate the processing algorithm in the second processing node; the processing step is to obtain the left child node and the right child node of the first node according to the binary classification result of the first node; repeating the above determination step multiple times to form a binary tree; traversing the binary tree to determine the last node in the leaf node layer of the binary tree as the second processing node.

[0112] Optionally, the first node is used to determine the income / expenditure type of the data, the left child node of the first node is used to determine the income type of the data, the right child node of the first node is used to determine the expenditure type of the data, the left child node of the left child node of the first node is used to determine whether the data is interest, the right child node of the left child node of the first node is used to determine the digest code of the data, the left child node of the right child node of the first node is used to determine whether the data is a NetsUnion transaction or a UnionPay transaction, and the right child node of the right child node of the first node is used to determine whether to add the data to the second processing node.

[0113] Optionally, after performing the above processing task, the method further includes: calling an income and expenditure analysis task according to a first interface, wherein the first interface is a subclass interface, and the income and expenditure analysis task is a task for performing income and expenditure analysis processing on the above data; and executing the income and expenditure analysis task to obtain the income and expenditure analysis results of the user's data.

[0114] Optionally, after performing the above processing tasks, the method further includes: after all the above second processing nodes have been added, calling the income and expenditure analysis task according to the second interface, wherein the above second interface is a parent interface, and the above income and expenditure analysis task is to perform income and expenditure analysis processing on the above data; executing the above income and expenditure analysis task to obtain the income and expenditure analysis results of the above user's data.

[0115] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0116] Step S101, obtaining the user's data, which is the user's current account transaction details.

[0117] Step S102, Determine step, determine whether the data of the above user meets the judgment condition of the first processing node, wherein the first processing node is used to process the above data with multiple transaction initiation channels, and the judgment condition is that the above data is income type data or expenditure type data.

[0118] Step S103: Execute the following step: If the user's data meets the judgment conditions of the first processing node, add the first processing node to the processing task.

[0119] Step S104: Repeat the above acquisition step, determination step and execution step at least once until the predetermined condition is met and the above processing task is established. The predetermined condition is to determine whether the data of all the above users meets the judgment condition of the first processing node.

[0120] Step S105: Execute the above processing task according to the order in which the first processing node in the above processing task is added.

[0121] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0122] Optionally, after performing the steps, the method further includes: adding a second processing node to the processing task after all the first processing nodes have been added, wherein the second processing node is used to process the data having one of the transaction initiation channels.

[0123] Optionally, before the above determination step, generating the first processing node includes: initializing the parameters in the first operator, wherein the first operator is used to generate the processing algorithm in the first processing node, and the parameters are used to characterize the attributes of the user's data; and establishing pre-processing tasks according to the same parameters, wherein the pre-processing tasks include multiple processing nodes, and each processing node of the pre-processing tasks is the first processing node.

[0124] Optionally, before the above determination step, the second processing node is generated, including: establishing a first node according to a second operator, wherein the second operator is used to generate the processing algorithm in the second processing node; the processing step is to obtain the left child node and the right child node of the first node according to the binary classification result of the first node; repeating the above determination step multiple times to form a binary tree; traversing the binary tree to determine the last node in the leaf node layer of the binary tree as the second processing node.

[0125] Optionally, the first node is used to determine the income / expenditure type of the data, the left child node of the first node is used to determine the income type of the data, the right child node of the first node is used to determine the expenditure type of the data, the left child node of the left child node of the first node is used to determine whether the data is interest, the right child node of the left child node of the first node is used to determine the digest code of the data, the left child node of the right child node of the first node is used to determine whether the data is a NetsUnion transaction or a UnionPay transaction, and the right child node of the right child node of the first node is used to determine whether to add the data to the second processing node.

[0126] Optionally, after performing the above processing task, the method further includes: calling an income and expenditure analysis task according to a first interface, wherein the first interface is a subclass interface, and the income and expenditure analysis task is a task for performing income and expenditure analysis processing on the above data; and executing the income and expenditure analysis task to obtain the income and expenditure analysis results of the user's data.

[0127] Optionally, after performing the above processing tasks, the method further includes: after all the above second processing nodes have been added, calling the income and expenditure analysis task according to the second interface, wherein the above second interface is a parent interface, and the above income and expenditure analysis task is to perform income and expenditure analysis processing on the above data; executing the above income and expenditure analysis task to obtain the income and expenditure analysis results of the above user's data.

[0128] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0129] Step S101, obtaining the user's data, which is the user's current account transaction details.

[0130] Step S102, Determine step, determine whether the data of the above user meets the judgment condition of the first processing node, wherein the first processing node is used to process the above data with multiple transaction initiation channels, and the judgment condition is that the above data is income type data or expenditure type data.

[0131] Step S103: Execute the following step: If the user's data meets the judgment conditions of the first processing node, add the first processing node to the processing task.

[0132] Step S104: Repeat the above acquisition step, determination step and execution step at least once until the predetermined condition is met and the above processing task is established. The predetermined condition is to determine whether the data of all the above users meets the judgment condition of the first processing node.

[0133] Step S105: Execute the above processing task according to the order in which the first processing node in the above processing task is added.

[0134] Optionally, after performing the steps, the method further includes: adding a second processing node to the processing task after all the first processing nodes have been added, wherein the second processing node is used to process the data having one of the transaction initiation channels.

[0135] Optionally, before the above determination step, generating the first processing node includes: initializing the parameters in the first operator, wherein the first operator is used to generate the processing algorithm in the first processing node, and the parameters are used to characterize the attributes of the user's data; and establishing pre-processing tasks according to the same parameters, wherein the pre-processing tasks include multiple processing nodes, and each processing node of the pre-processing tasks is the first processing node.

[0136] Optionally, before the above determination step, the second processing node is generated, including: establishing a first node according to a second operator, wherein the second operator is used to generate the processing algorithm in the second processing node; the processing step is to obtain the left child node and the right child node of the first node according to the binary classification result of the first node; repeating the above determination step multiple times to form a binary tree; traversing the binary tree to determine the last node in the leaf node layer of the binary tree as the second processing node.

[0137] Optionally, the first node is used to determine the income / expenditure type of the data, the left child node of the first node is used to determine the income type of the data, the right child node of the first node is used to determine the expenditure type of the data, the left child node of the left child node of the first node is used to determine whether the data is interest, the right child node of the left child node of the first node is used to determine the digest code of the data, the left child node of the right child node of the first node is used to determine whether the data is a NetsUnion transaction or a UnionPay transaction, and the right child node of the right child node of the first node is used to determine whether to add the data to the second processing node.

[0138] Optionally, after performing the above processing task, the method further includes: calling an income and expenditure analysis task according to a first interface, wherein the first interface is a subclass interface, and the income and expenditure analysis task is a task for performing income and expenditure analysis processing on the above data; and executing the income and expenditure analysis task to obtain the income and expenditure analysis results of the user's data.

[0139] Optionally, after performing the above processing tasks, the method further includes: after all the above second processing nodes have been added, calling the income and expenditure analysis task according to the second interface, wherein the above second interface is a parent interface, and the above income and expenditure analysis task is to perform income and expenditure analysis processing on the above data; executing the above income and expenditure analysis task to obtain the income and expenditure analysis results of the above user's data.

[0140] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0141] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0142] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0143] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0144] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0145] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0146] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0147] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0148] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0149] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0150] 1) The data processing method of this application first acquires user data and determines whether the user data meets the judgment conditions of the first processing node. If the user data meets the judgment conditions of the first processing node, the first processing node is added to the processing task. The acquisition, determination, and execution steps are repeated at least once until the predetermined conditions are met, thus completing the establishment of the processing task. Finally, the processing task is executed according to the order in which the first processing nodes are added. By constructing a chain-like processing task, the first processing node that needs to be executed can be directly identified, making the processing chain clearer, simplifying the processing flow, and thus reducing the code complexity in the data processing process.

[0151] 2) The data processing apparatus of this application includes an acquisition unit for acquiring user data, a determination unit for determining whether the user data meets the judgment conditions of the first processing node, a first execution unit for adding the first processing node to the processing task if the user data meets the judgment conditions of the first processing node, a processing unit for repeating the acquisition step, the determination step, and the execution step at least once until predetermined conditions are met, thus completing the establishment of the processing task, and a second execution unit for executing the processing task according to the order in which the first processing nodes are added. By constructing a chain-like processing task, the first processing node that needs to be executed can be directly identified, making the processing chain clearer, simplifying the processing flow, and thus reducing the code complexity in the data processing process.

[0152] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A data processing method, characterized in that, include: The acquisition step involves acquiring the user's data, which is the user's current account transaction details. The determination step is to determine whether the user's data meets the judgment condition of the first processing node, wherein the first processing node is used to process the data with multiple transaction initiation channels, and the judgment condition is that the data is income type data or expenditure type data; The execution step involves adding the first processing node to the processing task if the user's data meets the judgment conditions of the first processing node. Repeat the acquisition step, determination step, and execution step at least once until a predetermined condition is met to complete the establishment of the processing task. The predetermined condition is whether the data of all users meets the judgment condition of the first processing node. The processing task is executed according to the order in which the first processing nodes are added in the processing task; After performing the steps, the method further includes: adding a second processing node to the processing task after all the first processing nodes have been added, wherein the second processing node is used to process the data having one of the transaction initiation channels; Before the determination step, the second processing node is generated, including: establishing a first node according to a second operator, the second operator being used to generate the processing algorithm in the second processing node; the processing step, obtaining the left child node and right child node of the first node according to the binary classification result of the first node; repeating the determination step multiple times to form a binary tree; traversing the binary tree to determine the last node in the leaf node layer of the binary tree as the second processing node; The first node is used to determine the income / expenditure type of the data. The left child node of the first node is used to determine the income type of the data. The right child node of the first node is used to determine the expenditure type of the data. The left child node of the left child node of the first node is used to determine whether the data is interest. The right child node of the left child node of the first node is used to determine the digest code of the data. The left child node of the right child node of the first node is used to determine whether the data is a NetsUnion transaction or a UnionPay transaction. The right child node of the right child node of the first node is used to determine whether to add the data to the second processing node.

2. The method according to claim 1, characterized in that, Before the determination step, the first processing node is generated, including: The parameters within the first operator are initialized, wherein the first operator is used to generate the processing algorithm in the first processing node, and the parameters are used to characterize the attributes of the user's data; Based on the same parameters, pre-processing tasks are established respectively, wherein each pre-processing task includes multiple processing nodes, and each processing node of the pre-processing task is the first processing node.

3. The method according to claim 1, characterized in that, After performing the processing task, the method further includes: According to the first interface, the income and expenditure analysis task is invoked, wherein the first interface is a subclass interface, and the income and expenditure analysis task is a task that performs income and expenditure analysis on the data. The income and expenditure analysis task is executed to obtain the income and expenditure analysis results of the user's data.

4. The method according to claim 1, characterized in that, After performing the processing task, the method further includes: After all the second processing nodes have been added, the income and expenditure analysis task is invoked according to the second interface, wherein the second interface is the parent interface and the income and expenditure analysis task is to perform income and expenditure analysis processing on the data. The income and expenditure analysis task is executed to obtain the income and expenditure analysis results of the user's data.

5. A data processing apparatus, characterized in that, include: The acquisition unit is used to acquire user data, which is the user's current account transaction details. A determining unit is used to determine whether the user's data meets the judgment conditions of the first processing node, wherein the first processing node is used to process the data with multiple transaction initiation channels, and the judgment conditions are income type data or expenditure type data. The first execution unit is used to execute the step of adding the first processing node to the processing task when the user's data meets the judgment conditions of the first processing node. The processing unit is used to repeat the acquisition step, the determination step, and the execution step until a predetermined condition is met to complete the establishment of the processing task. The predetermined condition is whether the data of all users meets the judgment condition of the first processing node. The second execution unit is used to execute the processing task according to the order in which the first processing nodes are added in the processing task; Following the first execution unit, there is also an addition unit, which is used to add a second processing node to the processing task after all the first processing nodes have been added, wherein the second processing node is used to process the data having a transaction initiation channel. The joining unit further includes: a second establishment module, a first processing module, a second processing module, and a determination module. The second establishment module is used to establish a first node based on a second operator, and the second operator is used to generate the processing algorithm in the second processing node. The first processing module is used to execute processing steps, obtaining the left and right child nodes of the first node based on the binary classification result of the first node. The second processing module is used to repeat the determination step multiple times to form a binary tree. The determination module is used to traverse the binary tree and determine the last node in the leaf node layer of the binary tree as the second processing node. The first node is used to determine the income / expenditure type of the data. The left child node of the first node is used to determine the income type of the data. The right child node of the first node is used to determine the expenditure type of the data. The left child node of the left child node of the first node is used to determine whether the data is interest. The right child node of the left child node of the first node is used to determine the digest code of the data. The left child node of the right child node of the first node is used to determine whether the data is a NetsUnion transaction or a UnionPay transaction. The right child node of the right child node of the first node is used to determine whether to add the data to the second processing node.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 4.

7. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method of any one of claims 1 to 4 through the computer program.

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