Data processing method and apparatus, device, and storage medium

Abstract

The application provides a data processing method, device and equipment and a storage medium. The data processing method comprises the following steps: in response to a data processing request for a target object, collecting object income and expenditure data of the target object; performing data analysis on the object income and expenditure data according to a corresponding income and expenditure category label and target prompt information of the object income and expenditure data, to obtain first income and expenditure analysis data; performing correlation analysis on the object income and expenditure data and associated income and expenditure data, to obtain second income and expenditure analysis data; and outputting income and expenditure prompt information corresponding to the target object according to the first income and expenditure analysis data and / or the second income and expenditure analysis data. The technical solution of the application can improve the accuracy and comprehensiveness of income and expenditure records, analyze and remind the income and expenditure data, improve the intelligent degree of income and expenditure statistics, and optimize the user income and expenditure record and control experience.

Description

Technical Field

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

[0002] Currently, with the rapid development and widespread adoption of internet technology and mobile payment, more and more users are using smart terminals to perform mobile payments, transfers, and receipts. Existing smart terminals record these mobile payment transactions through applications such as bill management tools. However, these tools rely on users manually entering or importing bank statements to record mobile payment information, leading to cumbersome operations, data omissions, and a lack of automated data analysis, thus increasing the user's workload. Summary of the Invention

[0003] This application provides a data processing method, apparatus, device, and storage medium, aiming to solve the technical problem that the management of income and expenditure data in the prior art is cumbersome and prone to data omission, increasing the burden of income and expenditure management operations for users.

[0004] On one hand, embodiments of this application provide a data processing method, which includes the following steps: In response to a data processing request for a target object, collect the object's income and expenditure data; Based on the income and expenditure category labels and target prompt information corresponding to the income and expenditure data of the object, data analysis is performed on the income and expenditure data of the object to obtain the first income and expenditure analysis data; Based on the object's income and expenditure data and related income and expenditure data, a correlation analysis is performed to obtain the second income and expenditure analysis data; Output income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data.

[0005] In one possible implementation of this application, the step of performing data analysis on the object's income and expenditure data based on the income and expenditure category tags and target prompt information corresponding to the object's income and expenditure data to obtain first income and expenditure analysis data includes: The income and expenditure data of the objects are classified according to the preset classification rules to obtain the income and expenditure category labels of each object's income and expenditure data; The target large model is used to perform data analysis on the object's income and expenditure data based on the income and expenditure category labels and the target prompt information to obtain the first income and expenditure analysis data.

[0006] In one possible implementation of this application, the step of using a target big model to perform data analysis on the object's income and expenditure data based on the income and expenditure category labels and the target prompt information to obtain first income and expenditure analysis data includes: Obtain the candidate consumption patterns corresponding to the income and expenditure category tags, and the target prompt information corresponding to the candidate consumption patterns; Using the large model, the target consumption pattern corresponding to the object's income and expenditure data is analyzed according to the target prompt information to determine the target consumption pattern among the candidate consumption patterns; The target consumption data associated with the target consumption pattern and the target income and expenditure data of the object are determined as the first income and expenditure analysis data.

[0007] In one possible implementation of this application, the step of performing correlation analysis based on the object's income and expenditure data and related income and expenditure data to obtain second income and expenditure analysis data includes: Obtain the object attribute information of the target object, and read the associated income and expenditure data corresponding to the object attribute information; Based on the object's income and expenditure data and related income and expenditure data, a correlation analysis is performed to obtain the second income and expenditure analysis data.

[0008] In one possible implementation of this application, the step of performing correlation analysis based on the object's income and expenditure data and related income and expenditure data to obtain second income and expenditure analysis data includes: Obtain the object income and expenditure categories from the object income and expenditure data, and the income and expenditure category data corresponding to the object income and expenditure categories; Obtain the associated income and expenditure categories corresponding to the income and expenditure categories of the object from the associated income and expenditure data, and the associated category data corresponding to the associated income and expenditure categories; When the income and expenditure category data and the related category data meet the preset association analysis conditions, the object's income and expenditure category is identified as an abnormal income and expenditure category; The abnormal income and expenditure categories and the income and expenditure category data are identified as the second income and expenditure analysis data.

[0009] In one possible implementation of this application, the step of outputting income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data includes: Based on the target consumption pattern and target consumption data in the first income and expenditure analysis data, abnormal income and expenditure data in the object income and expenditure data are determined, and a first income and expenditure reminder message corresponding to the abnormal income and expenditure data is generated. A second income and expenditure reminder message is generated based on the abnormal income and expenditure categories and income and expenditure category data in the second income and expenditure analysis data; The first income and expenditure reminder information and / or the second income and expenditure prompt information are determined as the income and expenditure reminder information corresponding to the target object.

[0010] In one possible implementation of this application, the collection of the target object's income and expenditure data includes: Detect the target interaction event corresponding to the target object, and obtain the interaction event data corresponding to the target interaction event; The target large model is used to identify income and expenditure in the interaction event data to obtain the initial income and expenditure data of the target object. The initial income and expenditure data are preprocessed to obtain preprocessed income and expenditure data; By summarizing the preprocessed income and expenditure data, the object income and expenditure data of the target object are obtained.

[0011] On the other hand, this application provides a data processing apparatus, the data processing apparatus comprising: The data acquisition module is configured to respond to data processing requests for a target object and collect the object's income and expenditure data. The data analysis module is configured to perform data analysis on the object's income and expenditure data based on the income and expenditure category tags and target prompt information corresponding to the object's income and expenditure data, and obtain the first income and expenditure analysis data; The correlation analysis module is configured to perform correlation analysis based on the object's income and expenditure data and the related income and expenditure data to obtain second income and expenditure analysis data. The income and expenditure reminder module is configured to output income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data.

[0012] On the other hand, this application also provides a data processing device, the data processing device comprising: One or more processors; Memory; and One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the steps of the data processing method.

[0013] On the other hand, this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the data processing method.

[0014] This application collects the target object's income and expenditure data in response to a data processing request. It then analyzes the data based on income and expenditure category tags and target prompts to obtain first income and expenditure analysis data. Next, it performs correlation analysis on the target object's income and expenditure data and related income and expenditure data to obtain second income and expenditure analysis data. Finally, it outputs income and expenditure reminder information corresponding to the target object based on the first and / or second income and expenditure analysis data. This achieves accurate recording and analysis of the target object's income and expenditure data by performing multiple rounds of dynamic analysis after collection. This identifies income and expenditure analysis data containing the target object's consumption patterns and abnormal income and expenditure data. Based on this data, it dynamically provides income and expenditure reminders to the target object, improving the accuracy and comprehensiveness of income and expenditure records. Furthermore, it enhances the intelligence of income and expenditure statistics, optimizes the user's income and expenditure recording and control experience, and provides analysis and reminders for the data. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram illustrating a scenario of the data processing method in an embodiment of this application. Figure 2 This is a flowchart illustrating one embodiment of the data processing method in this application. Figure 3 A flowchart illustrating an embodiment of the data processing method for determining first income and expenditure analysis data provided in this application; Figure 4 A flowchart illustrating one embodiment of the data processing method for determining second income and expenditure analysis data provided in this application; Figure 5 A schematic diagram of the structure of one embodiment of the data processing apparatus provided in this application; Figure 6 This is a schematic diagram of the structure of one embodiment of the data processing device provided in this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0019] In this application, the term "exemplary" is used to mean "serving as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0020] Currently, with the rapid development and widespread adoption of internet technology and mobile payment, more and more users are using smart terminals to perform mobile payments, transfers, and receipts. Existing smart terminals record these mobile payment transactions through applications such as bill management tools. However, these tools rely on users manually entering or importing bank statements to record mobile payment information, leading to cumbersome operations, data omissions, and a lack of automated data analysis, thus increasing the user's workload.

[0021] Based on this, this application proposes a data processing method, apparatus, device, and computer-readable storage medium to solve the technical problem that the operation of income and expenditure data management in the prior art is cumbersome and prone to data omission, which increases the burden of income and expenditure management operations for users.

[0022] The data processing method in this embodiment of the invention is applied to a data processing device, which is disposed in a data processing equipment. The data processing equipment is provided with one or more processors, a memory, and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the data processing method. The data processing equipment can be a smart terminal, such as a mobile phone, tablet computer, smart wearable device, and smart computer. Optionally, the data processing equipment can also be a server or a service cluster composed of multiple servers.

[0023] like Figure 1 As shown, Figure 1 This is a schematic diagram of a data processing method according to an embodiment of the present application. The data processing scenario in this embodiment includes a data processing device 100 (the data processing device 100 integrates a data processing unit), and the data processing device 100 is equipped with a computer-readable storage medium corresponding to the data processing method to execute the steps of the data processing method.

[0024] Understandable, Figure 1 The data processing device in the data processing method scenario shown, or the device included in the data processing device, does not constitute a limitation on the embodiments of the present invention. That is, the number or type of data processing device in the data processing method scenario, or the number or type of device included in each device, does not affect the overall implementation of the technical solution in the embodiments of the present invention, and can all be considered as equivalent substitutions or derivatives of the technical solutions claimed in the embodiments of the present invention.

[0025] In this embodiment of the invention, the data processing device 100 is mainly used to: respond to a data processing request for a target object and collect the object income and expenditure data of the target object; Based on the income and expenditure category labels and target prompt information corresponding to the income and expenditure data of the object, data analysis is performed on the income and expenditure data of the object to obtain the first income and expenditure analysis data; Based on the object's income and expenditure data and related income and expenditure data, a correlation analysis is performed to obtain the second income and expenditure analysis data; Output income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data. The data processing device 100 in this embodiment of the invention can be an independent data processing device, such as a mobile phone, tablet computer, network device, server and smart computer, or a data processing network or data processing cluster composed of multiple data processing devices.

[0026] This application provides a data processing method, apparatus, device, and computer-readable storage medium, which will be described in detail below.

[0027] It will be understood by those skilled in the art that Figure 1 The application environment shown is only one application scenario related to the solution of this application and does not constitute a limitation on the application scenario of this application. Other application environments may include more than one application scenario. Figure 1 The number of data processing devices shown, or the data processing network connections, for example Figure 1 Only one data processing device is shown in the figure. It is understood that the scenario of this data processing method may also include one or more data processing devices, which are not specifically limited here. The data processing device 100 may also include a memory for storing income and expenditure data, first income and expenditure analysis data, related income and expenditure data and other data.

[0028] It should be noted that, Figure 1 The schematic diagram of the data processing method shown is merely an example. The scenarios of the data processing method described in the embodiments of the present invention are intended to more clearly illustrate the technical solutions of the embodiments of the present invention and do not constitute a limitation on the technical solutions provided in the embodiments of the present invention.

[0029] Based on the scenarios described above, various embodiments of the data processing method disclosed in this invention are proposed.

[0030] like Figure 2 As shown, Figure 2 This is a flowchart illustrating one embodiment of the data processing method in this application. The data processing method includes the following steps 201 to 204: 201. In response to a data processing request for the target object, collect the object's income and expenditure data; The data processing method in this embodiment is applied to a data processing device. The type and number of data processing devices are not specifically limited. That is, the data processing device can be one or more smart terminals or servers. In a specific embodiment, the data processing device is a smartphone capable of making mobile payments, transferring funds, and receiving payments.

[0031] Optionally, the target object is the entity that interacts with the data processing device. For example, in one specific embodiment, the target object can be the user of the data processing device themselves.

[0032] Optionally, the data processing request is a processing request that drives the data processing device to record and analyze the income and expenditure data of the target object. It controls the data processing device to record the income and expenditure data of the target object and output the corresponding income and expenditure analysis data and income and expenditure reminder information, thereby recording, analyzing, and reminding users of the target object's income and expenditure situation to optimize the target object's expenditure structure. The triggering method of this data processing request is not specifically limited here; that is, the data processing request can be actively triggered by the user. For example, the target object can actively trigger the data processing request by manually inputting its income and expenditure data or interactive event data associated with the income and expenditure data into the data processing device. Optionally, in other embodiments, the data processing request can also be automatically triggered by the data processing device. For example, the data processing device may have an automatic accounting function pre-set (this automatic accounting function may be provided by the data processing device's operating system or a third-party accounting application). After the target object enables the automatic accounting function in the settings interface corresponding to the automatic accounting function, the data processing device can automatically trigger the data processing request when the target object performs interactive operations such as payment, transfer, and receipt, and collect the object's income and expenditure data corresponding to the mobile payment and receipt operations, and then perform data processing operations on the object's income and expenditure data in subsequent steps. Optionally, in a specific embodiment, data processing of the object's income and expenditure data includes any one or more of the following: data preprocessing, data classification, data analysis, correlation data analysis, and generation of reminder information. Optionally, in other embodiments, data processing of the object's income and expenditure data may also include other data processing operations, which are not specifically limited in this embodiment.

[0033] Optionally, the object's payment data refers to the payment data information obtained by the target object when making payments, transferring funds, and receiving payments using data processing equipment or electronic devices that are connected to the data processing equipment. This data is used to record the transaction time, transaction amount, and transaction object of the target object when making mobile payments.

[0034] Optionally, after receiving a data processing request, the data processing device responds to the request, collects the target object's income and expenditure data, and processes the data in subsequent steps to record, analyze, and remind users of the target object's income and expenditure situation, thereby optimizing the target object's expenditure structure.

[0035] Optionally, in one specific embodiment, the data processing device detects a target interaction event corresponding to the target object and obtains the interaction event data corresponding to the target interaction event. The target interaction event is an interaction event corresponding to the target object performing mobile payment operations such as payment, transfer, and receipt using the data processing device or an electronic device communicatively connected to the data processing device. For example, in one specific embodiment, the target interaction event is an interaction event triggered when the target object uses a mobile payment application (such as Alipay and WeChat) to perform payment, transfer, and receipt. The interaction event data is multimodal interaction data corresponding to the target interaction event. Optionally, in one specific embodiment, the interaction event data can be text interaction data, electronic billing data, image interaction data, and interaction data in any format generated by the payment platform interface. For example, in one specific embodiment, after detecting a target interaction event of the target object performing mobile payment, the data processing device pops up a text interaction data prompting the user to manually input the expenditure and / or income corresponding to the target interaction event to generate interaction event data. The text interaction data is data information recorded in text format, including the transaction amount, transaction time, and transaction object corresponding to the target interaction event. For example, in one specific embodiment, the text interaction data is a payment of xx yuan to a person / organization at a certain time on a certain day. Optionally, in another specific embodiment, the electronic bill data is an electronic transaction record associated with the target interaction event. The data processing device can also receive electronic transaction records imported by the target object and / or read the electronic transaction record corresponding to the target interaction event from the corresponding bill interface of the e-banking application in the data processing device as interaction event data in the form of electronic bill data. Optionally, in other embodiments, the image interaction data is image data recording the interactive interface (e.g., the payment interface and / or the receipt interface) corresponding to the target interaction event. After triggering the target interaction event, the data processing device uses the image acquisition function to acquire the interactive interface corresponding to the target interaction event to obtain the interaction event data. Optionally, in other embodiments, the data processing device can also connect with a third-party payment platform after authorization by the target object to obtain the consumption data output by the third-party payment platform associated with the target object and the target interaction event as interaction event data.

[0036] Optionally, after acquiring the interaction event data, the data processing device further utilizes the target large-scale model to identify the income and expenditure of the interaction event data, thereby obtaining the initial income and expenditure data of the target object. The target large-scale model is an artificial intelligence large-scale model used for identifying the interaction event data and / or processing the object's income and expenditure data. Optionally, in one specific embodiment, the target large-scale model can be a multimodal large-scale language model such as DeepSeek, ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Tongyi Qianwen model, MiniMax model, Xinghuo model, Xingzhi model, Llama model, 360GPT model, Qwen model, Baichuan model, Yunque model, vivoLM model, and Wenxin Yiyan, etc., which is not limited in this application embodiment. Optionally, in other embodiments, the target large-scale model can also be other large-scale language models.

[0037] Optionally, in one specific embodiment, the data processing device uses the acquired interaction event data as model input, inputs the interaction event data into a target large model, and uses the target large model to perform income and expenditure identification on the interaction event data, thereby identifying the transaction amount, transaction time, and transaction object data associated with the target interaction event in the interaction event data, and generating initial income and expenditure data. The initial income and expenditure data is the income and expenditure data obtained from the preliminary detection of the interaction event data.

[0038] For example, in one specific embodiment, the data processing device acquires image interaction data, inputs the image interaction data into a target large model, and uses the target large model to perform image processing such as image text recognition on the image interaction data to extract the income and expenditure data in the image interaction data and obtain initial income and expenditure data.

[0039] Optionally, after acquiring the initial income and expenditure data, the data processing device performs data preprocessing on the initial income and expenditure data to obtain preprocessed income and expenditure data. That is, after acquiring the initial income and expenditure data, the data processing device also performs preprocessing operations such as data cleaning to remove duplicate, erroneous, or invalid income and expenditure data from the initial data, thereby generating preprocessed income and expenditure data. The device then summarizes all the preprocessed income and expenditure data to obtain the object income and expenditure data for the target object. In other words, the data processing device combines at least one preprocessed income and expenditure data to obtain the object income and expenditure data for the target object. The preprocessed income and expenditure data refers to the income and expenditure data obtained after preprocessing the initial income and expenditure data.

[0040] 202. Perform data analysis on the object's income and expenditure data based on the income and expenditure category tags and target prompt information corresponding to the object's income and expenditure data to obtain the first income and expenditure analysis data; Optionally, after acquiring the object's income and expenditure data, the data processing device also performs data analysis on the object's income and expenditure data based on the income and expenditure category labels and target prompt information corresponding to the object's income and expenditure data to obtain the first income and expenditure analysis data.

[0041] Optionally, the income and expenditure category label is label information that characterizes the income and expenditure category to which the object's income and expenditure data belongs, used to characterize the consumption and / or purpose category of the object's income and expenditure data. Optionally, in a specific embodiment, the income and expenditure category label includes income and expenditure category labels such as dining, shopping, transportation, leisure and entertainment, recharge and payment, education, and medical care.

[0042] Optionally, the first income and expenditure analysis data is income and expenditure analysis data characterizing the consumption patterns and corresponding consumption data of the target object. Optionally, the first income and expenditure analysis data includes the target consumption pattern and the target consumption data corresponding to the target consumption pattern. The target consumption pattern is pattern information characterizing the target object's income and expenditure behavior and habits in the income and expenditure process. For example, in a specific embodiment, the target consumption pattern includes a first consumption pattern divided according to time patterns, a second consumption pattern divided according to consumption behavior, and a third consumption pattern divided according to income and expenditure categories. The first consumption pattern is an evaluation pattern that statistically analyzes fixed consumption behaviors and / or consumption habits within any time range. The second consumption pattern is an evaluation pattern that statistically analyzes combinations of fixed consumption behaviors. The third consumption pattern is an evaluation pattern that statistically analyzes the frequency and / or amount of income and expenditure behaviors corresponding to each income and expenditure category.

[0043] Optionally, after acquiring the object's income and expenditure data, the data processing device classifies the data according to a preset classification rule to obtain income and expenditure category labels for each data point. The preset classification rule is rule information used to classify the object's income and expenditure data into consumption categories and / or expenditure categories. This preset classification rule can be a classification rule corresponding to different consumption categories and / or expenditure categories, or a large model prompt. For example, the preset classification rule includes keyword data corresponding to a specified consumption category and / or expenditure category (e.g., transaction object labels corresponding to each consumption category and / or expenditure category). By matching this keyword data with the text information in the transaction objects within the object's income and expenditure data, the income and expenditure category label corresponding to the object's income and expenditure data is determined. That is, if the text information corresponding to the transaction object is the same as the keyword data, then the consumption category and / or expenditure category corresponding to the keyword data is determined as the income and expenditure category data corresponding to the object's income and expenditure data. Optionally, in other embodiments, the preset classification rule can also be a large model prompt used to guide the target large model to identify the consumption category and / or expenditure category to which the object's income and expenditure data belongs. In one specific embodiment, the prompt message of the large model can be "Please help me identify the consumption category and / or expenditure category to which the object's income and expenditure data belongs." That is, the data processing device uses the preset classification rules and the object's income and expenditure data as model inputs to the target large model, and performs classification in the target large model to obtain the income and expenditure category labels of the object's income and expenditure data.

[0044] Optionally, after acquiring the income and expenditure category labels, the data processing device categorizes the object's income and expenditure data according to these labels. The categorized data, along with target prompt information, is then input into the target big model. The target big model analyzes the object's income and expenditure data based on the income and expenditure category labels and the target prompt information to obtain the first income and expenditure analysis data. Specifically, after acquiring the object's income and expenditure data, the target big model evaluates the target consumption mode to which the data belongs based on the target prompt information corresponding to each candidate consumption mode, thereby identifying the first income and expenditure analysis data corresponding to that mode. The target prompt information guides the target big model in determining whether the object's income and expenditure data belongs to a candidate consumption mode. Candidate consumption modes are pre-set mode information containing income and expenditure behaviors and habits by the data processing device.

[0045] 203. Perform correlation analysis based on the object's income and expenditure data and related income and expenditure data to obtain the second income and expenditure analysis data; Optionally, after acquiring the object income and expenditure data of the target object, the data processing device further acquires the associated income and expenditure data related to the target object, and then performs correlation analysis based on the object income and expenditure data and the associated income and expenditure data to obtain second income and expenditure analysis data used to judge the difference between the average expenditure of the target object and the objects corresponding to the same object attribute information.

[0046] Optionally, the data processing device can acquire object attribute information of the target object and query associated income and expenditure data related to the target object using this object attribute information. The object attribute information refers to attribute information characterizing the user characteristics of the target object. This object attribute information includes one or more of object attributes such as age, gender, marital status, and place of residence. In one specific embodiment, the object attribute information is the age attribute. That is, the data processing device acquires the average income and expenditure data of users of the same age as the target object as associated income and expenditure data. In other words, the data processing device acquires the average income and expenditure data of users with the same object attribute information as the target object as associated income and expenditure data.

[0047] Optionally, after acquiring the object's income and expenditure data and the corresponding related income and expenditure data, the data processing device compares the differences between the object's income and expenditure data and the related income and expenditure data to determine the difference between the target object and the average income and expenditure situation, thereby determining that the target object has abnormal income and expenditure categories with unnecessary expenditures and the corresponding income and expenditure category data, and generating second income and expenditure analysis data.

[0048] 204. Output income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data.

[0049] Optionally, after acquiring the first income and expenditure analysis data and / or the second income and expenditure analysis data, the data processing device may also output income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data.

[0050] Optionally, the data processing device generates a detailed income and expenditure report based on the first income and expenditure analysis data, the second income and expenditure analysis data, and the income and expenditure data of the target, and displays the detailed income and expenditure report to the target when the target sends a query request or other corresponding triggering conditions.

[0051] Optionally, the data processing device determines abnormal income and expenditure data in the object's income and expenditure data based on the target consumption pattern and target consumption data in the first income and expenditure analysis data, and generates a first income and expenditure reminder message corresponding to the abnormal income and expenditure data. The first income and expenditure reminder message is used to remind the target object of its target consumption model and the existence of unnecessary and / or excessively high abnormal income and expenditure data within that target consumption model. Abnormal income and expenditure data refers to income and expenditure data within the target consumption model where the target object has unnecessary and / or excessively high expenditures.

[0052] Optionally, the data processing device also generates a second income and expenditure reminder message based on the abnormal income and expenditure categories and data in the second income and expenditure analysis data. That is, after the data processing device identifies abnormal income and expenditure categories and data that are higher than the corresponding income and expenditure categories in the associated income and expenditure data, it outputs the second income and expenditure reminder message. This second income and expenditure reminder message is a reminder to the target entity to reduce expenses corresponding to the abnormal income and expenditure categories and adjust its expenditure structure.

[0053] Optionally, after generating the first income and expenditure reminder information and the second income and expenditure reminder information, the data processing device determines the first income and expenditure reminder information and / or the second income and expenditure reminder information as the income and expenditure reminder information corresponding to the target object, and outputs the income and expenditure reminder information.

[0054] In this embodiment, the data processing device collects the target object's income and expenditure data in response to a data processing request for the target object; performs data analysis on the target object's income and expenditure data based on the corresponding income and expenditure category tags and target prompt information to obtain first income and expenditure analysis data; performs correlation analysis on the target object's income and expenditure data and related income and expenditure data to obtain second income and expenditure analysis data; and outputs income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data. This achieves accurate recording and analysis of the target object's income and expenditure data by performing multiple rounds of dynamic analysis after collection, thereby determining income and expenditure analysis data that includes the target object's consumption patterns and abnormal income and expenditure data. Based on this income and expenditure analysis data, the device dynamically provides income and expenditure reminders to the target object, improving the accuracy and comprehensiveness of income and expenditure records, enhancing the intelligence of income and expenditure statistics, and optimizing the user's income and expenditure recording and control experience.

[0055] like Figure 3 As shown, Figure 3 This is a flowchart illustrating one embodiment of the data processing method for determining first income and expenditure analysis data provided in this application. Figure 3 In the illustrated embodiment, the data processing method further includes steps 301 to 303: 301. Obtain the candidate consumption patterns corresponding to the income and expenditure category tags, and the target prompt information corresponding to the candidate consumption patterns; 302. Using the large model, perform data analysis on the object's income and expenditure data according to the target prompt information to determine the target consumption pattern that corresponds to the object's income and expenditure data among the candidate consumption patterns; 303. The target consumption data associated with the target consumption pattern in the target consumption pattern and the target income and expenditure data of the object are determined as the first income and expenditure analysis data.

[0056] Based on the above embodiments, in this embodiment, after obtaining the income and expenditure category tags of the object's income and expenditure data, the data processing device also obtains the candidate consumption patterns corresponding to the income and expenditure category tags, and the target prompt information corresponding to the candidate consumption patterns. The candidate consumption patterns are pattern information pre-set by the data processing device, containing income and expenditure behaviors and habits. Optionally, in one specific embodiment, the candidate consumption pattern is a fixed combination of consumption behaviors corresponding to at least one income and expenditure category tag within a specified time period. For example, in one specific embodiment, the candidate consumption pattern may be a fixed food and beverage consumption behavior (e.g., purchasing milk tea) within a specified time period, or multiple consecutive food and beverage consumption behaviors within a specified time period.

[0057] Among them, the candidate consumption patterns corresponding to the income and expenditure category labels are those containing the candidate consumption patterns corresponding to that income and expenditure category label. The target hint information is the large model hint information that guides the target large model to determine whether the income and expenditure data of the object belongs to the candidate consumption patterns.

[0058] Optionally, after acquiring the object's income and expenditure data, the data processing device uses this data and the target prompt information as model inputs to a target-oriented large model. The target-oriented large model then analyzes the object's income and expenditure data according to the target prompt information to determine whether the consumption behavior corresponding to the object's income and expenditure data matches the candidate consumption pattern. This determines the target consumption pattern among the candidate consumption patterns that corresponds to the object's income and expenditure data. In other words, the target-oriented large model compares the consumption behavior corresponding to the object's income and expenditure data with the combinations of consumption behaviors in the candidate consumption patterns, identifying the candidate consumption patterns where the consumption behavior corresponding to the object's income and expenditure data matches all the consumption behaviors in the combinations as the target consumption data for that object's income and expenditure data.

[0059] Optionally, after determining the target consumption pattern of the target object, the data processing device further acquires consumption data from the income and expenditure data of each object associated with the target consumption pattern, compares this consumption data with a preset consumption data threshold, and identifies consumption data exceeding the threshold as target consumption data. The target consumption pattern and the target consumption data associated with it from the object's income and expenditure data are then identified as the first income and expenditure analysis data. Specifically, the target consumption data refers to unnecessary expenditure data from the object's income and expenditure data that is associated with the target consumption pattern and has a high expenditure level.

[0060] In this embodiment, the data processing device acquires candidate consumption patterns corresponding to the income and expenditure category tags, and target prompt information corresponding to the candidate consumption patterns; it then uses the target big model to analyze the object's income and expenditure data according to the target prompt information to determine the target consumption pattern corresponding to the object's income and expenditure data among the candidate consumption patterns; and finally, it determines the target consumption pattern and the target consumption data associated with the target consumption pattern in the object's income and expenditure data as the first income and expenditure analysis data. This achieves the goal of accurately identifying the target object's consumption patterns and unnecessary expenditures within those patterns using the target big model, thereby timely and effectively reminding the target object to reduce or avoid unnecessary spending and optimize their consumption habits.

[0061] like Figure 4 As shown, Figure 4 This is a flowchart illustrating one embodiment of the data processing method for determining second income and expenditure analysis data provided in this application. Figure 4 In the illustrated embodiment, the data processing method further includes steps 401 to 404: 401. Obtain the object income and expenditure category from the object income and expenditure data, and the income and expenditure category data corresponding to the object income and expenditure category; 402. Obtain the associated income and expenditure categories corresponding to the income and expenditure categories of the object in the associated income and expenditure data, and the associated category data corresponding to the associated income and expenditure categories; 403. When the income and expenditure category data and the related category data meet the preset association analysis conditions, the income and expenditure category of the object is determined as an abnormal income and expenditure category; 404. The abnormal income and expenditure categories and the income and expenditure category data are determined as the second income and expenditure analysis data.

[0062] Based on the above embodiments, in this embodiment, after the data processing device obtains the object income and expenditure data of the target object, it further obtains the associated income and expenditure data related to the target object, and then performs association analysis based on the object income and expenditure data and the associated income and expenditure data to obtain second income and expenditure analysis data for judging the difference between the average expenditure of the target object and the objects corresponding to the same object attribute information.

[0063] Optionally, the data processing device can acquire object attribute information of the target object and query associated income and expenditure data related to the target object using this object attribute information. The object attribute information refers to attribute information characterizing the user characteristics of the target object. This object attribute information includes one or more of object attributes such as age, gender, marital status, and place of residence. In one specific embodiment, the object attribute information is the age attribute. That is, the data processing device acquires the average income and expenditure data of users of the same age as the target object as associated income and expenditure data. In other words, the data processing device acquires the average income and expenditure data of users with the same object attribute information as the target object as associated income and expenditure data.

[0064] Optionally, the data processing device reads the income and expenditure category tags corresponding to the object's income and expenditure data, thereby determining the object's income and expenditure category to which the object's income and expenditure data belongs, and statistically analyzing the income and expenditure category data corresponding to that object's income and expenditure category. Here, the object's income and expenditure category refers to the classification information of the target object's income and expenditure data. The income and expenditure category data refers to the statistical data of the income and expenditure data for that object's income and expenditure category.

[0065] Optionally, the data processing device acquires the related income and expenditure categories corresponding to the income and expenditure categories of the target object from the related income and expenditure data, as well as the related category data corresponding to the related income and expenditure categories. That is, the data processing device acquires each income and expenditure category of the related income and expenditure data, determines the income and expenditure categories corresponding to the target object's income and expenditure categories as related income and expenditure categories, and calculates the related income and expenditure data according to each related income and expenditure category to obtain related category data. Here, the related category data is the average income and expenditure data of other objects with the same object attribute information as the target object in that object's income and expenditure category.

[0066] Optionally, after acquiring the income and expenditure category data and related category data, the data processing device performs joint analysis on the income and expenditure category data and related income and expenditure data. That is, when the income and expenditure category data and the related category data meet preset correlation analysis conditions, the income and expenditure category of the object is identified as an abnormal income and expenditure category. Optionally, in a specific embodiment, the correlation analysis condition is that the difference between the income and expenditure category data and the related category data is greater than a preset category data threshold. That is, when the difference between the income and expenditure category data and the related category data is greater than the preset category data threshold, the data processing device determines that the target object's expenditure in the corresponding object income and expenditure category of the income and expenditure category data exceeds the average level of users with the same object attributes, and judges the object's income and expenditure as an abnormal income and expenditure category with abnormal expenditure.

[0067] Optionally, after acquiring an abnormal income and expenditure category, the data processing device may identify the abnormal income and expenditure category and the data of that category as the second income and expenditure analysis data.

[0068] In this embodiment, the data processing device acquires the object's income and expenditure categories from the object's income and expenditure data, as well as the corresponding income and expenditure category data; acquires the related income and expenditure categories corresponding to the object's income and expenditure categories from the related income and expenditure data, as well as the related category data corresponding to the related income and expenditure categories; when the income and expenditure category data and the related category data meet preset association analysis conditions, the object's income and expenditure category is identified as an abnormal income and expenditure category; the abnormal income and expenditure category and the income and expenditure category data are identified as second income and expenditure analysis data. This achieves a joint comparison between the target object and the average income and expenditure data of objects with the same attribute information as the target object, thereby determining the abnormal expenditure structure of the target object and providing a data foundation for providing suggestions for adjusting the expenditure structure of the target object.

[0069] To better implement the data processing method in the embodiments of this application, based on the data processing method, the embodiments of this application also provide a data processing apparatus, such as... Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of one embodiment of the data processing apparatus provided in this application. Specifically, the data processing apparatus 500 includes: The data acquisition module 501 is configured to collect the object income and expenditure data of the target object in response to a data processing request for the target object; The data analysis module 502 is configured to perform data analysis on the object's income and expenditure data based on the income and expenditure category labels and target prompt information corresponding to the object's income and expenditure data, and obtain first income and expenditure analysis data; The correlation analysis module 503 is configured to perform correlation analysis based on the object's income and expenditure data and the related income and expenditure data to obtain second income and expenditure analysis data. The income and expenditure reminder module 504 is configured to output income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data.

[0070] In one possible implementation of this embodiment, the data processing device performs data analysis on the object's income and expenditure data based on the income and expenditure category tags and target prompt information corresponding to the object's income and expenditure data to obtain first income and expenditure analysis data, including: The income and expenditure data of the objects are classified according to the preset classification rules to obtain the income and expenditure category labels of each object's income and expenditure data; The target large model is used to perform data analysis on the object's income and expenditure data based on the income and expenditure category labels and the target prompt information to obtain the first income and expenditure analysis data.

[0071] In one possible implementation of this embodiment, the data processing device uses a target large model to perform data analysis on the object's income and expenditure data based on the income and expenditure category labels and the target prompt information to obtain first income and expenditure analysis data, including: Obtain the candidate consumption patterns corresponding to the income and expenditure category tags, and the target prompt information corresponding to the candidate consumption patterns; Using the large model, the target consumption pattern corresponding to the object's income and expenditure data is analyzed according to the target prompt information to determine the target consumption pattern among the candidate consumption patterns; The target consumption data associated with the target consumption pattern and the target income and expenditure data of the object are determined as the first income and expenditure analysis data.

[0072] In one possible implementation of this embodiment, the data processing device performs correlation analysis based on the object's income and expenditure data and related income and expenditure data to obtain second income and expenditure analysis data, including: Obtain the object attribute information of the target object, and read the associated income and expenditure data corresponding to the object attribute information; Based on the object's income and expenditure data and related income and expenditure data, a correlation analysis is performed to obtain the second income and expenditure analysis data.

[0073] In one possible implementation of this embodiment, the data processing device performs correlation analysis based on the object's income and expenditure data and related income and expenditure data to obtain second income and expenditure analysis data, including: Obtain the object income and expenditure categories from the object income and expenditure data, and the income and expenditure category data corresponding to the object income and expenditure categories; Obtain the associated income and expenditure categories corresponding to the income and expenditure categories of the object from the associated income and expenditure data, and the associated category data corresponding to the associated income and expenditure categories; When the income and expenditure category data and the related category data meet the preset association analysis conditions, the object's income and expenditure category is identified as an abnormal income and expenditure category; The abnormal income and expenditure categories and the income and expenditure category data are identified as the second income and expenditure analysis data.

[0074] In one possible implementation of this embodiment, the data processing device outputs income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data, including: Based on the target consumption pattern and target consumption data in the first income and expenditure analysis data, abnormal income and expenditure data in the object income and expenditure data are determined, and a first income and expenditure reminder message corresponding to the abnormal income and expenditure data is generated. A second income and expenditure reminder message is generated based on the abnormal income and expenditure categories and income and expenditure category data in the second income and expenditure analysis data; The first income and expenditure reminder information and / or the second income and expenditure prompt information are determined as the income and expenditure reminder information corresponding to the target object.

[0075] In one possible implementation of this embodiment, the data processing device collects the object income and expenditure data of the target object, including: Detect the target interaction event corresponding to the target object, and obtain the interaction event data corresponding to the target interaction event; The target large model is used to identify income and expenditure in the interaction event data to obtain the initial income and expenditure data of the target object. The initial income and expenditure data are preprocessed to obtain preprocessed income and expenditure data; By summarizing the preprocessed income and expenditure data, the object income and expenditure data of the target object are obtained.

[0076] In this embodiment, the data processing device, in response to a data processing request for a target object, collects the target object's income and expenditure data; performs data analysis on the target object's income and expenditure data based on the corresponding income and expenditure category tags and target prompt information to obtain first income and expenditure analysis data; performs correlation analysis on the target object's income and expenditure data and related income and expenditure data to obtain second income and expenditure analysis data; and outputs income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data. This achieves accurate recording and analysis of the target object's income and expenditure data by performing multiple rounds of dynamic analysis after collection, thereby determining income and expenditure analysis data that includes the target object's consumption patterns and abnormal income and expenditure data. Based on this income and expenditure analysis data, the device dynamically provides income and expenditure reminders to the target object, improving the accuracy and comprehensiveness of income and expenditure records, enhancing the intelligence of income and expenditure statistics, and optimizing the user's income and expenditure recording and control experience.

[0077] This invention also provides a data processing device, such as... Figure 6 As shown, Figure 6 This is a schematic diagram of one embodiment of the data processing device provided in this application.

[0078] The data processing device integrates any of the data processing apparatuses provided in the embodiments of the present invention, and the data processing device includes: One or more processors; Memory; and One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor of the steps in the data processing method described in any of the embodiments of the above data processing method.

[0079] Specifically, the data processing device may include components such as a processor 601 with one or more processing cores, a memory 602 with one or more computer-readable storage media, a power supply 603, and an input unit 604. Those skilled in the art will understand that... Figure 6 The data processing device structure shown does not constitute a limitation on the data processing device. It may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 601 is the control center of the data processing device. It connects various parts of the data processing device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 602, and by calling data stored in the memory 602, it performs various functions of the data processing device and processes data, thereby providing overall monitoring of the data processing device. Optionally, the processor 601 may include one or more processing cores; preferably, the processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 601.

[0080] The memory 602 can be used to store software programs and modules. The processor 601 executes various functional applications and data processing by running the software programs and modules stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the data processing device, etc. In addition, the memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 602 may also include a memory controller to provide the processor 601 with access to the memory 602.

[0081] The data processing device also includes a power supply 603 that supplies power to the various components. Preferably, the power supply 603 can be logically connected to the processor 601 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 603 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0082] The data processing device may also include an input unit 604, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0083] Although not shown, the data processing device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 601 in the data processing device loads the executable files corresponding to the processes of one or more applications into the memory 602 according to the following instructions, and the processor 601 runs the applications stored in the memory 602 to realize various functions, as follows: In response to a data processing request for a target object, collect the object's income and expenditure data; Based on the income and expenditure category labels and target prompt information corresponding to the income and expenditure data of the object, data analysis is performed on the income and expenditure data of the object to obtain the first income and expenditure analysis data; Based on the object's income and expenditure data and related income and expenditure data, a correlation analysis is performed to obtain the second income and expenditure analysis data; Output income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data.

[0084] Therefore, embodiments of the present invention provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. A computer program is stored thereon, which is loaded by a processor to execute the steps of any of the data processing methods provided in the embodiments of the present invention. For example, the computer program loaded by the processor can execute the following steps: In response to a data processing request for a target object, collect the object's income and expenditure data; Based on the income and expenditure category labels and target prompt information corresponding to the income and expenditure data of the object, data analysis is performed on the income and expenditure data of the object to obtain the first income and expenditure analysis data; Based on the object's income and expenditure data and related income and expenditure data, a correlation analysis is performed to obtain the second income and expenditure analysis data; Output income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data.

[0085] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the detailed descriptions of other embodiments above, which will not be repeated here.

[0086] In practice, each of the above units or structures can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units or structures, please refer to the previous method embodiments, which will not be repeated here.

[0087] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0088] The above provides a detailed description of a data processing method provided by the embodiments of this application. Specific embodiments have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A data processing method, characterized in that, The data processing method includes: In response to a data processing request for a target object, collect the object's income and expenditure data; Based on the income and expenditure category labels and target prompt information corresponding to the income and expenditure data of the object, data analysis is performed on the income and expenditure data of the object to obtain the first income and expenditure analysis data; Based on the object's income and expenditure data and related income and expenditure data, a correlation analysis is performed to obtain the second income and expenditure analysis data; Output income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data.

2. The data processing method according to claim 1, characterized in that, The step of performing data analysis on the object's income and expenditure data based on the income and expenditure category tags and target prompt information corresponding to the object's income and expenditure data to obtain first income and expenditure analysis data includes: The income and expenditure data of the objects are classified according to the preset classification rules to obtain the income and expenditure category labels of each object's income and expenditure data; The target large model is used to perform data analysis on the object's income and expenditure data based on the income and expenditure category labels and the target prompt information to obtain the first income and expenditure analysis data.

3. The data processing method according to claim 2, characterized in that, The method of using a target large model to perform data analysis on the object's income and expenditure data based on the income and expenditure category labels and the target prompt information to obtain first income and expenditure analysis data includes: Obtain the candidate consumption patterns corresponding to the income and expenditure category tags, and the target prompt information corresponding to the candidate consumption patterns; Using the target big model, the target income and expenditure data of the object are analyzed according to the target prompt information to determine the target consumption pattern that corresponds to the income and expenditure data of the object among the candidate consumption patterns; The target consumption data associated with the target consumption pattern and the target income and expenditure data of the object are determined as the first income and expenditure analysis data.

4. The data processing method according to claim 1, characterized in that, The step of performing correlation analysis based on the object's income and expenditure data and related income and expenditure data to obtain second income and expenditure analysis data includes: Obtain the object attribute information of the target object, and read the associated income and expenditure data corresponding to the object attribute information; Based on the object's income and expenditure data and related income and expenditure data, a correlation analysis is performed to obtain the second income and expenditure analysis data.

5. The data processing method according to claim 4, characterized in that, The step of performing correlation analysis based on the object's income and expenditure data and related income and expenditure data to obtain second income and expenditure analysis data includes: Obtain the object income and expenditure categories from the object income and expenditure data, and the income and expenditure category data corresponding to the object income and expenditure categories; Obtain the associated income and expenditure categories corresponding to the income and expenditure categories of the object from the associated income and expenditure data, and the associated category data corresponding to the associated income and expenditure categories; When the income and expenditure category data and the related category data meet the preset association analysis conditions, the object's income and expenditure category is identified as an abnormal income and expenditure category; The abnormal income and expenditure categories and the income and expenditure category data are identified as the second income and expenditure analysis data.

6. The data processing method according to claim 1, characterized in that, The step of outputting income and expense reminder information corresponding to the target object based on the first income and expense analysis data and / or the second income and expense analysis data includes: Based on the target consumption pattern and target consumption data in the first income and expenditure analysis data, abnormal income and expenditure data in the object income and expenditure data are determined, and a first income and expenditure reminder message corresponding to the abnormal income and expenditure data is generated. A second income and expenditure reminder message is generated based on the abnormal income and expenditure categories and income and expenditure category data in the second income and expenditure analysis data; The first income and expenditure reminder information and / or the second income and expenditure prompt information are determined as the income and expenditure reminder information corresponding to the target object.

7. The data processing method according to any one of claims 1-6, characterized in that, The collection of the target object's income and expenditure data includes: Detect the target interaction event corresponding to the target object, and obtain the interaction event data corresponding to the target interaction event; The target large model is used to identify income and expenditure in the interaction event data to obtain the initial income and expenditure data of the target object. The initial income and expenditure data are preprocessed to obtain preprocessed income and expenditure data; By summarizing the preprocessed income and expenditure data, the object income and expenditure data of the target object are obtained.

8. A data processing apparatus, characterized in that, The data processing device includes: The data acquisition module is configured to respond to data processing requests for a target object and collect the object's income and expenditure data. The data analysis module is configured to perform data analysis on the object's income and expenditure data based on the income and expenditure category tags and target prompt information corresponding to the object's income and expenditure data, and obtain the first income and expenditure analysis data; The correlation analysis module is configured to perform correlation analysis based on the object's income and expenditure data and the related income and expenditure data to obtain second income and expenditure analysis data. The income and expenditure reminder module is configured to output income and expenditure reminder information corresponding to the target object based on the first income and expenditure analysis data and / or the second income and expenditure analysis data.

9. A data processing device, characterized in that, The data processing device includes: One or more processors; Memory; and One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the steps of the data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to execute the steps of the data processing method according to any one of claims 1 to 7.

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