CN116228384A

CN116228384A - Data processing method, device, electronic equipment and computer readable medium

Info

Publication number: CN116228384A
Application number: CN202310033268.4A
Authority: CN
Inventors: 陈桂生; 胡劲英; 杨佳
Original assignee: CCB Finetech Co Ltd
Current assignee: CCB Finetech Co Ltd
Priority date: 2023-01-10
Filing date: 2023-01-10
Publication date: 2023-06-06

Abstract

The application discloses a data processing method, a data processing device, electronic equipment and a computer readable medium, and relates to the technical field of big data processing, wherein a specific implementation mode comprises the steps of receiving a data processing request and obtaining corresponding data to be processed; clustering the data to be processed based on a preset function to obtain each cluster; determining the corresponding transaction types of each cluster, and establishing a mapping relation between each cluster and the corresponding transaction type; and calling an estimation engine to determine and output an estimation value corresponding to the data to be processed according to the cluster and the mapping relation. The data processing efficiency and the flexibility of complex business scene processing can be improved.

Description

Data processing method, device, electronic equipment and computer readable medium

Technical Field

The present disclosure relates to the field of big data processing technologies, and in particular, to a data processing method, a device, an electronic apparatus, and a computer readable medium.

Background

In a banking institution, transaction attributes corresponding to various financial derivatives are different, corresponding evaluation methods are also different, so that market data required to be used are also different, the data volume in the banking institution is large, and complicated business rules are required to complete mapping. The transaction and market association method is complex, and based on a large amount of stored data, complex business scenes are difficult to process, and the flexibility is poor.

Disclosure of Invention

In view of this, the embodiments of the present application provide a data processing method, apparatus, electronic device, and computer readable medium, which can solve the problems that the existing transaction and market association method is complicated, and based on a large amount of stored data, it is difficult to process a complex business scenario, and the flexibility is poor.

To achieve the above object, according to one aspect of the embodiments of the present application, there is provided a data processing method, including:

receiving a data processing request, and acquiring corresponding data to be processed;

clustering the data to be processed based on a preset function to obtain each cluster;

determining the corresponding transaction types of each cluster, and establishing a mapping relation between each cluster and the corresponding transaction type;

and calling an estimation engine to determine and output an estimation value corresponding to the data to be processed according to the cluster and the mapping relation.

Optionally, clustering the data to be processed based on a preset function to obtain clusters, including:

acquiring attribute data corresponding to a preset function;

and calculating the similarity between the data to be processed and each attribute data, and clustering the data to be processed based on the similarity to obtain each cluster.

Optionally, determining the transaction type corresponding to each cluster includes:

extracting keywords corresponding to each cluster, and further obtaining the utilization rate of each keyword;

determining target keywords according to the utilization rate and a preset utilization rate range;

and determining the transaction type according to the target keywords.

Optionally, determining the target keyword according to the usage rate and the preset usage rate range includes:

and determining the keywords corresponding to the utilization rate within the preset utilization rate range as target keywords.

Optionally, after establishing the mapping relationship between each cluster and the corresponding transaction type, the method further includes:

determining a change attribute in response to the transaction attribute changing;

updating the corresponding transaction type based on the change attribute, and further updating the corresponding mapping relation based on the updated transaction type.

Optionally, determining an estimate corresponding to the data to be processed includes:

invoking an evaluation engine to determine each transaction data corresponding to the data to be processed according to the cluster and the mapping relation;

and carrying out valuation on each transaction data based on a preset valuation algorithm.

Optionally, clustering the data to be processed based on the similarity to obtain clusters, including:

and classifying the corresponding data to be processed into one cluster when the similarity is larger than a preset similarity threshold value, and further obtaining each cluster.

In addition, the application also provides a data processing device, which comprises:

the receiving unit is configured to receive a data processing request and acquire corresponding data to be processed;

the clustering unit is configured to cluster the data to be processed based on a preset function so as to obtain each cluster;

the mapping unit is configured to determine the transaction type corresponding to each cluster, and further establish a mapping relation between each cluster and the corresponding transaction type;

and the estimation unit is configured to call an estimation engine to determine and output an estimation corresponding to the data to be processed according to the cluster and the mapping relation.

Optionally, the clustering unit is further configured to:

acquiring attribute data corresponding to a preset function;

Optionally, the mapping unit is further configured to:

and determining the transaction type according to the target keywords.

Optionally, the mapping unit is further configured to:

Optionally, the data processing apparatus further comprises an updating unit configured to:

Optionally, the estimation unit is further configured to:

Optionally, the clustering unit is further configured to:

In addition, the application also provides data processing electronic equipment, which comprises: one or more processors; and a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the data processing method as described above.

In addition, the application also provides a computer readable medium, on which a computer program is stored, which when executed by a processor implements a data processing method as described above.

To achieve the above object, according to yet another aspect of the embodiments of the present application, a computer program product is provided.

A computer program product of an embodiment of the present application includes a computer program, which when executed by a processor implements a data processing method provided by the embodiment of the present application.

One embodiment of the above invention has the following advantages or benefits: the method comprises the steps of obtaining corresponding data to be processed by receiving a data processing request; clustering the data to be processed based on a preset function to obtain each cluster; determining the corresponding transaction types of each cluster, and establishing a mapping relation between each cluster and the corresponding transaction type; and calling an estimation engine to determine and output an estimation value corresponding to the data to be processed according to the cluster and the mapping relation. The data processing efficiency and the flexibility of complex business scene processing can be improved.

Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.

Drawings

The drawings are included to provide a better understanding of the present application and are not to be construed as unduly limiting the present application. Wherein:

FIG. 1 is a schematic diagram of the main flow of a data processing method according to one embodiment of the present application;

FIG. 2 is a schematic diagram of the main flow of a data processing method according to one embodiment of the present application;

FIG. 3 is a schematic diagram of the main flow of a data processing method according to one embodiment of the present application;

FIG. 4 is a schematic diagram of the main units of a data processing apparatus according to an embodiment of the present application;

FIG. 5 is an exemplary system architecture diagram in which embodiments of the present application may be applied;

fig. 6 is a schematic diagram of a computer system suitable for use in implementing the terminal device or server of the embodiments of the present application.

Detailed Description

Exemplary embodiments of the present application are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness. In the technical scheme of the application, the aspects of acquisition, analysis, use, transmission, storage and the like of the related user personal information all meet the requirements of related laws and regulations, are used for legal and reasonable purposes, are not shared, leaked or sold outside the aspects of legal use and the like, and are subjected to supervision and management of a supervision department. Necessary measures should be taken for the personal information of the user to prevent illegal access to such personal information data, ensure that personnel having access to the personal information data comply with the regulations of the relevant laws and regulations, and ensure the personal information of the user. Once these user personal information data are no longer needed, the risk should be minimized by limiting or even prohibiting the data collection and/or deletion.

User privacy is protected by de-identifying data when used, including in some related applications, such as by removing a particular identifier, controlling the amount or specificity of stored data, controlling how data is stored, and/or other methods.

FIG. 1 is a schematic diagram of the main flow of a data processing method according to an embodiment of the present application, as shown in FIG. 1, the data processing method includes:

step S101, receiving a data processing request and obtaining corresponding data to be processed.

In this embodiment, the execution body (for example, may be a server) of the data processing method may receive the data processing request by means of a wired connection or a wireless connection. The data processing request may be, for example, a data valuation request. The content of the data processing request in the embodiment of the present application is not particularly limited.

The data to be processed may be, for example, market data for transactions. Specifically, the market data category is divided into commodity futures, fluctuation rate curved surfaces, commodity spot forward curves, commodity futures, credit default loss rates, credit default probabilities, stock index futures fluctuation rate curved surfaces, stock BETA, stock fluctuation rate curved surfaces, stock prices, stock index futures prices, foundation prices BETA, foreign exchange forward curves, foreign exchange fluctuation rate curved surfaces, interest rate discount curves, interest rate reference indexes, interest rate credit differential curves, interest rate liquidity differential curves, risk-free return rate curves, interest rate fluctuation rate curved surfaces, advance payrates, bond prices, bond futures prices, and the like

Step S102, clustering the data to be processed based on a preset function to obtain each cluster.

The preset functions may include, for example, a stock trading function, a fund trading function. The preset functions are not particularly limited in the embodiment of the present application, and may include a rate of return analysis function, a rate of interest credit analysis function, a risk-free rate of return analysis function, a rate of interest fluctuation analysis function, and an early reimbursement analysis function.

The execution main body can perform similarity matching on the data to be processed and the preset functions, further determine the data to be processed matched with each preset function, and obtain each corresponding cluster.

The method specifically comprises the steps of word segmentation of data to be processed, mapping each word segment corresponding to the data to be processed into each first vector based on a word embedding mode, mapping a preset function into each second vector based on the word embedding mode, calculating cosine similarity between each first vector and each second vector, and matching each preset function with the data to be processed according to each obtained cosine similarity to obtain each cluster. Specifically, clustering the word corresponding to each first vector when the cosine similarity corresponding to each second vector is greater than a preset threshold value to obtain each cluster. For example, a commodity futures data cluster, a fluctuation rate curved surface data cluster, a commodity futures data cluster, a credit default loss rate data cluster, a stock price data cluster, and the like, and the content and the type of each cluster are not particularly limited in the embodiment of the present application.

Step S103, determining the transaction type corresponding to each cluster, and further establishing a mapping relation between each cluster and the corresponding transaction type.

And acquiring the names of the clusters, and further determining the corresponding transaction types according to the names of the clusters. The trade type may be, for example, stock trade, futures trade, bond trade, etc., and the trade type is not specifically limited in the embodiments of the present application.

As another implementation manner of the embodiment of the present application, determining a transaction type corresponding to each cluster includes: extracting keywords corresponding to each cluster, and further obtaining the utilization rate of each keyword; determining target keywords according to the utilization rate and a preset utilization rate range; and determining the transaction type according to the target keywords.

For example, when the keywords in each cluster are suitable for use in a suitable preset usage range, the keywords corresponding to the usage in the preset usage range may be determined as target keywords. For example, the executing body may ignore keywords whose usage rate exceeds a preset usage rate range (the keywords whose usage rate exceeds the preset usage rate range may have no practical meaning, for example, exist as a connective word, so that the usage rate is high) to obtain target keywords, such as bonds, regular periods, etc., within the preset usage rate range, so that it may be determined that the corresponding transaction type may be bond transactions, regular transactions, etc., according to the target keywords. The embodiment of the application does not specifically limit the transaction type.

Specifically, determining the target keyword according to the usage rate and the preset usage rate range includes: and determining the keywords corresponding to the utilization rate within the preset utilization rate range as target keywords.

For example, the preset usage may range from 10% to 20%. When the usage rate is 15%, the corresponding keyword is determined as the target keyword. When the usage rate is 60%, the corresponding keywords are ignored because the keywords may be the connective words with higher usage rate, and the like.

Upon determining the transaction type, the execution body may invoke a mapping engine to establish and store a mapping relationship, e.g., in the form of key-value, for each cluster with the corresponding transaction type.

Specifically, after the mapping relation is established between each cluster and the corresponding transaction type, the data processing method further comprises the following steps: determining a change attribute in response to the transaction attribute changing; updating the corresponding transaction type based on the change attribute, and further updating the corresponding mapping relation based on the updated transaction type. By way of example, the transaction attribute may be greater than, equal to or greater than, equal to, less than, equal to or less than, contain, not contain, etc. The updated mapping relationship may include: and, or, no, or, etc.

Step S104, an estimation engine is called to determine and output estimation corresponding to the data to be processed according to the clustering and the mapping relation.

Specifically, determining an estimate corresponding to the data to be processed includes: invoking an evaluation engine to determine each transaction data corresponding to the data to be processed according to the cluster and the mapping relation; and carrying out valuation on each transaction data based on a preset valuation algorithm.

For example, the execution body may invoke the valuation engine to determine a corresponding transaction type according to the cluster and the mapping relationship, and thereby obtain transaction data (e.g., each of the periodic transaction data in the data to be processed) corresponding to the transaction type (e.g., the periodic transaction). The executing body may then invoke a valuation algorithm in the valuation engine to calculate a valuation for the acquired transaction data. The evaluation may be, for example, the net value of periodic financial accounting at a preset time point, or the trade price of stocks at a preset time point, etc., and the embodiment of the present application does not specifically limit the content of the evaluation.

In the embodiment, corresponding data to be processed is obtained by receiving a data processing request; clustering the data to be processed based on a preset function to obtain each cluster; determining the corresponding transaction types of each cluster, and establishing a mapping relation between each cluster and the corresponding transaction type; and calling an estimation engine to determine and output an estimation value corresponding to the data to be processed according to the cluster and the mapping relation. The data processing efficiency and the flexibility of complex business scene processing can be improved.

FIG. 2 is a schematic flow chart of a data processing method according to an embodiment of the present application, as shown in FIG. 2, the data processing method includes:

step S201, a data processing request is received, and corresponding data to be processed is obtained.

The data processing request may be, for example, a request for performing evaluation processing on data. The evaluation of the data may be, for example, a request to score the collected performance data. The data to be processed may be, for example, performance data of individual users within a certain entity.

Step S202, obtaining attribute data corresponding to a preset function.

The preset function may be a function for evaluating the work enthusiasm of the user, and the corresponding attribute data may be attribute fields such as "late", "early-back", "leave-out number". The preset function may also be a function of evaluating the quality of work completion of the user, and the corresponding attribute data may be attribute fields such as "complaint times", "return exceeding times", "times subjected to the surfacing". The content of the attribute data corresponding to the preset function is not particularly limited in the embodiment of the present application.

Step S203, calculating the similarity between the data to be processed and each attribute data, and clustering the data to be processed based on the similarity to obtain each cluster.

Each attribute data such as an attribute field, for example, "late", "early", "leave", and the like. After obtaining attribute data corresponding to the preset function, the execution main body can perform similarity matching on each attribute field contained in the attribute data and the data to be processed so as to obtain a field value corresponding to each attribute field. Specifically, each attribute field is mapped into each attribute field vector, and a word segmentation program is called to perform word segmentation on data to be processed so as to obtain each word segment, and then mapped into each word segment vector. The execution body may perform cosine similarity matching on each attribute field vector and each word segmentation vector corresponding to the data to be processed, so as to cluster the data to be processed based on a matching result.

Specifically, clustering the data to be processed based on the similarity to obtain clusters, including: and classifying the corresponding data to be processed into one cluster when the similarity is larger than a preset similarity threshold value, and further obtaining each cluster.

By way of example, each cosine similarity is compared with a preset similarity threshold to group the segmented words corresponding to the cosine similarity greater than the preset similarity threshold into one class, and then each clustered cluster can be obtained.

Step S204, determining the transaction type corresponding to each cluster, and further establishing a mapping relation between each cluster and the corresponding transaction type.

In the embodiment of the application, one transaction may be interaction of one piece of work performance data. The transaction type may be represented as a type of interaction of the primary performance data, such as whether the interaction is actively initiated or initiated by receiving a subscription message, and the embodiment of the present application does not specifically limit the transaction type. For example, a higher attention weight for the corresponding user is indicated when the transaction type is actively initiated interactions, and a lower attention weight for the corresponding user is indicated when the transaction type is interactions initiated by receiving subscription messages.

The mapping relation between the cluster and the corresponding transaction type can be established, so that the attention condition data of the scoring node to the user to be evaluated can be conveniently counted, and a data association foundation is laid for subsequent evaluation.

Step S205, an estimation engine is called to determine and output an estimation corresponding to the data to be processed according to the cluster and the mapping relation.

And the evaluation engine can call an evaluation algorithm to determine the attention weight of the evaluation node to the user corresponding to the transaction type according to the clustering and the mapping relation, and then evaluate and output the attention weight and the data in the clustering.

The embodiment of the application can improve the efficiency and accuracy of data processing.

Fig. 3 is an application scenario diagram of a data processing method according to an embodiment of the present application. The data processing method can be applied to a scene of estimating data. As shown in fig. 3, a server receives a data processing request and acquires corresponding data to be processed; the server clusters the data to be processed based on a preset function to obtain each cluster; the server determines the transaction type corresponding to each cluster, and then establishes a mapping relation between each cluster and the corresponding transaction type; the server calls an estimation engine to determine and output an estimation corresponding to the data to be processed according to the cluster and the mapping relation.

For example, the market data category may be divided into commodity futures, fluctuation rate curved surfaces, commodity spot forward curves, commodity futures, credit breach loss rates, credit breach probabilities, stock index futures fluctuation rate curved surfaces, stock BETA, stock fluctuation rate curved surfaces, stock prices, stock index futures prices, foundation prices BETA, foreign exchange forward curves, foreign exchange fluctuation rate curved surfaces, interest rate discount curves, interest rate reference indexes, interest rate credit difference curves, interest rate liquidity difference curves, risk-free yield curves, interest rate fluctuation rate curved surfaces, advance payouts, bond prices, bond futures prices, and the like. A transaction data screening method is defined, and a transaction attribute table is mapped to specified market data, such as a certain interest rate discount curve or a certain foreign exchange, namely a long-term curve, according to screening rules of attribute fields (the attribute can comprise more than, more than or equal to, less than or equal to, including, not including and the like). The transaction data screening method can be configured on a page, flexibly adjust according to the attribute, and define the association relation of the screening rule, including sum, or, no, or both. The estimation engine performs corresponding estimation operation according to the configured rule or the mapping relation between the transaction and the market data and the type of the market data.

According to the embodiment of the application, the transaction can be flexibly screened according to the requirements and mapped with market data. The configuration method is flexible, and can meet the mapping of complex transaction and market data; the data storage quantity is small, only the rule needs to be configured, and the mapping relation between each transaction and market data does not need to be stored.

Fig. 4 is a schematic diagram of main units of a data processing apparatus according to an embodiment of the present application. As shown in fig. 4, the data processing apparatus 400 includes a receiving unit 401, a clustering unit 402, a mapping unit 403, and an estimating unit 404.

A receiving unit 401 configured to receive a data processing request, and obtain corresponding data to be processed;

a clustering unit 402 configured to cluster data to be processed based on a preset function to obtain clusters;

a mapping unit 403, configured to determine a transaction type corresponding to each cluster, and further establish a mapping relationship between each cluster and the corresponding transaction type;

and the estimation unit 404 is configured to call an estimation engine to determine and output an estimation corresponding to the data to be processed according to the cluster and the mapping relation.

In some embodiments, the clustering unit 402 is further configured to: acquiring attribute data corresponding to a preset function; and calculating the similarity between the data to be processed and each attribute data, and clustering the data to be processed based on the similarity to obtain each cluster.

In some embodiments, the mapping unit 403 is further configured to: extracting keywords corresponding to each cluster, and further obtaining the utilization rate of each keyword; determining target keywords according to the utilization rate and a preset utilization rate range; and determining the transaction type according to the target keywords.

In some embodiments, the mapping unit 403 is further configured to: and determining the keywords corresponding to the utilization rate within the preset utilization rate range as target keywords.

In some embodiments, the data processing apparatus further comprises an updating unit, not shown in fig. 4, configured to: determining a change attribute in response to the transaction attribute changing; updating the corresponding transaction type based on the change attribute, and further updating the corresponding mapping relation based on the updated transaction type.

In some embodiments, the estimation unit 404 is further configured to: invoking an evaluation engine to determine each transaction data corresponding to the data to be processed according to the cluster and the mapping relation; and carrying out valuation on each transaction data based on a preset valuation algorithm.

In some embodiments, the clustering unit 402 is further configured to: and classifying the corresponding data to be processed into one cluster when the similarity is larger than a preset similarity threshold value, and further obtaining each cluster.

Note that the data processing method and the data processing apparatus of the present application have a corresponding relationship in terms of implementation contents, and therefore, the description is not repeated.

Fig. 5 illustrates an exemplary system architecture 500 in which the data processing methods or data processing apparatus of embodiments of the present application may be applied.

As shown in fig. 5, the system architecture 500 may include

terminal devices

501, 502, 503, a network 504, and a server 505. The network 504 is used as a medium to provide communication links between the

terminal devices

501, 502, 503 and the server 505. The network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

A user may interact with the server 505 via the network 504 using the

terminal devices

501, 502, 503 to receive or send messages or the like. Various communication client applications may be installed on the

terminal devices

501, 502, 503, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

501, 502, 503 may be a variety of electronic devices having a data processing screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.

The server 505 may be a server providing various services, such as a background management server (by way of example only) providing support for data processing requests submitted by users using the

terminal devices

501, 502, 503. The background management server can receive the data processing request and acquire corresponding data to be processed; clustering the data to be processed based on a preset function to obtain each cluster; determining the corresponding transaction types of each cluster, and establishing a mapping relation between each cluster and the corresponding transaction type; and calling an estimation engine to determine and output an estimation value corresponding to the data to be processed according to the cluster and the mapping relation. The data processing efficiency and the flexibility of complex business scene processing can be improved.

It should be noted that, the data processing method provided in the embodiment of the present application is generally executed by the server 505, and accordingly, the data processing apparatus is generally disposed in the server 505.

It should be understood that the number of terminal devices, networks and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 6, a schematic diagram of a computer system 600 suitable for use in implementing the terminal device of an embodiment of the present application is shown. The terminal device shown in fig. 6 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiments of the present application.

As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data required for the operation of the computer system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a liquid crystal credit authorization query processor (LCD), and the like, and a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments disclosed herein include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 601.

It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware. The described units may also be provided in a processor, for example, described as: a processor includes a receiving unit, a clustering unit, a mapping unit, and an estimating unit. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

As another aspect, the present application also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs, which when executed by one of the devices, cause the device to receive a data processing request, and acquire corresponding data to be processed; clustering the data to be processed based on a preset function to obtain each cluster; determining the corresponding transaction types of each cluster, and establishing a mapping relation between each cluster and the corresponding transaction type; and calling an estimation engine to determine and output an estimation value corresponding to the data to be processed according to the cluster and the mapping relation.

The computer program product of the present application comprises a computer program which, when executed by a processor, implements the data processing method in the embodiments of the present application.

According to the technical scheme of the embodiment of the application, the data processing efficiency can be improved, and the flexibility of processing complex business scenes can be improved.

The above embodiments do not limit the scope of the application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application are intended to be included within the scope of the present application.

Claims

1. A method of data processing, comprising:

determining the transaction type corresponding to each cluster, and further establishing a mapping relation between each cluster and the corresponding transaction type;

2. The method according to claim 1, wherein the clustering the data to be processed based on a preset function to obtain clusters includes:

acquiring attribute data corresponding to a preset function;

3. The method of claim 1, wherein the determining the transaction type corresponding to the respective cluster comprises:

and determining the transaction type according to the target keyword.

4. The method of claim 3, wherein the determining the target keyword according to the usage rate and a preset usage rate range comprises:

5. The method of claim 1, wherein after said mapping said respective clusters to said corresponding transaction types, the method further comprises:

and updating the corresponding transaction type based on the change attribute, and further updating the corresponding mapping relation based on the updated transaction type.

6. The method of claim 1, wherein determining the estimate corresponding to the data to be processed comprises:

7. The method according to claim 2, wherein clustering the data to be processed based on the similarity to obtain clusters comprises:

and classifying the data to be processed corresponding to the similarity larger than a preset similarity threshold value into one cluster, and further obtaining each cluster.

8. A data processing apparatus, comprising:

the mapping unit is configured to determine transaction types corresponding to the clustering clusters, and further establish a mapping relation between the clustering clusters and the corresponding transaction types;

and the estimation unit is configured to call an estimation engine to determine and output an estimation value corresponding to the data to be processed according to the cluster and the mapping relation.

9. The apparatus of claim 8, wherein the clustering unit is further configured to:

acquiring attribute data corresponding to a preset function;

10. The apparatus of claim 8, wherein the mapping unit is further configured to:

and determining the transaction type according to the target keyword.

11. The apparatus of claim 10, wherein the mapping unit is further configured to:

12. The apparatus of claim 8, further comprising an updating unit configured to:

13. The apparatus of claim 8, wherein the estimation unit is further configured to:

14. A data processing electronic device, comprising:

one or more processors;

storage means for storing one or more programs,

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-7.

15. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-7.

16. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-7.