Data analysis method and device, storage medium, electronic device and program product

Abstract

The embodiment of the invention provides a data analysis method and device, a storage medium, an electronic device and a program product, and the method comprises the steps: obtaining first data of a target user on a first platform, and the first data comprises data generated when the target user carries out financial asset exchange on the first platform; second data of the target user on a second platform are obtained, and the second data are data generated by triggering when the target user executes target operation included in commodity transaction on the second platform; and analyzing transaction information of the target user according to the first data and the second data to determine feature information of the target user.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of communications, and in particular, to a data analysis method, device, storage medium, electronic device, and program product. Background Art

[0002] The rise of Internet platforms in recent years has greatly promoted the transformation of the financial industry. User data on Internet platforms plays an important role in platform operations and user services. The customer data analysis methods in related technologies usually only analyze data based on the collected financial platform data. Therefore, there is a problem of inaccurate analysis results due to limited data sources. Summary of the invention

[0003] The embodiments of the present invention provide a data analysis method, device, storage medium, electronic device and program product to at least solve the problem of inaccurate analysis results due to limited data sources in the customer data analysis method in the related art. Since the customer data analysis method in the related art usually only performs data analysis based on the collected financial platform data, there is a problem of inaccurate analysis results due to limited data sources.

[0004] According to one embodiment of the present invention, there is provided a data analysis method, comprising: obtaining first data of a target user on a first platform, wherein the first data includes data generated by the target user performing financial asset transactions on the first platform; obtaining second data of the target user on a second platform, wherein the second data is data triggered and generated when the target user performs a target operation included in a commodity transaction on the second platform; and analyzing the transaction information of the target user based on the first data and the second data to determine characteristic information of the target user.

[0005] In an exemplary embodiment, before obtaining the second data of the target user on the second platform, the method also includes: determining the target type of the target operation that triggers the generation of the second data; determining the target method for identifying the target user to perform the target operation; and setting a trigger mechanism for triggering the generation of the second data when the target user performs the target operation based on the target type and the target method.

[0006] In an exemplary embodiment, analyzing the transaction information of the target user according to the first data and the second data to determine the characteristic information of the target user includes: filtering abnormal data included in the first data and the second data to obtain target data; and analyzing the transaction information of the target user based on the target data to determine the characteristic information of the target user.

[0007] In an exemplary embodiment, filtering abnormal data included in the first data and the second data to obtain target data includes: filtering data whose values ​​are within an abnormal range and repeated data included in the first data to obtain third data; filtering data whose values ​​are within an abnormal range and repeated data included in the second data to obtain fourth data; and merging the third data and the fourth data to obtain the target data.

[0008] In an exemplary embodiment, analyzing the target user's transaction information based on the target data to determine the target user's characteristic information includes: analyzing the target user's consumption preferences, asset status, and credit attributes based on the target data to determine the target user's financial profile, wherein the transaction information includes the consumption preferences, asset status, and credit attributes, and the characteristic information includes the financial profile.

[0009] In an exemplary embodiment, analyzing the transaction information of the target user according to the first data and the second data to determine the characteristic information of the target user includes: inputting the first data and the second data into a target model to obtain an output result of the target model, wherein the target model is a pre-trained neural network model capable of analyzing the user's transaction information; and determining the characteristic information of the target user based on the output result.

[0010] According to another embodiment of the present invention, there is provided a data analysis device, comprising: a first acquisition module, used to acquire first data of a target user on a first platform, wherein the first data includes data generated by the target user performing financial asset transactions on the first platform; a second acquisition module, used to acquire second data of the target user on a second platform, wherein the second data is data triggered and generated when the target user performs a target operation included in a commodity transaction on the second platform; and an analysis module, used to analyze the transaction information of the target user based on the first data and the second data to determine the characteristic information of the target user.

[0011] According to yet another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any one of the above method embodiments when run.

[0012] According to yet another embodiment of the present invention, there is provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0013] According to yet another embodiment of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0014] Through the present invention, since the information of the target user is obtained from multiple data sources and the user feature analysis is performed based on the information obtained from the multiple data sources, it is possible to solve the problem of inaccurate analysis results caused by limited data sources in the analysis method of customer data in the related art, thereby achieving the effect of improving the comprehensiveness and accuracy of data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a hardware structure block diagram of a mobile terminal according to a data analysis method according to an embodiment of the present invention;

[0016] Figure 2 is a flow chart of a data analysis method according to an embodiment of the present invention;

[0017] Figure 3 is a structural block diagram of a data analysis device according to an embodiment of the present invention;

[0018] Figure 4 is a schematic diagram of a data analysis system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0021] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 is a hardware structure block diagram of a mobile terminal according to a data analysis method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0022] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the data analysis method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a 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 a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0023] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0024] In this embodiment, a data analysis method running on the above mobile terminal is provided. Figure 2 is a flow chart of a data analysis method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0025] Step S202, obtaining first data of the target user on the first platform, wherein the first data includes data generated by the target user performing financial asset transactions on the first platform;

[0026] Step S204, obtaining second data of the target user on the second platform, wherein the second data is data triggered and generated when the target user performs a target operation included in a commodity transaction on the second platform;

[0027] Step S206: Analyze the transaction information of the target user according to the first data and the second data to determine the characteristic information of the target user.

[0028] In the above steps, the first platform includes but is not limited to: financial service institution platform (for example, bank platform, stock trading platform, lending platform, fund platform, etc.), the second platform includes but is not limited to: consumption platform (for example, payment platform, e-commerce platform, etc.), the first data includes but is not limited to: financial transaction data, position data, account operation data and risk preference data, etc., and the second data includes: consumption data, financial management data and transaction data, etc.

[0029] The execution subject of the above steps may be a terminal capable of running a data analysis program, etc., but is not limited thereto.

[0030] The execution order of step S202 and step S204 is interchangeable, that is, step S204 may be executed first, and then step S202.

[0031] In the above steps, by obtaining information of target users from multiple data sources and performing user feature analysis based on the information obtained from multiple data sources, the problem of inaccurate analysis results caused by limited data sources in the customer data analysis method in related technologies is solved, and the comprehensiveness and accuracy of data analysis are improved.

[0032] In an optional embodiment, before obtaining the second data of the target user on the second platform, the method also includes: determining the target type of the target operation that triggers the generation of the second data; determining the target method for identifying the target user to perform the target operation; and setting a trigger mechanism for triggering the generation of the second data when the target user performs the target operation based on the target type and the target method.

[0033] In the above steps, exemplarily, the target types include but are not limited to: transaction operations, fund transfer operations, etc., determining the target type of the target operation that triggers the generation of the second data includes but is not limited to: determining the target operation corresponding to the target user in combination with the identity information of the target user, and determining the target method for identifying the target user to perform the target operation includes but is not limited to: determining the location for burying the target operation (for example, the interface, interface, interaction process for deploying the burying point), and determining the tool for burying the target operation (for example, Mixpanel).

[0034] In the above steps, by setting a trigger mechanism for collecting data on the second platform, the second data can be dynamically generated according to the actual behavior of the user, thereby helping to capture user behavior in real time or near real time and improving the timeliness of the data.

[0035] In an optional embodiment, the method further includes: after setting a trigger mechanism for triggering the generation of the second data when the target user performs the target operation, verifying the trigger mechanism.

[0036] In an optional embodiment, illustratively, verification of the trigger mechanism includes but is not limited to: after setting the embedding point, verification testing of the validity of the user data collected by the embedding point.

[0037] In an optional embodiment, the method further includes: after obtaining the original behavior data generated by the target user's execution of the target operation through tracking, adjusting the content of the data included in the original behavior data (for example, the target user's identity information, the attribute information of the target operation, the type information of the target operation, etc.) and the format of the original behavior data to obtain the target data.

[0038] In the above steps, by collecting data by setting up tracking points, relevant behavioral data can be collected more accurately, reducing computing pressure and improving the accuracy of data analysis.

[0039] In an optional embodiment, analyzing the transaction information of the target user according to the first data and the second data to determine the characteristic information of the target user includes: filtering abnormal data included in the first data and the second data to obtain target data; and analyzing the transaction information of the target user based on the target data to determine the characteristic information of the target user.

[0040] In the above steps, exemplarily, when the span from the beginning to the end of the target user's transaction behavior exceeds a time threshold (for example, 12 hours, 24 hours, 48 ​​hours, etc.), a sliding window method is used to analyze the behavior changes of the target user in the adjacent time period. For example, the average transaction amount in the last 30 days is calculated, and the fluctuation of user activity is reflected as the time window slides. For periodic behaviors (for example, holiday consumption, seasonal purchases, etc.), time-related features (for example, weekly / monthly / seasonal periodic features) are added to the target user feature information.

[0041] In an optional embodiment, filtering abnormal data included in the first data and the second data to obtain target data includes: filtering data whose values ​​are within an abnormal range and repeated data included in the first data to obtain third data; filtering data whose values ​​are within an abnormal range and repeated data included in the second data to obtain fourth data; and merging the third data and the fourth data to obtain the target data.

[0042] In the above steps, exemplarily, filtering the abnormal data included in the first data and the second data to obtain the target data includes but is not limited to: performing a rationality check, performing range or domain verification on the value of the first data and the value of the second data, and when the value of the first data and the value of the second data exceed a reasonable range, determining the first data that exceeds a reasonable range (for example, a reasonable range of age, a reasonable range of income) and the second data that exceeds a reasonable range as the abnormal data; performing a duplicate data check, deduplicating the target user's identification, timestamp, behavior and other fields, identifying and filtering repeatedly submitted data; checking for empty values ​​or missing data, where the value of a field is empty or missing, it is considered abnormal data; checking a statistical model, detecting anomalies in the data by statistical methods, and if a certain value exceeds the specified standard deviation range, it is considered an anomaly; checking a time series analysis method, for time data, using a time series analysis method to identify anomalies that do not conform to the time trend; and checking a machine learning model, using a machine learning algorithm to automatically identify abnormal data.

[0043] In the above steps, by performing user feature analysis based on the data after removing outliers and duplicate data, the overall quality of the data set can be improved and the accuracy of the analysis results can be ensured.

[0044] In an optional embodiment, analyzing the target user's transaction information based on the target data to determine the target user's characteristic information includes: analyzing the target user's consumption preferences, asset status, and credit attributes based on the target data to determine the target user's financial profile, wherein the transaction information includes the consumption preferences, asset status, and credit attributes, and the characteristic information includes the financial profile.

[0045] In the above steps, by analyzing consumption preferences, asset status and credit attributes, we can fully understand the user's financial behavior and characteristics, build a more complete user financial portrait, and thus provide users with more personalized services and product recommendations to meet their specific needs and improve user experience.

[0046] In an optional embodiment, analyzing the transaction information of the target user according to the first data and the second data to determine the characteristic information of the target user includes: inputting the first data and the second data into a target model to obtain an output result of the target model, wherein the target model is a pre-trained neural network model capable of analyzing the user's transaction information; and determining the characteristic information of the target user based on the output result.

[0047] In the above steps, exemplarily, the target model includes but is not limited to: a deep convolutional neural network model, a RBF neural network model, and the like.

[0048] In an optional embodiment, the method further includes: updating the characteristic information of the target user according to a predetermined period (for example, updating the financial profile of the target user according to a predetermined period).

[0049] In an optional embodiment, the method further includes: anonymizing the acquired identity information of the target user by encryption, hashing, etc., and desensitizing the fields containing user sensitive information (for example, bank card number, address, etc.) included in the first data and the second data.

[0050] In an optional embodiment, the method also includes: encrypting the first data and the second data obtained (for example, performing SSL encryption, TLS encryption, AES encryption, etc.) and performing compliance checks on the first data and the second data obtained to ensure that data processing complies with data privacy regulations such as GDPR and CCPA to avoid infringing on user privacy rights.

[0051] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0052] In this embodiment, a data analysis device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0053] Figure 3 is a structural block diagram of a data analysis device according to an embodiment of the present invention. Figure 3 As shown, the device includes: a first acquisition module 32, used to acquire first data of the target user on the first platform, wherein the first data includes data generated by the target user performing financial asset transactions on the first platform; a second acquisition module 34, used to acquire second data of the target user on the second platform, wherein the second data is data triggered and generated when the target user performs a target operation included in a commodity transaction on the second platform; an analysis module 36, used to analyze the transaction information of the target user based on the first data and the second data to determine the characteristic information of the target user.

[0054] In an optional embodiment, the device also includes: a first determination module, used to determine the target type of the target operation that triggers the generation of the second data before obtaining the second data of the target user on the second platform; a second determination module, used to determine the target mode for identifying the target user to perform the target operation; and a setting module, used to set a trigger mechanism for triggering the generation of the second data when the target user performs the target operation based on the target type and the target mode.

[0055] In an optional embodiment, the analysis module 36 includes: a filtering unit, used to filter abnormal data included in the first data and the second data to obtain target data; and a first analysis unit, used to analyze the transaction information of the target user based on the target data to determine the characteristic information of the target user.

[0056] In an optional embodiment, the filtering unit includes: a first filtering subunit, used to filter the data whose values ​​are within an abnormal range and the repeated data included in the first data to obtain third data; a second filtering subunit, used to filter the data whose values ​​are within an abnormal range and the repeated data included in the second data to obtain fourth data; and a merging subunit, used to merge the third data and the fourth data to obtain the target data.

[0057] In an optional embodiment, the first analysis unit includes: an analysis subunit, used to analyze the consumption preferences, asset status and credit attributes of the target user based on the target data to determine the financial profile of the target user, wherein the transaction information includes the consumption preferences, asset status and credit attributes, and the characteristic information includes the financial profile.

[0058] In an optional embodiment, the analysis module 36 includes: a second analysis unit, used to input the first data and the second data into a target model to obtain an output result of the target model, wherein the target model is a pre-trained neural network model capable of analyzing the user's transaction information; and determine the characteristic information of the target user based on the output result.

[0059] In an optional embodiment, the present invention further provides a data analysis system, Figure 4 is a schematic diagram of a data analysis system according to an embodiment of the present invention. Figure 4 As shown, the system includes: the first acquisition module, the second acquisition module, the analysis module and a data center, wherein the data center receives and stores the first data acquired by the first acquisition module and the second data acquired by the second acquisition module, and outputs the received and stored first data and the second data to the analysis module, wherein the data center is communicatively connected and / or electrically connected to the first acquisition module and the first acquisition module, and the data center is communicatively connected and / or electrically connected to the analysis module.

[0060] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0061] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0062] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0063] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0064] An embodiment of the present invention further provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0065] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0066] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data analysis method, characterized in that: include: Acquire first data of a target user on a first platform, wherein the first data includes data generated by the target user performing financial asset transactions on the first platform; Acquire second data of the target user on the second platform, wherein the second data is data triggered and generated when the target user performs a target operation included in a commodity transaction on the second platform; The transaction information of the target user is analyzed according to the first data and the second data to determine the characteristic information of the target user.

2. The method according to claim 1, characterized in that Before obtaining the second data of the target user on the second platform, the method further includes: Determining a target type of the target operation that triggers generation of the second data; Determining a target mode for identifying the target user performing the target operation; A trigger mechanism for generating the second data when the target user performs the target operation is set based on the target type and the target mode.

3. The method according to claim 1, characterized in that Analyzing the transaction information of the target user according to the first data and the second data to determine the characteristic information of the target user includes: Filtering abnormal data included in the first data and the second data to obtain target data; The transaction information of the target user is analyzed based on the target data to determine the characteristic information of the target user.

4. The method according to claim 3, characterized in that Filtering the abnormal data included in the first data and the second data to obtain target data includes: filtering data whose values ​​are within an abnormal range and repeated data included in the first data to obtain third data; filtering data whose values ​​are within an abnormal range and repeated data included in the second data to obtain fourth data; The third data and the fourth data are combined to obtain the target data.

5. The method according to claim 4, characterized in that Analyzing the transaction information of the target user based on the target data to determine the characteristic information of the target user includes: The consumption preferences, asset status and credit attributes of the target user are analyzed based on the target data to determine the financial profile of the target user, wherein the transaction information includes the consumption preferences, asset status and credit attributes, and the characteristic information includes the financial profile.

6. The method according to claim 1, characterized in that Analyzing the transaction information of the target user according to the first data and the second data to determine the characteristic information of the target user includes: Inputting the first data and the second data into a target model to obtain an output result of the target model, wherein the target model is a pre-trained neural network model capable of analyzing transaction information of a user; Determine the characteristic information of the target user based on the output result.

7. A data analysis device, characterized in that: include: A first acquisition module, configured to acquire first data of a target user on a first platform, wherein the first data includes data generated by the target user performing financial asset transactions on the first platform; A second acquisition module is used to acquire second data of the target user on the second platform, wherein the second data is data triggered and generated when the target user performs a target operation included in a commodity transaction on the second platform; An analysis module is used to analyze the transaction information of the target user according to the first data and the second data to determine the characteristic information of the target user.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 6 when executed by a processor.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

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