CN111667321A

CN111667321A - Data processing method and device, computer and readable storage medium

Info

Publication number: CN111667321A
Application number: CN202010589720.1A
Authority: CN
Inventors: 施雯洁; 聂晓楠; 栾朝阳; 钱波
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2020-06-24
Filing date: 2020-06-24
Publication date: 2020-09-15

Abstract

The embodiment of the application discloses a data processing method, a data processing device, a computer and a readable storage medium, which can use data computing technology in the field of big data, and the method comprises the following steps: acquiring at least two second sampling indexes associated with the first sampling index; taking a second sampling index which has a one-way association relation with a target second sampling index as an associated second sampling index in the at least two second sampling indexes; acquiring a first coupling degree of a target second sampling index aiming at the first sampling index, and acquiring a second coupling degree of the associated second sampling index and the target second sampling index aiming at the first sampling index; and adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree. By the method and the device, interference among different second sampling indexes can be reduced, and accuracy of influence quantization of the second sampling indexes on the first sampling indexes is improved.

Description

Data processing method and device, computer and readable storage medium

Technical Field

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

Background

In each industry, a first sampling index of a certain service in the industry may be sorted and analyzed, where the first sampling index refers to an index that needs to be paid attention when a service person analyzes the service, such as Gross trade Volume (GMV) in a retail industry. When the first sampling index is analyzed, the first sampling index is generally decomposed into a plurality of second sampling indexes by a dupont analysis method, and the influence of each second sampling index on the first sampling index is obtained. However, when the influence of each second sampling index on the first sampling index is obtained by the conventional variable control method, the influence result of the obtained second sampling index on the first sampling index may have deviation or deficiency due to the possible interference between different second sampling indexes.

Disclosure of Invention

The embodiment of the application provides a data processing method, a data processing device, a computer and a readable storage medium, which can reduce interference among different second sampling indexes and improve the accuracy of quantization of the influence of the second sampling indexes on the first sampling indexes.

An embodiment of the present application provides a data processing method, including:

acquiring at least two second sampling indexes associated with the first sampling index; one of the at least two second sampling indices is a target second sampling index;

taking a second sampling index which has a one-way association relation with a target second sampling index as an associated second sampling index in the at least two second sampling indexes;

acquiring a first coupling degree of a target second sampling index aiming at the first sampling index, and acquiring a second coupling degree of the associated second sampling index and the target second sampling index aiming at the first sampling index;

and adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree.

Wherein, among the at least two second sampling indicators, determining an associated second sampling indicator of the target second sampling indicator comprises:

acquiring the index priorities of at least two second sampling indexes;

acquiring a second sampling index of which the index priority is smaller than that of the target second sampling index in at least two second sampling indexes as a to-be-associated sampling index;

acquiring a superposition relation between the sampling index to be correlated and a target second sampling index;

if the superposition relationship is a cross superposition relationship, determining the sampling index to be correlated as a correlated second sampling index of the target second sampling index; and the index priority is used for representing that the target second sampling index has a one-way incidence relation to the associated second sampling index.

Wherein, obtaining the index priority of at least two second sampling indexes comprises:

acquiring the index content types of at least two second sampling indexes, and acquiring the index priorities corresponding to the at least two second sampling indexes respectively based on the index content types; the index content type has a corresponding index priority.

obtaining the historical coupling degree of each second sampling index in at least two second sampling indexes;

and sequencing at least two second sampling indexes based on the historical coupling degree, and determining the index priority of each second sampling index according to the sequencing result.

Wherein, obtaining a first coupling degree of a target second sampling index for the first sampling index comprises:

acquiring a first sampling index value of the first sampling index at a first sampling time point and a second sampling index value at a second sampling time point, and determining the index change rate of the first sampling index based on the first sampling index value and the second sampling index value;

acquiring a first target index value of a target second sampling index at a first sampling time point and a second target index value at a second sampling time point, and determining a target sampling index change rate of the target second sampling index based on the first target index value and the second target index value;

and determining a first coupling degree of a target second sampling index based on the index change rate and the target sampling index change rate.

Wherein, obtaining a second coupling degree of the associated second sampling index and the target second sampling index for the first sampling index together comprises:

acquiring a first relevance index value of a relevance second sampling index at a first sampling time point and a second relevance index value at a second sampling time point, and determining a change rate of the relevance sampling index of the relevance second sampling index based on the first relevance index value and the second relevance index value;

determining a common change rate of the target second sampling index and the associated second sampling index based on the associated sampling index change rate and the target sampling index change rate;

and determining a second coupling degree of the associated second sampling index and the target second sampling index for the first sampling index based on the common change rate and the index change rate.

Wherein, based on the associated sampling index change rate and the target sampling index change rate, determining a common change rate of the target second sampling index and the associated second sampling index comprises:

taking the product of the change rate of the associated sampling index and the change rate of the target sampling index as the common change rate of the target second sampling index and the associated second sampling index;

determining a second degree of coupling of the associated second sampling indicator and the target second sampling indicator to the first sampling indicator together based on the common rate of change and the indicator rate of change, including:

the ratio of the common rate of change to the index rate of change is determined as a second degree of coupling that relates the second sampling index to the target second sampling index.

Wherein, according to first degree of coupling and second degree of coupling, the index data of the target second sampling index in the adjustment first sampling index includes:

taking the sum of the first coupling degree and the second coupling degree as a target coupling degree of a target second sampling index aiming at the first sampling index;

if the target coupling degree is a negative number, determining that an inverse relation exists between the target second sampling index and the first sampling index, and adjusting index data of the target second sampling index based on the target coupling degree and the inverse relation;

and if the target coupling degree is positive, determining that a forward relation exists between the target second sampling index and the first sampling index, and adjusting the index data of the target second sampling index based on the target coupling degree and the forward relation.

Wherein, adjusting the index data of the target second sampling index includes:

acquiring an index adjustment type of a target second sampling index;

if the index adjustment type is the trigger adjustment type, determining that the index data comprises triggering a second sampling index based on the trigger adjustment type, and adjusting and triggering the second sampling index; triggering the second sampling index to have a one-way incidence relation with the target second sampling index;

and if the index adjustment type is the operation adjustment type, determining that the index data comprises a target second sampling index based on the operation adjustment type, and adjusting the target second sampling index.

Wherein, the method also comprises:

and acquiring the coupling degrees of the at least two second sampling indexes, and if the second sampling index with the maximum coupling degree in the at least two second sampling indexes is the target second sampling index, executing the step of adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree.

Wherein, the method also comprises:

acquiring an index channel forming a target second sampling index, and acquiring the level coupling degree of the index channel aiming at the target second sampling index;

determining a channel coupling degree of the index channel aiming at the first sampling index based on the level coupling degree, the first coupling degree and the second coupling degree;

the index data includes an index channel constituting a target second sampling index;

adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree, including:

determining a first change coupling relation of a target second sampling index aiming at the first sampling index based on the first coupling degree and the second coupling degree; the first variable coupling relationship comprises a forward relationship and a reverse relationship;

determining a second variation coupling relation of the index channel aiming at the target second sampling index based on the hierarchical coupling degree; the second variable coupling relationship comprises a forward relationship and a reverse relationship;

and updating the index channel based on the first change coupling relation, the second change coupling relation and the channel coupling degree so as to adjust the target second sampling index.

Wherein, based on the hierarchical coupling degree, the first coupling degree and the second coupling degree, determining the channel coupling degree of the index channel for the first sampling index comprises:

and taking the product of the target coupling degree and the level coupling degree as the channel coupling degree of the index channel aiming at the first sampling index.

An embodiment of the present application provides a data processing apparatus, where the apparatus includes:

the index acquisition module is used for acquiring at least two second sampling indexes associated with the first sampling index; one of the at least two second sampling indices is a target second sampling index;

the index correlation module is used for taking a second sampling index which has a one-way correlation relationship with the target second sampling index as a correlated second sampling index in at least two second sampling indexes;

the coupling acquisition module is used for acquiring a first coupling degree of a target second sampling index aiming at the first sampling index and acquiring a second coupling degree of the associated second sampling index and the target second sampling index aiming at the first sampling index;

and the index adjusting module is used for adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree.

Wherein, index correlation module includes:

a priority acquiring unit, configured to acquire index priorities of at least two second sampling indexes;

the index acquisition unit is used for acquiring a second sampling index of which the index priority is smaller than that of the target second sampling index from at least two second sampling indexes as a to-be-associated sampling index;

the stacking relation obtaining unit is used for obtaining the stacking relation between the sampling index to be associated and the target second sampling index;

the correlation index determining unit is used for determining the sampling index to be correlated as a correlated second sampling index of the target second sampling index if the superposition relation is a cross superposition relation; and the index priority is used for representing that the target second sampling index has a one-way incidence relation to the associated second sampling index.

Wherein, the priority acquisition unit includes:

the priority determining subunit is configured to obtain the index content types of the at least two second sampling indexes, and obtain, based on the index content types, index priorities corresponding to the at least two second sampling indexes, respectively; the index content type has a corresponding index priority.

Wherein, the priority acquisition unit includes:

the history obtaining subunit is used for obtaining the history coupling degree of each second sampling index in at least two second sampling indexes;

and the index sorting subunit is used for sorting the at least two second sampling indexes based on the historical coupling degree and determining the index priority of each second sampling index according to the sorting result.

In obtaining a first degree of coupling of a target second sampling index with respect to a first sampling index, the coupling obtaining module includes:

the core change acquiring unit is used for acquiring a first sampling index value of the first sampling index at a first sampling time point and a second sampling index value at a second sampling time point, and determining the index change rate of the first sampling index based on the first sampling index value and the second sampling index value;

a target change acquiring unit, configured to acquire a first target index value of a target second sampling index at a first sampling time point and a second target index value at a second sampling time point, and determine a target sampling index change rate of the target second sampling index based on the first target index value and the second target index value;

and the first coupling determination unit is used for determining a first coupling degree of a target second sampling index based on the index change rate and the target sampling index change rate.

Wherein, in obtaining a second degree of coupling of the associated second sampling indicator and the target second sampling indicator together with respect to the first sampling indicator, the coupling obtaining module includes:

an association change acquisition unit configured to acquire a first association index value associated with a second sampling index at a first sampling time point and a second association index value associated with the second sampling time point, and determine an association sampling index change rate associated with the second sampling index based on the first association index value and the second association index value;

the common change acquiring unit is used for determining the common change rate of the target second sampling index and the associated second sampling index based on the associated sampling index change rate and the target sampling index change rate;

and the second coupling determination unit is used for determining a second coupling degree of the associated second sampling index and the target second sampling index for the first sampling index based on the common change rate and the index change rate.

Wherein, the common change acquiring unit is specifically configured to:

the second coupling determination unit is specifically configured to:

Wherein, index adjustment module includes:

the target coupling determining unit is used for taking the sum of the first coupling degree and the second coupling degree as a target coupling degree of a target second sampling index aiming at the first sampling index;

the index data adjusting unit is used for determining that an inverse relation exists between the target second sampling index and the first sampling index if the target coupling degree is a negative number, and adjusting the index data of the target second sampling index based on the target coupling degree and the inverse relation;

and the index data adjusting unit is further used for determining that a forward relation exists between the target second sampling index and the first sampling index if the target coupling degree is a positive number, and adjusting the index data of the target second sampling index based on the target coupling degree and the forward relation.

Wherein, in adjusting the index data of the target second sampling index, the index data adjusting unit includes:

the adjustment type obtaining subunit is used for obtaining an index adjustment type of the target second sampling index;

the index data adjusting subunit is used for determining that the index data comprises a triggering second sampling index and adjusting the triggering second sampling index based on the triggering adjustment type if the index adjustment type is the triggering adjustment type; triggering the second sampling index to have a one-way incidence relation with the target second sampling index;

and the index data adjusting subunit is further configured to determine that the index data includes the target second sampling index based on the operation adjustment type and adjust the target second sampling index if the index adjustment type is the operation adjustment type.

Wherein, the device still includes:

and the coupling comparison module is used for acquiring the coupling degrees of the at least two second sampling indexes, and if the second sampling index with the largest coupling degree in the at least two second sampling indexes is the target second sampling index, the step of adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree is executed.

Wherein, the device still includes:

the hierarchical coupling acquisition module is used for acquiring an index channel forming a target second sampling index and acquiring the hierarchical coupling degree of the index channel aiming at the target second sampling index;

the channel coupling acquisition module is used for determining the channel coupling degree of the index channel aiming at the first sampling index based on the level coupling degree, the first coupling degree and the second coupling degree;

the index adjustment module further comprises:

the first relation obtaining unit is used for determining a first change coupling relation of a target second sampling index aiming at the first sampling index based on the first coupling degree and the second coupling degree; the first variable coupling relationship comprises a forward relationship and a reverse relationship;

the second relation acquisition unit is used for determining a second variation coupling relation of the index channel aiming at the target second sampling index based on the hierarchical coupling degree; the second variable coupling relationship comprises a forward relationship and a reverse relationship;

and the channel updating unit is used for updating the index channel based on the first change coupling relation, the second change coupling relation and the channel coupling degree so as to adjust the target second sampling index.

The channel coupling acquisition module is specifically configured to:

One aspect of the embodiments of the present application provides a computer device, including a processor, a memory, and an input/output interface;

the processor is connected to the memory and the input/output interface, respectively, where the input/output interface is configured to receive data and output data, the memory is configured to store program codes, and the processor is configured to call the program codes to execute the data processing method according to an aspect of the embodiment of the present application.

An aspect of the embodiments of the present application provides a computer-readable storage medium, in which a computer program is stored, where the computer program includes program instructions, and when the program instructions are executed by a processor, the data processing method in the aspect of the embodiments of the present application is executed.

An aspect of an embodiment of the present application provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method provided in the various alternatives in one aspect of the embodiments of the application.

The embodiment of the application has the following beneficial effects:

the embodiment of the application acquires at least two second sampling indexes associated with the first sampling index; one of the at least two second sampling indices is a target second sampling index; taking a second sampling index which has a one-way association relation with a target second sampling index as an associated second sampling index in the at least two second sampling indexes; acquiring a first coupling degree of a target second sampling index aiming at the first sampling index, and acquiring a second coupling degree of the associated second sampling index and the target second sampling index aiming at the first sampling index; and adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree. The influence of the target second sampling index on the first sampling index is subjected to quantization splitting to obtain the coupling degree, and the coupling degree can represent the influence result of the corresponding second sampling index on the first sampling index, so that the interference among different second sampling indexes is reduced while the integrity of the obtained influence result of the second sampling index on the first sampling index is ensured, and the accuracy of the influence quantization of the second sampling index on the first sampling index is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in 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 for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1a is a diagram of a data processing network architecture provided by an embodiment of the present application;

FIG. 1b is a functional block diagram of a data processing system according to an embodiment of the present disclosure;

fig. 2 is a schematic diagram of a data processing scenario provided in an embodiment of the present application;

fig. 3 is a flowchart of a data processing method provided in an embodiment of the present application;

fig. 4 is a schematic diagram of a data acquisition scenario provided in an embodiment of the present application;

fig. 5a is a schematic view of a data processing scenario in a split mode according to an embodiment of the present application;

fig. 5b is a schematic diagram of a data processing scenario in another splitting manner provided in the embodiment of the present application;

FIG. 6 is a diagram of an index processing architecture according to an embodiment of the present application;

FIG. 7 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the 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.

The method is used for obtaining the coupling degree of the second sampling indexes aiming at the first sampling indexes, each first sampling index can be associated with at least two second sampling indexes, each second sampling index can comprise at least two index channels, each index channel can be split again, in other words, the first sampling indexes can be split into a plurality of second sampling indexes, each second sampling index can be split continuously based on needs, and the first sampling indexes are split into a plurality of levels. The first sampling index is split into a plurality of levels, and each level may have a plurality of elements, so that a large amount of data may be generated.

The Big data (Big data) refers to a data set which cannot be captured, managed and processed by a conventional software tool within a certain time range, and is a massive, high-growth-rate and diversified information asset which can have stronger decision-making power, insight discovery power and flow optimization capability only by a new processing mode. With the advent of the cloud era, big data has attracted more and more attention, and the big data needs special technology to effectively process a large amount of data within a tolerance elapsed time. The method is suitable for the technology of big data, and comprises a large-scale parallel processing database, data mining, a distributed file system, a distributed database, a cloud computing platform, the Internet and an extensible storage system.

And the computing efficiency when the data needs to be processed in the embodiment of the application can be improved based on the cloud computing technology. Among them, cloud computing (cloud computing) is a computing mode that distributes computing tasks over a resource pool formed by a large number of computers, so that various application systems can acquire computing power, storage space, and information services as needed. The network that provides the resources is referred to as the "cloud". Resources in the "cloud" appear to the user as being infinitely expandable and available at any time, available on demand, expandable at any time, and paid for on-demand.

As a basic capability provider of cloud computing, a cloud computing resource pool (called as an ifas (Infrastructure as a Service) platform for short is established, and multiple types of virtual resources are deployed in the resource pool and are selectively used by external clients.

According to the logic function division, a PaaS (Platform as a Service) layer can be deployed on an IaaS (Infrastructure as a Service) layer, a SaaS (Software as a Service) layer is deployed on the PaaS layer, and the SaaS can be directly deployed on the IaaS. PaaS is a platform on which software runs, such as a database, a web container, etc. SaaS is a variety of business software, such as web portal, sms, and mass texting. Generally speaking, SaaS and PaaS are upper layers relative to IaaS.

Specifically, please refer to fig. 1a, where fig. 1a is a data processing network architecture diagram provided in the embodiment of the present application, and the embodiment of the present application may be implemented by a computer device, where the computer device may be composed of a server and a terminal device; the computer device may also be a server or a terminal device, which is not limited herein. In this embodiment of the present application, data used by a local device (computer device) may be stored in a local memory, or may be stored in other computer devices, and the storage manner is not limited. As shown in fig. 1a, it is assumed that data (such as a first sampling index or a second sampling index) used in the embodiment of the present application is stored in other computer devices (including the computer device 101a, the computer device 101b, the computer device 101c, and the like), and the local device 100 obtains data, such as the first sampling index or the second sampling index, required in the embodiment of the present application from the other computer devices. The local device 100 processes the first sampling index and the second sampling index to obtain a coupling degree of each second sampling index for the first sampling index, so as to adjust the second sampling index based on the coupling degree, thereby enabling the first sampling index to better meet the service requirement. Wherein the first sampling index can be split into at least two second sampling indices.

Taking any one of at least two second sampling indexes as an example, taking the second sampling index as a target second sampling index, and determining the coupling degree of the target second sampling index to the first sampling index by acquiring the first coupling degree of the target second sampling index and the second coupling degree of the target second sampling index and the associated second sampling index to the first sampling index together, wherein a unidirectional association relationship exists between the target second sampling index and the associated second sampling index. Wherein the one-way correlation is used to represent a change in the target second sampling index, which may result in a change in the correlated second sampling index. The coupling degree in this embodiment of the present application may represent an influence degree of one index on another index, for example, the first coupling degree may represent an influence degree of a target second sampling index on a first sampling index alone, and the second coupling degree may represent an influence degree of the target second sampling index and an associated second sampling index for the first sampling index together.

For example, the first sampling index is GMV, and for the service personnel, the larger the GMV is, the better the profit is, so that the GMV can be increased by adjusting the second sampling index of the GMV. Assume that the first sampling metric "GMV" is split into at least two second sampling metrics, including the number of purchasers and the unit price of guests. The local equipment takes the second sampling index 'passenger unit price' as a target second sampling index, and obtains a first coupling degree of the 'passenger unit price' to the first sampling index, wherein the first coupling degree refers to an influence ratio of the change of the 'passenger unit price' to the first sampling index 'GMV'. Since the change of the passenger unit price affects the number of purchasers, for example, the decrease of the passenger unit price increases the number of purchasers, so that a one-way association relationship exists between the number of purchasers and the passenger unit price, and the number of purchasers is an associated second sampling index of the passenger unit price. And adjusting the index data of the passenger unit price to increase the GMV according to the second coupling degree of the passenger unit price and the number of the purchasers to the GMV and the first coupling degree of the passenger unit price to the GMV. Through the process, the interference among different second sampling indexes is reduced, and the quantization accuracy of the second sampling indexes to the first sampling indexes is improved.

It is understood that the computer device mentioned in the embodiments of the present application includes, but is not limited to, a terminal device or a server. In other words, the computer device may be a server or a terminal device, or may be a system of a server and a terminal device. The above-mentioned terminal device may be an electronic device, including but not limited to a mobile phone, a tablet computer, a desktop computer, a notebook computer, a palm-top computer, an Augmented Reality/virtual Reality (AR/VR) device, a helmet-mounted display, a wearable device, a smart speaker, and other Mobile Internet Devices (MID) with network access capability.

Further, please refer to fig. 1b, where fig. 1b is a functional architecture diagram of data processing according to an embodiment of the present application. As shown in fig. 1b, the local device 100 in the embodiment of the present application may include a data storage area 102, a data acquisition area 103, a data analysis area 104, a data display area 105, a data adjustment area 106, and the like. The data storage area 102 is used for recording or caching data generated when the local device processes the first sampling index and the second sampling index; the data acquisition area 103 is used for acquiring data from the data storage area 102 and feeding the acquired data back to the data analysis area 104 for analysis; the data analysis area 104 is configured to analyze the acquired data, including the first sampling index and the second sampling index, to obtain a coupling degree of the second sampling index to the first sampling index; the data display area 105 may be configured to display an analysis result of the data analysis area 104, so that a service person may obtain the analysis result; the data adjusting area 106 is configured to adjust the second sampling indicator according to the analysis result of the data analyzing area 104, so that the first sampling indicator can better meet the service requirement.

The data storage area 102 may store a first sampling indicator and a second sampling indicator, or the first sampling indicator or the second sampling indicator may also be stored in other computer devices, and a storage location of the first sampling indicator or the second sampling indicator is not limited herein. Fig. 1b is only one possible functional architecture diagram in the embodiment of the present application, and other architecture diagrams for dividing the local device based on the function may also be used as an optional functional architecture diagram in the embodiment of the present application.

Further, please refer to fig. 2, and fig. 2 is a schematic diagram of a data processing scenario provided in an embodiment of the present application. As shown in fig. 2, it is assumed that the first sampling indicator can be split into at least two second sampling indicators, including a second sampling indicator 1, a second sampling indicator 2, a second sampling indicator 3, and a second sampling indicator 4. The local device uses the second sampling index 1 as a target second sampling index, acquires a second sampling index having a one-way association relationship with the target second sampling index from other second sampling indexes, and assumes that the acquired second sampling index 2 and the acquired second sampling index 3 have the one-way association relationship with the target second sampling index, that is, the second sampling index 2 and the second sampling index 3 are associated second sampling indexes of the target second sampling index. The local equipment acquires a first coupling degree of a target second sampling index aiming at the first sampling index, and acquires a second coupling degree of the target second sampling index and a related second sampling index aiming at the first sampling index; the second coupling degree comprises a second coupling degree 1 of a target second sampling index and a second sampling index 2 aiming at the first sampling index, a second coupling degree 2 of a target second sampling index and a second sampling index 3 aiming at the first sampling index, and a second coupling degree 3 of the target second sampling index, the second sampling index 2 and the second sampling index 3 aiming at the first sampling index.

The local device can adjust the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree, so that the first sampling index can better meet the service requirement based on the index data. Through the process, the integrity of the influence result of the obtained second sampling index on the first sampling index is guaranteed, meanwhile, the interference among different second sampling indexes is reduced, and the accuracy of the influence quantization of the second sampling index on the first sampling index is improved.

Further, referring to fig. 3, fig. 3 is a flowchart of a data processing method provided by an embodiment of the present application, where the data processing process may be executed by a local device, and the local device is a computer device. As shown in fig. 3, the data processing procedure includes the following steps:

in step S301, at least two second sampling indexes associated with the first sampling index are obtained, and one of the at least two second sampling indexes is a target second sampling index.

Specifically, the first sampling index may be split into a plurality of second sampling indexes, and the first sampling index may have a plurality of splitting manners. For example, if the first sampling index is GMV, the first sampling index GMV can be split into second sampling indexes such as the number of purchasers and the passenger order; or, the first sampling index GMV may be split into second sampling indexes such as a premium turn-over amount and an original price turn-over amount. In summary, a first sampling index may be split into n second sampling indexes, and a first sampling index may be split in m splitting manners, where n and m are positive integers.

Taking a splitting manner of a first sampling index as an example, the local device obtains at least two second sampling indexes associated with the first sampling index, and takes any one of the at least two second sampling indexes as a target second sampling index. For example, the first sampling index is payment amount, the payment amount can be divided into second sampling indexes such as visitor number, visitor conversion rate and visitor unit price, and any one of the visitor number, the visitor conversion rate and the visitor unit price is taken as a target second sampling index; or the payment amount can be split into second sampling indexes such as preferential payment amount and non-preferential payment amount, and any one of the preferential payment amount and the non-preferential payment amount is taken as a target second sampling index.

The different splitting modes of the first sampling index can correspond to different business analysis tasks, so that the first sampling index can be analyzed from different angles, the second sampling index forming the first sampling index is adjusted, and the first sampling index can better meet business requirements. For example, the first sampling index 'payment amount' is split into second sampling indexes such as the number of visitors, the visitor conversion rate and the passenger order price, and the service analysis task corresponding to the splitting mode is an operation analysis task; and splitting the first sampling index 'payment amount' into second sampling indexes such as preferential payment amount and non-preferential payment amount, wherein the service analysis task corresponding to the splitting mode is a preferential activity analysis task and the like.

The first sampling index, the second sampling index and the like can be input by service personnel; the first sampling index and the second sampling index can also be collected from historical data; each splitting mode of the first sampling index can be preset, and at least two second sampling indexes related to the first sampling index are obtained based on the obtained first sampling index input by the service personnel. For example, please refer to fig. 4, where fig. 4 is a schematic diagram of a data acquisition scenario provided in an embodiment of the present application, and in this example, specific data is acquired based on index names by acquiring index names of a first sampling index and a second sampling index that need to be acquired and are input by a service person for description. Specifically, after the service personnel inputs the index names of the first sampling index and the second sampling index to be acquired and the analysis time in the index name acquisition page 401, and after the confirmation, the local device 402 responds to the confirmation operation in the index name acquisition page 401 to acquire that the first sampling index is GMV, the second sampling index includes the number of purchasers and the passenger order, and the analysis time is acquired, wherein the analysis time is used for representing the sampling time points of the first sampling index and the second sampling index. The local device 402 obtains, based on the obtained index name, data of a first sampling index at a sampling time point and data of a second sampling index at the sampling time point from the index database 403 to obtain index analysis data 404 required in the embodiment of the present application, where the index analysis data 404 is only a generic term for the first sampling index, the second sampling index, the sampling time point, and the like in the embodiment of the present application, and the index analysis data 404 may be stored in any data format that can perform statistical analysis, such as a chart or a text.

Optionally, the service person may also directly input a splitting formula in which the first sampling index and the at least two second sampling indexes are common in the index name obtaining page 401, the local terminal obtains the splitting formula, and determines the first sampling index and the at least two second sampling indexes associated with the first sampling index, where the service person may input one or at least two splitting formulas, and each splitting formula corresponds to a splitting manner of the first sampling index. For example, the local device acquires a splitting formula "GMV ═ number of purchasers × unit price of passengers", acquires a first sampling index as GMV based on the splitting formula, and at least two second sampling indexes associated with the first sampling index include the number of purchasers and the unit price of passengers.

In fig. 4, the sampling time points obtained by the local device 402 include a first sampling time point and a second sampling time point, and data "GMV 1" of the first sampling indicator "GMV" at the first sampling time point and data "GMV 2" at the second sampling time point are obtained; the local device 402 acquires the data "number of purchasers 1" at the first sampling time point and the data "number of purchasers 2" at the second sampling time point, which are the second sampling index "number of purchasers"; the local device 402 acquires the data "passenger price 1" at the first sampling time point and the data "passenger price 2" at the second sampling time point of the second sampling index "passenger price". Alternatively, the sampling time points may include at least two sampling time points.

Step S302, of the at least two second sampling indexes, a second sampling index having a one-way association relationship with the target second sampling index is used as an associated second sampling index.

Specifically, the local device obtains an associated second sampling index of the target second sampling index from at least two second sampling indexes, where the target second sampling index has a one-way association relationship with the associated second sampling index, and the one-way association relationship indicates that a change in the target second sampling index may cause a change in the associated second sampling index, for example, a change in the unit price of the guest may cause a change in the number of people purchased, and when the unit price of the guest is the target second sampling index, the number of people purchased may be the associated second sampling index of the unit price of the guest. Optionally, the target second sampling index may have at least one associated second sampling index, or may not have an associated second sampling index.

The local device may obtain the associated second sampling index of the target second sampling index according to the index priority of each second sampling index. Specifically, the local device obtains the index priorities of at least two second sampling indexes; acquiring a second sampling index of which the index priority is smaller than that of the target second sampling index in at least two second sampling indexes as a to-be-associated sampling index; acquiring a superposition relation between the sampling index to be correlated and a target second sampling index; if the superposition relationship is a cross superposition relationship, determining the sampling index to be correlated as a correlated second sampling index of the target second sampling index; the index priority may be used to represent that the target second sampling index has a unidirectional association relationship with the associated second sampling index.

Wherein, the stacking relationship comprises a parallel stacking relationship and a cross stacking relationship. When the superposition relationship between the target second sampling index and the sampling index to be associated is the parallel superposition relationship, it indicates that the target second sampling index and the sampling index to be associated are independent from each other and do not affect each other, so that the first coupling degree of the target second sampling index can be used as the target coupling degree of the target second sampling index, the index data of the target second sampling index is adjusted according to the target coupling degree, and the adjustment process can refer to the adjustment process in step S304. When the superposition relationship between the target second sampling index and the sampling index to be associated is the cross superposition relationship, the target second sampling index and the sampling index to be associated are not mutually independent and have interference with each other, so that the sampling index to be associated is taken as the associated second sampling index of the target second sampling index.

Optionally, the local device may store a correspondence between the index content type and the index priority in advance. Specifically, the local terminal acquires the index content types of at least two second sampling indexes, and acquires the index priorities corresponding to the at least two second sampling indexes respectively based on the index content types; the index content type has a corresponding index priority. Optionally, each index content type may correspond to an index priority set, where the index priority set includes an association relationship between an index priority and a second sampling index; if the index content types of two or more second sampling indexes in the at least two second sampling indexes are the same, the index priority associated with the index content types is obtained, and the index priority of each second sampling index is further determined based on the index priority.

Optionally, the local device may obtain a historical coupling degree of each of the at least two second sampling indexes; and sequencing at least two second sampling indexes based on the historical coupling degree, and determining the index priority of each second sampling index according to the sequencing result. It can be considered that the index priority of the second sampling index is larger as the historical coupling degree is larger.

Optionally, the local device may obtain an index priority of each second sampling index input by the service person. For example, referring to fig. 4, a service person inputs at least two second sampling indexes in the index name obtaining page 401, and the local device obtains an input sequence of the service person for the at least two second sampling indexes, and determines an index priority of each second sampling index. Or, the service person directly inputs at least two second sampling indexes and the index priority of each second sampling index in the index name obtaining page 401, and the local device directly obtains the at least two second sampling indexes and the index priority of each second sampling index input by the service person.

Further, please refer to fig. 5a, where fig. 5a is a schematic diagram of a data processing scenario in a splitting manner according to an embodiment of the present application. As shown in fig. 5a, assuming that the first sampling indicator is the payment amount, in the splitting mode 501 in fig. 5a, the payment amount is split into second sampling indicators, such as the number of visitors, the conversion rate of the visitors, and the order of the visitors, wherein the indicator priority of the order of the visitors is greater than the indicator priority of the number of visitors, and the indicator priority of the number of visitors is greater than the indicator priority of the conversion rate of the visitors. And when the passenger order is taken as a target second sampling index, the local terminal acquires a second sampling index with the index priority lower than that of the target second sampling index as a to-be-associated sampling index, wherein the to-be-associated sampling index comprises the number of visitors and the visitor conversion rate. Acquiring a superposition relationship between the sampling index to be associated and a target second sampling index, and assuming that the payment sum is the visitor number and the visitor conversion rate and the visitor unit price, determining that the superposition relationship between the visitor unit price and the visitor conversion rate is a cross superposition relationship 502, the superposition relationship between the visitor unit price and the visitor conversion rate is a cross superposition relationship 503, and meanwhile, a superposition relationship exists among the visitor unit price, the visitor number and the visitor conversion rate, and the superposition relationship is a cross superposition relationship 504. The local device determines the number of visitors and the visitor conversion rate as the associated second sampling index of the target second sampling index. The cross-over relationship 502, the cross-over relationship 503, and the cross-over relationship 504 all refer to the same stacking relationship (multiplication-division relationship), and the number (502, 503, or 504) is only the second sampling index associated with distinguishing the corresponding cross-over relationship.

Similarly, the associated second sample indicator for the number of visitors includes a visitor conversion rate, and the visitor conversion rate has no associated second sample indicator.

Further, please refer to fig. 5b, where fig. 5b is a schematic diagram of a data processing scenario in another splitting manner provided in the embodiment of the present application, where index priorities of at least two second sampling indexes in which the superposition relationship is a parallel superposition relationship may be considered to be the same. As shown in fig. 5b, assuming that the first sampling index is a payment amount, the payment amount is split into second sampling indexes such as an offer payment amount and a non-offer payment amount in the splitting method 505 in fig. 5 b. Assuming that the payment amount is equal to the preferential payment amount plus the non-preferential payment amount, the local device determines that the superposition relationship between the preferential payment amount and the non-preferential payment amount is a parallel superposition relationship (plus-minus relationship), and determines that no associated second sampling index exists in the preferential payment amount or the non-preferential payment amount.

Optionally, when the service staff directly inputs the splitting formula, the local device obtains the splitting formula, where the splitting formula includes "number of visitors" and visitor conversion rate "and" payment sum "and preferential payment sum + non-preferential payment sum", and two splitting modes for the first sampling index "payment sum" and a superposition relationship between the second sampling indexes in each splitting mode can be obtained. The splitting formula may represent at least two second sampling indicators associated with the first sampling indicator, and a stacking relationship between the second sampling indicators, for example, "+" or "/" indicates that the associated two second sampling indicators are in a cross-stacking relationship, and "+" or "-" indicates that the associated two second sampling indicators are in a parallel stacking relationship. Further optionally, the splitting formula may further represent the index priority of each second sampling index, for example, the index priority of each second sampling index may be represented by an arrangement order of the second sampling indexes in the splitting formula. For example, if the splitting formula is "payment amount per unit price per visitor number per visitor conversion rate", the index priority of the unit price per visitor may be greater than the index priority of the visitor number, and the index priority of the visitor number may be greater than the index priority of the visitor conversion rate.

Step S303, a first coupling degree of the target second sampling index with respect to the first sampling index is obtained, and a second coupling degree of the associated second sampling index and the target second sampling index together with respect to the first sampling index is obtained.

Specifically, when the target second sampling index does not have a related second sampling index, only the first coupling degree of the target second sampling index to the first sampling index needs to be obtained; when the target second sampling index has the associated second sampling index, acquiring a first coupling degree of the target second sampling index aiming at the first sampling index, and acquiring a second coupling degree of the associated second sampling index and the target second sampling index aiming at the first sampling index together. When the index priority of the target second sampling index is the minimum among the at least two second sampling indexes, or when the superposition relationship between the target second sampling index and any one of the other second sampling indexes is a parallel superposition relationship, it may be considered that the target second sampling index does not have a related second sampling index, specifically refer to the related description in step S302.

The local equipment acquires a first sampling index value of a first sampling index at a first sampling time point and a second sampling index value at a second sampling time point, and determines the index change rate of the first sampling index based on the first sampling index value and the second sampling index value; acquiring a first target index value of a target second sampling index at a first sampling time point and a second target index value at a second sampling time point, and determining a target sampling index change rate of the target second sampling index based on the first target index value and the second target index value; and determining a first coupling degree of a target second sampling index based on the index change rate and the target sampling index change rate. The local equipment takes the ratio of the difference value of the second sampling index value and the first sampling index value to the first sampling index value as the index change rate of the first sampling index; taking the ratio of the difference value between the second target index value and the first target index value to the first target index value as the target sampling index change rate of the target second sampling index; and determining the ratio of the target sampling index change rate to the index change rate as a first coupling degree of a target second sampling index to the first sampling index.

Further, the local device obtains a first relevance index value associated with a second sampling index at a first sampling time point and a second relevance index value at a second sampling time point, and determines a change rate of the relevance sampling index associated with the second sampling index based on the first relevance index value and the second relevance index value. And determining the common change rate of the target second sampling index and the associated second sampling index based on the associated sampling index change rate and the target sampling index change rate. And determining a second coupling degree of the associated second sampling index and the target second sampling index for the first sampling index based on the common change rate and the index change rate. And taking the ratio of the difference value of the second relevance index value and the first relevance index value to the first relevance index value as the change rate of the relevance sampling index of the relevance second sampling index.

When the common change rate of the target second sampling index and the associated second sampling index is determined based on the associated sampling index change rate and the target sampling index change rate, the local device may use the product of the associated sampling index change rate and the target sampling index change rate as the common change rate of the target second sampling index and the associated second sampling index. When determining a second degree of coupling of the second sampling index and the target second sampling index to the first sampling index in common based on the common rate of change and the index rate of change, the local device may determine a ratio of the common rate of change to the index rate of change as the second degree of coupling of the second sampling index and the target second sampling index in common.

For example, the first sampling index and the at least two second sampling indexes may be as shown in table 1 below:

TABLE 1

Sampling time point	GMV	Number of purchasers	Passenger unit price
				First sampling time point d1	The first sampling index value t1	Number of purchasers a1	Guest unit price b1
Second sampling time point d2	The second sampling index value t2	Number of purchasers a2	Guest unit price b2
				Rate of change	Index change rate lt	lt₁	lt₂

As shown in table 1, the first sampling index is GMV, the at least two second sampling indices include the number of purchasers and the unit price of passengers, the first sampling index value of the GMV at the first sampling time point d1 is denoted as t1, the second sampling index value of the GMV at the second sampling time point d2 is denoted as t2, and the index change rate of the first sampling index is (t2-t1)/t 1. Assuming that the guest unit price is the target second sampling index, the splitting formula and the above step S302 show that the associated second sampling index of the guest unit price is the number of purchasers. When the first target index value of the guest unit price at the first sampling time point d1 is denoted as b1, and the second target index value of the guest unit price at the second sampling time point d2 is denoted as b2, the target sampling index change rate is lt₂(b2-b1)/b 1; if the first relevance index value of the number of the purchasers at the first sampling time point d1 is recorded as a1, and the second relevance index value of the number of the purchasers at the second sampling time point d2 is recorded as a2, the change rate of the relevance sampling index is lt₁(a2-a1)/a1, wherein t1 is a1b1, t2 is a2b2, and the first sampling index is obtained according to the splitting formula "GMV (purchaser) passenger unit priceThe index change rate is (t2-t1)/t1 (a2b2-a1b1)/a1b1 ═ lt₁+(1+lt₁)*lt₂。

Wherein the first degree of coupling of the target second sample index with respect to the first sample index is "(a 1b2-a1b1)/(t2-t1) ═ lt₂And similarly, associating a first coupling degree of a second sampling index to the first sampling index as lt₁The second coupling degree of the target second sampling index and the associated second sampling index for the first sampling index is lt₁*lt₂And/lt. It can be found that the degree of coupling of the number of purchasers to the first sampling index is

The target coupling degree of the passenger unit price for the first sampling index is the first coupling degree lt of the passenger unit price for the first sampling index₂And a second coupling degree lt of the unit price of the passenger and the number of the purchasers for the first sampling index₁*lt₂And/lt is obtained.

For example, when t1 is 2, t2 is 6, a1 is 2, a2 is 3, b1 is 1, and b2 is 2, the index change rate is 200%, and when the guest unit is used as the target second sampling index, the target sampling index change rate is lt ₂100%, the associated sampling index change rate is lt₁50%. It can be concluded that the coupling degree of the passenger order to the first sampling index is

The degree of coupling of the number of purchasers to the first sampling index is

Step S304, adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree.

Specifically, the sum of the first coupling degree and the second coupling degree is determined as a target second sampling indexA target degree of coupling for the first sampling indicator. And adjusting the index data of the target second sampling index in the first sampling index according to the target coupling degree. As shown in Table 1 in step S303, when the target second sampling index is the number of purchasers, the target degree of coupling of the target second sampling index is

When the target second sampling index is the guest unit price, the target coupling degree of the target second sampling index is

In summary, when the first sampling index is split into n second sampling indexes, and the superposition relationship between every two of the n second sampling indexes is a cross superposition relationship, the coupling degree of the ith second sampling index with respect to the first sampling index is:

wherein i is a positive integer, i can represent the index priority of each second sampling index, wherein the larger i is, the larger the index priority of the corresponding second sampling index is, wherein ii represents multiplicative multiplication, i.e. the product of multiple numbers and multiplication. Wherein i is less than or equal to n.

When the superposition relationship between every two of the n second sampling indexes is a parallel superposition relationship, the coupling degree of the ith second sampling index to the first sampling index is as follows:

further, if the target coupling degree is a negative number, determining that an inverse relation exists between the target second sampling index and the first sampling index, and adjusting the index data of the target second sampling index based on the target coupling degree and the inverse relation. And if the target coupling degree is positive, determining that a forward relation exists between the target second sampling index and the first sampling index, and adjusting the index data of the target second sampling index based on the target coupling degree and the forward relation. For example, if the service demand is that the GMV is increased, the target second sampling index is the customer order price, and the target coupling degree of the customer order price to the first sampling index is 44.6%, the index data of the target second sampling index is adjusted to increase the customer order price, thereby increasing the GMV.

Optionally, an index adjustment type of the target second sampling index is obtained; if the index adjustment type is the trigger adjustment type, determining that the index data comprises triggering a second sampling index based on the trigger adjustment type, and adjusting and triggering the second sampling index; and triggering the second sampling index to have a one-way incidence relation with the target second sampling index. And if the index adjustment type is the operation adjustment type, determining that the index data comprises a target second sampling index based on the operation adjustment type, and adjusting the target second sampling index. When the triggering second sampling index has a one-way incidence relation with the target second sampling index, the triggering change of the second sampling index may cause the target second sampling index to change. The trigger adjustment type can be regarded as an index adjustment type of passive modification, such as visitor number and the like, and the visitor number cannot be modified and can only be changed according to the change of other indexes; the operation adjustment type can be regarded as an index adjustment type of active modification, such as a guest unit price, and business personnel can directly set the guest unit price for modification.

Optionally, when the first sampling index may be split into multiple layers, the local device obtains an index channel forming the target second sampling index, and obtains a hierarchical coupling degree of the index channel for the target second sampling index. And determining the channel coupling degree of the index channel aiming at the first sampling index based on the level coupling degree, the first coupling degree and the second coupling degree, wherein the index data comprises the index channel forming the target second sampling index. At this time, when the index data of the target second sampling index in the first sampling index is adjusted according to the first coupling degree and the second coupling degree, specifically, a first change coupling relation of the target second sampling index to the first sampling index is determined based on the first coupling degree and the second coupling degree; the first variable coupling relationship comprises a forward relationship and a reverse relationship; determining a second variation coupling relation of the index channel aiming at the target second sampling index based on the hierarchical coupling degree; the second variable coupling relationship comprises a forward relationship and a reverse relationship; and updating the index channel based on the first change coupling relation, the second change coupling relation and the channel coupling degree so as to adjust the target second sampling index.

The method for acquiring the hierarchical coupling degree of the index channel for the target second sampling index can refer to the method for acquiring the target coupling degree of the target second sampling index for the first sampling index, wherein when the hierarchical coupling degree of the index channel for the target second sampling index is acquired, the index channel is equivalent to the second sampling index, and the target second sampling index is equivalent to the first sampling index, so that the process for acquiring the hierarchical coupling degree is not repeated here.

Optionally, an index channel with the largest channel coupling degree may be obtained as a target index channel, and the target index channel is updated based on the first change coupling relation and the second change coupling relation to adjust the target second sampling index. Optionally, the channel coupling degree may be directly obtained, a third variation coupling relationship of the index channel for the first sampling index is determined based on the channel coupling degree, and the index channel is updated based on the third variation coupling relationship to adjust the target second sampling index.

Taking the sum of the first coupling degree and the second coupling degree as a target coupling degree of a target second sampling index aiming at the first sampling index; and taking the product of the target coupling degree and the level coupling degree as the channel coupling degree of the index channel aiming at the first sampling index.

Further, assuming that the first sampling index is split into p layers, the coupling degree of the element of the j layer to the element of the (j-1) layer is recorded as

The coupling degree of the element of the j-th layer to the first sampling index is:

wherein, p and j are positive integers, p is the total number of layers of the first sampling index after being split, and j is less than or equal to p.

For example, referring to fig. 5a, a first sampling indicator is a payment amount, and the first sampling indicator is divided into second sampling indicators such as the number of visitors, the conversion rate of visitors, and the order of visitors. Further, dividing the visitor number into index channels such as a first-mode visitor number, a second-mode visitor number and a third-mode visitor number, wherein the visitor number is the first-mode visitor number, the second-mode visitor number and the third-mode visitor number; splitting the visitor conversion rate into index channels such as an access click conversion rate, a click purchase adding conversion rate and a purchase payment conversion rate, wherein the visitor conversion rate is the access click conversion rate and the click purchase adding conversion rate and the purchase payment conversion rate; and splitting the guest unit price into index channels such as the per-person purchase quantity, the commodity unit price and the like, wherein the guest unit price is the per-person purchase quantity and the commodity unit price. For example, the coupling degree of the number of the first-mode entering visitors to the payment amount is the product of the coupling degree of the number of the first-mode entering visitors to the number of the visitors and the coupling degree of the number of the visitors to the payment amount.

Similarly, any one of the at least two second sampling index values may be used as a target second sampling index, and step S301 to step S304 are executed to obtain the coupling degree of each of the two second sampling indexes, and if the second sampling index with the highest coupling degree of the at least two second sampling indexes is the target second sampling index, the step of adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree is executed. For example, assuming that a first sampling index, which is the payment amount, is divided into second sampling indexes, such as the number of visitors, the visitor conversion rate, the passenger unit price, and the like, through steps S301 to S304, it is obtained that the coupling degree of the number of visitors to the first sampling index is 30%, the coupling degree of the visitor conversion rate to the first sampling index is 25%, and the coupling degree of the passenger unit price to the first sampling index is 45%, and it is known that the coupling degree of the passenger unit price to the first sampling index is the maximum, the index data of the passenger unit price is adjusted.

Optionally, after obtaining the coupling degree of each second sampling index to the first sampling index, the local device may display the coupling degree of each second sampling index to the first sampling index, so that service personnel may adjust each second sampling index and the like based on the coupling degree, thereby improving autonomy of adjustment of the second sampling index.

The first sampling index may be an index that needs to be analyzed in any industry, such as the financial industry (GMV, payment amount, performance, etc.), the game industry (game income, copy use frequency, user use duration, etc.), or the entertainment industry (video play amount, information popularity, etc.), and is not limited herein. Taking the video playing amount as an example, assuming that the video playing amount is used as a first sampling index, and a splitting formula of the video playing amount is "video playing amount is equal to video playing user number equal to average playing duration", then at least two second sampling indexes associated with the video playing amount are obtained, where the second sampling indexes include the video playing user number and the average playing duration.

According to a splitting formula of 'the number of video playing users x the average playing time', the superposition relationship between the number of video playing users and the average playing time is a cross superposition relationship, the number of video playing users is used as a target second sampling index, and the average playing time is a related second sampling index of the number of video playing users on the assumption that the index priority of the average playing time is smaller than the index priority of the number of video playing users. The method comprises the steps of obtaining a first coupling degree of the number of video playing users to the video playing amount, obtaining a second coupling degree of the number of video playing users and the average playing time length to the video playing amount, wherein the first coupling degree and the second coupling degree form a target coupling degree of the number of video playing users to the video playing amount. The target coupling degree is 52.3%, and the channel coupling degree of the acquired first platform playing user number for the video playing amount is the maximum, so that the video can be promoted in the first platform in an increased manner or the first page recommendation can be performed, so that more users can acquire the video, and the video playing amount of the video is further increased.

Further, please refer to fig. 6, where fig. 6 is an index processing architecture diagram provided in an embodiment of the present application, where the index processing architecture diagram includes a first sampling index, an index splitting module, and an executable module, where the index splitting module is at least two second sampling indexes obtained by splitting the first sampling index, and the executable module is a service analysis task corresponding to the first sampling index or an element obtained by splitting the index. For example, assuming that the first sampling index is the payment amount, the payment amount is divided into second sampling indexes such as the number of visitors, the conversion rate of the visitors and the passenger order price. The method comprises the steps that a splitting mode of visitor number is analyzed, and a service analysis task of channel analysis can be executed; the splitting mode of the access conversion rate is analyzed, and business analysis tasks such as commodity analysis and page analysis can be executed; analyzing the splitting mode of the guest unit price, and analyzing the associated strategy; the splitting mode of the payment amount is analyzed, marketing activities and the like can be analyzed, and the method is not limited.

The executable module may be preset, or may be a service analysis task input by a service person based on a service requirement, so that the local device may obtain a coupling degree of a corresponding element to be analyzed with respect to a target element based on each service analysis task in the executable module to obtain an analysis result of the service analysis task, where the first sampling index, a second sampling index obtained after splitting the first sampling index, an index channel after further splitting the second sampling index, and the like may be referred to as elements. Wherein, the element to be analyzed and the target element are determined based on the business analysis task. For example, the element to be analyzed corresponding to the business analysis task of channel analysis is an element obtained after splitting the visitor number, the target element is the visitor number, and other business analysis tasks are similar and not described herein too much.

The embodiment of the application acquires at least two second sampling indexes associated with the first sampling index; one of the at least two second sampling indices is a target second sampling index; taking a second sampling index which has a one-way association relation with a target second sampling index as an associated second sampling index in the at least two second sampling indexes; acquiring a first coupling degree of a target second sampling index aiming at the first sampling index, and acquiring a second coupling degree of the associated second sampling index and the target second sampling index aiming at the first sampling index; and adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree. The influence of each second sampling index on the first sampling index is subjected to quantization splitting to obtain the coupling degree of each second sampling index on the first sampling index so as to represent the influence degree of the corresponding second sampling index on the first sampling index, so that the interference among different second sampling indexes is reduced while the integrity of the obtained influence result of the second sampling index on the first sampling index is ensured, and the accuracy of the influence quantization of the second sampling index on the first sampling index is improved.

Referring to fig. 7, fig. 7 is a schematic diagram of a data processing apparatus according to an embodiment of the present application. The data processing means may be a computer program (comprising program code) running on a computer device, for example the data processing means being an application software; the apparatus may be used to perform the corresponding steps in the methods provided by the embodiments of the present application. As shown in fig. 7, the data processing apparatus 700 may be used in the computer device in the embodiment corresponding to fig. 3, and specifically, the apparatus may include: the system comprises an index acquisition module 11, an index association module 12, a coupling acquisition module 13 and an index adjustment module 14.

An index obtaining module 11, configured to obtain at least two second sampling indexes associated with the first sampling index; one of the at least two second sampling indices is a target second sampling index;

the index correlation module 12 is configured to use, as a correlated second sampling index, a second sampling index having a one-way correlation with the target second sampling index, among the at least two second sampling indexes;

the coupling obtaining module 13 is configured to obtain a first coupling degree of a target second sampling index with respect to the first sampling index, and obtain a second coupling degree of the associated second sampling index and the target second sampling index with respect to the first sampling index;

and the index adjusting module 14 is configured to adjust the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree.

The index association module 12 includes:

a priority obtaining unit 121, configured to obtain index priorities of at least two second sampling indexes;

the index obtaining unit 122 is configured to obtain, as a to-be-associated sampling index, a second sampling index of which the index priority is smaller than the index priority of the target second sampling index, among the at least two second sampling indexes;

a stacking relation obtaining unit 123, configured to obtain a stacking relation between the to-be-associated sampling index and the target second sampling index;

the correlation index determining unit 124 is configured to determine the to-be-correlated sampling index as a correlated second sampling index of the target second sampling index if the stacking relationship is a cross stacking relationship; and the index priority is used for representing that the target second sampling index has a one-way incidence relation to the associated second sampling index.

The priority acquiring unit 121 includes:

a priority determining subunit 1211, configured to obtain index content types of the at least two second sampling indexes, and obtain index priorities corresponding to the at least two second sampling indexes, respectively, based on the index content types; the index content type has a corresponding index priority.

The priority acquiring unit 121 includes:

a history obtaining subunit 1212, configured to obtain a history coupling degree of each of the at least two second sampling indexes;

and the index sorting subunit 1213 is configured to sort the at least two second sampling indexes based on the historical coupling degree, and determine an index priority of each second sampling index according to a sorting result.

In obtaining the first degree of coupling of the target second sampling index with respect to the first sampling index, the coupling obtaining module 13 includes:

a core change acquiring unit 131, configured to acquire a first sampling index value of the first sampling index at a first sampling time point and a second sampling index value at a second sampling time point, and determine an index change rate of the first sampling index based on the first sampling index value and the second sampling index value;

a target change acquiring unit 132, configured to acquire a first target index value of a target second sampling index at a first sampling time point and a second target index value at a second sampling time point, and determine a target sampling index change rate of the target second sampling index based on the first target index value and the second target index value;

the first coupling determining unit 133 is configured to determine a first coupling degree of the target second sampling indicator based on the indicator change rate and the target sampling indicator change rate.

In obtaining a second degree of coupling of the associated second sampling index and the target second sampling index with respect to the first sampling index, the coupling obtaining module 13 includes:

an association change acquiring unit 134, configured to acquire a first association index value associated with the second sampling index at the first sampling time point and a second association index value associated with the second sampling time point, and determine an association sampling index change rate associated with the second sampling index based on the first association index value and the second association index value;

a common change obtaining unit 135, configured to determine a common change rate of the target second sampling index and the associated second sampling index based on the associated sampling index change rate and the target sampling index change rate;

a second coupling determination unit 136, configured to determine, based on the common change rate and the index change rate, a second coupling degree of the first sampling index in common for the associated second sampling index and the target second sampling index.

Wherein, the common change acquiring unit 135 is specifically configured to:

the second coupling determination unit 136 is specifically configured to:

Wherein, the index adjusting module 14 includes:

a target coupling determination unit 141, configured to use a sum of the first degree of coupling and the second degree of coupling as a target degree of coupling of the target second sampling index with respect to the first sampling index;

an index data adjusting unit 142, configured to determine that an inverse relationship exists between the target second sampling index and the first sampling index if the target coupling degree is a negative number, and adjust the index data of the target second sampling index based on the target coupling degree and the inverse relationship;

the index data adjusting unit 142 is further configured to determine that a forward relationship exists between the target second sampling index and the first sampling index if the target coupling degree is a positive number, and adjust the index data of the target second sampling index based on the target coupling degree and the forward relationship.

Wherein, in terms of adjusting the index data of the target second sampling index, the index data adjusting unit 142 includes:

an adjustment type obtaining subunit 1421, configured to obtain an index adjustment type of the target second sampling index;

an index data adjusting subunit 1422, configured to determine, based on the trigger adjustment type, that the index data includes a trigger second sampling index and adjust the trigger second sampling index if the index adjustment type is the trigger adjustment type; triggering the second sampling index to have a one-way incidence relation with the target second sampling index;

the index data adjusting subunit 1422 is further configured to, if the index adjustment type is the operation adjustment type, determine that the index data includes the target second sampling index based on the operation adjustment type, and adjust the target second sampling index.

Wherein, the apparatus 700 further comprises:

and the coupling comparison module 15 is configured to obtain a coupling degree of the at least two second sampling indexes, and if a second sampling index with the largest coupling degree in the at least two second sampling indexes is the target second sampling index, perform a step of adjusting index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree.

Wherein, the apparatus 700 further comprises:

the hierarchical coupling acquisition module 16 is configured to acquire an index channel forming a target second sampling index, and acquire a hierarchical coupling degree of the index channel for the target second sampling index;

the channel coupling acquisition module 17 is configured to determine a channel coupling degree of the index channel for the first sampling index based on the hierarchical coupling degree, the first coupling degree and the second coupling degree;

the index adjustment module 14 includes:

a first relation obtaining unit 143, configured to determine a first variation coupling relation of the target second sampling index with respect to the first sampling index based on the first coupling degree and the second coupling degree; the first variable coupling relationship comprises a forward relationship and a reverse relationship;

a second relation obtaining unit 144, configured to determine, based on the hierarchical coupling degree, a second variation coupling relation of the index channel for the target second sampling index; the second variable coupling relationship comprises a forward relationship and a reverse relationship;

the channel updating unit 145 is configured to update the index channel based on the first variation coupling relationship, the second variation coupling relationship, and the channel coupling degree, so as to adjust the target second sampling index.

The channel coupling obtaining module 17 is specifically configured to:

The embodiment of the application provides a data processing device, which obtains at least two second sampling indexes associated with a first sampling index; one of the at least two second sampling indices is a target second sampling index; taking a second sampling index which has a one-way association relation with a target second sampling index as an associated second sampling index in the at least two second sampling indexes; acquiring a first coupling degree of a target second sampling index aiming at the first sampling index, and acquiring a second coupling degree of the associated second sampling index and the target second sampling index aiming at the first sampling index; and adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree. The coupling degree of each second sampling index to the first sampling index is obtained by carrying out quantization splitting on the influence of each second sampling index to the first sampling index, and the coupling degree can represent the influence degree of the corresponding second sampling index to the first sampling index, so that the interference among different second sampling indexes is reduced while the integrity of the influence result of the obtained second sampling index to the first sampling index is ensured, and the accuracy of the influence quantization of the second sampling index to the first sampling index is improved.

Referring to fig. 8, fig. 8 is a schematic structural diagram of a computer device according to an embodiment of the present application. As shown in fig. 8, the computer device in the embodiment of the present application may include: one or more processors 801, a memory 802, and an input-output interface 803. The processor 801, the memory 802, and the input/output interface 803 are connected by a bus 804. The memory 802 is used for storing a computer program including program instructions, and the input/output interface 803 is used for receiving data and outputting data; the processor 801 is configured to execute program instructions stored in the memory 802 to perform the following operations:

In some possible implementations, the processor 801 may be a Central Processing Unit (CPU), and the processor may be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 802 may include both read-only memory and random-access memory, and provides instructions and data to the processor 801 and the input/output interface 803. A portion of the memory 802 may also include non-volatile random access memory. For example, the memory 802 may also store device type information.

In a specific implementation, the computer device may execute the implementation manners provided in the steps in fig. 3 through each built-in functional module thereof, which may specifically refer to the implementation manners provided in the steps in fig. 3, and details are not described herein again.

The embodiment of the present application provides a computer device, including: the system comprises a processor, an input/output interface and a memory, wherein the processor acquires computer instructions in the memory, and executes the steps of the method shown in the figure 3 to perform data processing operation. According to the embodiment of the application, the influence of the second sampling index on the first sampling index is quantized, the interference among different second sampling indexes is reduced while the integrity of the obtained influence result of the second sampling index on the first sampling index is guaranteed, and therefore the accuracy of the influence quantization of the second sampling index on the first sampling index is improved. Meanwhile, multiple splitting modes can be provided for one first sampling index, each splitting mode can be used for splitting the first sampling index into multiple layers, any one element obtained after the first sampling index is split can be obtained through the method no matter what the number of the splitting modes for one first sampling index is, and what the number of layers corresponds to each splitting mode is, and the simplicity of analysis for the first sampling index is improved for the coupling degree of the first sampling index.

An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by the processor, the data processing method provided in each step in fig. 3 may be implemented, which may specifically refer to an implementation manner provided in each step in fig. 3, and is not described herein again. In addition, the beneficial effects of the same method are not described in detail. For technical details not disclosed in embodiments of the computer-readable storage medium referred to in the present application, reference is made to the description of embodiments of the method of the present application. By way of example, program instructions may be deployed to be executed on one computer device or on multiple computer devices at one site or distributed across multiple sites and interconnected by a communication network.

The computer readable storage medium may be the data processing apparatus provided in any of the foregoing embodiments or an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) card, a flash card (flash card), and the like, provided on the computer device. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the computer device. The computer-readable storage medium is used for storing the computer program and other programs and data required by the computer device. The computer readable storage medium may also be used to temporarily store data that has been output or is to be output.

Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instruction from the computer-readable storage medium, and executes the computer instruction, so that the computer device executes the method provided in the various optional manners in fig. 3, obtains the coupling degree of each second sampling index to the first sampling index, and adjusts the index data of the second sampling index, so that the first sampling index can better meet the service requirement.

The term "comprises" and any variations thereof in the description and claims of the embodiments of the present application and in the drawings is intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or apparatus that comprises a list of steps or elements is not limited to the listed steps or modules, but may alternatively include other steps or modules not listed or inherent to such process, method, apparatus, product, or apparatus.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the specification for the purpose of clearly illustrating the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The method and the related apparatus provided by the embodiments of the present application are described with reference to the flowchart and/or the structural diagram of the method provided by the embodiments of the present application, and each flow and/or block of the flowchart and/or the structural diagram of the method, and the combination of the flow and/or block in the flowchart and/or the block diagram can be specifically implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block or blocks of the block diagram. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block or blocks of the block diagram. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block or blocks.

The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.

Claims

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

taking a second sampling index having a one-way association relationship with the target second sampling index as an associated second sampling index in the at least two second sampling indexes;

acquiring a first coupling degree of the target second sampling index aiming at the first sampling index, and acquiring a second coupling degree of the associated second sampling index and the target second sampling index aiming at the first sampling index together;

2. The method of claim 1, wherein determining an associated one of the target second sampling metrics among the at least two second sampling metrics comprises:

acquiring the index priorities of the at least two second sampling indexes;

acquiring a second sampling index of the at least two second sampling indexes, wherein the index priority is smaller than the index priority of the target second sampling index, and taking the second sampling index as a to-be-associated sampling index;

acquiring a superposition relation between the sampling index to be correlated and the target second sampling index;

if the superposition relationship is a cross superposition relationship, determining the sampling index to be correlated as a correlated second sampling index of the target second sampling index; the index priority is used for representing that the target second sampling index has a one-way incidence relation to the associated second sampling index.

3. The method of claim 2, wherein obtaining the metric priorities of the at least two second sampling metrics comprises:

acquiring the index content types of the at least two second sampling indexes, and acquiring the index priorities corresponding to the at least two second sampling indexes respectively based on the index content types; the index content type has a corresponding index priority.

4. The method of claim 2, wherein obtaining the metric priorities of the at least two second sampling metrics comprises:

obtaining the historical coupling degree of each second sampling index in the at least two second sampling indexes;

and sequencing the at least two second sampling indexes based on the historical coupling degree, and determining the index priority of each second sampling index according to a sequencing result.

5. The method of claim 1, wherein the obtaining the first degree of coupling of the target second sampling metric to the first sampling metric comprises:

acquiring a first sampling index value of the first sampling index at a first sampling time point and a second sampling index value at a second sampling time point, and determining an index change rate of the first sampling index based on the first sampling index value and the second sampling index value;

acquiring a first target index value of the target second sampling index at the first sampling time point and a second target index value at the second sampling time point, and determining a target sampling index change rate of the target second sampling index based on the first target index value and the second target index value;

and determining a first coupling degree of the target second sampling index based on the index change rate and the target sampling index change rate.

6. The method of claim 5, wherein the obtaining a second degree of coupling of the associated second sampling metric and the target second sampling metric in common for the first sampling metric comprises:

acquiring a first relevance index value of the relevance second sampling index at the first sampling time point and a second relevance index value at the second sampling time point, and determining a relevance sampling index change rate of the relevance second sampling index based on the first relevance index value and the second relevance index value;

determining a common rate of change of the target second sampling indicator and the associated second sampling indicator based on the associated sampling indicator rate of change and the target sampling indicator rate of change;

determining a second degree of coupling of the associated second sampling indicator with the target second sampling indicator for the first sampling indicator in common based on the common rate of change and the indicator rate of change.

7. The method of claim 6, wherein determining the common rate of change of the target second sample metric and the associated second sample metric based on the associated sample metric rate of change and the target sample metric rate of change comprises:

taking the product of the associated sampling indicator change rate and the target sampling indicator change rate as the common change rate of the target second sampling indicator and the associated second sampling indicator;

the determining a second degree of coupling of the associated second sampling metric and the target second sampling metric together for the first sampling metric based on the common rate of change and the metric rate of change comprises:

determining a ratio of the common rate of change to the index rate of change as a second degree of coupling of the associated second sampling index and the target second sampling index.

8. The method of claim 1, wherein adjusting the indicator data of the target second sampling indicator in the first sampling indicator according to the first degree of coupling and the second degree of coupling comprises:

taking the sum of the first coupling degree and the second coupling degree as a target coupling degree of the target second sampling index for the first sampling index;

and if the target coupling degree is a positive number, determining that a forward relation exists between the target second sampling index and the first sampling index, and adjusting the index data of the target second sampling index based on the target coupling degree and the forward relation.

9. The method of claim 8, wherein said adjusting the target second sample metric data comprises:

acquiring an index adjustment type of the target second sampling index;

if the index adjustment type is a trigger adjustment type, determining that the index data comprises a trigger second sampling index based on the trigger adjustment type, and adjusting the trigger second sampling index; the triggering second sampling index has the one-way incidence relation to the target second sampling index;

and if the index adjustment type is the operation adjustment type, determining that the index data comprises the target second sampling index based on the operation adjustment type, and adjusting the target second sampling index.

10. The method of claim 1, wherein the method further comprises:

11. The method of claim 1, wherein the method further comprises:

acquiring an index channel forming the target second sampling index, and acquiring the level coupling degree of the index channel aiming at the target second sampling index;

determining a channel coupling degree of the index channel for the first sampling index based on the hierarchical coupling degree, the first coupling degree and the second coupling degree;

the index data comprises an index channel constituting the target second sampling index;

the adjusting the index data of the target second sampling index in the first sampling index according to the first coupling degree and the second coupling degree includes:

determining a first variation coupling relation of the target second sampling index for the first sampling index based on the first coupling degree and the second coupling degree; the first varying coupling relationship comprises a forward relationship and a reverse relationship;

determining a second variation coupling relation of the index channel for the target second sampling index based on the hierarchical coupling degree; the second varying coupling relationship comprises the forward relationship and the reverse relationship;

updating the index channel based on the first change coupling relation, the second change coupling relation and the channel coupling degree to adjust the target second sampling index.

12. The method of claim 11, wherein determining the channel coupling degree of the indicator channel for the first sampling indicator based on the hierarchical coupling degree, the first coupling degree, and the second coupling degree comprises:

and taking the product of the target coupling degree and the hierarchical coupling degree as the channel coupling degree of the index channel for the first sampling index.

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

the index correlation module is used for taking a second sampling index which has a one-way correlation relationship with the target second sampling index in the at least two second sampling indexes as a correlated second sampling index;

a coupling obtaining module, configured to obtain a first coupling degree of the target second sampling indicator with respect to the first sampling indicator, and obtain a second coupling degree of the associated second sampling indicator and the target second sampling indicator with respect to the first sampling indicator;

14. A computer device comprising a processor, a memory, an input output interface;

the processor is connected to the memory and the input/output interface, respectively, wherein the input/output interface is configured to receive data and output data, the memory is configured to store program code, and the processor is configured to call the program code to perform the method according to any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions which, when executed by a processor, perform the method according to any one of claims 1-12.