CN112837140A

CN112837140A - Data processing method, device, equipment and storage medium

Info

Publication number: CN112837140A
Application number: CN202110169912.1A
Authority: CN
Inventors: 吴杰
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2021-02-02
Filing date: 2021-02-02
Publication date: 2021-05-25

Abstract

The application relates to a data processing method, a device, equipment and a storage medium, wherein the method comprises the following steps: receiving a parameter configuration request, wherein the parameter configuration request carries characteristic data; forming a data processing rule based on the feature data; receiving a trial operation instruction aiming at the data processing rule, and obtaining a trial operation result by using the characteristic conditions of each characteristic in the data processing rule and the operational relationship among the characteristic conditions; acquiring historical black and white sample data; analyzing the test run result according to the historical black and white sample data to obtain a first evaluation index; and performing online processing on the data processing rule when the first evaluation index meets a preset online condition. The method and the device can avoid complex operation of data processing rules on line, improve the on-line efficiency of the data processing rules, and increase the hit rate of the data processing rules.

Description

Data processing method, device, equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a data processing method, apparatus, device, and storage medium.

Background

With the social development, illegal behaviors are increasingly performed through the business of the financial institution, so that the financial institution needs to perform targeted behavior monitoring business.

In the prior art, a financial institution mainly monitors risk behaviors in a system through processes, data processing rules and other modes. Data processing rules are generally developed and evaluated on line, and then whether the rules agree to be on line or not is determined according to personal preference through manual preliminary sampling. The offline pass-through of code development rules is time-consuming and labor-consuming, and the developed rules are prone to data logic errors due to carelessness of the developer. Meanwhile, the on-line test operation result and the verification result of manual spot check have the problems of retention, non-uniform rule effect evaluation standard, non-standard approval process and the like, so that the on-line efficiency and the analysis effect of the data processing rule are not ideal, and the development of monitoring service is not facilitated.

Disclosure of Invention

The application provides a data processing method, a data processing device, data processing equipment and a storage medium, which can avoid the complex operation of online of data processing rules and improve the online efficiency and the analysis effect of the data processing rules.

In one aspect, the present application provides a data processing method, including:

receiving a parameter configuration request, wherein the parameter configuration request carries characteristic data;

forming a data processing rule based on the feature data;

receiving a trial operation instruction aiming at the data processing rule, and obtaining a trial operation result by using the characteristic conditions of each characteristic in the data processing rule and the operational relationship among the characteristic conditions;

acquiring historical black and white sample data;

analyzing the test run result according to the historical black and white sample data to obtain a first evaluation index;

and performing online processing on the data processing rule when the first evaluation index meets a preset online condition.

Another aspect provides a data processing apparatus, the apparatus comprising:

a parameter configuration request receiving module, configured to receive a parameter configuration request, where the parameter configuration request carries feature data;

a rule generation module for forming a data processing rule based on the feature data;

the rule trial operation module is used for receiving a trial operation instruction aiming at the data processing rule and obtaining a trial operation result by utilizing the characteristic conditions of each characteristic in the data processing rule and the operational relationship among the characteristic conditions;

the sample data acquisition module is used for acquiring historical black and white sample data;

the trial operation result analysis module is used for analyzing the trial operation result according to the historical black and white sample data to obtain a first evaluation index;

and the online processing module is used for performing online processing on the data processing rule when the first evaluation index meets a preset online condition.

Another aspect provides an electronic device, which includes a processor and a memory, where at least one instruction or at least one program is stored in the memory, and the at least one instruction or at least one program is loaded by the processor and executes the data processing method described above.

Another aspect provides a computer storage medium having at least one instruction or at least one program stored therein, the at least one instruction or the at least one program being loaded and executed by a processor to implement the data processing method as described above.

According to the method and the device, the parameter configuration interface is provided for the user to configure the rule, the data processing rule is automatically generated, and the data processing rule development efficiency and the online efficiency are improved while the transparency and the interpretability of the rule are improved; the rule test operation is provided before the data processing rule is on line, so that the rule test efficiency is improved; after the rule is commissioned, preliminarily evaluating the commissioning result by using historical black and white sample data to provide a reference basis for the effectiveness evaluation of the subsequent rule; the system automatically performs the initial evaluation without manual operation, so that the rule effect evaluation standards used by the off-line test operation result and the verification result of manual spot check are unified, and the manpower loss is reduced; and the rule is processed on-line after the initial evaluation result, namely the first evaluation index, reaches the on-line standard, so that the hit rate of the data processing rule is increased, and the analysis effect of the data processing rule is further improved.

Drawings

In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a schematic structural diagram of a data processing system according to an embodiment of the present application.

Fig. 2 is a schematic architecture diagram of a data processing system according to an embodiment of the present application.

Fig. 3 is a schematic structural diagram of a distributed system applied to a blockchain system according to an embodiment of the present disclosure.

Fig. 4 is a schematic diagram of a block structure according to an embodiment of the present disclosure.

Fig. 5 is a schematic flowchart of a data processing method according to an embodiment of the present application.

Fig. 6 is an exemplary diagram of a parameter configuration interface provided in an embodiment of the present application.

Fig. 7 is an exemplary diagram of a basic information configuration interface provided in an embodiment of the present application.

Fig. 8 is a schematic flowchart of another data processing method according to an embodiment of the present application.

Fig. 9 is a schematic flowchart of another data processing method according to an embodiment of the present application.

Fig. 10 is an exemplary diagram of a template configuration interface provided in an embodiment of the present application.

Fig. 11 is a schematic flowchart of forming a data processing rule according to an embodiment of the present application.

Fig. 12 is a schematic flowchart of determining a first evaluation index according to an embodiment of the present application.

FIG. 13a is a block diagram of an alternative data processing system according to an embodiment of the present application.

FIG. 13b is an exemplary diagram of the cumulative number of hits provided by an embodiment of the present application.

Fig. 14 is a flowchart illustrating a process of determining a preset online condition according to an embodiment of the present application.

Fig. 15 is a schematic flowchart of another data processing method according to an embodiment of the present application.

Fig. 16 is an exemplary diagram of a spot check interface provided in an embodiment of the present application.

Fig. 17 is an exemplary diagram of a check top line interface provided in an embodiment of the present application.

FIG. 18 is a schematic diagram of an execution result viewing interface provided by an embodiment of the present application.

Fig. 19 is an exemplary diagram of an evaluation result provided in an embodiment of the present application.

Fig. 20 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.

Fig. 21 is a hardware structural diagram of an apparatus for implementing the method provided by the embodiment of the present application.

Detailed Description

Cloud technology refers to a hosting technology for unifying serial resources such as hardware, software, network and the like in a wide area network or a local area network to realize calculation, storage, processing and sharing of data.

The cloud technology is a general term of network technology, information technology, integration technology, management platform technology, application technology and the like applied based on a cloud computing business model, can form a resource pool, is used as required, and is flexible and convenient. Cloud computing technology will become an important support. Background services of the technical network system require a large amount of computing and storage resources, such as video websites, picture-like websites and more web portals. With the high development and application of the internet industry, each article may have its own identification mark and needs to be transmitted to a background system for logic processing, data in different levels are processed separately, and various industrial data need strong system background support and can only be realized through cloud computing.

Cloud technology has been widely used in various fields such as government, transportation, finance and enterprise, and the scheme that this application embodiment provided relates to finance application field. In the financial field, in order to develop a targeted behavior monitoring task, a financial institution mainly monitors risk behaviors in a system through processes, data processing rules and other modes. Data processing rules are generally developed and evaluated on line, and then whether the rules agree to be on line or not is determined according to personal preference through manual preliminary sampling. The offline pass-through of code development rules is time-consuming and labor-consuming, and the developed rules are prone to data logic errors due to carelessness of the developer. Meanwhile, the on-line test operation result and the verification result of manual spot check have the problems of retention, non-uniform rule effect evaluation standard, non-standard approval process and the like, so that the on-line efficiency and the analysis effect of the data processing rule are not ideal, and the development of monitoring service is not facilitated.

Based on the above description, the embodiments of the present application provide a data processing method to improve the online efficiency and the analysis effect of the data processing rule. Embodiments of the present application will be described in further detail below with reference to the accompanying drawings, and it is to be understood that the described embodiments are merely a few embodiments of the application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or service that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

First, the following explanations are made with respect to the terms involved in the embodiments of the present specification:

auditing: the method refers to preliminarily delineating suspicious risk users through a model or a rule.

Checking: in risk prevention and control, suspicious users subjected to rule audit need to manually investigate and confirm whether the suspicious users are real or suspicious, and then report the suspicious users or not.

Is characterized in that: an attribute of a subject, such as "height", "age", "transaction amount of the last 7 days", is called suspicious when some feature or combination of features of the user is different from normal persons.

And (3) testing: the rule needs to be simulated to operate before being on line, and the audit effect of the rule is checked and then whether the rule reaches the on-line standard is evaluated.

Data processing rules: refers to a rule or model that audits risk behavior, and is generally composed of a variety of features.

And (3) getting on line: and the rule formally begins to audit risk users and pushes the risk users to an auditing platform for manual auditing.

And (3) online evaluation: and (4) evaluating whether the rule meets the online condition when the rule is online, wherein the evaluation comprises dimensions such as the hit rate, the coverage amount, the prevention and control importance of target crowds and the like of the rule.

Referring to fig. 1, a schematic structural diagram of a data processing system according to an embodiment of the present disclosure is shown, and as shown in fig. 1, the system may include at least a client 01 and a server 02.

The client 01 may be a smart phone, a desktop computer, a tablet computer, a notebook computer, a digital assistant, a smart wearable device, a monitoring device, a voice interaction device, or other types of devices, or may be software running in the devices, such as web pages provided by some service providers to users, or applications provided by the service providers to users. Specifically, the client 01 may be configured to display a configuration interface for the data processing rule and the operation result data.

The server 02 may be an independently operating server, or a distributed server, or a server cluster composed of a plurality of servers, or may be a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network service, cloud communication, middleware service, a domain name service, a security service, a CDN, and a big data and artificial intelligence platform. Specifically, the server 02 may receive a request corresponding to the client 01 to complete the processes of configuration, commissioning, verification, online processing, and the like of the data processing rule.

Specifically, as shown in fig. 2, it is a schematic diagram of an architecture of a data processing system. And converting the rule template configured by the client and the information related to the template parameter into specific executable codes such as sql, python or java and the like, and then calling feature indexes in the low-level feature pool according to the data processing rule to perform data calculation periodically to determine the risk user.

In some possible implementations, the system related to the embodiments of the present application may also be a distributed system formed by connecting a client, a plurality of nodes (any form of computing device in an access network, such as a server and a user terminal) through a network communication form.

Taking a distributed system as an example of a blockchain system, referring To fig. 3, fig. 3 is an optional structural schematic diagram of the distributed system 100 applied To the blockchain system, which is formed by a plurality of nodes (computing devices in any form in an access network, such as servers and user terminals) and clients, and a Peer-To-Peer (P2P, Peer To Peer) network is formed between the nodes, and the P2P Protocol is an application layer Protocol operating on top of a Transmission Control Protocol (TCP). In a distributed system, any machine, such as a server or a terminal, can join to become a node, and the node comprises a hardware layer, a middle layer, an operating system layer and an application layer.

Referring to the functions of each node in the blockchain system shown in fig. 3, the functions involved include: routing, a basic function that a node has for supporting communication between nodes; the application is used for being deployed in a block chain, realizing specific services according to actual service requirements, recording data related to the realization functions to form recording data, carrying a digital signature in the recording data to represent a source of task data, and sending the recording data to other nodes in the block chain system, so that the other nodes can add the recording data to a temporary block when the source and integrity of the recording data are verified successfully; and the Block chain comprises a series of blocks (blocks) which are mutually connected according to the generated chronological order, new blocks cannot be removed once being added into the Block chain, and recorded data submitted by nodes in the Block chain system are recorded in the blocks.

For example, the services implemented by the application include:

1) the wallet is used for providing functions of carrying out transactions of electronic money, and comprises the steps of initiating the transactions (namely, sending the transaction records of the current transactions to other nodes in the blockchain system, and storing the record data of the transactions into a temporary block of the blockchain as a response for confirming that the transactions are valid after the other nodes are verified successfully; of course, the wallet also supports the querying of the electronic money remaining in the electronic money address.

2) The shared account book is used for providing functions of operations such as storage, query and modification of account data, sending the record data of the operations on the account data to other nodes in the block chain system, and after the other nodes verify that the record data are valid, storing the record data into a temporary block as a response for acknowledging that the account data are valid, and also sending confirmation to the node initiating the operations.

3) Intelligent contracts, computerized agreements, which can enforce the terms of a contract, are implemented by codes deployed on a shared ledger for execution when certain conditions are met, are used to complete automated transactions according to actual business requirement codes, such as querying the logistics status of goods purchased by a buyer, transferring the buyer's electronic money to a merchant's address after the buyer signs for goods; of course, smart contracts are not limited to executing contracts for trading, but may also execute contracts that process received information.

Referring to fig. 4, fig. 4 is an optional schematic diagram of a Block Structure (Block Structure) provided in this embodiment, each Block includes a hash value of a transaction record stored in the Block (hash value of the Block) and a hash value of a previous Block, and the blocks are connected by the hash values to form a Block chain. The block may include information such as a time stamp at the time of block generation. A block chain (Blockchain), which is essentially a decentralized database, is a string of data blocks associated by using cryptography, and each data block contains related information for verifying the validity (anti-counterfeiting) of the information and generating a next block.

A data processing method provided by the embodiment of the present application is described below, and the method can be applied to the server shown in fig. 1 or the node shown in fig. 3. It is noted that the present specification provides the method steps as described in the examples or flowcharts, but may include more or less steps based on routine or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In practice, the system or server product may be implemented in a sequential or parallel manner (e.g., parallel processor or multi-threaded environment) according to the embodiments or methods shown in the figures. Specifically, as shown in fig. 5, the method may include:

s510, a parameter configuration request is received, and the parameter configuration request carries characteristic data.

The user can configure the feature data of each feature forming the data processing rule in the parameter configuration interface, so that the data processing rule can be prevented and controlled from falling to the ground. As shown in fig. 6, which is an exemplary diagram of a parameter configuration interface. One or more feature conditions may be configured in the parameter configuration interface, such that the feature data includes at least a feature identifier, a threshold parameter, and a comparator parameter for each feature of each feature, and if there are multiple feature conditions, the feature data further includes a feature association parameter.

Wherein, the feature identifier is used for uniquely identifying the feature, such as a feature ID or a feature name, etc.; the threshold parameter represents a threshold value for comparing with a characteristic value corresponding to the characteristic, and the threshold parameter can be a specific numerical value, a character string or a date and the like; the comparator parameter is used to determine the relationship between the feature value and the threshold parameter, such as ">", "<", "≧" or "═ or the like, for comparing the signs of the relationship between the sizes of two objects, or to determine whether there are signs of the inclusion relationship between two objects, such as in or not int, etc.; the feature association parameter characterizes an association relationship between features, such as an "and" relationship or an "or" relationship.

In specific implementation, the feature data may further include a data type, an operation parameter, and version information of each feature. The data type characterization feature corresponds to the type of the characteristic value, such as integer, character string type or date type, so that the value of the threshold parameter can be limited by the data type, and reference is provided for a user when the threshold parameter is configured; the operation parameters may include an operation period representing a period of automatic operation after the data processing rule is online and a first operation time representing a time of first operation after the data processing rule is online.

In order to unify the configuration mode of the feature data and avoid the confusion phenomena of inconsistency, logic errors and the like of the feature data configured by different users, in practical application, some rule templates can be provided for the users, and a parameter configuration interface is generated based on the rule templates. As shown in fig. 7, which is an exemplary diagram of a basic information configuration interface. In the basic information configuration interface, a user can select a rule module of a data processing rule to be defined through a risk mode selection field, and the server automatically reads template parameters based on the selected rule template to generate a parameter configuration interface.

Version information of the data processing rules, such as version descriptions, version names, and the like, can also be configured in the basic information configuration interface, and the version descriptions are verified for risk control of a certain category, for example, at the time of verification, and the like. It can be understood that the parameter configuration interface and the basic information configuration interface may be the same interface, and the existence form of the basic information configuration interface is not specifically limited in this specification.

In view of this, in some possible embodiments, as shown in fig. 8, before step S510 is implemented, the method may further include:

s507, receiving a template reading request, wherein the template reading request carries a template identifier;

s508, acquiring a rule template matched with the template identifier;

s509, reading template parameters in the rule template, and sending the template parameters to the client so that the client generates a parameter configuration interface based on the template parameters.

The template identifier is used for uniquely identifying a rule template, and the rule template can be a language template written by a target language, such as an SQL language template, a python language template or a java language template, or a text template written by a text editing tool, such as a json template, an xml template or an excel template, and the like. The template parameters correspond to the feature data, and may include a feature association parameter, a feature name of each feature, a feature identification, a comparator parameter, a default value, a data type, and the like.

For the generation of the rule template, besides writing by using a target language or a text editing tool, in order to enable the rule template to have expandability and prevent the written template parameters from generating normative errors when the rule template is written, online writing can be provided for a user. And when writing on line, the user can directly utilize the imported features to avoid configuration errors of the related attributes of the features, such as feature types or feature values.

Based on the above description, in some possible embodiments, as shown in fig. 9, before step S507 is implemented, the method may further include:

s502, receiving a search characteristic request, wherein the search characteristic request carries characteristic keywords.

As shown in fig. 10, which is an exemplary diagram of a template configuration interface. In fig. 10, the user may enter the features to be added for the feature keyword search screening in the search box. In the template configuration interface, a user can add new features in two ways: the new entry behind each feature record is used, and the new condition entry is used, but different ways have corresponding different association relations. As shown in fig. 10, if a new entry is added after the first feature record (i.e. the record with sequence number 1), the new entry is a parallel or feature, that is, only one of the two entries needs to be satisfied, for example, only one of the 1 st and 2 nd entries needs to be satisfied, and it is intuitively understood from the figure that a sequence number is not added; if the condition entries are newly added, parallel and characteristics are added, that is, each condition needs to be met, for example, line 1, line 2, line 3, line 4 and line 5, and it is intuitively understood from the figure that a new serial number is added.

The audit logic shown in FIG. 6 is required to satisfy the following three conditions: 1. the authentication field is: y; 2. the user registration time is as follows: less than a specified value; 3. the number of transfer opponents in the last 7 days is greater than the specified value, or the transfer amount in the last 7 days is greater than the specified value.

Corresponding to the new addition, the user can also delete according to the deletion entry behind the characteristic record after a certain characteristic condition is not needed.

And S503, screening target characteristic information from a preset characteristic pool according to the characteristic keywords.

The preset feature pool stores features which are pre-imported and subjected to preprocessing such as cleaning, and the target feature information may include a feature name, a feature identifier and a data type. It is understood that the preset feature pool may be a single feature pool table or a plurality of feature pool tables.

S504, the target characteristic information is sent to the client side, so that the client side can display the target characteristic information in the template configuration interface.

When the client displays the target characteristic information, a corresponding default value input control is generated according to the data type in the target characteristic information, for example, an input box control is generated if the data type is integer (int), a date plug-in control is generated if the data type is date (datatime), and the like. The user may enter default values for the features in the default value entry controls, or may use default values provided by the client.

It will be appreciated that the range of default values may be defined by the data type, for example when the data type characterizes the default value as a numerical value, such as integer (int), any recommended numerical value may be filled in; when the data type characterization default value is an enumerated value, the enumerated value can be selected through a pull-down control. While different comparators may be selected for different data types, for example, the values may be selected >, < ≧ and ≦ and the enumerated values may be selected as either in, not in, and so forth.

And S505, receiving a template configuration request from the template configuration interface, wherein the template configuration request carries template parameters.

After each feature and the condition corresponding to the feature are completed, the user can complete the operation by triggering the control such as completion or saving. And after receiving the completion operation, the client reads the feature information of each feature in the template configuration interface to generate a template parameter, encapsulates the template parameter in the template configuration request and sends the template parameter to the server.

S506, generating a rule template based on the template parameters.

As described in step S509, the rule template may have various expression modes such as a language template and a text template, and accordingly, may also have various storage modes such as a file storage and a database storage. For example, for an SQL language template, the formed rule template is an SQL code composition, and the SQL code corresponding to fig. 10 is as follows:

select userid

from user_base

where(amt_7day>‘parameterA’or user_7day>‘parameterB’)

and ifcent＝‘parameterC’

and createdate<‘parameterD’

wherein, the user _ base is the name of the data table to be inquired; the userid is a main key and refers to the main user id of the feature; amt _7day refers to the transfer amount of the last 7 days of the feature; user _7day refers to the number of transferring opponents in the last 7 days; the ifcent indicates whether authentication is performed; createdate refers to user registration time; the parameters ParameterA, ParameterB, ParameterC and ParameterD are pending threshold parameters and are filled in after the specific rule is configured. It should be understood that the data table used in the SQL code is a single data table, and in a specific implementation, multiple data tables may be used to perform join operation generation, which is not specifically limited in this application.

And S520, forming a data processing rule based on the characteristic data.

Since the feature data includes at least the feature identifier, the threshold parameter, and the comparator parameter of each feature, the feature data further includes a feature association parameter when there are a plurality of features. Thus, in one possible embodiment, as shown in fig. 11, step S520 may be implemented to include:

and S521, generating a characteristic condition of the characteristic according to the characteristic identifier, the threshold parameter and the comparator parameter of the characteristic aiming at each characteristic in the characteristic data.

In the embodiment of the application, the characteristic condition represents a risk rule of the characteristic, and if the characteristic value of the characteristic meets the characteristic condition of the characteristic, the risk is considered to be suspicious. Specifically, the key representation of the feature in the data table is obtained through the feature identifier, and then the key representation, the threshold parameter and the comparator parameter are expressed according to the target language to obtain the feature condition. For example, if the target language is SQL, the key representation is amt _7day, the threshold value represented by the threshold value parameter is 10000, and the comparator represented by the comparator parameter is ">", the generated characteristic condition is "amt _7day > 10000"; similarly, if the key indicates user _7day, the threshold value indicated by the threshold value parameter is 1000, and the comparator indicated by the comparator parameter is ">", the generated characteristic condition is "user _7day > 1000".

And S522, determining the number of the features in the feature data.

The feature quantity indicates the number of features in the feature data, and if the feature quantity is a preset feature threshold, the preset feature threshold represents only 1 feature, that is, only one record is present in the interface shown in fig. 6, step S523 is executed; if the number of features is greater than the preset feature threshold, that is, there are multiple records in the interface shown in fig. 6, step S524 is executed.

S523, a data processing rule is formed from the characteristic conditions of the characteristics.

For example, if the data processing rule is expressed by SQL code, assuming that only feature 1 exists and the feature condition of feature 1 is "amt _7day > 10000", the data processing rule is "amt _7day > 10000".

And S524, determining the operational relationship among the characteristic conditions of each characteristic according to the characteristic association parameters in the characteristic data, and forming a data processing rule according to the characteristic conditions of each characteristic and the operational relationship among the characteristic conditions.

The characteristic association parameters characterize the association relationship among the characteristics, and when the operation relationship among the characteristic conditions is determined, the operator parameters are used for representing. For example, if the feature associated parameter is an or relationship between the feature 1 and the feature 2, the operator parameter between the feature condition of the feature 1 and the feature condition of the feature 2 is "or".

If the data processing rule is expressed by SQL code, and it is assumed that there are two features, feature 1 and feature 2, and the feature condition of feature 1 is "amt _7day > 10000", the feature condition of feature 2 is "user _7day > 1000", and the operator parameter between feature 1 and feature 2 is "or", the data processing rule is "amt _7day >10000or user _7day > 1000".

S530, receiving a trial operation instruction aiming at the data processing rule, and obtaining a trial operation result by using the characteristic conditions of each characteristic in the data processing rule and the operation relation among the characteristic conditions.

In the embodiment of the application, the test operation is also a test, and the test operation result mainly comprises each suspicious user meeting the data processing rule. The data processing rules may be expressed in various forms, such as text or code, and if the code is the code, the running operation may be directly performed, and if the code is the text, the text needs to be converted into executable code. Thus, step S530, when implemented, may include: generating an executable code by using the characteristic conditions of each characteristic in the data processing rule and the operational relationship among the characteristic conditions; and calling the executable code to obtain a test run result. With the configuration shown in FIG. 6, the executable code generated using the SQL language is as follows:

Create table online_rule_18237389as

select userid

from user_base

where(amt_7day>10000or user_7day>1000)

and ifcent＝’Y’

and createdate<’2019-01-01’

through the executable statements, the user id (userid) of the suspicious user can be stored in the calculation result for the auditing platform to call and perform manual auditing, as shown in table 1:

TABLE 1

User ID
	XXXX1
XXXX2
	XXXX9

And S540, acquiring historical black and white sample data.

The historical black and white sample data represent a black sample and a white sample which are marked by historical manual marking, wherein the black sample is a risk user sample, and the white sample is a non-risk user sample. The historical black and white sample data can comprise user identification, whether to report and a risk category, whether to report indicates whether the sample is a black sample, and the risk category indicates the type of the risk. As shown in table 2, this is an example of the acquired historical black and white sample data:

TABLE 2

And S550, analyzing the test run result according to the historical black and white sample data to obtain a first evaluation index.

The first evaluation index comprises a risk user hit rate and hit rates of various risk categories, the risk user hit rate represents the probability of risk users in the index, and the risk users are black sample users; the hit rate for a risk category characterizes the probability of the risk category among the hit users.

In one possible implementation, as shown in fig. 12, the step S550 may include, in specific implementation:

and S551, matching the historical black and white sample data with the trial operation result, and determining the hit user.

The effect that the commissioning needs to achieve is to audit by using the real data on the line, and the audit task is not really generated and pushed to the auditing platform, as shown in fig. 13a, which is a schematic diagram of the architecture of another data processing system. The online rule audit results are all pushed to an online database table, the offline test operation rules are not online, the audit results are pushed to an offline rule audit table with the same structure, and the format of the offline rule audit table is shown in table 3:

TABLE 3

User ID	Audit rule identification	Audit time
			XXXX1	xxx1	11/12/15/58/12 in 2020
XXXX2	xxx2	Year 2020, 11, 12, 14:56:09
			XXXX3	xxx3	11/12/09/30/05 in 2020
XXXX4	xxx4	11/13/12/46/36 in 2020
			XXXX5	xxx4	Year 2020, 11, 15, 13:07:56

The process of matching the trial operation result with the historical black and white sample data is also the process of determining the audit result. It can be understood that, for the same data processing rule, it may perform auditing in multiple runs, and after counting the first evaluation index of each auditing, it may be determined whether the data processing rule has stability, such as cumulative number of hits. As shown in table 4, it is an example of the audit result corresponding to a certain test rule, that is, the hit user list:

TABLE 4

And S552, screening out the risk users from the users in the life.

According to the audit time, the risk users of the trial operation can be determined, namely the reported users in the user list are hit.

And S553, determining the ratio of the number of the risky users to the total number of the users in the historical black and white sample data as the hit rate of the risky users.

And S554, classifying the risk users according to the risk categories to obtain the risk users corresponding to each risk category.

And S555, aiming at each risk category, determining the ratio of the number of the risk users corresponding to the risk category to the total number of the users corresponding to the risk category in the historical black and white sample as the hit rate of the risk category.

For example, assume that the historical black and white sample data includes 100 users, and among the 100 users, there are 20 white samples, where the number of users in category 1 is 10, the number of users in category 2 is 50, and the number of users in category 3 is 20. The number of hit users determined by matching the historical black and white sample data with the test run result is 80, the number of risk users in the number of hit users is 40, and the number of risk users is 5 for the category 1 user number, 20 for the category 2 user number and 15 for the category 3 user number respectively. Then the risk user hit rate is 40/100-0.4, the hit rate for category 1 is 5/10-0.5, the hit rate for category 2 is 20/50-0.4, and the hit rate for category 3 is 15/20-0.75.

After the first evaluation index of the trial operation is determined, the server can perform statistical analysis on the first evaluation index for service personnel to perform overall effectiveness evaluation. As shown in fig. 13b, which is an exemplary graph of the number of accumulated hits, it can be seen in the graph at which time period the data processing rule has a stronger validity.

It can be understood that it is necessary to determine whether the first evaluation index meets the preset online condition, and the online processing of the data processing rule is allowed only when the preset online condition is met. In some possible embodiments, as shown in fig. 14, the determining whether the first evaluation index satisfies the preset on-line condition may include, in a specific implementation:

and S548, determining whether the hit rate of the risk user meets a first preset online condition, and determining whether the hit rate of each risk category meets a second preset online condition corresponding to the risk category.

The first preset online condition represents a standard that the hit rate of the risky user needs to reach, for example, the hit rate of the risky user may be compared with a first hit rate threshold, and when the hit rate of the risky user is greater than the first hit rate threshold, the standard is considered to be reached. Similarly, the second preset online condition represents a standard that the hit rate of the risk category needs to reach, for example, the hit rate of the risk category may be compared with a second hit rate threshold corresponding to the risk category, and when the hit rate of the risk category is greater than the second hit rate threshold corresponding to the risk category, the standard is considered to be reached.

It should be noted that the hit rate of each risk category may have a different second preset on-line condition, i.e. a different second hit rate threshold. For example, as long as the hit rate of the category 1 is greater than 50%, the hit rate of the category 2 satisfies a second preset on-line condition corresponding to the category 2; and the hit rate of the category 3 needs to be greater than 80%, and the hit rate of the category 3 is considered to meet the second preset on-line condition corresponding to the category 3.

It can be understood that the first preset online condition and the second preset online condition corresponding to each risk category may be adjusted in real time, that is, the first hit rate threshold and the second hit rate threshold may be set to be the same or different, and this specification is not limited specifically.

S549, if the first preset on-line condition is satisfied and a second preset on-line condition corresponding to each risk category is satisfied, determining that the first hit indicator satisfies the preset on-line condition.

It should be noted that the hit rate of the risk user and the hit rate of each risk category are core indexes of the first evaluation index, and in some embodiments, the first evaluation index may further include indexes such as an audit quantity fluctuation range, a historical audit ratio, and a historical report ratio. Correspondingly, when determining whether the first hit index meets the preset on-line condition, it is necessary to determine whether the indexes such as the audit quantity fluctuation range, the historical audit ratio, the historical report ratio and the like meet the corresponding preset on-line condition, and under the condition that all the indexes meet the corresponding preset on-line condition, it can be determined that the first hit index meets the preset on-line condition. In specific implementation, the first evaluation index may also be adjusted according to different current business situations.

And S560, performing online processing on the data processing rule when the first evaluation index meets a preset online condition.

When each index in the first evaluation index basically meets the requirement, further evaluation links such as manual sampling inspection or system sampling inspection can be carried out. If the index effect obtained from the evaluation result is not ideal, the features, feature conditions, threshold parameters, or the like in the data processing rule may be re-optimized, and after the optimization, the process of commissioning in step S530 is executed again.

In view of this, in a possible implementation, as shown in fig. 15, before the step of performing online processing on the data processing rule is implemented, the method may further include:

s561, receiving a verification processing request for the data processing rule, where the verification processing request carries a second evaluation index.

If the data processing rule is manually selected, the data processing rule historical operation records are selected and checked by the selection staff, and the effectiveness of indexes such as hit rate, hit type and the like is evaluated again. The second evaluation index may include information such as the number of audited users, the number of sampling inspection people, and the like, besides the first evaluation index, as shown in fig. 16, which is an exemplary diagram of the verification interface. The user can input the related information in the second evaluation index into a selective examination evaluation text box, for example, the number of the audited users is 100; the number of the spot check people is 90; the number of people hit in spot inspection is 80; the hit rate of spot check is 80%; type of spot check hit: category 1 is 50 people and category 2 is 30 people.

And S562, auditing the data processing rule according to the second evaluation index, and performing online processing on the data processing rule after the auditing is passed.

The process of performing the audit according to the second evaluation index may refer to step S550, that is, whether each index meets the corresponding preset on-line condition is audited, and if all the indexes meet the corresponding preset on-line condition, the audit is passed. If not, the data processing rule may be re-optimized and then step S530 is executed.

In some possible embodiments, the step of performing online processing on the data processing rule may be implemented by: receiving an online processing request, wherein the online processing request carries applicant information; and creating an application flow according to the applicant information, and starting the processing of each application node in the application flow. As shown in fig. 17, which is an exemplary diagram of a check-up line interface.

Because the data processing rule comprises the operation parameters, after the data processing rule is subjected to online processing, the server can start the operation of the data processing rule according to the operation parameters in the data processing rule. The operation flow of each operation is similar to the operation flow of the trial operation, and the detailed description is not repeated here.

In some feasible implementation manners, in order to enable the user to more intuitively know the trial operation result of the trial operation, the server side may send the trial operation result to the client side, so that the user can view details of the operation result through the client side. Thus, after step S530 is implemented, the method may further include:

(1) and sending the execution condition to the client so that the client displays the execution condition in the execution result viewing interface.

As shown in fig. 18, which is a schematic view of an execution results viewing interface. In each data processing rule operation or trial operation, adding a task record of the task corresponding to the data processing rule of the time, such as the task number 142 in fig. 18, in the execution result viewing interface, wherein the state is in operation; and after the data processing rule is run or the trial run is finished every time, the server sends the execution condition to the client. The execution condition characterizes the execution result status, e.g., success or failure.

(2) And receiving a result viewing request from the execution result viewing interface, wherein the result viewing request at least carries the task number.

(3) And acquiring a test run result corresponding to the task number.

The user can check the trial operation result through the detail entry in the operation result, and the server analyzes the trial operation result and historical black and white sample data to obtain a first evaluation index. In order to enable the user to view the evaluation index more intuitively, the method may further include, after step S550 is implemented:

and evaluating the data processing rule based on the first evaluation index to generate evaluation data, and sending the evaluation data to the client so that the client can visually display the evaluation data.

The evaluation data comprises the total number of hit users, the trend of the number of hit users (if the simulation is periodically run for a plurality of times), the data of the history of the hit users which is examined, the number of the history judged as risk clients and the risk category of the history judged as the risk clients. As shown in fig. 19, which is an exemplary diagram of the evaluation result.

Through experimental verification, in the aspect of efficiency improvement, by using the data processing method provided by the embodiment of the method, the development efficiency of the data processing rule is reduced to within 1 day from 2-4 weeks of technically pure code development; the production completion of the test run is reduced to less than 1 hour from 1-2 days depending on the technology; in the aspect of compliance, the on-line of all data processing rules is measured by specific data indexes, so that the data processing rules are more reasonable and standard, and the standards are more uniform and objective; moreover, spot check, evaluation and the like are completed on line, and follow-up is well documented; the auditing process is also completed on line, so that the examination is convenient and quick.

According to the scheme provided by the embodiment, the data processing rule is automatically generated by providing the parameter configuration interface for the user to configure the rule, so that the development efficiency and the online efficiency of the data processing rule are improved while the transparency and the interpretability of the rule are improved; the rule test operation is provided before the data processing rule is on line, so that the rule test efficiency is improved; after the rule is commissioned, preliminarily evaluating the commissioning result by using historical black and white sample data to provide a reference basis for the effectiveness evaluation of the subsequent rule; the system automatically performs the initial evaluation without manual operation, so that the rule effect evaluation standards used by the off-line test operation result and the verification result of manual spot check are unified, and the manpower loss is reduced; and the rule is processed on-line after the initial evaluation result, namely the first evaluation index, reaches the on-line standard, so that the hit rate of the data processing rule is increased, and the analysis effect of the data processing rule is further improved.

By providing the system rule template, the user can rapidly configure the data processing rule, and the user experience is improved; the special risk is prevented and controlled by utilizing the cleaned and imported features and the customized combined feature conditions, so that the development efficiency of the data processing rule is further improved; by means of functional design of template configuration, rule configuration, testing, evaluation, examination and approval and the like, all processes of the data processing rules are transferred from off-line to on-line, the problems that time and labor are consumed for code development rules, the data processing rules are slow in on-line efficiency due to factors such as off-line non-standardization in all stages are solved, and the data processing rules can be rapidly on-line.

Based on the same inventive concept as the method embodiment, the present application embodiment further provides a data processing apparatus, as shown in fig. 20, the apparatus 200 may include:

a parameter configuration request receiving module 210, configured to receive a parameter configuration request, where the parameter configuration request carries feature data;

a rule generation module 220 for forming a data processing rule based on the feature data;

a rule commissioning module 230, configured to receive a commissioning instruction for the data processing rule, and obtain a commissioning result by using the feature condition of each feature in the data processing rule and an operation relationship between the feature conditions;

a sample data obtaining module 240, configured to obtain historical black and white sample data;

the trial operation result analysis module 250 is configured to analyze a trial operation result according to historical black and white sample data to obtain a first evaluation index;

and the online processing module 260 is configured to perform online processing on the data processing rule when the first evaluation index meets a preset online condition.

In some possible embodiments, the apparatus 200 may further include: the reading request receiving module is used for receiving a template reading request, and the template reading request carries a template identifier; the template acquisition module is used for acquiring a rule template matched with the template identifier; and the template parameter reading module is used for reading the template parameters in the rule template and sending the template parameters to the client so that the client generates a parameter configuration interface based on the template parameters.

In some possible embodiments, the apparatus 200 may further include: the characteristic searching module is used for receiving a searching characteristic request, and the searching characteristic request carries characteristic keywords; the characteristic screening module is used for screening target characteristic information from a preset characteristic pool according to the characteristic keywords; the characteristic returning module is used for sending the target characteristic information to the client so that the client can display the target characteristic information in the template configuration interface; the template configuration request receiving module is used for receiving a template configuration request from a template configuration interface, and the template configuration request carries template parameters; and the rule template generating module is used for generating a rule template based on the template parameters.

In some possible implementations, the rule generation module 220 may include: the logic generation unit is used for generating characteristic conditions of the characteristics according to the characteristic identification, the threshold parameter and the comparator parameter of the characteristics aiming at each characteristic in the characteristic data; the characteristic quantity determining unit is used for determining the quantity of the characteristics in the characteristic data; the first rule generating unit is used for forming a data processing rule according to the characteristic condition of the characteristic under the condition that the characteristic quantity is a preset characteristic threshold value; and the second rule generating unit is used for determining the operation relation among the characteristic conditions of the characteristics according to the characteristic association parameters in the characteristic data under the condition that the number of the characteristics is greater than the preset characteristic threshold value, and forming a data processing rule by the characteristic conditions of each characteristic and the operation relation among the characteristic conditions.

In some possible embodiments, the first evaluation index may include a hit rate of the risk user and a hit rate of each risk category; the commissioning result analysis module 250 may include: the hit user determining unit is used for matching the historical black and white sample data with the trial operation result and determining a hit user; the risk user screening unit is used for screening risk users from the hit users; the first calculating unit is used for determining the ratio of the number of the risky users to the total number of the users in the historical black and white sample data as the hit rate of the risky users; the risk user classification unit is used for classifying the risk users according to risk categories to obtain the risk user corresponding to each risk category; and the second calculating unit is used for determining the ratio of the number of the risk users corresponding to the risk category to the total number of the risk users corresponding to the risk category in the historical black and white sample as the hit rate of the risk category.

In some possible embodiments, the apparatus 200 may further include: and the online condition judging module is used for determining whether the first evaluation index meets the preset online condition.

Specifically, the online condition determining module may include: the first condition judgment unit is used for determining whether the hit rate of the risk user meets a first preset online condition; the second condition judgment unit is used for determining whether the hit rate of each risk category meets a second preset online condition corresponding to the risk category; and the result determining unit is used for judging that the first hit index meets the preset on-line condition under the condition that the first preset on-line condition is met and the second preset on-line condition corresponding to each risk category is met.

In some possible embodiments, the apparatus 200 may further include: the verification processing request receiving module is used for receiving a verification processing request aiming at the data processing rule, and the verification processing request carries a second evaluation index; and the verification module is used for verifying the data processing rule according to the second evaluation index, and executing the step of performing online processing on the data processing rule after the verification is passed.

It should be noted that, when the apparatus provided in the foregoing embodiment implements the functions thereof, only the division of the functional modules is illustrated, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the apparatus may be divided into different functional modules to implement all or part of the functions described above. In addition, the apparatus and method embodiments provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments for details, which are not described herein again.

The embodiment of the present application further provides an electronic device, where the device includes a processor and a memory, where the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded by the processor and executes the data processing method provided by the foregoing method embodiment.

Further, fig. 21 shows a hardware structure diagram of an apparatus for implementing the method provided in the embodiment of the present application, and the apparatus may participate in constituting or containing the device or system provided in the embodiment of the present application. As shown in fig. 21, the device 21 may include one or more (shown as 2102a, 2102b, … …, 2102 n) processors 2102 (the processors 2102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 2104 for storing data, and a transmission 2106 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 21 is merely illustrative and is not intended to limit the structure of the electronic device. For example, device 21 may also include more or fewer components than shown in FIG. 21, or have a different configuration than shown in FIG. 21.

It should be noted that the one or more processors 2102 and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuitry may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the device 21 (or mobile device). As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).

The memory 2104 can be used for storing software programs and modules of application software, such as program instructions/data storage devices corresponding to the methods described in the embodiments of the present application, and the processor 2102 executes various functional applications and data processing by running the software programs and modules stored in the memory 2104, so as to implement one of the data processing methods described above. The memory 2104 may include high speed random access memory, and may also include 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 2104 may further include memory located remotely from the processor 2102, which may be connected to the device 21 over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 2106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the device 21. In one example, the transmission device 2106 includes a network adapter (NIC) that can be connected to other network devices through a base station to communicate with the internet. In one example, the transmission device 2106 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the device 21 (or mobile device).

The embodiment of the present application further provides a computer storage medium, where at least one instruction or at least one program is stored in the computer storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the data processing method provided by the foregoing method embodiment.

Alternatively, in this embodiment, the computer storage medium may be located on at least one of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer storage medium. The processor of the electronic device reads the computer instructions from the computer storage medium, and the processor executes the computer instructions, so that the electronic device executes the data processing method provided by the method embodiment.

It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the device and electronic apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.

The foregoing description has disclosed fully embodiments of the present application. It should be noted that those skilled in the art can make modifications to the embodiments of the present application without departing from the scope of the claims of the present application. Accordingly, the scope of the claims of the present application is not to be limited to the particular embodiments described above.

Claims

1. A method of data processing, the method comprising:

forming a data processing rule based on the feature data;

acquiring historical black and white sample data;

2. The method of claim 1, wherein forming data processing rules based on the feature data comprises:

generating a feature condition of each feature according to the feature identifier, the threshold parameter and the comparator parameter of the feature for each feature in the feature data;

determining the number of features in the feature data;

if the characteristic quantity is a preset characteristic threshold value, forming the data processing rule according to the characteristic condition of the characteristic;

if the feature quantity is larger than the preset feature threshold, determining an operational relationship among feature conditions of each feature according to feature association parameters in the feature data, and forming the data processing rule by the feature conditions of each feature and the operational relationship among the feature conditions.

3. The method of claim 1, wherein the first evaluation index comprises a risk user hit rate and a hit rate for each risk category;

analyzing the test run result according to the historical black and white sample data to obtain a first evaluation index, wherein the method comprises the following steps:

matching the historical black and white sample data with the trial operation result to determine a hit user;

screening out risk users from the hit users;

determining the ratio of the number of the risky users to the total number of users in the historical black and white sample data as the hit rate of the risky users;

classifying the risk users according to the risk categories to obtain the risk user corresponding to each risk category;

and for each risk category, determining the ratio of the number of the risk users corresponding to the risk category to the total number of the risk users corresponding to the risk category in the historical black and white sample as the hit rate of the risk category.

4. The method according to claim 1, wherein when the first evaluation index meets a preset on-line condition, before the data processing rule is subjected to on-line processing, the method further comprises the step of determining whether the first evaluation index meets the preset on-line condition;

the determining whether the first evaluation index meets the preset online condition includes:

determining whether the hit rate of the risk user meets a first preset online condition, and determining whether the hit rate of each risk category meets a second preset online condition corresponding to the risk category;

and if the first preset online condition is met and a second preset online condition corresponding to each risk category is met, judging that the first hit index meets the preset online condition.

5. The method of claim 1, wherein prior to said receiving a parameter configuration request, the method further comprises:

receiving a template reading request, wherein the template reading request carries a template identifier;

acquiring a rule template matched with the template identifier;

and reading template parameters in the rule template, and sending the template parameters to a client so that the client generates a parameter configuration interface based on the template parameters.

6. The method of claim 5, wherein prior to said receiving a template read request, the method further comprises:

receiving a search characteristic request, wherein the search characteristic request carries characteristic keywords;

screening target characteristic information in a preset characteristic pool according to the characteristic keywords;

sending the target characteristic information to a client so that the client can display the target characteristic information in a template configuration interface;

receiving a template configuration request from the template configuration interface, wherein the template configuration request carries template parameters;

and generating a rule template based on the template parameters.

7. The method of claim 1, wherein prior to said inline-processing the data processing rule, the method further comprises:

receiving a verification processing request aiming at the data processing rule, wherein the verification processing request carries a second evaluation index;

and verifying the data processing rule according to the second evaluation index, and executing the step of performing online processing on the data processing rule after the verification is passed.

8. A data processing apparatus, characterized in that the apparatus comprises:

9. An electronic device, characterized in that the device comprises a processor and a memory, in which at least one instruction or at least one program is stored, which is loaded by the processor and executes the data processing method according to any one of claims 1 to 7.

10. A computer storage medium, in which at least one instruction or at least one program is stored, which is loaded and executed by a processor to implement the data processing method according to any one of claims 1 to 7.