CN113052582A - Bill checking method, device and equipment and computer storage medium - Google Patents

Bill checking method, device and equipment and computer storage medium Download PDF

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CN113052582A
CN113052582A CN201911376569.7A CN201911376569A CN113052582A CN 113052582 A CN113052582 A CN 113052582A CN 201911376569 A CN201911376569 A CN 201911376569A CN 113052582 A CN113052582 A CN 113052582A
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雷文丽
周士伟
何勇强
刘长利
范颖
卢佳
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China Mobile Communications Group Co Ltd
China Mobile Information Technology Co Ltd
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Abstract

The invention discloses a bill checking method, a device, equipment and a computer storage medium, which specifically comprise the following steps: acquiring service data of a bill to be verified, wherein the service data comprises first service data and second service data; determining a first credibility of the first service data according to the first service data; and determining the second credibility of the second service data according to the second service data, and obtaining the credibility of the bill to be verified by using a mean algorithm according to the first credibility and the second credibility. According to the embodiment of the invention, the accuracy and the reasonability of the bill data can be quantitatively evaluated, and the accuracy of the service bill to be verified is determined.

Description

Bill checking method, device and equipment and computer storage medium
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a bill checking method, device and equipment and a computer storage medium.
Background
The operator can make business account settlement according to the flow and call volume and other indexes every month. The whole amount of the bill is huge, the types of related indexes are many, the bill needs to be checked after monthly payment is finished, and possible problems are eliminated. At present, after monthly payment is finished, a worker analyzes the fluctuation condition of monthly bill data through the ring ratio and the proportion of the bill data so as to judge the accuracy of the bill.
However, by adopting the method for checking and verifying the bill, only abnormal points of bill fluctuation can be found, and if the bill data in a month has large geometric fluctuation after the bill is checked, the accuracy and the reasonability of the bill data cannot be quantitatively evaluated, and business personnel cannot conveniently master the accuracy of the bill data, so that the problem of finding a problem bill cannot be found in time, and further the problem of bill complaint increase and the like can be caused.
Disclosure of Invention
The embodiment of the invention provides a bill checking method, a bill checking device and a computer storage medium, which can quantitatively evaluate the accuracy and the reasonability of bill data, facilitate business personnel to master the accuracy of the bill data and find out problem bills in time.
In a first aspect, an embodiment of the present invention provides a method for checking a bill, where the method includes:
acquiring service data of a bill to be verified, wherein the service data comprises first service data and second service data;
determining a first credibility of the first service data according to the first service data;
determining a second credibility of the second service data according to the second service data;
and obtaining the credibility of the bill to be verified by using a mean algorithm according to the first credibility and the second credibility.
Optionally, determining the first reliability of the first service data according to the first service data includes:
obtaining a first correlation degree by utilizing a correlation degree analysis algorithm according to the monthly data of the first service and the historical data of the first service in the first service data;
inputting the first service historical data into a preset first time sequence analysis model to obtain a first fitting degree;
and obtaining the first reliability of the first service data by using a weight algorithm according to the first correlation and the first fitting degree.
Optionally, the first business month data and the first business history data respectively include at least one business index data; the obtaining of the first correlation degree by using a correlation degree analysis algorithm according to the monthly data of the first service and the historical data of the first service in the first service data includes:
and obtaining a first correlation degree by utilizing a correlation degree analysis algorithm according to the first service monthly data and at least one service index data in the first service historical data.
Optionally, the correlation analysis algorithm is a euclidean distance-based correlation analysis algorithm.
Optionally, the second service data includes service data of a plurality of areas, the service data of each area includes first index data and second index data, the first index data includes first index historical data, and the second index data includes second index historical data; the determining a second reliability of the second service data according to the second service data includes:
respectively inputting the plurality of first index historical data into a preset second time series analysis model to obtain a plurality of second fitting degrees;
inputting the plurality of second index historical data into a preset third time series analysis model respectively to obtain a plurality of third fitting degrees;
and determining a second credibility of the second service data according to the second fitting degrees and the third fitting degrees.
Optionally, the determining a second degree of confidence of the second traffic data according to the second plurality of degrees of fits and the third plurality of degrees of fits includes:
determining the reliability of the service data of the corresponding region according to the second fitting degree obtained by the first index historical data of the first index data of each region and the third fitting degree obtained by the second index historical data of the second index data of the corresponding region;
and determining the second credibility of the second service data according to the credibility of the service data of the plurality of areas.
Optionally, the first time series analysis model, the second time series analysis model, and the third time series analysis model are autoregressive moving average models.
Optionally, the first service data includes a total settlement amount, and the second service data includes a total provincial settlement amount.
Optionally, the first index data is a sum to be paid, and the second index data is a sum to be paid.
In a second aspect, an embodiment of the present invention provides a device for checking a bill, where the device includes:
the system comprises an acquisition module, a verification module and a verification module, wherein the acquisition module is used for acquiring business data of a bill to be verified, and the business data comprises first business data and second business data;
the first determining module is used for determining the first credibility of the first service data according to the first service data;
the second determining module is used for determining the second credibility of the second service data according to the second service data;
and the third determining module is used for obtaining the credibility of the bill to be verified by using a mean algorithm according to the first credibility and the second credibility.
Optionally, the first determining module includes:
the correlation unit is used for obtaining a first correlation degree by utilizing a correlation degree analysis algorithm according to the monthly data of the first service and the historical data of the first service in the first service data;
the fitting unit is used for inputting the first service historical data into a preset first time sequence analysis model to obtain a first fitting degree;
and the determining unit is used for obtaining the first reliability of the first service data by using a weight algorithm according to the first correlation degree and the first fitting degree.
In a third aspect, an embodiment of the present invention provides a device for bill verification, where the device includes: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the method of bill verification as described above.
In a fourth aspect, an embodiment of the present invention provides a computer storage medium, where computer program instructions are stored, and when executed by a processor, implement the method for checking bills in the first aspect or any optional manner of the first aspect.
In the technical scheme, the service data of the bill to be verified comprises first service data and second service data, the first credibility is determined through the first service data, the second credibility is determined through the second service data, and finally the credibility of the bill to be verified can be determined after the first credibility and the second credibility are combined. Therefore, the accuracy of the bill can be effectively evaluated through the quantitative analysis of the bill credibility, the accuracy and the reasonability of the bill data can be quantitatively checked, business personnel can conveniently master the accuracy of the bill data, problem bills can be found in time, the problem bills can be processed in time, and then the occurrence of bill complaint events can be reduced.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a bill checking method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another method for bill verification according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a tree-shaped calculation model of bill credibility in an actual application scenario according to an embodiment of the present invention;
fig. 4 is a schematic flowchart of determining bill credibility in an actual application scenario according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of another bill checking apparatus provided in the embodiment of the present invention;
fig. 6 is a schematic diagram of a hardware structure for bill verification according to an embodiment of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
In order to solve the problem of the prior art, embodiments of the present invention provide a method, an apparatus, a device, and a computer storage medium for bill verification. First, a method for checking bills provided by the embodiment of the present invention is described below.
In the embodiment of the invention, when the accuracy verification analysis is carried out on the service bill, the evaluation can be carried out according to the first service and the second service, so that the summary of the first service data and the second service data can reflect the bill value to a certain extent. Therefore, the method and the device can summarize and evaluate the reliability of the business bill from two aspects of the first business data reliability and the second business data reliability, and can check and analyze the bill.
Fig. 1 is a schematic flowchart of a method for checking a bill according to an embodiment of the present invention. As shown in fig. 1, in the embodiment of the present invention, the method for checking the account bill may include the following steps:
step 101: and acquiring the service data of the bill to be verified.
Here, the service data includes first service data and second service data. The first business data and the second business data may be divided according to different business dimensions of the bill to be verified. For example: the service dimension can be province dimension or dimension such as mobile phone number. The first service data may be a settlement total; the second service data may be a total amount of provincial settlement, that is, a summary of the provincial settlement amount.
Step 102: and determining the first credibility of the first service data according to the first service data.
Here, the first reliability of the first service data may be, for example, reliability of a settlement total amount.
Step 103: and determining the second credibility of the second service data according to the second service data.
Here, the second reliability of the second service data may be, for example, reliability of the total amount of the provincial settlement.
Step 104: and obtaining the credibility of the bill to be verified by using a mean algorithm according to the first credibility and the second credibility.
Here, the obtained first reliability and the second reliability are combined by using a mean algorithm to obtain the reliability of the bill to be verified, and specifically, the mean algorithm may be an algorithm such as weighted average.
In summary, in the embodiment of the present invention, in the bill verification method, the service data of the bill to be verified includes the first service data and the second service data. And determining the first credibility through the first service data and determining the second credibility through the second service data. And finally, after the first credibility and the second credibility are combined and processed, the credibility of the bill to be checked is obtained, so that the accuracy and the reasonableness of the bill data can be quantitatively checked, and business personnel can conveniently master the accuracy of the bill data. Based on the data of bill credibility, the problem bills can be found in time, and the problem bills can be processed in time, so that the occurrence of bill complaint events can be reduced.
In another embodiment of the present invention, as shown in fig. 2, fig. 2 is a schematic flowchart of another bill checking method provided in the embodiment of the present invention. The bill checking method can be specifically realized as the following steps:
step 201: and acquiring the service data of the bill to be verified.
Here, the service data includes first service data and second service data. The first business data and the second business data may be divided according to different business dimensions of the bill to be verified. Specifically, the first service data includes a settlement total amount; the second service data comprises provincial total settlement amount, wherein the total settlement amount is the total settlement amount according to the mobile phone number and with no provincial dimensionality; the total provincial settlement amount is the sum of the provincial settlement amounts of 31 provincial and municipal autonomous districts.
Specifically, the first service data includes first service monthly data and first service historical data, where the first service historical data may be first service data of a time period before the month, and does not include the first service data of the month, for example, the first service historical data may be first service data of 20 months before the month.
Specifically, the second service data includes service data of a plurality of areas, and the service data of each area includes first index data and second index data. The first index data includes first index history data, and the second index data includes second index history data. Wherein the first index history data and the second index history data respectively represent the first index data and the second index data one time period before (not including) the present month. Specifically, the first index data may be an amount to be credited, and the second index data may be an amount to be credited.
Step 202: and obtaining a first correlation degree by utilizing a correlation degree analysis algorithm according to the monthly data of the first service and the historical data of the first service in the first service data.
Here, the first service data includes first service month data and first service history data, wherein the first service month data and the first service history data include at least one service index data, respectively. Specifically, the service index data includes: settlement amount, call amount, audit error unification calculation amount and the like.
Specifically, a first correlation degree is obtained by using a correlation degree analysis algorithm according to at least one service index data in the first service monthly data and the first service historical data.
The correlation degree analysis algorithm is a correlation degree analysis algorithm based on Euclidean distance, and the correlation degree between the monthly data of the first service and the historical data of the first service can be calculated through a similarity coefficient calculation mode based on Euclidean distance, so that the first correlation degree of the first service data is obtained.
Specifically, the relevancy score is basic data for calculating the bill credibility score, in the embodiment of the present invention, the relevancy between the monthly data of the first service and the historical data of the first service is calculated by using a similarity coefficient calculation mode based on the euclidean distance, and the specific calculation steps are as follows:
and S11, constructing a correlation analysis index matrix.
And abstracting monthly service index data such as settlement amount, call amount, audit error amount and audit error amount calculation amount into an analysis object.
n months can obtain n objects X, wherein n is a natural number greater than 1, and X is represented as:
X＝{x1,x2,x3,…,xn} (1)
each object comprises m business index data which can respectively refer to settlement amount, call amount, audit error amount and audit error settlement amount, wherein m is a natural number more than 1, namely xiExpressed as:
xi＝{xi1,xi2,xi3,…,xim} (2)
the above relationship is represented by a matrix as:
Figure BDA0002341136030000081
and S12, solving a similarity coefficient matrix between the objects.
Euclidean distance, also known as euclidean distance, refers to the distance between two points in an m-dimensional space and can be used to measure the correlation between objects. The invention uses Euclidean distance as the distance between different indexes of different objects, and the calculation mode is as follows:
Figure BDA0002341136030000082
wherein r isijRepresents a distance, x, y represent calculation variables, respectively, and k, n, and m are natural numbers greater than 1, respectively.
The similarity coefficient matrix R among all objects can be obtained by the method
Figure BDA0002341136030000083
And S13, calculating the difference degree between the objects.
By calculating the degree of difference p between the ith object and other objectsiAnd measuring the correlation degree among the objects. Degree of difference piThe larger the distance between the object i and other objects, the smaller the correlation. The difference degree is used as the score of the correlation degree between the data of the first business month and the data of the first business history, namely other months. The degree of difference is calculated as follows:
Figure BDA0002341136030000084
wherein i represents the ith object, j represents the jth object, and k, n and m are natural numbers greater than 1.
Step 203: and inputting the first service historical data into a preset first time sequence analysis model to obtain a first fitting degree.
Here, the preset first time-series analysis model may be an Autoregressive moving average model (ARMA). The parameters of the ARMA model are determined according to the time series to be fitted, and specifically, the specific model parameters are determined according to the time series autocorrelation and partial autocorrelation systems to be fitted. Specifically, the first service history data may be represented as a time series, and a first fitting degree of the first service data is obtained by using an ARMA model.
The fitness score is the basis for calculating the confidence of the bill. In the embodiment of the invention, an ARMA model is utilized to analyze the historical number of the first service to be analyzedFitting the monthly data value of the first service according to the historical trend, and utilizing R2The degree of fit is measured as a fitness score. The obtaining of the first degree of fit of the first service data using the ARMA model may be embodied as the following steps:
and S21, smoothing the sequence to be analyzed.
And performing time sequence analysis on the sequence to be analyzed, observing the long-term change trend of the data, wherein if the value of the sequence to be analyzed fluctuates up and down in a constant and the fluctuation range is limited, the sequence is a stable sequence, and if not, the sequence is a non-stable sequence. If the data is a non-stationary sequence, the sequence needs to be converted into a stationary sequence by using differential processing.
And S22, determining model parameters.
And performing autocorrelation analysis and partial autocorrelation analysis on the stationary time sequence subjected to the difference processing, and respectively solving autocorrelation coefficients and partial autocorrelation coefficients to assist in confirming the parameters of the model p and the model q.
1) Autoregressive model (Autoregressive model, AR)
Autoregressive model ar (p): time series y if the sequence length is ntSatisfies the following conditions:
Figure BDA0002341136030000091
wherein epsilontIs a sequence of independent and identically distributed random variables, and epsilontSatisfies the following conditions:
E(εt)＝0 (8)
Figure BDA0002341136030000092
then the time series is called ytObeying an autoregressive model of order p. Wherein E (ε)t) Representing the expectation of a variable, var (ε)t) Representing the variance of the variables. σ represents a variable, which is used to indicate that the variance is a variable greater than 0.
Autocorrelation coefficient ρ of the kth sample of the sequencekThe calculation formula is as follows:
Figure BDA0002341136030000093
where x represents a variable for performing a fitness analysis, for example, when performing a fitness analysis on the first service data, the variable refers to the first service data in different months, for example: x6 represents the first traffic data of month 6.
2) Moving Average model (Moving-Average, MA)
Moving average model ma (q): if the time series ytSatisfies the following conditions:
yt＝εt-θ1εt-1-…-θqεt-q (11)
then the time series is called ytObeying a moving average model of order q. Wherein, thetaqRepresenting a variable, ε, in a modeltRepresenting a random error term. Specifically, the moving average model MA determines q by analyzing the relationship between the value at time t and the random interference term at other times
Partial autocorrelation coefficient of the kth sample of the sequence
Figure BDA0002341136030000101
The calculation method is as follows:
Figure BDA0002341136030000102
wherein D iskAnd D represents a matrix.
Figure BDA0002341136030000103
Figure BDA0002341136030000104
3) And determining p and q coefficients.
The known autocorrelation coefficient ρkAnd partial auto-correlationCoefficient of performance
Figure BDA0002341136030000105
All obey normal distribution and utilize 2 times of standard deviation range
Figure BDA0002341136030000106
The truncation condition of the correlation coefficient can be judged, and the coefficients p and q can be further determined in an auxiliary mode.
Correlation coefficient truncation: if the autocorrelation coefficient or partial autocorrelation coefficient of a sample is significantly greater than 2 times the standard deviation range in the initial d-order, then almost 95% of the autocorrelation coefficient ρkAll fall within the range of 2 times of standard deviation, and the process that the non-zero correlation coefficient is attenuated to small value fluctuation is very sudden, the correlation coefficient is regarded as correlation coefficient truncation, and the truncation order is d.
Tailing of correlation coefficient: correlation coefficient tailing is considered if more than 5% of the sample correlation coefficient falls outside the 2-fold standard deviation range, or if the non-zero correlation coefficient fluctuates slowly.
And determining a parameter p according to the truncation order of the partial autocorrelation coefficient, and determining a parameter q according to the truncation order of the autocorrelation coefficient.
S23, constructing an ARMA (p, q) model.
The data sequence formed by the prediction index along with the time is regarded as a random sequence, and the dependency relationship of the group of random variables represents the continuity of the original data in time. The random sequence has its own variation law and is influenced by external factors. Considering the predicted object ytInfluenced by self-variation, therefore, the model expression of the autoregressive moving average mixed model ARMA obeying the (p, q) order is as follows:
yt＝β1yt-1+…+βpyt-p+εt-θ1εt-1-…-θqεt-q (15)
using least square method to measure parameters in model
Figure BDA0002341136030000111
And (3) estimating:
Figure BDA0002341136030000112
where β, θ represent different parameters in the model.
The residual terms are:
Figure BDA0002341136030000113
the sum of the squares of the residuals is:
Figure BDA0002341136030000114
finally, the parameter that minimizes the sum of the squares of the residuals is the parameter
Figure BDA0002341136030000115
And calculating a goodness of fit R2The goodness-of-fit may be expressed as a first goodness-of-fit for the first traffic data.
Step 204: and obtaining the first reliability of the first service data by using a weight algorithm according to the first correlation and the first fitting degree.
Specifically, the influence degree of a first correlation degree and the first fitting degree on a first reliability of first service data is analyzed, the first correlation degree and the first fitting degree are respectively weighted by using a weighting algorithm, the weighting values can be the same, and finally the first reliability is calculated by using a weighted average.
Here, it is understood that the credibility is used to analyze whether the monthly bill has a large abnormality, and the credibility is determined by analyzing the degree of fitting of the relevancy of the first business monthly data and the first business historical data and the time sequence. The high degree of fitting represents that the error between the actual value of the bill data and the model fitting value is small, and the high degree of correlation represents that the data in the first service month, namely the total settlement amount in the month has a correlation with the historical data in the first service month, namely the total settlement amount in other months, so that the degree of fitting and the degree of correlation both reflect whether the bill is abnormal or not to a certain extent.
Step 205: and respectively inputting the plurality of first index historical data into a preset second time series analysis model to obtain a plurality of second fitting degrees.
Here, the second service data includes service data of a plurality of areas, the service data of each area includes first index data and second index data, the first index data includes first index history data, and the second index data includes second index history data. That is, there are a plurality of first index history data and a plurality of second index history data. Further, specifically, the first index data may be an amount to be credited, and the second index data may be an amount to be credited.
Here, the preset second time series analysis model may be an ARMA model, the parameters of which are determined according to the time series to be fitted, specifically, the specific model parameters are determined according to the time series autocorrelation and partial autocorrelation system to be fitted. The first index historical data can be represented as the time series to be fitted, and a second fitting degree of the first index historical data is obtained by using an ARMA model.
In the embodiment of the invention, an ARMA model is utilized, the monthly data value of the first index data is fitted according to the historical trend of the first index historical data to be analyzed, and R is utilized2And measuring the fitting degree to obtain the fitting degree. The specific implementation steps of obtaining the second fitting degree by using the ARMA model may be as S21-S23 in the above step 203, which are not described herein again.
Further, a plurality of first index historical data are respectively input into a second time series analysis model, namely an ARMA model, and a plurality of second fitting degrees can be obtained.
Further, it is understood that the first index history data uses the second time series analysis model, i.e., the model parameters of the ARMA model may be the same as the model parameters of the first time series analysis model, or may be different. In practical application, a user can adjust and set according to own requirements, which is not described herein again.
Step 206: and respectively inputting the plurality of second index historical data into a preset third time series analysis model to obtain a plurality of third fitting degrees.
Here, the preset third time series analysis model may be an ARMA model, and parameters of the ARMA model are determined according to the time series to be fitted, specifically, specific model parameters are determined according to the time series autocorrelation and partial autocorrelation system to be fitted. The second index historical data can be represented as the time series to be fitted, and the ARMA model is utilized to obtain the degree of triple fit of the second index historical data.
In the embodiment of the invention, an ARMA model is utilized, the monthly data value of the second index data is fitted according to the historical trend of the second index historical data to be analyzed, and R is utilized2And measuring the fitting degree to obtain the fitting degree. The specific implementation steps of obtaining the third fitting degree by using the ARMA model may be as described in steps S21-S23 in step 203, and will not be described herein again.
Further, a plurality of second index historical data are respectively input into a third time series analysis model, namely an ARMA model, and a plurality of third fitting degrees can be obtained.
Further, it is understood that the second index history data uses the third time series analysis model, i.e., the model parameters of the ARMA model may be the same as the model parameters of the first time series analysis model, the model parameters of the second time series analysis model, or may be different. That is, the model parameters of the first time-series analysis model, and the model parameters of the second time-series analysis model may be the same or different. In practical application, a user can adjust and set according to own requirements, which is not described herein again.
Step 207: and determining the reliability of the service data of the corresponding region according to the second fitting degree obtained by the first index historical data of the first index data of each region and the third fitting degree obtained by the second index historical data of the second index data of the corresponding region.
Here, the reliability of the traffic data in any one of the plurality of regions may be determined based on the second degree of fitting of the first index data and the third degree of fitting of the second index data in the region. Specifically, the weighted average method is used to perform weighted average on the second fitting degree and the third fitting degree respectively to obtain the reliability of the second service data in the region.
Further, by analyzing the correlation degree of the first index data and the second index data with the second service data, the weight value may be determined. Through correlation analysis, a correlation coefficient matrix of the first index data, the second index data and the second service data can be obtained, and the weight values of the first index data and the second index data to the second service data are calculated according to a conversion formula of the correlation coefficient and the weight. In general, the weight values may be set equal.
Specifically, the confidence level of each region may be calculated by using a mean algorithm of a multi-layer tree.
In addition, the first index data may be a sum to be paid, and the second index data may be a sum to be paid. The plurality of areas may be 31 provincial municipalities, and the service data in any one area may be the settlement amount of the provincial municipality.
Step 208: and determining the second credibility of the second service data according to the credibility of the service data of the plurality of areas.
Here, the second reliability of the second traffic data is calculated by an averaging method based on the reliabilities of the traffic data of the plurality of areas. Specifically, the second reliability of the second traffic data may be determined by an arithmetic average of the reliabilities of the traffic data of the plurality of regions.
Specifically, the plurality of areas may be 31 provincial and municipal municipalities, and the credibility of the 31 provincial and municipal municipalities is collected and averaged to obtain a summary of the credibility of the provincial settlement amount, that is, the credibility of the total provincial settlement amount.
Step 209: and obtaining the credibility of the bill to be verified by using a mean algorithm according to the first credibility and the second credibility.
Here, the averaging algorithm may be a weighted averaging algorithm. And respectively carrying out assignment according to the influence degrees of the first credibility and the second credibility on the bill credibility, setting the evaluation values to be the same weight values, and obtaining a bill credibility score, namely the bill credibility, in a weighted average mode.
Specifically, the determination manner of the influence weight values of the bill credibility of the first credibility and the second credibility may be through expert evaluation, and the first credibility and the second credibility are respectively scored to determine the weight values, that is, the weight values corresponding to the first credibility and the second credibility are respectively obtained.
In summary, in the embodiment of the present invention, in the bill verification method, the service data of the bill to be verified includes the first service data and the second service data; the first service data comprises first service monthly data and first service historical data; the second service data comprises service data of a plurality of areas, and the service data of each area comprises first index data and second index data; the first index data includes first index history data, and the second index data includes second index history data.
Determining a first correlation degree and a first fitting degree of the first service data according to the first service monthly data and the first service historical data, and determining a first credibility according to the first correlation degree and the first fitting degree; for the second reliability, the reliability of each region may be determined based on the second degree of fitting of the first index data and the third degree of fitting of the second index data for each region, and the second reliability may be obtained by summarizing the reliability of the plurality of regions. And finally, after the first credibility and the second credibility are combined and processed, the credibility of the bill to be checked is obtained, so that the accuracy and the reasonableness of the bill data can be quantitatively checked, and business personnel can conveniently master the accuracy of the bill data. Based on the data of bill credibility, the problem bills can be found in time, and the problem bills can be processed in time, so that the occurrence of bill complaint events can be reduced.
In order to better understand the technical scheme of the invention, the method for checking the bill in the embodiment of the invention is explained in detail in combination with an actual application scenario. Fig. 3 is a schematic diagram of a tree-shaped calculation model of bill credibility in a practical application scenario according to an embodiment of the present invention.
Specifically, the mobile operator performs accuracy check analysis on a mobile service bill, namely a bill, and constructs a tree-shaped bill reliability calculation model with a root node as service bill reliability. As shown in fig. 3, fig. 3 is a schematic diagram of a tree-shaped calculation model of bill credibility in a practical application scenario according to an embodiment of the present invention. Specifically, in the model, the root node is the bill reliability, and the child nodes of the root node are the summary of the total settlement amount reliability and the provincial settlement amount reliability, that is, the reliability of the total provincial settlement amount. The reliability of the settlement total can be determined by the correlation degree of the settlement total and the fitting degree of the settlement total. The credibility of the provincial settlement amounts is summarized as the summary of the credibility of the provincial settlement amounts, and the credibility of any one provincial settlement amount can be determined by the degree of fitting of the amount to be intervened and the degree of fitting of the amount to be settled.
The tree structure as shown in fig. 3 is an embodiment of one idea of the tree-like computational model modeling of bill credibility. The method comprises the steps of analyzing a business settlement process, selecting a business index capable of representing a bill, further analyzing the credibility of the business index, quantifying through two mathematical indexes of fitting degree and correlation degree, further constructing a calculation method of the business bill credibility layer by layer, and realizing evaluation of bill accuracy. The settlement total amount and the provincial settlement total amount are obtained by counting settlement fees from different dimensions, and corresponding bills are generated only after the settlement amount indexes and the provincial settlement amount indexes are counted.
For the total settlement amount, the degree of reliability is defined by analyzing the correlation degree of the total settlement amount in the current month and other months and the fitting degree of the time series. The high degree of fitting indicates that the error between the actual value and the fitting value of the model is small, and the high degree of correlation indicates that the total settlement amount in the month has a correlation with other months, and reflects whether the bill is abnormal or not to a certain extent.
On the other hand, as for the total amount of the provincial settlement, the total amount of the provincial settlement is a summary of each of the provincial settlement amounts. Each provincial settlement amount is determined according to two indexes of the provincial settlement amount and the provincial settlement amount. And determining whether a large difference exists between the actual value and the predicted value of the amount to be paid in the province in the month or not by performing fitting analysis on the amount to be paid in the province in the month, and determining whether a large difference exists between the actual value and the predicted value of the amount to be paid out in the province in the month or not by performing fitting analysis on the amount to be paid out in the province in the month. If the gap is large, the data in this month may have large fluctuation, resulting in low fitness, affecting the credibility of the provincial settlement amount, and further possibly affecting the credibility of the provincial settlement total amount.
Specifically, the settlement amount credibility is calculated according to the correlation analysis algorithm, the difference degree of the settlement amount, the call amount, the mischeck amount and the mischeck amount of different months is calculated, and the settlement amount goodness of fit R in the month is calculated according to the ARMA fitness algorithm2And finally, calculating the reliability of the settlement amount by using a weighted averaging mode. And summarizing the credibility of the provincial settlement amounts, and averaging according to the credibility of the 31 provincial settlement amounts to obtain the credibility of each provincial settlement amount, wherein the credibility of each provincial settlement amount is obtained by calculating the fitting degree score of the corresponding income amount and the fitting degree score of the corresponding out amount according to an ARMA (autoregressive moving average) fitting degree algorithm, and carrying out weighted average on the corresponding fitting degree scores of the income amount and the corresponding out amount to obtain the credibility of each provincial settlement amount. The values of different nodes can be respectively obtained according to the method, and finally the value of the root node is calculated according to a weighted average value method, namely the bill credibility score is obtained.
Specifically, as shown in fig. 4, fig. 4 is a schematic flow chart illustrating a process of determining bill credibility in an actual application scenario according to an embodiment of the present invention. In an actual application scenario, the method for determining the bill reliability may be implemented as follows: step 41: screening key indexes capable of representing bills according to the service settlement characteristics, analyzing service indexes influencing the key indexes by more urgent service characteristics, and constructing a tree-shaped bill reliability analysis model based on an analysis result; step 42: based on historical data, an ARMA (p, q) fitting degree analysis model is constructed, and the fitting goodness of the total settlement amount, the fitting goodness of the amount of money to be paid in each province and the fitting goodness of the amount of money to be paid out are calculated; step 43: constructing a correlation degree analysis model based on the Euclidean distance, and calculating the difference degree of the settlement amount, the call amount, the audit fault amount and the audit fault unification calculation amount in the month; step 44: calculating credibility of settlement amounts of each province by using a mean algorithm (MUL method) of a multi-layer tree based on the goodness of fit of the settlement amounts of the income amounts and the settlement amounts of each province, and calculating the credibility of the settlement amounts of each province by using a mean algorithm (AVG method) to be summarized; step 45: calculating the reliability of the total settlement amount by using a weight algorithm (RIG method) based on the goodness of fit and the degree of difference of the total settlement amount; step 46: and calculating the bill credibility by using an RIG method based on the total settlement amount credibility and provincial settlement amount credibility summary.
In summary, in the bill verification method in the embodiment of the present invention, it is considered that the service settlement can be evaluated according to the total settlement list and each provincial settlement list, and the summary of the service settlement amount and the provincial settlement amount can reflect the value of the service bill to a certain extent. And evaluating the reliability of the service bill from two aspects of the reliability of the settlement amount and the reliability of the provincial settlement amount. And the settlement amount reliability is obtained by weighted calculation according to the goodness of fit of the settlement amount in the current month and the difference between the settlement amount, the phone bill amount, the misaudit bill amount and the misaudit bill settlement amount in different months, the provincial settlement amount reliability is summarized by weighted average according to the settlement amount reliability of 31 provinces, the settlement amount of each province is determined by the corresponding settlement amount and the settlement amount, and finally, the obtained bill reliability method is formed, namely, the bill can be verified.
Therefore, the technical scheme realizes the bill credibility analysis method by utilizing the correlation algorithm and the fitting degree algorithm, perfects the bill accuracy evaluation system, solves the problems of system shortage, failure discovery lag, complaint rise and the like, and has the following beneficial effects: the relevance and the fitting degree of the basic index are calculated by utilizing the Euclidean distance and the ARMA time sequence, so that the accuracy and the efficiency of the obtained index result are ensured; the reliability of the business bill is determined by the reliability of a plurality of business indexes, so that the reliability accuracy of the bill is improved, and the excessive action of a single factor is avoided; weighting each index according to the influence degree of each index on the bill, and calculating a credibility score according to the weighted average, so that the calculation result of the bill credibility is more accurate; the bill credibility tree structure calculation model is suitable for different service scenes in the field of mobile communication, and ensures the flexibility of model configurability while having high reusability.
In another embodiment of the present invention, a bill verification apparatus is provided, as shown in fig. 5, fig. 5 is a schematic structural diagram of a bill verification apparatus according to another embodiment of the present invention. This account bill check-up's device includes:
an obtaining module 501, configured to obtain service data of a bill to be verified, where the service data includes first service data and second service data;
a first determining module 502, configured to determine a first reliability of the first service data according to the first service data
A second determining module 503, configured to determine a second reliability of the second service data according to the second service data;
a third determining module 504, configured to obtain, according to the first reliability and the second reliability, the reliability of the bill to be verified by using a mean algorithm.
In summary, in this embodiment, the bill verification apparatus is provided to implement the bill verification method in the above embodiments, where the service data of the bill to be verified includes first service data and second service data, the first reliability is determined by the first service data, the second reliability is determined by the second service data, and finally, after the first reliability and the second reliability are combined, the reliability of the bill to be verified can be determined. Therefore, the accuracy of the bill can be effectively evaluated through the quantitative analysis of the bill credibility, the accuracy and the reasonability of the bill data can be quantitatively checked, business personnel can conveniently master the accuracy of the bill data, problem bills can be found in time, the problem bills can be processed in time, and then the occurrence of bill complaint events can be reduced.
Fig. 6 is a schematic diagram illustrating a hardware structure of bill verification provided in the embodiment of the present invention.
The bill verification device may include a processor 601 and memory 302 having stored thereon computer program instructions.
Specifically, the processor 601 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present invention.
Memory 602 may include mass storage for data or instructions. By way of example, and not limitation, memory 602 may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, tape, or Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 602 may include removable or non-removable (or fixed) media, where appropriate. The memory 602 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 602 is a non-volatile solid-state memory. In a particular embodiment, the memory 602 includes Read Only Memory (ROM). Where appropriate, the ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or a combination of two or more of these.
The processor 601 may implement any of the bill verification methods of the embodiments shown in fig. 1 or fig. 2 by reading and executing computer program instructions stored in the memory 602.
In one example, the bill verification device may also include a communication interface 603 and a bus 610. As shown in fig. 6, the processor 601, the memory 602, and the communication interface 603 are connected via a bus 610 to complete communication therebetween.
The communication interface 603 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present invention.
The bus 610 includes hardware, software, or both to couple the components at the bill checking device to each other. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hypertransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 610 may include one or more buses, where appropriate. Although specific buses have been described and shown in the embodiments of the invention, any suitable buses or interconnects are contemplated by the invention.
The bill verification device may execute the bill verification method in the embodiment of the present invention, so as to implement the bill verification method described in conjunction with fig. 1 or fig. 2.
In addition, in combination with the bill checking method in the above embodiments, the embodiments of the present invention may provide a computer storage medium to implement. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by the processor, implement any of the bill checking methods in the above embodiments.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.

Claims (13)

1. A method of bill verification, comprising:
acquiring service data of a bill to be verified, wherein the service data comprises first service data and second service data;
determining a first credibility of the first service data according to the first service data;
determining a second credibility of the second service data according to the second service data;
and obtaining the credibility of the bill to be verified by using a mean algorithm according to the first credibility and the second credibility.
2. The method of claim 1, wherein determining a first trustworthiness of the first traffic data based on the first traffic data comprises:
obtaining a first correlation degree by utilizing a correlation degree analysis algorithm according to the monthly data of the first service and the historical data of the first service in the first service data;
inputting the first service historical data into a preset first time sequence analysis model to obtain a first fitting degree;
and obtaining the first reliability of the first service data by using a weight algorithm according to the first correlation and the first fitting degree.
3. The method of claim 2, wherein the first business month data and the first business history data respectively comprise at least one business index data; the obtaining of the first correlation degree by using a correlation degree analysis algorithm according to the monthly data of the first service and the historical data of the first service in the first service data includes:
and obtaining a first correlation degree by utilizing a correlation degree analysis algorithm according to the first service monthly data and at least one service index data in the first service historical data.
4. The method of claim 3, wherein the correlation analysis algorithm is a Euclidean distance based correlation analysis algorithm.
5. The method according to any one of claims 2 to 4, wherein the second service data comprises service data of a plurality of areas, the service data of each area comprising first index data and second index data, the first index data comprising first index history data, the second index data comprising second index history data; the determining a second reliability of the second service data according to the second service data includes:
respectively inputting the plurality of first index historical data into a preset second time series analysis model to obtain a plurality of second fitting degrees;
inputting the plurality of second index historical data into a preset third time series analysis model respectively to obtain a plurality of third fitting degrees;
and determining a second credibility of the second service data according to the second fitting degrees and the third fitting degrees.
6. The method of claim 5, wherein determining a second confidence level for the second traffic data based on the second plurality of fits and the third plurality of fits comprises:
determining the reliability of the service data of the corresponding region according to the second fitting degree obtained by the first index historical data of the first index data of each region and the third fitting degree obtained by the second index historical data of the second index data of the corresponding region;
and determining the second credibility of the second service data according to the credibility of the service data of the plurality of areas.
7. The method of claim 5, wherein the first time series analysis model, the second time series analysis model, and the third time series analysis model are autoregressive moving average models.
8. The method of claim 1, wherein the first service data comprises a settlement total and the second service data comprises a provincial settlement total.
9. The method of claim 5, wherein the first metric data is a settlement amount and the second metric data is a settlement amount.
10. An apparatus for bill verification, the apparatus comprising:
the system comprises an acquisition module, a verification module and a verification module, wherein the acquisition module is used for acquiring business data of a bill to be verified, and the business data comprises first business data and second business data;
the first determining module is used for determining the first credibility of the first service data according to the first service data;
the second determining module is used for determining the second credibility of the second service data according to the second service data;
and the third determining module is used for obtaining the credibility of the bill to be verified by using a mean algorithm according to the first credibility and the second credibility.
11. The apparatus of claim 10, wherein the first determining module comprises:
the correlation unit is used for obtaining a first correlation degree by utilizing a correlation degree analysis algorithm according to the monthly data of the first service and the historical data of the first service in the first service data;
the fitting unit is used for inputting the first service historical data into a preset first time sequence analysis model to obtain a first fitting degree;
and the determining unit is used for obtaining the first reliability of the first service data by using a weight algorithm according to the first correlation degree and the first fitting degree.
12. A bill verification apparatus, the apparatus comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the method of bill verification as claimed in any one of claims 1-9.
13. A computer storage medium having computer program instructions stored thereon which, when executed by a processor, implement the bill verification method of any one of claims 1-9.
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