CN108829638B

CN108829638B - Business data fluctuation processing method and device

Info

Publication number: CN108829638B
Application number: CN201810556156.6A
Authority: CN
Inventors: 张长江; 杨陆毅; 赵华; 朱通
Original assignee: Advanced New Technologies Co Ltd
Current assignee: Advanced New Technologies Co Ltd; Advantageous New Technologies Co Ltd
Priority date: 2018-06-01
Filing date: 2018-06-01
Publication date: 2022-12-16
Anticipated expiration: 2038-06-01
Also published as: CN108829638A

Abstract

The application provides a method and a device for processing business data fluctuation. The method comprises the following steps: acquiring a plurality of influence dimensions corresponding to the service indexes, wherein the influence dimensions comprise at least one sub-dimension; calculating the overall fluctuation quantity of the service index and the sub-fluctuation quantity of the service index on the sub-dimension; respectively calculating the discrete degree of the contribution degree of each sub-dimension in the multiple influencing dimensions to the service index fluctuation according to the overall fluctuation amount and the sub-fluctuation amount; and determining a target influence dimension influencing the business index fluctuation from the plurality of influence dimensions according to the discrete degree of the contribution degree of each sub-dimension in the influence dimensions to the business index fluctuation. By utilizing the embodiments in the application, monitoring and analysis of different service indexes are realized, and the accuracy of service data fluctuation analysis processing is improved.

Description

Business data fluctuation processing method and device

Technical Field

The application belongs to the technical field of internet, and particularly relates to a method and a device for processing business data fluctuation.

Background

With the development of internet technology, people can monitor some network service data by using a computer, such as: the presence or absence of a risk for a certain service can be monitored. In daily business monitoring, when business indexes fluctuate, the fluctuation amplitude, the fluctuation main attribution, business influence and severity caused by fluctuation and the like need to be located, wherein the fluctuation main attribution needs to be detected and refined and analyzed, and the main dimensionality and the main sub-dimensionality of fluctuation are located.

In the prior art, a mode of directly calculating a tabulation increment is usually adopted to perform fluctuation analysis on service index data, the method is simple, and the result of the service data fluctuation processing may be inaccurate, especially the fluctuation data analysis of a composite index type service index (such as a wind control rejection rate (a ratio of rejection amount to payment amount)). Therefore, an embodiment capable of improving the accuracy of the service data fluctuation processing result is needed.

Disclosure of Invention

The application aims to provide a method and a device for processing business data fluctuation, which improve the accuracy of business data fluctuation processing.

In one aspect, an embodiment of the present application provides a method for processing service data fluctuation, including:

obtaining a plurality of influence dimensions corresponding to the service indexes, wherein the influence dimensions comprise at least one sub-dimension;

calculating the overall fluctuation quantity of the service index and the sub-fluctuation quantity of the service index on the sub-dimension;

respectively calculating the discrete degree of the contribution degree of each sub-dimension in the plurality of influence dimensions to the service index fluctuation according to the overall fluctuation amount and the sub-fluctuation amount;

and determining a target influence dimension influencing the business index fluctuation from a plurality of influence dimensions according to the discrete degree of the contribution degree of each sub-dimension in the influence dimensions to the business index fluctuation.

Further, in another embodiment of the method, the calculating the overall fluctuation amount of the service index and the sub-fluctuation amounts of the service index in the sub-dimensions includes:

acquiring a sub-numerical value and a reference sub-numerical value of the service index on the sub-dimension, and an overall numerical value and an overall reference numerical value of the service index;

and taking the difference between the sub-numerical value and the reference sub-numerical value as the sub-fluctuation quantity with the matching degree meeting the preset requirement, and taking the difference between the integral numerical value and the integral reference numerical value as the integral fluctuation quantity.

Further, in another embodiment of the method, the calculating, according to the overall fluctuation amount and the sub-fluctuation amounts, a discrete degree of a contribution degree of each of the sub-dimensions of the plurality of influence dimensions to the fluctuation of the business indicator includes:

calculating the contribution value of the fluctuation of the business index on the sub-dimension to the fluctuation of the business index according to the overall fluctuation amount and the sub-fluctuation amount;

and calculating the discrete degree of the contribution degree of each sub-dimension in the influence dimension to the fluctuation of the business index according to the contribution degree value corresponding to the sub-dimension in the influence dimension.

Further, in another embodiment of the method, the calculating, according to the contribution value corresponding to the sub-dimension in the influence dimension, a discrete degree of the contribution degree of each of the sub-dimensions in the influence dimension to the fluctuation of the business index includes:

calculating the average value of the contribution values corresponding to the sub-dimensions in the influence dimension according to the contribution values corresponding to the sub-dimensions in the influence dimension;

calculating a standard deviation or a variance of the contribution degree corresponding to the sub-dimension in the influence dimension according to the average value of the contribution degree corresponding to each sub-dimension in the influence dimension and the contribution degree corresponding to the sub-dimension;

and taking the standard deviation or variance of the contribution degree as the dispersion degree of the contribution degree of each sub-dimension in the influence dimension to the fluctuation of the service index.

Further, in another embodiment of the method, the service indicators include a first type service indicator and a second type service indicator, the second type service indicator is obtained by calculation based on at least two of the first type service indicators, and the calculating, according to the overall fluctuation amount and the sub-fluctuation amount, a contribution degree of fluctuation of the service indicators in the sub-dimension to fluctuation of the service indicators includes:

if the service index is the first class service index, taking the ratio of the sub-dimensional fluctuation amount of the first class service index to the overall fluctuation amount of the first class service index as the contribution degree of the fluctuation of the first class service index on the sub-dimension to the first class service index;

if the service index is the second type service index, calculating a contribution coefficient of the fluctuation of the second type service index on the sub-dimension to the second type service index according to the following formula;

in the above formula, the removing the fluctuation amount includes: and removing the fluctuation amount of the service index after the sub-fluctuation amount of the second type of service index on the sub-dimension.

Further, in another embodiment of the method, after calculating the overall fluctuation amount of the service index and the sub-fluctuation amounts of the service index in the sub-dimensions, the method further includes:

respectively acquiring fluctuation trend data corresponding to at least one sub-dimension according to the sub-momentum of the service index on the at least one sub-dimension;

acquiring integral fluctuation trend data of the service index according to the integral fluctuation amount;

respectively comparing fluctuation trend data corresponding to at least one sub-dimension with the overall fluctuation trend data, and calculating the matching degree between the fluctuation trend data corresponding to the sub-dimension and the overall fluctuation trend data;

and screening out the sub-dimension with the matching degree meeting the preset requirement from at least one sub-dimension.

Further, in another embodiment of the method, the calculating a discrete degree of the contribution degree of each of the sub-dimensions of the plurality of influence dimensions to the fluctuation of the business index includes:

and calculating the discrete degree of the contribution degree of each sub-dimension with the matching degree meeting the preset requirement in the plurality of influence dimensions to the service index fluctuation according to the sub-momentum of the service index on the sub-dimension with the matching degree meeting the preset requirement.

Further, in another embodiment of the method, the method further comprises:

if the difference value between the discrete degrees of the contribution degrees of the sub-dimensions to the service index fluctuation in the plurality of influence dimensions is smaller than a preset threshold value, acquiring a service weight coefficient corresponding to the influence dimensions according to historical data of the service index;

accordingly, the determining a target impact dimension from the plurality of impact dimensions that impacts the business metric fluctuation includes:

and when the difference value between the discrete degrees of the contribution degrees of the sub-dimensions to the service index fluctuation in the plurality of influence dimensions is smaller than the preset threshold value, acquiring the target influence dimension according to the service weight coefficient.

Further, in another embodiment of the method, the method further comprises:

and determining a target sub-dimension influencing the business index fluctuation from the target influence dimension according to the contribution degree of each sub-dimension in the target influence dimension to the business index fluctuation.

On the other hand, the present application provides a service data fluctuation processing apparatus, including:

the influence dimension determining module is used for acquiring a plurality of influence dimensions corresponding to the business indexes, and the influence dimensions comprise at least one sub-dimension;

the fluctuation amount calculation module is used for calculating the whole fluctuation amount of the service index and the sub-fluctuation amount of the service index on the sub-dimension;

the contribution degree processing module is used for respectively calculating the discrete degree of the contribution degree of each sub-dimension in a plurality of influence dimensions to the service index fluctuation according to the integral fluctuation amount and the sub-fluctuation amount;

and the fluctuation analysis module is used for determining a target influence dimension influencing the business index fluctuation from a plurality of influence dimensions according to the dispersion degree of the contribution degree of each sub-dimension in the influence dimensions to the business index fluctuation.

Further, in another embodiment of the apparatus, a sub-value and a reference sub-value of the service index in the sub-dimension, and an overall value and an overall reference value of the service index are obtained;

and taking the difference value between the sub-numerical value and the reference sub-numerical value as the sub-fluctuation amount of the service index on the sub-dimension, and taking the difference value between the overall numerical value and the overall reference numerical value as the overall fluctuation amount.

Further, in another embodiment of the apparatus, the contribution processing module includes:

the contribution degree calculating unit is used for calculating the contribution degree value of the fluctuation of the business index on the sub-dimension to the fluctuation of the business index according to the whole fluctuation amount and the sub-fluctuation amount;

and the dispersion degree calculating unit is used for calculating the dispersion degree of the contribution degree of each sub-dimension in the influence dimension to the business index fluctuation according to the contribution degree value corresponding to the sub-dimension in the influence dimension.

Further, in another embodiment of the apparatus, the dispersion degree calculating unit is specifically configured to:

Further, in another embodiment of the apparatus, the service indicators include a first type of service indicator and a second type of service indicator, the second type of service indicator is obtained by calculation based on at least two first type of service indicators, and the contribution degree calculating unit is specifically configured to:

when the service index is the first-class service index, taking the ratio of the sub-dimension of the first-class service index to the overall fluctuation amount as the contribution of the fluctuation of the first-class service index on the sub-dimension to the first-class service index;

when the service index is the second type service index, calculating a contribution coefficient of the fluctuation of the second type service index on the sub-dimension to the second type service index according to the following formula;

Further, in another embodiment of the apparatus, the apparatus further comprises:

the sub-dimension fluctuation processing module is used for respectively acquiring fluctuation trend data corresponding to at least one sub-dimension according to the sub-momentum of the service index on at least one sub-dimension;

the integral fluctuation processing module is used for acquiring integral fluctuation trend data of the service index according to the integral fluctuation amount;

the trend comparison module is used for respectively comparing fluctuation trend data corresponding to at least one sub-dimension with the overall fluctuation trend data and calculating the matching degree between the fluctuation trend data corresponding to the sub-dimension and the overall fluctuation trend data;

and the sub-dimension screening module is used for screening out the sub-dimension of which the matching degree meets the preset requirement from at least one sub-dimension.

Further, in another embodiment of the apparatus, the contribution processing module is specifically configured to:

the weight coefficient setting module is used for acquiring a business weight coefficient corresponding to the influence dimension according to historical data of the business index when the difference value between the dispersion degrees of the contribution degrees of the sub-dimensions to the business index fluctuation in the plurality of influence dimensions is smaller than a preset threshold value;

correspondingly, the fluctuation analysis module is configured to, when a difference between the discrete degrees of the contribution degrees of the sub-dimensions to the fluctuation of the service index in the plurality of influence dimensions is smaller than the preset threshold, obtain the target influence dimension according to the service weight coefficient.

and the target dimension value determining module is used for determining a target sub-dimension influencing the business index fluctuation from the target influence dimension according to the contribution degree of each sub-dimension in the target influence dimension to the business index fluctuation.

In another aspect, an embodiment of the present application provides a computer storage medium, on which a computer program is stored, and when the computer program is executed, the method for processing service data fluctuation is implemented as claimed in the following claims.

In another aspect, an embodiment of the present application provides a service data fluctuation processing system, which includes at least one processor and a memory for storing processor-executable instructions, where the processor executes the instructions to implement the service data fluctuation processing method.

The method, the device and the system for processing the business data fluctuation introduce a method for calculating the overall fluctuation amount of the business index and the sub-fluctuation amount of the business index on a specific sub-dimension, and simultaneously calculate the discrete degree of the contribution degree of each dimension influencing the sub-dimension to the business index fluctuation based on the overall fluctuation amount and the sub-fluctuation amount. The method for analyzing the business index fluctuation by downward detection has rigorous calculation logic, higher business interpretability and higher accuracy of business data fluctuation analysis. Meanwhile, the method of the embodiment of the application can support combined downward exploration with any number of dimensions, can help complex services to rapidly perform fluctuation detection, fluctuation disassembly, main cause positioning and the like, and the whole method for analyzing the fluctuation of the service indexes and analyzing the downward exploration is very intelligent, so that the accuracy of analyzing and processing the fluctuation of the service data 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 description below are only some embodiments described in the present application, and for those skilled in the art, other drawings may be obtained according to these drawings without creative efforts.

Fig. 1 is a schematic flowchart of a method for processing service data fluctuation in an embodiment provided in the present application;

fig. 2 is a schematic flow chart of a method for handling service data fluctuation in another embodiment of the present application;

fig. 3 is a schematic flowchart of another service data fluctuation processing method in this embodiment;

fig. 4 is a schematic flowchart of another service data fluctuation processing method according to the present application;

fig. 5 is a schematic block diagram of an embodiment of a service data fluctuation processing apparatus provided in the present application;

FIG. 6 is a block diagram of a contribution level processing module according to an embodiment of the present application;

fig. 7 is a schematic block diagram of a traffic data fluctuation processing apparatus according to another embodiment of the present application;

fig. 8 is a schematic block diagram of a service data fluctuation processing apparatus in another embodiment of the present application;

fig. 9 is a schematic block diagram of a traffic data fluctuation processing apparatus according to another embodiment of the present application;

fig. 10 is a schematic block structure diagram of an embodiment of a service data fluctuation processing system provided in the present application.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present application, 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 obtained by a person of ordinary skill in the art based on the embodiments in the present application without making creative efforts shall fall within the protection scope of the present application.

With the development of internet technology, business data can be acquired through the internet, and the business data can also be understood as business index data, which can include sales volume of a certain commodity, payment volume of a user, payment mode, purchase volume of commodity categories purchased by the user, user activity of a certain business and the like. The monitoring of the business data can be realized according to the obtained business data, such as wind control analysis and payment analysis, which business investment has risks, which business payment or sales fluctuates, and the factors influencing the fluctuation and the like are analyzed.

The method for processing the business data fluctuation provided by the embodiment of the application can analyze the fluctuation reason of the business data which fluctuates when the business data is monitored, and acquire the main factors influencing the business data fluctuation so as to take corresponding countermeasures against the fluctuation. The method for processing the service data fluctuation provided by the embodiment of the application is not only suitable for the fluctuation processing of some first-class service indexes (absolute service indexes), but also suitable for the fluctuation processing of second-class service indexes (complex service indexes), and can improve the adaptability and accuracy of the service data fluctuation analysis processing.

Specifically, fig. 1 is a schematic flow diagram of a service data fluctuation processing method in an embodiment provided in the present application, and as shown in fig. 1, the service data fluctuation processing method provided in the embodiment of the present application includes:

s10, obtaining a plurality of influence dimensions corresponding to the service indexes, wherein the influence dimensions comprise at least one sub-dimension.

Monitoring of business data may include monitoring payment status, risk status, user activity, etc. for a business. When monitoring the service data, the service index influencing the fluctuation of the service data can be selected in advance according to historical data, expert experience and the like, then the service index is monitored, and the influence dimension when monitoring the service index is obtained. The service index may include a first type service index and a second type service index, and the first type service index may include an absolute quantity type index such as: payment amount, payment success amount, rejection amount, etc., and the second type of service index may include a rate-type service index such as: success rate of payment, rate of wind-controlled refusal (ratio of refusal amount to payment amount), etc. The second type of service index can be obtained by performing mathematical operations on at least two first type of service indexes, such as: the payment success rate = payment success amount/payment amount, and is composed of a ratio of two first-class service indexes. The influence dimension may represent factors influencing the business index and the business data fluctuation, such as: card country (which country's card), logistics country (which country's logistics), card issuer, categories of goods, etc. Specific sub-dimensions may be included in the influence dimension, such as the influence dimension-which country is specific in the card country "in the card may represent a sub-dimension of the card country, such as: usa, china, uk, etc., representing us, china, and uk cards, respectively; if the influence dimension is provincial, the sub-dimensions may include Zhejiang, jiangsu, anhui, shanghai, etc.

For example: if the user activity of a certain service needs to be observed and evaluated, the embodiment of the application can analyze what indexes are used for measuring the user activity reasonably according to historical data, expert experience and the like, and set the service indexes for evaluating the user activity of the service. And setting an influence dimension which needs to carry out downward analysis on the service index (namely specifically analyzing which factors have influence on the fluctuation of the service index) aiming at the service index, and carrying out fluctuation analysis on the service index according to the influence dimension. Such as: the user activity of the service can be evaluated by analyzing and obtaining the payment amount of the user, so that the payment amount can be used as a service index, fluctuation factors influencing the payment amount of the service index are specifically analyzed, and influence dimensions are set as follows: the card country, the logistics country, etc. Through specific analysis of the fluctuation situation of the service index-payment amount on the influence dimension, specific reasons influencing the user activity of the service are analyzed. For another example: for wind control, it is often of interest: according to the embodiment of the application, risk measurement indexes can be set according to historical data, expert experience and the like, namely, what data have fluctuation to indicate that the data are risks, corresponding business indexes are set, influence dimensions of the business indexes are set, and specific risk analysis is carried out on the business indexes and the influence dimensions.

S20, calculating the whole fluctuation quantity of the service index and the sub-fluctuation quantity of the service index on the sub-dimension.

After the influence dimensionality required to be subjected to downward exploration analysis is obtained for monitoring the service index, the overall fluctuation amount of the service index in the preset time and the sub-fluctuation amount of the service index in each sub-dimensionality can be determined according to the specific data of the service index. For example: the data of the payment amount of the user in the preset time in a certain service can be collected, the overall fluctuation condition of the payment amount can be obtained by analyzing the data of the payment amount of the user, and the overall fluctuation amount of the payment amount can be obtained. By carrying out downward detection analysis on the payment amount data, the payment amount change of the user on different sub-dimensions can be obtained. Such as: in the payment amount of the service, the data paid by using the bank card and the credit card can be obtained, and the corresponding sub-wavelets of the payment amount on the sub-dimensions of the U.S., china, british and the like in the dimension-card country can be analyzed, for example, the sub-wavelets of the payment amount paid by using the U.S. card in the preset time by a user can be calculated, and the corresponding sub-wavelets of the payment amount on the sub-dimensions of the U.S., china, british and the like in the dimension-logistics country can be calculated.

In an embodiment of the application, the calculating an overall fluctuation amount of the service index and a sub-fluctuation amount of the service index in the sub-dimension may include:

and taking the difference value between the sub-numerical value and the reference sub-numerical value as the sub-fluctuation amount, and taking the difference value between the overall numerical value and the overall reference value as the overall fluctuation amount.

Specifically, the sub-value and the whole value may represent data corresponding to a current preset time period, and the reference sub-value and the whole reference value may represent data in a historical synchronous preset time period, such as: the overall value of the payment amount may be the payment amount data from 8 am to 8 pm today, and the overall reference value of the payment amount may be the payment amount data from 8 am to 8 pm yesterday or may be an average of the payment amount data from 8 am to 8 pm each day in the last 7 days. The difference of the sub-value from the reference sub-value or the difference of the overall value from the overall reference value may represent the amount of fluctuation. Such as: if the fluctuation amount of the service index, i.e., the payment amount, is to be calculated, a difference between the payment amount from 8 am to 8 pm (an overall value) today and the payment amount from 8 am to 8 pm (an overall reference value) yesterday can be used as the overall fluctuation amount of the payment amount. If the preset influence dimension for carrying out the downward analysis on the service index payment amount comprises a card country, the payment amount (sub-value) from 8 am to 8 pm using the bank card in the united states is subtracted from 8 am to 8 pm using the bank card in the united states (reference sub-value) to serve as the sub-dimension of the payment amount in the influence dimension-the card country-the sub-momentum of the united states.

The specific definitions of the current data and the comparison data may be set according to actual needs, and the embodiments of the present application are not particularly limited.

And S30, respectively calculating the discrete degree of the contribution degree of each sub-dimension in the plurality of influence dimensions to the service index fluctuation according to the overall fluctuation amount and the sub-fluctuation amount.

The overall fluctuation amount of the service index can reflect the fluctuation condition of the overall service, and the sub-fluctuation amount of the service index on a certain sub-dimension can reflect the fluctuation condition of a certain service index on a specific sub-dimension. By comparing the sub-momentum with the overall fluctuation, the influence degree of the fluctuation of the service index on the overall fluctuation of the service index can be seen to a certain extent. Different sub-dimensions in the influence dimension may correspond to different sub-waveamounts, and by comparing the overall fluctuation amount of the service index with the sub-waveamounts of the service index in all the sub-dimensions in a certain influence dimension, the discrete degree of the contribution of the fluctuation of the service index in each sub-dimension in the influence dimension to the overall fluctuation of the service index can be obtained.

For example: the overall fluctuation amount of the service index-payment amount is 300, and the sub-momentum amounts of the service index-payment amount in the influencing dimension-country, namely the sub-momentum amounts in the sub-dimension-united states, china and great Britain are respectively 100, 80 and 50, so that the different contribution degrees, namely the different influencing degrees, of the sub-momentum amounts of the service index-payment amount in the sub-dimension-united states, china and great Britain to the overall fluctuation amount of the payment amount can be seen, namely, the contribution degrees of each sub-dimension in the lower exploration dimension to the overall fluctuation amount of the service index can have certain dispersion. According to the influence dimension, namely the neutron dimension in the country card, the sub-momentum corresponding to the United states, china and the United kingdom and the overall fluctuation amount of the payment amount, the dispersion degree of the contribution degree of the influence dimension, namely the neutron dimension in the country card, namely the United states, china and the United kingdom to the overall fluctuation amount of the payment amount can be determined.

S40, determining a target influence dimension influencing the business index fluctuation from the plurality of influence dimensions according to the discrete degree of the contribution degree of each sub-dimension in the influence dimensions to the business index fluctuation.

After the discrete degree of the contribution degree of each sub-dimension to the business index fluctuation in different influence dimensions is determined, the target influence dimension with large influence on the business index fluctuation can be determined from the multiple influence dimensions according to the calculated discrete degree. The higher the dispersion degree of the contribution degree of each sub-dimension to the business index fluctuation in the influence dimension is, the more obvious the difference of the contribution degree of the sub-dimension to the business index fluctuation in the influence dimension can be reflected, and the higher the probability that the influence dimension is the main factor of the business index fluctuation can be considered. In the embodiment of the application, the target influence dimensionality can be determined according to the discrete degree of the contribution degree of the neutron dimensionality to the business index fluctuation, the influence dimensionality can be sequenced according to the discrete degree corresponding to the influence dimensionality, and the target influence dimensionality with large influence on the business index fluctuation is obtained. Such as: the influence dimension with the highest dispersion degree can be used as the target influence dimension, the influence dimension with the dispersion degree arranged in the previous preset name can be used as the target influence dimension, or the influence dimension with the dispersion degree larger than the preset dispersion threshold can be used as the target influence dimension.

It should be noted that, the method provided in the embodiment of the present application may perform service monitoring on one service index, such as payment amount, at a time, and may also perform service monitoring on multiple service indexes, such as: and the payment amount, the payment success rate and the like are monitored simultaneously to obtain fluctuation influence factors of the whole service data. Such as: the payment success rate is not changed, which does not mean that all services are stable, downward exploration analysis can be performed by combining the payment success rate, and which payment success rate affecting dimensionality is greatly increased or decreased is seen to determine whether the stable condition of each service exists or not.

The business data fluctuation processing method introduces the overall fluctuation amount of the business index and the calculation method of the sub-fluctuation amount of the business index on the specific sub-dimension, and simultaneously calculates the discrete degree of the contribution degree of the sub-dimension to the business index fluctuation in each influence dimension based on the overall fluctuation amount and the sub-fluctuation amount. The method for analyzing the business index fluctuation by downward exploration has rigorous calculation logic, higher business interpretability and higher accuracy of business data fluctuation analysis. Meanwhile, the method of the embodiment of the application can support combined downward exploration with any number of dimensions, can help complex services to rapidly perform fluctuation detection, fluctuation disassembly, main cause positioning and the like, and the whole method for analyzing the fluctuation of the service indexes and analyzing the downward exploration is very intelligent, so that the accuracy of analyzing and processing the fluctuation of the service data is improved.

On the basis of the foregoing embodiment, in an embodiment of the present application, the calculating, according to the overall fluctuation amount and the sub-fluctuation amount, a discrete degree of a contribution degree of each of the sub-dimensions in the multiple influence dimensions to the fluctuation of the business index respectively includes:

Specifically, the influence degree of the fluctuation of the service index on the fluctuation of the service index in the sub-dimension may also represent the contribution degree corresponding to the sub-dimension, and the specific calculation method of the contribution degree value corresponding to the sub-dimension may be set according to actual needs, for example: the ratio of the sub-momentum of the service index in a certain sub-dimension to the overall fluctuation of the service index, or the ratio multiplied by a specified coefficient, or the difference between the sub-momentum of the service index in a certain sub-dimension and the overall fluctuation of the service index multiplied by a specified coefficient, etc. may be used as the contribution value of the fluctuation of the service index in the sub-dimension to the fluctuation of the service index, which is not specifically limited in the embodiments of the present application.

After the contribution degree value corresponding to each sub-dimension in the influence dimension is determined, the dispersion degree of the contribution degree of each sub-dimension in the influence dimension to the business index can be obtained on the basis of the contribution degree value corresponding to each sub-dimension, and whether the difference of the contribution degree of each sub-dimension in the influence dimension to the business index fluctuation is obvious can be reflected. The degree of dispersion may be calculated by using a method in mathematical statistics, or may be determined by a form of a drawing, and the embodiment of the present application is not particularly limited.

On the basis of the foregoing embodiment, in an embodiment of the present application, the calculating, according to the contribution value corresponding to the sub-dimension in the influence dimension, a discrete degree of the contribution degree of each of the sub-dimensions in the influence dimension to the fluctuation of the service index may include:

For example: if the influence dimension-a sub-dimension in the country of the card includes: in china, the united states and the united kingdom, the method according to the above embodiment calculates and obtains a value of contribution of the service index, namely the payment amount, in the sub-dimension-united states of 1.2, a value of contribution of the payment amount, in the sub-dimension-china of 1.8, and a value of contribution of the payment amount, in the sub-dimension-united kingdom of-0.3. Then, the average value of the contribution degree of the payment amount corresponding to each sub-dimension in the affected dimension-country card is (1.2 + 1.8-0.3)/3 =0.9, and the standard deviation corresponding to the affected dimension-country card is

0.75 can be used as the discrete degree of influence on the contribution degree of each sub-dimension in the dimension-card country to the business index-payment amount. Of course, the variance corresponding to the influence dimension may also be calculated according to actual needs, and it may also be indicated whether the influence degrees of each sub-dimension in the influence dimension on the data fluctuation of the service index are consistent, so that the fluctuation influence condition of the influence dimension on the service index may be further reflected.

And determining the discrete condition of the contribution degree of each sub-dimension in the influence dimension to the fluctuation of the service index by using the form of standard deviation or variance according to the contribution values corresponding to different sub-dimensions in the influence dimension.

On the basis of the foregoing embodiment, in an embodiment of the present application, the service indicators include a first type service indicator and a second type service indicator, where the second type service indicator is obtained by calculation based on at least two first type service indicators, and the calculating, according to the overall fluctuation amount and the sub-fluctuation amount, a contribution degree of fluctuation of the service indicator in the sub-dimension to fluctuation of the service indicator may include:

if the service index is the first class service index, taking the ratio of the sub-dimension sub-momentum to the overall fluctuation quantity of the first class service index as the contribution of the fluctuation of the first class service index on the sub-dimension to the first class service index;

The service indexes in the embodiment of the application include a first class service index and a second class service index, and the calculation of the sub-momentum of the first class service index on the sub-dimension can adopt a formula:

and (4) performing calculation. Such as: if the total fluctuation amount of the service index-payment amount obtained by the above embodiment is 10 ten thousand, the fluctuation amount of the payment amount in the influencing dimension-the sub-dimension in the card country-the sub-momentum in china, that is, the fluctuation amount of the payment amount paid by using the card such as the bank card, the credit card, etc. in china is 2 ten thousand, the contribution degree of the payment amount in the influencing dimension-the sub-dimension in the card country-the china may be: 2/10=0.2.

When the service index is a second type service index, such as a payment success rate, a risk rejection rate, or the like, a formula may be adopted:

and calculating the corresponding contribution degree of the sub-dimension in the second class of service index. Table 1 shows the monitoring of the business index in one embodiment of the present applicationData comparison table, in table 1, the child dimension-china and the child dimension-usa respectively represent influence dimensions-child dimension-china and child dimension-usa in the country of cards. As shown in table 1, the service indicators in table 1 include payment amount, payment success amount, and payment success rate, where the payment success rate = payment success amount/payment amount, and it can be seen that the payment success rate is a proportional service indicator, and belongs to the second class of service indicators. According to the data statistics of the business indexes, yesterday data (overall reference value) and today data (overall value) of the whole business indexes, yesterday data (reference sub value) and today data (sub value) of the business indexes in the sub dimensions-China and the sub dimensions-America are obtained. By using the method of the embodiment, the overall fluctuation amount of the service index and the sub-fluctuation amounts of the service index on the sub-dimension-China and sub-dimension-United states can be obtained.

Table 1 data comparison table for service index monitoring

According to the data of the statistical service indexes, the overall fluctuation amount of the service indexes after the fluctuation amount of the service indexes in the sub-dimension is eliminated can be obtained, for example, the '78.71%' column of the 'success rate of the whole service indexes (after the fluctuation amount of the dimension is eliminated') in table 1 can represent the payment success rate after the sub-dimension-Chinese sub-momentum of the payment success rate is eliminated, and represent the overall numerical value of the service indexes after the sub-dimension-Chinese sub-momentum of the service indexes is eliminated.

Then, the whole data of the business indexes after the sub-momentum of the business indexes on the sub-dimension is removed is utilized to calculate the sub-dimension of the business indexesThe degree of contribution, such as:

the numerator 'the payment success rate after eliminating the sub-momentum of the payment success rate in the child dimension China-the payment success rate of the yesterday whole' can represent 'the fluctuation amount is removed', and the denominator 'the payment success rate of the today whole-the payment success rate of the yesterday whole' can represent the whole fluctuation amount. It can be seen that the "removal of the fluctuation amount" may represent a difference between a sub-value after removing the sub-momentum of the second type service indicator in the sub-dimension and a reference sub-value.

Similarly, the contribution degree of the payment success rate after the sub-dimensionality-the sub-momentum in the united states of america is removed can be obtained, and the specific calculation method is not described herein again.

The contribution degrees corresponding to the fluctuation of different types of service indexes on the sub-dimension are calculated through different calculation methods, particularly the calculation of the contribution degree of the second type of service indexes is not a simple incremental calculation method any longer, and the fluctuation influence of the first type of service indexes on the second type of service indexes together in the second type of service indexes is considered. The accuracy of analyzing the data fluctuation influence degree of the overall business index by the business index is improved, and an accurate data basis is provided for subsequently determining the target influence dimension and the target sub-dimension which influence the overall business data fluctuation.

On the basis of the foregoing embodiment, in an embodiment of the present application, the method may further include:

Fig. 2 is a schematic flowchart of a business data fluctuation processing method in another embodiment of the present application, and as shown in fig. 2, after a target influence dimension is determined, a target sub-dimension may be determined according to a contribution degree corresponding to each sub-dimension in the target influence dimension. The sub-dimension with the largest contribution degree in the target influence dimensions can be selected as the target sub-dimension, the sub-dimension with the contribution degree in the previous preset ranking in the target influence dimensions can be selected as the target sub-dimension, or the sub-dimension with the contribution degree larger than a preset contribution degree threshold value can be selected as the target sub-dimension. For example: if the target influence dimension determined by the method of the embodiment is the card country, the contribution degree corresponding to each sub-dimension in the card country can be obtained. If the contribution degree of the child dimension-china is the largest, the child dimension-china in the target influence dimension can be used as the target child dimension. The contribution degree can be judged by using the contribution degree, and the calculation method of the contribution degree can refer to the calculation of the above embodiment, which is not described herein again.

Based on the target influence dimension and the contribution degree corresponding to the sub-dimension in the target influence dimension, the sub-dimension with larger influence on the fluctuation of the service index can be further determined, and a more accurate and more detailed data basis is provided for the monitoring of the service index.

On the basis of the foregoing embodiment, in an embodiment of the present application, after calculating the overall fluctuation amount of the service index and the sub-fluctuation amount of the service index in the sub-dimension, the method may further include:

respectively acquiring fluctuation trend data corresponding to at least one sub-dimension according to the sub-momentum of the service index on at least one dimension value;

and screening out the sub-dimensions with the matching degrees meeting the preset requirements from at least one sub-dimension.

After screening out the sub-dimensions whose matching degrees meet the preset requirements, in an embodiment of the present application, the calculating a discrete degree of a contribution degree of each of the sub-dimensions to the service index fluctuation in the plurality of influence dimensions may include:

Fig. 3 is a schematic flow chart of another method for processing service data fluctuation in this embodiment, and as shown in fig. 3, according to the method in the foregoing embodiment, sub-wavelets of a service index in a sub-dimension are calculated, and fluctuation trend data corresponding to the sub-dimension is determined according to the size of the sub-wavelets of the sub-dimension, that is, a fluctuation trend of the service index in the sub-dimension is calculated. Specifically, the calculated sub-momentum may be compared with 0, if the sub-momentum is greater than 0, it may indicate that the fluctuation of the service indicator in the sub-dimension is in an increasing trend, and may indicate that the fluctuation is in an increasing trend by using specified data such as 1, that is, the fluctuation trend data of the service indicator in the sub-dimension is 1 at this time. If the sub-momentum is less than 0, it may indicate that the fluctuation of the service indicator in the sub-dimension is in a falling trend, and the specified data such as-1 may be used as the fluctuation trend data at this time. If the sub-momentum is equal to zero, it may indicate that the fluctuation of the service index in the sub-dimension is relatively smooth, and may use the specified data such as: 0 is taken as fluctuation tendency data at this time. Of course, the judgment method of the fluctuation trend can be adjusted correspondingly according to the actual situation, such as: the sub-momentum is compared with 1, and the embodiment of the present application is not particularly limited. Similarly, according to the relationship between the overall fluctuation amount corresponding to the service index and 0 (or other numerical values), the overall fluctuation trend data of the service index can be obtained. The overall fluctuation trend data of the service index can be compared with the fluctuation trend data of the service index on the sub-dimensions, and the sub-dimensions with the matching degree with the overall fluctuation trend meeting the preset requirements (such as the same fluctuation trend) are screened out. Such as: the sub-dimension with the same fluctuation trend of the service index on the sub-dimension and the whole fluctuation trend of the service index can be used as the sub-dimension with the fluctuation trend matching degree meeting the preset requirement.

Of course, other ways may also be selected to calculate the overall fluctuation trend data and the fluctuation trend data of the service index on the sub-dimension according to actual needs, such as: the fluctuation trend data of the service index on the sub-dimension can be determined by comparing the sub-momentum with the sub-numerical value or the reference sub-numerical value used when calculating the sub-momentum, and the overall fluctuation trend data can be determined by comparing the overall fluctuation with the overall numerical value (or the overall reference numerical value) used when calculating the overall fluctuation. Such as: and comparing the sub-wave momentum with the sub-numerical value used in the calculation of the sub-wave momentum to determine that the business index has an exponential rising trend in the sub-dimension, comparing the overall wave momentum with the overall numerical value used in the calculation of the overall wave momentum to determine that the overall wave fluctuation has a linear rising trend, and determining that the matching degree of the fluctuation trend data of the business index in the sub-dimension and the overall fluctuation trend data is lower.

The fluctuation trend data can be used for analyzing whether the fluctuation of the sub-dimension is consistent with the overall fluctuation trend, for example, the overall fluctuation is in an increase, but a certain sub-dimension is in a drop, and the fluctuation trend is opposite, which can indicate that the fluctuation of the service index in the sub-dimension is not the main reason of the overall fluctuation increase. The sub-dimension and even the influence dimension which have larger influence on the fluctuation of the service index can be screened out based on the overall fluctuation trend and the fluctuation trend corresponding to the sub-dimension. And calculating a contribution value and a discrete degree based on the screened sub-dimensions to obtain a target influence dimension and a target sub-dimension which have the largest influence on the fluctuation of the business indexes.

For example: if the overall fluctuation quantity of the user payment quantity of a certain service is larger than 0 through data acquisition and analysis, the overall fluctuation of the payment quantity is in an upward trend. The sub-momentum of the payment amount paid by using a credit card, a bank card and the like in China is greater than 0, namely the sub-momentum of the payment amount on the influencing dimension-the sub-dimension in the card country-China is greater than 0, and the fluctuation of the payment amount on the influencing dimension-the sub-dimension in the card country-China is in an upward trend and is the same as the fluctuation trend of the whole payment amount. The sub-momentum of the payment amount paid using a credit card, a bank card, or the like in the united states is less than 0, that is, the sub-momentum of the payment amount on the influence dimension-the sub-dimension in the card country-the united states is less than 0, and the fluctuation of the payment amount on the influence dimension-the sub-dimension in the card country-the united states is in a falling tendency, which is opposite to the fluctuation tendency of the entire payment amount. Through the analysis, the influence dimension, namely the child dimension in the card country, china can be used as the child dimension of which the matching degree meets the preset requirement, and further, the contribution degree corresponding to the influence dimension, namely the child dimension in the card country, namely China, can be calculated according to the child momentum of the payment amount in the influence dimension, namely the child dimension in the card country, namely China. Based on the contribution degree corresponding to the sub-dimension-China and the contribution degree corresponding to the sub-dimension with the influence dimension-Ka country fluctuation trend matching degree meeting the preset requirement, the discrete degree of the contribution degree of the sub-dimension to the business index fluctuation in the influence dimension-Ka country can be determined, and the target influence dimension and the target sub-dimension are determined by further using the method of the embodiment. The contribution degree of the payment amount on the influence dimension, namely the sub-dimension in the card country, namely the United states, and the corresponding discrete degree of the subsequent influence dimension, namely the card country, can be avoided from being processed, so that the data processing efficiency is improved.

By comparing the fluctuation trend of the service index on the sub-dimension with the overall fluctuation trend of the service index, the sub-dimension which is possibly in accordance with the preset requirement for the fluctuation trend matching degree with the larger influence degree on the service index is screened out, the contribution degree and the discrete degree corresponding to the influence dimension are further calculated for the sub-dimension of which the fluctuation trend matching degree is in accordance with the preset requirement, and an accurate data basis is provided for the subsequent determination of the target influence dimension and the target sub-dimension. And by screening the sub-dimensions of which the fluctuation trend matching degrees meet the preset requirements, the data processing and calculation can be omitted for the sub-dimensions of which the fluctuation trend matching degrees do not meet the preset requirements, so that the data processing and calculation processes are reduced, and the data processing efficiency is improved.

accordingly, the determining a target impact dimension from the plurality of impact dimensions that impacts the business metric fluctuation comprises:

Specifically, fig. 4 is a schematic flow chart of another service data fluctuation processing method of the present application, and as shown in fig. 4, by using the method in the foregoing embodiment, discrete degrees (e.g., standard deviations) corresponding to each influence dimension are obtained, and if the difference between the discrete degrees (e.g., standard deviations) corresponding to each influence dimension is not large, that is, the difference between the discrete degrees (e.g., standard deviations) corresponding to each influence dimension is smaller than a preset threshold, it may be reflected that one fluctuation item of each influence dimension on the service index may be at the same level. At this time, it is difficult to determine the target influence dimension by using the standard deviation corresponding to the influence dimension, that is, it is difficult to determine which influence dimension has a large fluctuation influence on the service index. The size of the preset threshold value may be set according to actual needs, and the embodiment of the present application is not particularly limited.

In the embodiment of the application, when the influence of the influence dimensionality on the service index is determined to be at the same level, the service weight coefficient can be introduced, that is, the service weight coefficients corresponding to different influence dimensionalities can be determined according to the historical data of the service index. Based on historical data corresponding to the service indexes, and by combining with expert experience, or through methods such as model training, the service weight coefficient influencing dimensionality is set. For different service indexes, service weight coefficients corresponding to the influence dimensions may be different, and may be specifically set according to actual needs. Moreover, the calculation method of the traffic weight coefficient may be different for different services, for example: in a wind control scene, data which is reported back to a case is used as a high-risk black sample, the ratio of the black samples in each dimension can be used as a high-risk weight of the influence dimension, and in some special scenes, the associated samples of the black samples can also be used as a high-risk weighting characteristic, so that a service weight coefficient suitable for a service scene is constructed. The method for determining the service weight coefficient affecting the dimension may be selected according to actual needs, and the embodiment of the present application is not particularly limited.

After the business weight coefficients of the influence dimensions corresponding to the business indexes are determined, the influence dimensions can be sorted based on the size of the business weight coefficients when the difference of the discrete degrees corresponding to the influence dimensions is not large, and the target influence dimensions are obtained. For example: the influence dimension with the largest business weight coefficient can be used as a target influence dimension, or the influence dimension with the business weight coefficient arranged in the previous preset ranking can be used as the target influence dimension, or the influence dimension with the business weight coefficient larger than the preset weight threshold can be used as the target influence dimension, and further the sub-dimension with the largest contribution degree in the target influence dimension can be used as the target sub-dimension.

In practical application, the fluctuation trend of the service index on the sub-dimension and the service weight coefficient of the influence dimension can be applied together in the fluctuation analysis of the service index. For example: the overall fluctuation trend of the business index and the fluctuation trend of the business index on the sub-dimension can be determined according to the overall fluctuation quantity of the business index and the sub-momentum on the sub-dimension, and the sub-dimension with the fluctuation trend meeting the preset requirement is screened out. And calculating the contribution degree corresponding to the sub-dimension of which the fluctuation trend meets the preset requirement by utilizing the sub-dimension of which the fluctuation trend meets the preset requirement, and further calculating the dispersion degree of the contribution degree of the sub-dimension in the influence dimension corresponding to the sub-dimension of which the fluctuation trend meets the preset requirement. If the difference value of the discrete degrees corresponding to each influence dimension is smaller than a preset threshold value, a service weight coefficient of the influence dimension can be set, and the target influence dimension is determined based on the service weight coefficient. If the difference value of the standard deviation corresponding to each influence dimension is larger than a preset threshold value, the target influence dimension can be directly determined according to the discrete degree corresponding to the influence dimension. And determining the target sub-dimension based on the contribution degree corresponding to the sub-dimension in the target influence dimension.

According to the embodiment of the application, when the dispersion degree corresponding to the influence dimension is small, such as the standard deviation difference, and the target influence dimension and the target sub-dimension which have large influence on the fluctuation of the service index cannot be well determined, the service weight coefficient is introduced. The weight coefficient can be set for the influence dimension corresponding to the service index based on historical data, expert experience and the like, and the target influence dimension and the target sub-dimension are determined based on the service weight coefficient corresponding to the influence dimension. The method and the device enable the downward exploration analysis of the service indexes to be more scientific and reasonable, and improve the accuracy of the fluctuation processing of the service data.

In the present specification, each embodiment of the method is described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. Reference is made to the description of the method embodiments in part.

Based on the above-mentioned service data fluctuation processing method, one or more embodiments of the present specification further provide a service data fluctuation processing apparatus. The apparatus may include systems (including distributed systems), software (applications), modules, components, servers, clients, etc. that use the methods described in the embodiments of this specification in conjunction with any necessary apparatus to implement the hardware. Based on the same innovative conception, embodiments of the present specification provide an apparatus as described in the following embodiments. Because the implementation scheme of the apparatus for solving the problem is similar to that of the method, the specific apparatus implementation in the embodiment of the present description may refer to the implementation of the foregoing method, and repeated details are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.

Specifically, fig. 5 is a schematic block structure diagram of an embodiment of a service data fluctuation processing apparatus provided in the present application, and as shown in fig. 5, the service data fluctuation processing apparatus provided in the present application includes: an influence dimension determining module 51, a fluctuation amount calculating module 52, a contribution degree processing module 53, and a fluctuation analyzing module 54, wherein:

an influence dimension determining module 51, configured to obtain a plurality of influence dimensions corresponding to the service index, where the influence dimensions include at least one sub-dimension;

a fluctuation amount calculation module 52, configured to calculate an overall fluctuation amount of the service index and sub-fluctuation amounts of the service index in the sub-dimensions;

a contribution processing module 53, configured to separately calculate, according to the overall fluctuation amount and the sub-fluctuation amounts, a discrete degree of a contribution of each of the sub-dimensions to the fluctuation of the business indicator in the multiple influence dimensions;

the fluctuation analysis module 54 may be configured to determine, according to a discrete degree of a contribution degree of each of the sub-dimensions to the business index fluctuation in the influence dimension, a target influence dimension that influences the business index fluctuation from the plurality of influence dimensions.

The business data fluctuation processing device provided by the embodiment of the application introduces the overall fluctuation amount of the business index and the calculation method of the sub-fluctuation amount of the business index on the specific sub-dimension, and simultaneously calculates the discrete degree of the contribution of the sub-dimension to the business index fluctuation in each influence dimension based on the overall fluctuation amount and the sub-fluctuation amount. The method for analyzing the business index fluctuation by downward exploration has rigorous calculation logic, higher business interpretability and higher accuracy of business data fluctuation analysis. Meanwhile, the method can support combined sounding of any number of dimensions, can help complex services to rapidly conduct fluctuation detection, fluctuation disassembly, main cause positioning and the like, and is very intelligent in the whole service index fluctuation disassembly and sounding analysis method, so that the accuracy of service data fluctuation analysis processing is improved.

On the basis of the foregoing embodiment, the fluctuation amount calculation module is specifically configured to:

The embodiment of the application provides a method for calculating the overall fluctuation quantity of the service index and the sub-fluctuation quantity of the service index on the sub-dimension, and provides an accurate data basis for the fluctuation analysis of the subsequent service index.

Fig. 6 is a schematic structural diagram of a contribution degree processing module in an embodiment of the present application, and as shown in fig. 6, on the basis of the above embodiment, the contribution degree processing module 53 includes:

a contribution calculating unit 61, configured to calculate, according to the overall fluctuation amount and the sub-fluctuation amount, a contribution value of the fluctuation of the business index in the sub-dimension to the fluctuation of the business index;

the dispersion degree calculating unit 62 may be configured to calculate, according to the contribution value corresponding to the sub-dimension in the influence dimension, a dispersion degree of a contribution degree of each of the sub-dimensions in the influence dimension to the business index fluctuation.

According to the embodiment of the application, the contribution degree of the fluctuation of the service index on the sub-dimension to the overall fluctuation of the service index is calculated based on the sub-momentum corresponding to each sub-dimension in the influence dimension. And determining the discrete degree of the contribution degree of each sub-dimension in the influence dimension to the fluctuation of the service index according to the contribution degree corresponding to each sub-dimension in the influence dimension. The method is simple, provides an accurate data base for the fluctuation analysis of the subsequent service indexes, and improves the accuracy of the fluctuation analysis of the service data.

On the basis of the above embodiment, the dispersion degree calculating unit is specifically configured to:

According to the embodiment of the application, the distribution condition of the influence degree of the sub-dimensionality in the influence dimensionality on the business index fluctuation is represented by the standard deviation or the variance of the contribution degree corresponding to each sub-dimensionality in the influence dimensionality, the method is simple, an accurate data basis is provided for the fluctuation analysis of the follow-up business index, and the accuracy of the business data fluctuation analysis is improved.

On the basis of the foregoing embodiment, the service indicators include a first type of service indicator and a second type of service indicator, the second type of service indicator is obtained by calculation based on at least two first type of service indicators, and the contribution degree calculation unit is specifically configured to:

According to the embodiment of the application, different contribution calculation methods are provided for different service indexes, especially for the second-class service indexes, the simple increase calculation is not performed any more, the fluctuation influence of each first-class service index in the second-class service indexes on the overall service index is considered, and an accurate data basis is provided for the overall fluctuation condition analysis of the subsequent service indexes.

Fig. 7 is a schematic block diagram of a service data fluctuation processing apparatus in another embodiment of the present application, and as shown in fig. 7, the service data fluctuation processing apparatus in an embodiment of the present application may further include:

the sub-dimension fluctuation processing module 71 may be configured to obtain fluctuation trend data corresponding to at least one of the sub-dimensions according to the sub-momentum of the service indicator in at least one of the sub-dimensions;

the overall fluctuation processing module 72 may be configured to obtain overall fluctuation trend data of the service index according to the overall fluctuation amount;

a trend comparison module 73, configured to compare fluctuation trend data corresponding to at least one of the sub-dimensions with the overall fluctuation trend data, and calculate a matching degree between the fluctuation trend data corresponding to the sub-dimensions and the overall fluctuation trend data;

and the sub-dimension screening module 74 is configured to screen out a sub-dimension, of which the matching degree meets a preset requirement, from at least one sub-dimension.

On the basis of the foregoing embodiment, the contribution processing module 53 is specifically configured to calculate, according to the sub-momentum of the service indicator on the sub-dimension of which the matching degree meets the preset requirement, a discrete degree of the contribution of each sub-dimension of which the matching degree meets the preset requirement in the multiple influence dimensions to the fluctuation of the service indicator.

According to the embodiment of the application, the fluctuation trend of the sub-dimension and the whole fluctuation trend of the service index are determined according to the whole fluctuation quantity of the service index and the sub-momentum of the service index on the sub-dimension, the sub-dimension which is the same as the whole fluctuation trend of the service index or the matching degree of the fluctuation trend meets the preset requirement is screened out, and then the fluctuation condition of the service index is analyzed based on the screened sub-dimension. The data processing amount is reduced, and the data processing efficiency is improved.

Fig. 8 is a schematic block diagram of a service data fluctuation processing apparatus in another embodiment of the present application, and as shown in fig. 8, the service data fluctuation processing apparatus in an embodiment of the present application may further include:

the weight coefficient setting module 81 may be configured to, when a difference between discrete degrees of contribution degrees of each of the sub-dimensions to the service index fluctuation in the plurality of influence dimensions is smaller than a preset threshold, obtain a service weight coefficient corresponding to the influence dimension according to historical data of the service index;

correspondingly, the fluctuation analysis module 54 may be configured to, when a difference between the dispersion degrees of the contribution degrees of each of the sub-dimensions to the business index fluctuation in the multiple influence dimensions is smaller than the preset threshold, obtain the target influence dimension according to the business weight coefficient.

According to the embodiment of the application, when the standard deviation difference corresponding to the influence dimension is small and the target influence dimension and the target sub-dimension which have large influence on the fluctuation of the service index cannot be well determined, the service weight coefficient is introduced. The weight coefficient can be set for the influence dimension corresponding to the service index based on historical data, expert experience and the like, and the target influence dimension and the target sub-dimension are determined based on the service weight coefficient corresponding to the influence dimension. The method and the device enable the sounding analysis of the service indexes to be more scientific and reasonable, and improve the accuracy of the service data fluctuation processing.

Fig. 9 is a schematic block diagram of a service data fluctuation processing apparatus in another embodiment of the present application, and as shown in fig. 8, the service data fluctuation processing apparatus in an embodiment of the present application may further include:

the target influence dimension value determining module 91 may be configured to determine, according to a contribution degree of each of the sub-dimensions in the target influence dimension to the business index fluctuation, a target sub-dimension that influences the business index fluctuation from the target influence dimension.

According to the embodiment of the application, the sub-dimension with larger influence on the fluctuation of the service index can be further determined based on the target influence dimension and the contribution degree corresponding to the sub-dimension in the target influence dimension, and a more accurate and more detailed data basis is provided for monitoring the service index.

In an embodiment of the present application, a computer storage medium may also be provided, where a computer program is stored, and when the computer program is executed, the method for processing video data in the foregoing embodiment may be implemented, for example, as follows:

respectively calculating the discrete degree of the contribution degree of each sub-dimension in a plurality of influence dimensions to the service index fluctuation according to the overall fluctuation amount and the sub-fluctuation amount;

and determining a target influence dimension influencing the business index fluctuation from a plurality of influence dimensions according to the dispersion degree of the contribution degree of each sub-dimension in the influence dimensions to the business index fluctuation.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The method or apparatus provided by the present specification and described in the foregoing embodiments may implement service logic through a computer program and record the service logic on a storage medium, where the storage medium may be read and executed by a computer, so as to implement the effect of the solution described in the embodiments of the present specification.

The method or the apparatus for processing service data fluctuation provided in the embodiments of the present specification may be implemented in a computer by a processor executing corresponding program instructions, for example, implemented in a PC end using a c + + language of a windows operating system, implemented in a linux system, or implemented in an intelligent terminal using android and iOS system programming languages, or implemented in processing logic based on a quantum computer. In an embodiment of a service data fluctuation processing system provided in this specification, fig. 10 is a schematic block diagram of an embodiment of a service data fluctuation processing system provided in this application, and as shown in fig. 10, a service data fluctuation processing system provided in this application may include a processor 101 and a memory 102 for storing processor-executable instructions,

the processor 101 and the memory 102 communicate with each other through the bus 103;

the processor 101 is configured to call the program instructions in the memory 102 to execute the methods provided in the above embodiments of the seismic data processing method, including: obtaining a plurality of influence dimensions corresponding to the service index, wherein the influence dimensions comprise at least one sub-dimension; calculating the overall fluctuation quantity of the service index and the sub-fluctuation quantity of the service index on the sub-dimension; respectively calculating the discrete degree of the contribution degree of each sub-dimension in the plurality of influence dimensions to the service index fluctuation according to the overall fluctuation amount and the sub-fluctuation amount; and determining a target influence dimension influencing the business index fluctuation from a plurality of influence dimensions according to the discrete degree of the contribution degree of each sub-dimension in the influence dimensions to the business index fluctuation.

It should be noted that descriptions of the apparatus, the computer storage medium, and the system described above according to the related method embodiments may also include other embodiments, and specific implementations may refer to the descriptions of the method embodiments and are not described in detail herein.

The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.

The embodiments of this specification are not limited to what must be in compliance with industry communication standards, standard computer data processing and data storage rules, or the description of one or more embodiments of this specification. Certain industry standards, or implementations modified slightly from those described using custom modes or examples, may also achieve the same, equivalent, or similar, or other, contemplated implementations of the above-described examples. The embodiments using the modified or transformed data acquisition, storage, judgment, processing and the like can still fall within the scope of the alternative embodiments of the embodiments in this specification.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical blocks. For example, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)) is an integrated circuit whose Logic functions are determined by a user programming the Device. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development, but the original code before compiling is also written in a specific Programming Language, which is called Hardware Description Language (HDL), and the HDL is not only one kind but many kinds, such as abll (Advanced boot Expression Language), AHDL (alternate hard Description Language), traffic, CUPL (computer universal Programming Language), HDCal (Java hard Description Language), lava, lola, HDL, PALASM, software, rhydl (Hardware Description Language), and vhul-Language (vhyg-Language), which is currently used in the field. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in purely computer readable program code means, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, apparatuses, modules or units described in the above embodiments may be specifically implemented by a computer chip or an entity, or implemented by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle human interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Although one or more embodiments of the present description provide method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded. The terms first, second, etc. are used to denote names, but not any particular order.

For convenience of description, the above devices are described as being divided into various modules by functions, which are described separately. Of course, when implementing one or more of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, etc. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be 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 diagram block or blocks.

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 diagram block or blocks.

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 diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage, graphene storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

As will be appreciated by one skilled in the art, one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the present specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

The above description is merely exemplary of one or more embodiments of the present disclosure and is not intended to limit the scope of one or more embodiments of the present disclosure. Various modifications and alterations to one or more embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims.

Claims

1. A method for processing service data fluctuation is characterized by comprising the following steps:

obtaining a plurality of influence dimensions corresponding to the business indexes, wherein the influence dimensions comprise at least one sub-dimension;

respectively calculating the discrete degree of the contribution degree of each sub-dimension in the plurality of influence dimensions to the service index fluctuation according to the overall fluctuation amount and the sub-fluctuation amount; the contribution degree of each sub-dimension to the business index fluctuation is the influence degree of the business index fluctuation on the business index fluctuation caused by the fluctuation of the business index on each sub-dimension;

2. The method of claim 1, wherein said calculating the overall amount of fluctuation of the traffic indicator and the sub-amounts of fluctuation of the traffic indicator in the sub-dimensions comprises:

and taking the difference value between the sub-numerical value and the reference sub-numerical value as the sub-momentum of the service index on the sub-dimension, and taking the difference value between the overall numerical value and the overall reference numerical value as the overall fluctuation amount of the service index.

3. The method of claim 1, wherein said calculating a discrete degree of contribution of each of said sub-dimensions of said plurality of said influencing dimensions to said business indicator fluctuation based on said overall fluctuation amount and said sub-fluctuation amount comprises:

calculating the contribution value of the fluctuation of the service index on the sub-dimension to the fluctuation of the service index according to the overall fluctuation amount and the sub-fluctuation amount;

and calculating the discrete degree of the contribution degree of each sub-dimension in the influence dimension to the fluctuation of the service index according to the contribution degree value corresponding to the sub-dimension in the influence dimension.

4. The method of claim 3, wherein the calculating a discrete degree of the contribution degree of each of the sub-dimensions in the influence dimension to the business index fluctuation according to the contribution degree value corresponding to the sub-dimension in the influence dimension comprises:

and taking the standard deviation or the variance of the contribution degree as the dispersion degree of the contribution degree of each sub-dimension in the influence dimension to the fluctuation of the business index.

5. The method of claim 3, wherein the service indicators include a first type service indicator and a second type service indicator, the second type service indicator is obtained by calculation based on at least two of the first type service indicators, and the calculating the contribution degree of the fluctuation of the service indicator in the sub-dimension to the fluctuation of the service indicator according to the overall fluctuation amount and the sub-fluctuation amount comprises:

if the service index is the first class service index, taking the ratio of the sub-dimension of the first class service index to the overall fluctuation amount as the contribution value of the fluctuation of the first class service index on the sub-dimension to the first class service index;

if the service index is the second type service index, calculating the contribution value of the fluctuation of the second type service index on the sub-dimension to the second type service index according to the following formula;

6. The method of claim 1, wherein after calculating the overall amount of fluctuation of the traffic indicator and the sub-amounts of fluctuation of the traffic indicator in the sub-dimensions, the method further comprises:

7. The method of claim 6, wherein said calculating a discrete degree of contribution of each of said sub-dimensions of said plurality of said influencing dimensions to said business metric fluctuation comprises:

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

9. The method of any one of claims 1-8, further comprising:

10. A traffic data fluctuation processing apparatus, comprising:

the influence dimension determining module is used for acquiring a plurality of influence dimensions corresponding to the service index, wherein the influence dimensions comprise at least one sub-dimension;

the contribution degree processing module is used for respectively calculating the discrete degree of the contribution degree of each sub-dimension in the plurality of influence dimensions to the service index fluctuation according to the whole fluctuation amount and the sub-fluctuation amount; the contribution degree of each sub-dimension to the business index fluctuation is the influence degree of the business index fluctuation on the business index fluctuation caused by the fluctuation of the business index on each sub-dimension;

11. The apparatus according to claim 10, wherein the fluctuation amount calculation module is specifically configured to:

12. The apparatus of claim 10, wherein the contribution degree processing module comprises:

and the dispersion degree calculating unit is used for calculating the dispersion degree of the contribution degree of each sub-dimension in the influence dimension to the fluctuation of the service index according to the contribution degree value corresponding to the sub-dimension in the influence dimension.

13. The apparatus according to claim 12, wherein the dispersion degree calculation unit is specifically configured to:

14. The apparatus according to claim 12, wherein the service indicators include a first type of service indicator and a second type of service indicator, the second type of service indicator is obtained by calculation based on at least two of the first type of service indicators, and the contribution degree calculation unit is specifically configured to:

15. The apparatus of claim 10, wherein the apparatus further comprises:

the overall fluctuation processing module is used for acquiring overall fluctuation trend data of the service index according to the overall fluctuation amount;

and the sub-dimension screening module is used for screening out the sub-dimensions with the matching degree meeting the preset requirement from at least one sub-dimension.

16. The apparatus according to claim 15, wherein the contribution degree processing module is specifically configured to:

and calculating the discrete degree of the contribution degree of each sub-dimension with the matching degree meeting the preset requirement in the plurality of influence dimensions to the business index fluctuation according to the sub-momentum of the business index on the sub-dimension with the matching degree meeting the preset requirement.

17. The apparatus of claim 10, wherein the apparatus further comprises:

the weight coefficient setting module is used for acquiring a business weight coefficient corresponding to the influence dimension according to historical data of the business index when the difference value between the discrete degrees of the contribution of each sub-dimension to the business index fluctuation in the plurality of influence dimensions is smaller than a preset threshold value;

18. The apparatus of any of claims 10-17, further comprising:

19. A computer storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the method of any of claims 1-9.

20. A traffic data fluctuation handling system comprising at least one processor and a memory for storing processor-executable instructions which, when executed by the processor, implement the method of any one of claims 1 to 9.