CN113052582B - Method, device, equipment and computer storage medium for checking bill - Google Patents
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Abstract
The invention discloses a method, a device, equipment and a computer storage medium for checking bills, which concretely comprise the following steps: the method comprises the steps of acquiring service data of a bill to be checked, wherein the service data comprise first service data and second service data; determining a first credibility of the first service data according to the first service data; and determining second credibility of the second service data according to the second service data, and obtaining the credibility of the bill to be checked 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 rationality of the bill data can be quantitatively evaluated, and the accuracy of the business bill to be checked is determined.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a computer storage medium for checking a bill.
Background
And the operator can carry out business account settlement according to the indexes such as flow, ticket quantity and the like. The whole bill is huge, the index types are more, and the bill needs to be checked after the monthly account is discharged, so that possible problems are eliminated. At present, after the monthly billing is finished, staff analyzes fluctuation conditions of the monthly bill data through ring ratio and same ratio of the bill data, so that the accuracy of the bill is judged.
However, by adopting the method for checking and checking the bill, only abnormal points of bill fluctuation can be found, for example, after the bill is checked, the bill data of a certain month has comparatively large fluctuation, the accuracy and rationality of the bill data cannot be quantitatively evaluated, and business personnel cannot grasp the accuracy of the bill data conveniently, so that a problem bill cannot be found in time, and further, the problem of rising of bill complaints and the like can be caused.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a computer storage medium for checking bills, which can quantitatively evaluate the accuracy and rationality of bill data, and is convenient for business personnel to master the accuracy of the bill data and discover problem bills in time.
In a first aspect, an embodiment of the present invention provides a method for checking a bill, including:
the method comprises the steps of acquiring service data of a bill to be checked, wherein the service data comprise 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 checked by using a mean algorithm according to the first credibility and the second credibility.
Optionally, determining the first credibility of the first service data according to the first service data includes:
obtaining a first correlation by using a correlation analysis algorithm according to first business month data and first business history data in the first business data;
inputting the first service history data into a preset first time sequence analysis model to obtain a first fitting degree;
and obtaining the first credibility of the first business data by using a weight algorithm according to the first relativity 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 a first correlation according to the first business month data and the first business history data in the first business data by using a correlation analysis algorithm includes:
and obtaining a first correlation by utilizing a correlation analysis algorithm according to at least one business index data in the first business month data and the first business history 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 history data, and the second index data includes second index history data; the determining the second credibility of the second service data according to the second service data comprises the following steps:
Respectively inputting the plurality of first index historical data into a preset second time sequence analysis model to obtain a plurality of second fitting degrees;
respectively inputting the plurality of second index historical data into a preset third time sequence analysis model to obtain a plurality of third fitting degrees;
and determining the second credibility of the second business data according to the plurality of second fitting degrees and the plurality of third fitting degrees.
Optionally, the determining the second credibility of the second service data according to the plurality of second fitness degrees and the plurality of third fitness degrees includes:
determining the credibility of the business data of the corresponding area according to the second fitting degree obtained by the first index historical data of the first index data of each area and the third fitting degree obtained by the second index historical data of the second index data of the corresponding area;
and determining 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 all autoregressive moving average models.
Optionally, the first service data includes a settlement total amount, and the second service data includes a provincial settlement total amount.
Optionally, the first index data is the amount to be bound, and the second index data is the amount to be bound.
In a second aspect, an embodiment of the present invention provides an apparatus for checking a bill, where the apparatus 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 comprise first business data and second business data;
the first determining module is used for determining first credibility of the first service data according to the first service data;
a second determining module, configured to determine a second reliability 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 checked by utilizing 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 by utilizing a correlation analysis algorithm according to the first business month data and the first business history data in the first business data;
the fitting unit is used for inputting the first service history 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 credibility of the first service data by utilizing 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 checking a bill, including: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method of bill verification as described above.
In a fourth aspect, embodiments of the present invention provide a computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement the first aspect, or the bill verification method in any alternative of the first aspect.
In the technical scheme, the service data of the bill to be checked 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 checked can be determined after the first credibility and the second credibility are combined. Therefore, the accuracy of the bill can be effectively evaluated through quantitative analysis of the reliability of the bill, the accuracy and rationality of the bill data can be quantitatively checked, business personnel can master the accuracy of the bill data conveniently, problem bills can be found out in time, the problem bills can be processed in time, and then the occurrence of bill complaint events can be reduced.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present invention, the drawings that are needed to be used in the embodiments of the present invention will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.
FIG. 1 is a flow chart of a method for bill verification according to an embodiment of the present invention;
FIG. 2 is a 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 flow chart of determining the reliability of a bill in an actual application scenario according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a billing verification apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a hardware structure of bill checking 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 the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and the detailed embodiments. It should be understood that the specific embodiments described herein are merely configured to illustrate the invention and are not configured to limit 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 invention by showing examples of the invention.
It is noted that relational terms such as first and second, and the like are 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. Moreover, 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 like elements in a process, method, article or apparatus that comprises the element.
In order to solve the problems in the prior art, the embodiment of the invention provides a method, a device, equipment and a computer storage medium for checking bills. The method for checking the bill provided by the embodiment of the invention is first described below.
In the embodiment of the invention, when the accuracy check analysis is carried out on the business bill, the business bill can be evaluated according to the first business and the second business, so that the summary of the first business data and the second business data can reflect the bill value to a certain extent. Therefore, the invention gathers and evaluates the credibility of the business bill from the two angles of the credibility of the first business data and the credibility of the second business data, and can verify and analyze the bill.
Fig. 1 is a flow chart of a method for checking a bill according to an embodiment of the present invention. As shown in fig. 1, in an embodiment of the present invention, the method for checking an account list may include the following steps:
step 101: and acquiring service data of the bill to be checked.
Here, the service data includes first service data and second service data. The first service data and the second service data may be divided according to different service dimensions of the bill to be checked. For example: the service dimension may be a province or a dimension such as a cell phone number. The first business data may be a settlement total amount; the second service data may be a summary of the provincial settlement total amount, i.e., 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 credibility of the first service data may be, for example, credibility of a settlement total amount.
Step 103: and determining a second credibility of the second service data according to the second service data.
Here, the second credibility of the second service data may be, for example, credibility of a provincial settlement total amount.
Step 104: and obtaining the credibility of the bill to be checked by using a mean algorithm according to the first credibility and the second credibility.
Here, the average algorithm is used to combine the obtained first reliability and the second reliability to obtain the reliability of the bill to be checked, and specifically, the average algorithm may be a weighted average algorithm or the like.
In summary, in the method for checking account, the service data of the bill to be checked 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. Finally, after the first credibility and the second credibility are combined, the credibility of the bill to be checked is obtained, so that the accuracy and rationality of the bill data can be quantitatively checked, and the accuracy of the bill data can be conveniently mastered by service personnel. Based on the data of the bill credibility, the problem bill can be found in time, the problem bill can be processed in time, and then 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 flow chart of another method for checking a bill according to an embodiment of the present invention. The bill checking method can be specifically implemented as the following steps:
step 201: and acquiring service data of the bill to be checked.
Here, the service data includes first service data and second service data. The first service data and the second service data may be divided according to different service dimensions of the bill to be checked. Specifically, the first business data includes a settlement total amount; the second business data comprises a provincial settlement total amount, wherein the settlement total amount is the settlement total amount according to the mobile phone number and in different provincial dimensions; the total amount of the provincial settlement is the sum of the provincial settlement amounts of 31 provincial municipalities.
Specifically, the first service data includes first service month data and first service history data, where the first service history data may be first service data of a period of time before the month, and the first service data without the month may be, for example, 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 of a period of time before the present month (without the present month). Specifically, the first index data may be the amount to be paid in, and the second index data may be the amount to be paid out.
Step 202: and obtaining a first correlation by using a correlation analysis algorithm according to the first business month data and the first business history data in the first business data.
Here, the first business data includes first business month data and first business history data, wherein the first business month data and the first business history data include at least one business index data, respectively. Specifically, the business index data includes: account amount, ticket amount, audit error amount, etc.
Specifically, according to at least one business index data in the first business month data and the first business history data, a correlation analysis algorithm is utilized to obtain a first correlation.
The correlation analysis algorithm is based on Euclidean distance, and the correlation between the first business month data and the first business history data can be calculated by a similarity coefficient calculation mode based on Euclidean distance, so that the first correlation of the first business data is obtained.
Specifically, the relevance score is basic data for calculating the bill reliability score, and in the embodiment of the present invention, the relevance between the first business month data and the first business history data is calculated by using a similarity coefficient calculation mode based on the euclidean distance, and the specific calculation steps are as follows:
S11, constructing a correlation analysis index matrix.
Monthly business index data such as the amount of settlement, the amount of bill, the amount of audit error bill and the amount of settlement of audit error bill are abstracted as analysis objects.
n months can result in n objects X, where n is a natural number greater than 1, and X is expressed as:
X＝{x 1 ,x 2 ,x 3 ,…,x n } (1)
each object contains m business index data, which can be respectively referred to as settlement amount, ticket amount, audit error amount and audit error amount, wherein m is a natural number greater than 1, namely x i Expressed as:
x i ＝{x i1 ,x i2 ,x i3 ,…,x im } (2)
the above relationship is represented by a matrix:
s12, solving a similarity coefficient matrix between objects.
Euclidean distance, also known as euclidean distance, refers to the distance between two points in 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:
wherein r is ij The distance, x, y represent the calculated variables, and k, n, m are natural numbers greater than 1, respectively.
The similarity coefficient matrix R between the objects can be obtained by the method
S13, calculating the difference degree among objects.
By calculating the firstDegree of difference p between i objects and other objects i And measuring the correlation degree among the objects. Degree of difference p i The larger the distance between the object i and other objects, the smaller the correlation. The difference is used as a correlation score between the first business month data and the first business history data, namely other months. The difference degree is calculated as follows:
Wherein i represents the ith object, j represents the jth object, and k, n and m are natural numbers greater than 1 respectively.
Step 203: and inputting the first service history 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 (Autoregressive moving average model, ARMA). Parameters of the ARMA model are determined from the time series to be fitted, in particular, from 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 the first fitting degree of the first service data is obtained by using an ARMA model.
The fitness score is the basis for calculating the reliability of the bill. In the embodiment of the invention, an ARMA model is utilized to fit a first business month data value according to the history trend of the first business history data to be analyzed, and R is utilized 2 The degree of fit is measured as a degree of fit score. The obtaining of the first fitting degree of the first service data using the ARMA model may be implemented as follows:
s21, stabilizing the sequence to be analyzed.
And carrying out time sequence analysis on the sequence to be analyzed, observing the long-term change trend of the data, and if the value of the sequence to be analyzed fluctuates up and down in a constant and the fluctuation range is limited, determining the sequence as a stable sequence, otherwise, determining the sequence as a non-stable sequence. If the data is a non-stationary sequence, differential processing is required to convert the sequence to a stationary sequence.
S22, determining model parameters.
And carrying out autocorrelation analysis and partial autocorrelation analysis on the stable time sequence after the difference processing, and respectively solving an autocorrelation coefficient and a partial autocorrelation coefficient to assist in confirming parameters of the model p and the model q.
1) Autoregressive model (Autoregressive model, AR)
Autoregressive model AR (p): if the sequence length is a time sequence y of n t The method meets the following conditions:
wherein ε t Is a random variable sequence which is independently and uniformly distributed and epsilon t The method meets the following conditions:
E(ε t )＝0 (8)
then the time series is called y t Obeying an autoregressive model of the p-th order. Wherein E (ε) t ) Representing the expectations of the variables, var (ε) t ) Representing the variance of the variable. σ represents a variable, which is used to represent a variable whose variance is greater than 0.
The autocorrelation coefficient ρ of the kth sample of the sequence k The calculation formula is as follows:
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 of different months, for example: x6 represents the first service data of 6 months.
2) Moving Average model (Moving-Average, MA)
Moving average model MA (q): if time series y t The method meets the following conditions:
y t ＝ε t -θ 1 ε t-1 -…-θ q ε t-q (11)
then the time series is called y t Obeys a q-order moving average model. Wherein θ q Representing the variables, ε, in the model t Representing 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 coefficients of the kth sample of the sequenceThe calculation method is as follows:
wherein D is k D represents a matrix.
3) And determining p and q coefficients.
Known autocorrelation coefficient ρ k And partial autocorrelation coefficientAll obey normal distribution and use 2 times standard deviation rangeThe tail-biting condition of the correlation coefficient can be judged, and the coefficients p and q are further determined in an auxiliary mode.
The correlation coefficient is truncated: if the autocorrelation coefficient or partial autocorrelation coefficient of the sample is significantly greater than 2 times the standard deviation in the initial d-order, then nearly 95% of the autocorrelation coefficient ρ k All fall within a standard deviation of 2 times, and the process of attenuating the non-zero correlation coefficient into small value fluctuation is very abrupt, the correlation coefficient is regarded as being truncated, and the truncated order is d.
Correlation coefficient tailing: if more than 5% of the sample correlation coefficients fall outside the standard deviation range of 2 times, or the non-zero correlation coefficient fluctuation process is slow, the correlation coefficient tailing is regarded.
And determining a parameter p according to the truncated order of the partial autocorrelation coefficient, and determining a parameter q according to the truncated order of the autocorrelation coefficient.
S23, constructing an ARMA (p, q) model.
The data sequence formed by the predictors with time is regarded as a random sequence, and the dependency relationship of the random variables represents the continuity of the original data with time. The random sequence has a self-variation rule and is influenced by external factors. Consider the predicted object y t Is influenced by self-variation, and therefore, the model expression of the (p, q) -order autoregressive moving average hybrid model ARMA is as follows:
y t ＝β 1 y t-1 +…+β p y t-p +ε t -θ 1 ε t-1 -…-θ q ε t-q (15)
using least squares to pair parameters in the modelAnd (3) estimating:
wherein, beta and theta represent different parameters in the model.
The residual term is:
the sum of squares of the residuals is:
finally, the parameter which minimizes the sum of squares of the residuals isLeast square estimated value of (2), and calculate the goodness of fit R 2 The goodness of fit may be expressed as a first goodness of fit for the first traffic data.
Step 204: and obtaining the first credibility of the first business data by using a weight algorithm according to the first relativity and the first fitting degree.
Specifically, the influence degree of the first correlation degree and the first fitting degree on the first credibility of the first service data is analyzed, a weight algorithm is utilized to respectively weight the first correlation degree and the first fitting degree, the weight values can be the same, and finally, the first credibility in a weighted average calculation mode is utilized.
Here, it can be understood that the reliability is used to analyze whether the present month bill has a large abnormality, and the reliability is determined by analyzing the fitting degree of the correlation degree and time sequence of the first business present month data and the first business history data. The high fitting degree indicates that the error between the actual value of the bill data and the fitting value of the model is smaller, the high correlation degree indicates that the first business month data, namely the month settlement total amount, has a correlation with the first business history data, namely the settlement total amount of other months, and therefore the fitting degree and the correlation degree can 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 sequence 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 including first index data including first index history data and second index data including second index history data. That is, there are a plurality of first index history data and a plurality of second index history data. In addition, specifically, the first index data may be the amount to be paid in, and the second index data may be the amount to be paid out.
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, in particular, the specific model parameters are determined according to the time series autocorrelation and partial autocorrelation system to be fitted. The first index history data may be represented as the time series to be fitted, and a second fitness of the first index history data is obtained by using an ARMA model.
In the embodiment of the invention, the ARMA model is utilized to fit the month data value of the first index data according to the history trend of the first index history data to be analyzed, and R is utilized 2 And measuring the fitting degree of the model to obtain the fitting degree. The specific implementation step of obtaining the second fitness by using the ARMA model may be S21-S23 in the step 203, which is not described herein.
Further, the plurality of first index historical data are respectively input into a second time series analysis model, namely an ARMA model, so that a plurality of second fitting degrees can be obtained.
Furthermore, it is understood that the model parameters of the first index history data using the second time series analysis model, i.e. 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, the user can adjust the setting according to the own requirement, which is not described here again.
Step 206: and respectively inputting the plurality of second index historical data into a preset third time sequence analysis model to obtain a plurality of third fitting degrees.
Here, the preset third time series analysis model may be an ARMA model, the parameters of which are determined according to the time series to be fitted, in particular, the specific model parameters are determined according to the time series autocorrelation and partial autocorrelation system to be fitted. The second index history data may be represented as the time series to be fitted, and three fitting degrees of the second index history data are obtained by using an ARMA model.
In the embodiment of the invention, the ARMA model is utilized to fit the month data value of the second index data according to the history trend of the second index history data to be analyzed, and R is utilized 2 And measuring the fitting degree of the model to obtain the fitting degree. The specific implementation step of obtaining the third fitting degree by using the ARMA model may be S21-S23 in the step 203, which is not described herein.
Further, the plurality of second index histories are respectively input into a third time series analysis model, namely an ARMA model, so that a plurality of third fitting degrees can be obtained.
Furthermore, it is understood that the model parameters of the second index history data using the third time series analysis model, i.e. 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, the user can adjust the setting according to the own requirement, which is not described here again.
Step 207: and determining the credibility of the business data of the corresponding area according to the second fitting degree obtained by the first index historical data of the first index data of each area and the third fitting degree obtained by the second index historical data of the second index data of the corresponding area.
Here, the reliability of the service data of any one of the plurality of areas may be determined according to the second fitting degree of the first index data and the third fitting degree of the second index data of the area. Specifically, a weighted average mode is utilized to carry out weighted average on the second fitting degree and the third fitting degree respectively, so that the credibility of the second service data of the area is obtained.
Further, the weight value may be determined by analyzing the correlation of the first index data and the second index data with the second service data. 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 weight values of the first index data and the second index data for the second service data respectively 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 reliability of each region can be calculated by using a mean algorithm of the multi-layer tree.
In addition, the first index data may be the amount to be added, and the second index data may be the amount to be added. The plurality of areas may be 31 provincial autonomous areas, and the service data of any area may be a settlement amount of the provincial autonomous area.
Step 208: and determining second credibility of the second service data according to the credibility of the service data of the plurality of areas.
Here, the second credibility of the second service data is calculated by means of a mean method according to the credibility of the service data of the plurality of areas. Specifically, the second reliability of the second service data may be determined by an arithmetic average of the reliability of the service data of the plurality of areas.
Specifically, the multiple areas may be 31 provincial autonomous areas, the credibility of the 31 provincial autonomous areas is summarized, and an average value is obtained to obtain the credibility summary of the provincial settlement amount, that is, the credibility of the provincial settlement total amount.
Step 209: and obtaining the credibility of the bill to be checked by using a mean algorithm according to the first credibility and the second credibility.
Here, the mean algorithm may be a weighted average algorithm. And respectively assigning values according to the influence degree of the first credibility and the second credibility on the credibility of the bill, wherein the values can be set to be the same weight value, and the credibility score of the bill, namely the credibility of the bill, is obtained in a weighted average mode.
Specifically, the determination mode of the influence weight value of the first credibility and the second credibility on the bill credibility can be used for respectively scoring the first credibility and the second credibility through expert evaluation to determine the weight value, namely respectively obtaining the weight values corresponding to the first credibility and the second credibility.
In summary, in the bill verification method according to the embodiment of the present invention, the service data of the bill to be verified includes the first service data and the second service data; the first business data comprises first business month data and first business history 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.
According to the first business month data and the first business history data, a first correlation degree and a first fitting degree of the first business data can be determined, and the first reliability is determined according to the first correlation degree and the first fitting degree; and for the second credibility, the credibility of each region can be determined according to the second fitting degree of the first index data and the third fitting degree of the second index data of each region, and the credibility of a plurality of regions is summarized to obtain the second credibility. 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 rationality of the bill data can be quantitatively checked, and the business personnel can grasp the accuracy of the bill data conveniently. Based on the data of the bill credibility, the problem bill can be found in time, the problem bill can be processed in time, and then the occurrence of bill complaint events can be reduced.
In order to better understand the technical scheme of the invention, a detailed description of the method for checking the bill in the embodiment of the invention is firstly provided by combining an actual application scene. 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.
Specifically, in order to perform accuracy check analysis on a mobile service bill, namely a bill, a mobile operator constructs a tree bill credibility calculation model with a root node as the service bill credibility. 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. Specifically, in the model, the root node is bill credibility, and the child node of the root node is sum of settlement total amount credibility and provincial settlement amount credibility, namely the credibility of the provincial settlement total amount. The settlement total amount credibility can be determined by the settlement total amount relatedness and the settlement total amount fitting degree. The provincial settlement amount credibility is summarized into the provincial settlement amount credibility, and any provincial settlement amount credibility can be determined through the intervention amount fitting degree and the settlement amount fitting degree.
The tree structure as shown in fig. 3 is an embodiment of one idea of tree-like computational model modeling of bill credibility. In the embodiment of the invention, the business index capable of representing the bill is selected by analyzing the business settlement flow, and the reliability of the business index is analyzed and quantified by two mathematical indexes of fitting degree and correlation degree, so that a calculation method of the reliability of the business bill is built layer by layer, and the accuracy of the bill is evaluated. The settlement total amount and the provincial settlement total amount are counted from different dimensionalities of settlement fees, and corresponding bills are generated after the settlement amount indexes and the provincial settlement amount indexes are counted.
And for the settlement total amount, the credibility is defined by analyzing the correlation degree of the settlement total amount of the current month and other months and the fitting degree of time sequences. The high fitting degree indicates that the error between the actual value and the fitting value of the model is smaller, the high correlation degree indicates that the total settlement amount in the month has correlation with other months, and whether the bill is abnormal or not is reflected to a certain extent.
On the other hand, the provincial settlement total amount is a sum of the per-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 larger gap exists between the actual value and the predicted value of the amount to be joined of the province in the current month by carrying out fitting analysis on the amount to be joined of the province, and determining whether a larger gap exists between the actual value and the predicted value of the amount to be joined of the province in the current month by carrying out fitting analysis on the amount to be joined of the province. If the gap is large, the data of the month may have large fluctuation, so that the fitting degree is low, the credibility of the provincial settlement amount is influenced, and the credibility of the total provincial settlement amount is possibly influenced.
Specifically, the credibility of the settlement amount is calculated according to the correlation analysis algorithm, the difference degree of the settlement amount, the ticket amount and the ticket amount of the audit error in different months is calculated, and the fitting goodness R of the settlement amount in the present month is calculated according to the ARMA fitting degree algorithm 2 And finally, calculating the credibility of the settlement amount by using a weighted average value calculating mode. And summarizing the credibility of the settlement amount of the province, and obtaining an average value according to the credibility of the settlement amount of the province 31, wherein the credibility of the settlement amount of each province calculates the fitting degree score of the amount to be settled and the fitting degree score of the amount to be settled according to an ARMA fitting degree algorithm, and performs weighted average on the fitting degree of the corresponding access amount and the fitting degree of the amount to be settled, so as to obtain the credibility of the settlement amount of each province. According to the method, the values of different nodes can be respectively obtained, and finally, the value of the root node is calculated according to the weighted average value obtaining mode, namely the bill credibility score.
Specifically, as shown in fig. 4, fig. 4 is a schematic flow chart of determining the reliability of a bill 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 service settlement characteristics, analyzing service indexes affecting the key indexes by urgent service characteristics, and constructing a tree bill credibility analysis model based on analysis results; step 42: based on historical data, an ARMA (p, q) fitting degree analysis model is constructed, and the total settlement amount fitting goodness, the amount fitting goodness to be added for each province and the amount fitting goodness to be added for each province are calculated; step 43: constructing a correlation analysis model based on Euclidean distance, and calculating the difference degree of the settlement amount, the ticket amount, the audit error ticket amount and the audit error unijunction calculation amount of the month; step 44: calculating the credibility of the settlement amount of each province by using a mean value algorithm (MUL method) of the multi-layer tree based on the fitting goodness of the settlement amount of each province and the fitting goodness of the settlement amount of each province, and calculating the credibility summary of the settlement amount of each province by using a mean value algorithm (AVG method); step 45: calculating the credibility of the total settlement amount by using a weight algorithm (RIG method) based on the fitting goodness of the total settlement amount and the quasi-difference of the total settlement amount; step 46: and calculating the bill credibility by using a RIG method based on the total settlement amount credibility and the provincial settlement amount credibility summary.
In summary, according to the bill checking method in the embodiment of the invention, business settlement is considered to be evaluated according to the total settlement bill and the settlement bills of each province, and the sum of the business settlement amount and the province settlement amount can reflect the business bill value to a certain extent. And evaluating the credibility of the business bill from two angles of credibility of the settlement amount and credibility of the provincial settlement amount. The settlement amount credibility is obtained by weighting and calculating according to the fitting goodness of the settlement amount of the month and the settlement amount, bill amount, audit error amount and difference degree of audit error single calculation amount of different months, the settlement amount credibility of the province is obtained by weighting and averaging the settlement amount credibility of the province according to 31 provinces, the settlement amount of each province is determined by the settlement amount and the settlement amount, and finally the obtained bill credibility is formed, so that the bill can be checked.
Therefore, the technical scheme realizes the bill reliability analysis method by utilizing the correlation algorithm and the fitness algorithm, improves the bill accuracy evaluation system, solves the problems of system deficiency, fault discovery lag, complaint rising and the like, and has the following beneficial effects: the relevance and the fitting degree of the basic index are calculated by using the Euclidean distance and the ARMA time sequence, so that the accuracy and the efficiency of the obtained index result are ensured; the service bill credibility is determined by a plurality of service index credibility together, so that the bill credibility accuracy is improved, and the overaction 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 weighted average, so that the bill credibility calculation result is more accurate; the bill credibility tree structure calculation model is applicable to different service scenes in the field of mobile communication, and high in reusability and ensures the configurable flexibility of the model.
In another embodiment of the present invention, a device for checking a bill is provided, as shown in fig. 5, and fig. 5 is a schematic structural diagram of a device for checking a bill according to another embodiment of the present invention. The account checking device comprises:
the acquiring module 501 is configured to acquire 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;
and a third determining module 504, configured to obtain the reliability of the bill to be verified by using a mean algorithm according to the first reliability and the second reliability.
In summary, in this embodiment, the provided apparatus for checking a bill is used to implement the method for checking a bill in the foregoing embodiment, where the service data of the bill to be checked 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, the reliability of the bill to be checked may be determined after the first reliability and the second reliability are combined. Therefore, the accuracy of the bill can be effectively evaluated through quantitative analysis of the reliability of the bill, the accuracy and rationality of the bill data can be quantitatively checked, business personnel can master the accuracy of the bill data conveniently, problem bills can be found out 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 of a hardware structure of bill checking according to an embodiment of the present invention.
A processor 601 may be included in the bill checking means and a memory 302 storing computer program instructions.
In particular, the processor 601 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits that implement 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, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the above. The memory 602 may include removable or non-removable (or fixed) media, where appropriate. 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 particular embodiments, memory 602 includes Read Only Memory (ROM). 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, where appropriate.
Processor 601 implements a method of bill verification as in any of the embodiments shown in fig. 1 or 2 by reading and executing computer program instructions stored in 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 to each other through a bus 610 and perform communication with each other.
The communication interface 603 is mainly used for implementing communication between each module, apparatus, unit and/or device in the embodiment of the present invention.
Bus 610 includes hardware, software, or both, that couple the components of the billing verification device to one another. By way of example, and not limitation, the buses 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 the above. Bus 610 may include one or more buses, where appropriate. Although embodiments of the invention have been described and illustrated with respect to a particular bus, the invention contemplates any suitable bus or interconnect.
The bill verification device may perform the bill verification method in the embodiment of the present invention, thereby implementing the bill verification method described in connection with fig. 1 or fig. 2.
In addition, in combination with the bill checking method in the above embodiment, the embodiment of the invention may be implemented by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the bill checking methods of the above embodiments.
It should be understood that the invention is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. 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 shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented in 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, a plug-in, a 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 over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, 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 the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure 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, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
In the foregoing, only the specific embodiments of the present invention are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present invention is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present invention, and they should be included in the scope of the present invention.
Claims (7)
1. A method of bill verification, comprising:
the method comprises the steps that service data of a bill to be checked are obtained, wherein the service data comprise first service data and second service data, the first service data comprise settlement total amount, and the second service data comprise provincial settlement total amount;
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;
obtaining the credibility of the bill to be checked by using a mean algorithm according to the first credibility and the second credibility;
the determining the first credibility of the first service data according to the first service data comprises the following steps:
obtaining a first correlation by using a correlation analysis algorithm according to first business month data and first business history data in the first business data;
inputting the first service history data into a preset first time sequence analysis model to obtain a first fitting degree;
according to the first correlation degree and the first fitting degree, obtaining the first credibility of the first service data by using a weight algorithm;
the second service data comprises service data of a plurality of areas, the service data of each area comprises first index data and second index data, the first index data comprises first index historical data, the second index data comprises second index historical data, the first index data is the amount to be added, and the second index data is the amount to be added;
The determining the second credibility of the second service data according to the second service data comprises the following steps:
respectively inputting the plurality of first index historical data into a preset second time sequence analysis model to obtain a plurality of second fitting degrees;
respectively inputting the plurality of second index historical data into a preset third time sequence analysis model to obtain a plurality of third fitting degrees;
determining a second credibility of the second business data according to the plurality of second fitting degrees and the plurality of third fitting degrees;
the determining the second credibility of the second business data according to the plurality of second fitting degrees and the plurality of third fitting degrees comprises:
determining the credibility of the business data of the corresponding area according to the second fitting degree obtained by the first index historical data of the first index data of each area and the third fitting degree obtained by the second index historical data of the second index data of the corresponding area;
and determining second credibility of the second service data according to the credibility of the service data of the plurality of areas.
2. The method of claim 1, wherein the first business month data and the first business history data each comprise at least one business index data; the obtaining a first correlation according to the first business month data and the first business history data in the first business data by using a correlation analysis algorithm includes:
And obtaining a first correlation by utilizing a correlation analysis algorithm according to at least one business index data in the first business month data and the first business history data.
3. The method of claim 2, wherein the correlation analysis algorithm is a euclidean distance based correlation analysis algorithm.
4. The method of claim 1, wherein the first, second, and third time series analysis models are all autoregressive moving average models.
5. 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, the business data comprise first business data and second business data, the first business data comprise settlement total amount, and the second business data comprise provincial settlement total amount;
the first determining module is used for determining first credibility of the first service data according to the first service data;
a second determining module, configured to determine a second reliability of the second service data according to the second service data;
the third determining module is used for obtaining the credibility of the bill to be checked by utilizing a mean algorithm according to the first credibility and the second credibility;
The first determining module includes:
the correlation unit is used for obtaining a first correlation by utilizing a correlation analysis algorithm according to the first business month data and the first business history data in the first business data;
the fitting unit is used for inputting the first service history data into a preset first time sequence analysis model to obtain a first fitting degree;
the determining unit is used for obtaining the first credibility of the first service data by utilizing a weight algorithm according to the first correlation degree and the first fitting degree;
the second service data comprises service data of a plurality of areas, the service data of each area comprises first index data and second index data, the first index data comprises first index historical data, the second index data comprises second index historical data, the first index data is the amount to be added, and the second index data is the amount to be added;
the determining the second credibility of the second service data according to the second service data comprises the following steps:
respectively inputting the plurality of first index historical data into a preset second time sequence analysis model to obtain a plurality of second fitting degrees;
Respectively inputting the plurality of second index historical data into a preset third time sequence analysis model to obtain a plurality of third fitting degrees;
determining a second credibility of the second business data according to the plurality of second fitting degrees and the plurality of third fitting degrees;
the determining the second credibility of the second business data according to the plurality of second fitting degrees and the plurality of third fitting degrees comprises:
determining the credibility of the business data of the corresponding area according to the second fitting degree obtained by the first index historical data of the first index data of each area and the third fitting degree obtained by the second index historical data of the second index data of the corresponding area;
and determining second credibility of the second service data according to the credibility of the service data of the plurality of areas.
6. A bill verification apparatus, the apparatus comprising: a processor and a memory storing computer program instructions;
a method of bill verification as claimed in any one of claims 1 to 4 when said processor executes said computer program instructions.
7. A computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement a bill verification method according to any one of claims 1-4.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102869033A (en) * | 2012-09-21 | 2013-01-09 | 西南交通大学 | ARMA-ARCH model family based prediction method for GPRS (general packet radio service) data services of GSM (global system for mobile communications) communication system |
| CN106204151A (en) * | 2016-07-18 | 2016-12-07 | 南京坦道信息科技有限公司 | A kind of communication user propensity to consume detection method based on clustering algorithm |
| JP6124232B1 (en) * | 2016-11-15 | 2017-05-10 | シーロムパートナーズ税理士法人 | Financial forecasting system, financial forecasting method, and financial forecasting program |
| CN107346464A (en) * | 2016-05-06 | 2017-11-14 | 腾讯科技（深圳）有限公司 | Operational indicator Forecasting Methodology and device |
| CN107426759A (en) * | 2017-08-09 | 2017-12-01 | 广州杰赛科技股份有限公司 | The Forecasting Methodology and system of newly-increased base station data portfolio |
| CN107688876A (en) * | 2017-09-04 | 2018-02-13 | 北京京东尚科信息技术有限公司 | Business objective predictor method and device, storage medium and electronic equipment |
| CN107871190A (en) * | 2016-09-23 | 2018-04-03 | 阿里巴巴集团控股有限公司 | A kind of operational indicator monitoring method and device |
| WO2018075995A1 (en) * | 2016-10-21 | 2018-04-26 | DataRobot, Inc. | Systems for predictive data analytics, and related methods and apparatus |
| CN108052528A (en) * | 2017-11-09 | 2018-05-18 | 华中科技大学 | A kind of storage device sequential classification method for early warning |
| CN109409875A (en) * | 2018-09-25 | 2019-03-01 | 阿里巴巴集团控股有限公司 | A kind of bill method of calibration, device and electronic equipment |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9202200B2 (en) * | 2011-04-27 | 2015-12-01 | Credibility Corp. | Indices for credibility trending, monitoring, and lead generation |
| US9197511B2 (en) * | 2012-10-12 | 2015-11-24 | Adobe Systems Incorporated | Anomaly detection in network-site metrics using predictive modeling |
| US10115137B2 (en) * | 2013-03-14 | 2018-10-30 | Bill.Com, Inc. | System and method for enhanced access and control for connecting entities and effecting payments in a commercially oriented entity network |
-
2019
- 2019-12-27 CN CN201911376569.7A patent/CN113052582B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102869033A (en) * | 2012-09-21 | 2013-01-09 | 西南交通大学 | ARMA-ARCH model family based prediction method for GPRS (general packet radio service) data services of GSM (global system for mobile communications) communication system |
| CN107346464A (en) * | 2016-05-06 | 2017-11-14 | 腾讯科技（深圳）有限公司 | Operational indicator Forecasting Methodology and device |
| CN106204151A (en) * | 2016-07-18 | 2016-12-07 | 南京坦道信息科技有限公司 | A kind of communication user propensity to consume detection method based on clustering algorithm |
| CN107871190A (en) * | 2016-09-23 | 2018-04-03 | 阿里巴巴集团控股有限公司 | A kind of operational indicator monitoring method and device |
| WO2018075995A1 (en) * | 2016-10-21 | 2018-04-26 | DataRobot, Inc. | Systems for predictive data analytics, and related methods and apparatus |
| JP6124232B1 (en) * | 2016-11-15 | 2017-05-10 | シーロムパートナーズ税理士法人 | Financial forecasting system, financial forecasting method, and financial forecasting program |
| CN107426759A (en) * | 2017-08-09 | 2017-12-01 | 广州杰赛科技股份有限公司 | The Forecasting Methodology and system of newly-increased base station data portfolio |
| CN107688876A (en) * | 2017-09-04 | 2018-02-13 | 北京京东尚科信息技术有限公司 | Business objective predictor method and device, storage medium and electronic equipment |
| CN108052528A (en) * | 2017-11-09 | 2018-05-18 | 华中科技大学 | A kind of storage device sequential classification method for early warning |
| CN109409875A (en) * | 2018-09-25 | 2019-03-01 | 阿里巴巴集团控股有限公司 | A kind of bill method of calibration, device and electronic equipment |
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|---|---|
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