CN110413967A - Reconciliation chart generation method, device, computer equipment and storage medium - Google Patents
Reconciliation chart generation method, device, computer equipment and storage medium Download PDFInfo
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Abstract
This application involves big data technical fields, a kind of reconciliation chart generation method is provided, device, computer equipment and storage medium, by carrying out segmented cleaning to transaction data, remove noise data, reduce follow-up data treating capacity, the data after cleaning are sampled using different time dimension, target data after obtaining dilatation, ensure that target data more comprehensively correct can characterize transaction data, again by carrying out Accurate classification to target data based on Bayes Method, the target data and the classification data are subjected to visualization mapping respectively, generate the reconciliation chart for carrying different time dimension and type, it is convenient for transaction data reconciliation, transaction data reconciliation efficiency and accuracy can be significantly improved.
Description
Technical field
This application involves big data processing techniques, set more particularly to a kind of reconciliation chart generation method, device, computer
Standby and storage medium.
Background technique
With the rapid development of network technology, more and more transaction are moved from off-line transaction onto line, are submitted by line
Easily being paid, the means of payment becomes convenient, and consequent is the explosive growth of transaction data, and life becomes digitization,
People are also higher and higher for the processing requirement of big data.
The processing of existing transaction data is to record transaction amount, transaction by the order information for the transaction such as collect money and pay the bill
Trading situation each time is well understood in time etc., checks Transaction Details convenient for user.
But the prior art records trading situation each time, can not get information about overall income and expenditure
Record, transaction data carry out reconciliation when, need to search transaction record one by one, to transaction data carry out calculate and statistical disposition
Process is complicated, and is easy to appear mistake of statistics, causes reconciliation result inaccurate.
Summary of the invention
Based on this, it is necessary to which complicated for conventional transaction data reconciliation process and inaccuracy problem provides a kind of reconciliation
Chart generation method, device, computer equipment and storage medium significantly improve number of deals in order to carry out transaction data reconciliation
According to reconciliation efficiency and accuracy.
A kind of reconciliation chart generation method the described method includes:
Transaction data is obtained, and the transaction data is divided into multiple transaction data sections；
Noise data analysis is carried out to each transaction data section respectively, and is analyzed according to noise data as a result, to institute
It states transaction data to be cleaned, data have been cleaned in acquisition；
To the sampling processing cleaned data and carried out different time dimension, target data is obtained；
Based on Bayes Method, classify to the data of having cleaned, obtains classification data；
The target data and the classification data are subjected to visualization mapping respectively, generate visualization account chart.
It is described in one of the embodiments, that noise data analysis, and root are carried out to each transaction data section respectively
It is analyzed according to noise data as a result, being cleaned to the transaction data, acquisition has cleaned data and included:
Noise data analysis is carried out to each transaction data section respectively；
According to the corresponding noise data analysis of each transaction data section as a result, obtaining in each transaction data section to clear
Wash field；
It searches in the field to be cleaned and is augmented field, the field progress high order tensor that is augmented is augmented, is obtained
Obtain tensor sets of fields；
By tensor field relevant to the field to be cleaned in the tensor sets of fields to the field to be cleaned into
Data have been cleaned in row cleaning, acquisition.
It is described to the sampling processing cleaned data and carried out different time dimension in one of the embodiments, it obtains
The target data is taken to include:
Resample function setup first time dimension sample frequency is called, according to the first time dimension sample frequency
Sampling processing is carried out to the data of having cleaned, obtains first time dimension target data；
The second time dimension of resample function setup sample frequency is called, according to the second time dimension sample frequency
Sampling processing is carried out to the data of having cleaned, obtains the second time dimension target data；
The first time dimension target data and the second time dimension target data are combined, target data is obtained.
It is described in one of the embodiments, to be based on Bayes Method, classify to the data of having cleaned, obtains
Classification data includes:
It obtains historical sample and has cleaned data；
It has cleaned data to the historical sample using Naive Bayes Classification Algorithm to be trained, the Piao trained
Plain Bayes's classification function；
By the Naive Bayes Classification function trained, classify to the data of having cleaned, obtains classification
Data.
It is described in one of the embodiments, to carry out visualizing respectively reflecting by the target data and the classification data
It penetrates, generates target data visualization account chart and classification data visualization account chart includes:
By ring than calculation formula, ring ratio is carried out respectively to the target data and the classification data；
According to the ring of the target data and classification data ratio as a result, obtaining the target data and the classification number
Increase and decrease amplitude according to turnover in the respective unit time；
Increase and decrease amplitude and the ring according to the turnover and carry out visualization mapping than result, generating the classification data can
Account chart is visualized depending on changing account chart and the target data.
It is described in one of the embodiments, to carry out visualizing respectively reflecting by the target data and the classification data
It penetrates, after generation target data visualization account chart and classification data visualization account chart, further includes:
Account chart is visualized to the classification data and target data visualization account chart is analyzed, by institute
It states classification data visualization account chart and target data visualization account chart and analysis result pushes to user.
A kind of reconciliation chart generating means, described device include:
Transaction data obtains module, obtains transaction data, and the transaction data is divided into multiple transaction data sections；
Data acquisition module is cleaned, is used for respectively to each transaction data section progress noise data analysis, and according to
As a result, cleaning to the transaction data, data have been cleaned in acquisition for noise data analysis；
Target data obtains module, for obtaining to the sampling processing cleaned data and carried out different time dimension
Target data；
Classification data obtains module, for being based on Bayes Method, classifies to the data of having cleaned, obtains and divide
Class data；
Chart generating module is generated for the target data and the classification data to be carried out visualization mapping respectively
Visualize account chart.
The cleaning data acquisition module is also used to respectively to each transaction data section in one of the embodiments,
Carry out noise data analysis；According to the corresponding noise data analysis of each transaction data section as a result, obtaining each number of deals
According to field to be cleaned in section；It searches in the field to be cleaned and is augmented field, high-order is carried out to the field that is augmented
Amount is augmented, and obtains tensor sets of fields；By tensor field relevant to the field to be cleaned in the tensor sets of fields to institute
It states field to be cleaned to be cleaned, data have been cleaned in acquisition.
A kind of computer equipment, including memory and processor, the memory are stored with computer program, the processing
Device is realized when executing the computer program such as the step of the above method.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
It realizes when row such as the step of above-mentioned method.
Above-mentioned reconciliation chart generation method, device, computer equipment and storage medium, by being segmented to transaction data
Cleaning removes noise data, reduces follow-up data treating capacity, is sampled using different time dimension to the data after cleaning,
Target data after obtaining dilatation, it is ensured that target data more comprehensively correct can characterize transaction data, then by being based on pattra leaves
This classification carries out Accurate classification to target data, and the target data and the classification data are carried out visualization respectively and reflected
It penetrates, generates the reconciliation chart for carrying different time dimension and type, be convenient for transaction data reconciliation, friendship can be significantly improved
Easy data reconciliation efficiency and accuracy.
Detailed description of the invention
Fig. 1 is the application scenarios schematic diagram of above-mentioned reconciliation chart generation method；
Fig. 2 is the one of embodiment flow diagram of above-mentioned reconciliation chart generation method；
Fig. 3 is above-mentioned another embodiment flow diagram of reconciliation chart generation method；
Fig. 4 is the above-mentioned one of example structure schematic diagram of reconciliation chart generating means；
Fig. 5 is the one of embodiment schematic diagram of internal structure of computer equipment.
Specific embodiment
It is with reference to the accompanying drawings and embodiments, right in order to which the objects, technical solutions and advantages of the application are more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the application, not
For limiting the application.
Reconciliation chart generation method provided by the present application, can be applied in application environment as shown in Figure 1.Wherein, eventually
End 102 is communicated with server 104 by network by network.Terminal 102 sends transaction data to server by network
104, server 104 receives transaction data, and the transaction data is divided into multiple transaction data sections；Respectively to each described
Transaction data section carries out noise data analysis, and is analyzed according to noise data as a result, cleaning to the transaction data, acquisition
Data are cleaned；To the sampling processing cleaned data and carried out different time dimension, target data is obtained；Based on Bayes
Classification classifies to the data of having cleaned, and obtains classification data；The target data and the classification data are distinguished
Visualization mapping is carried out, visualization account chart is generated, server 104 pushes to terminal 102 for account chart is visualized.Its
In, terminal 102 can be, but not limited to be various personal computers, laptop, smart phone, tablet computer and it is portable can
Wearable device, server 104 can be realized with the server cluster of the either multiple server compositions of independent server.
In one of the embodiments, as shown in Fig. 2, providing a kind of reconciliation chart generation method, it is applied in Fig. 1
Server, specifically includes the following steps:
S110: transaction data is obtained, and the transaction data is divided into multiple transaction data sections.
Transaction data refers to through online trading, to the time of transaction, transaction amount, payment method, products transactions to hand over
The data that the information such as easy both sides are recorded.Further, when server responds customer transaction data extraction operation, extraction user
The associated All Activity data of account.It is non-essential, it can be by setting interval, server is in Fixed Time Interval
It is interior, data are acquired, the transaction data of fixed user is obtained.According to preset time price by transaction data according to generation
Time be divided into multiple transaction data sections.It, can be monthly as the period is divided, to divide such as 1 year transaction data
For 12 transaction data sections.
S120: carrying out noise data analysis to each transaction data section respectively, and according to noise data analysis as a result,
The transaction data is cleaned, data have been cleaned in acquisition.
Data cleansing refers to, the process that data are audited and verified again, it is therefore intended that deletes duplicate message, corrects
Existing mistake, and the consistency of data is provided, convenient for carrying out next step operation to data.In the present embodiment, to number of deals
According to audit inspection is carried out, suitable data cleaning method, such as Random Interpolation method, hot platform interpolation, modeling can be used
The methods of with noise treatment, missing values, repetition values, the error value in transaction data are deleted and are filled, it is possible to understand that
, in the present embodiment, for data cleaning method and without limitation, can be needed to select suitable data cleansing side according to business
Method cleans transaction data, and data have been cleaned in acquisition.The purpose of core of data cleansing is to remove noise number in transaction data
According to, therefore, can first carry out noise analysis, according to noise analysis result judge whether need current transaction data section is counted
According to cleaning, noise analysis is carried out to each transaction data section respectively, when noise data probability of occurrence is greater than preset threshold, determined
Current transaction data section carries out clearing noise data out, finally obtains and has cleaned data.Further, noise analysis has
Body are as follows: obtain the probability P that noise data value in each transaction data section occurs, wherein P=M N, M is in single transaction data section
The number that noise data occurs, N are that transaction record sum in single transaction data section marks the friendship when P value is greater than default P0
Easy data segment is field to be cleaned, and wherein noise data can be missing values, error value and inconsistent value etc..
S130: to the sampling processing cleaned data and carried out different time dimension, target data is obtained.
Different time dimension refers to made of the chronological order arrangement by the numerical value of same statistical indicator by its generation
Ordered series of numbers.The sampling of different time dimension refers to the treatment process by the time from a frequency conversion to another frequency, based on difference
Time dimension sampling can expand sampled data set, in order to which the Visual Chart being subsequently generated is more comprehensive, accurate response is handed over
Easy data.In the present embodiment, the generation of transaction data is recorded according to the time-sequencing of transaction creation, such as user A
Transaction data are as follows: 04 divides at 2017 2 months 2 days 3, food expenditure 250；04 divides when 11 days 16 November in 2018, cosmetics branch
Out 1000；59 divide when 14 days 17 December in 2018, dress ornament expenditure 500, by the way that resampling frequency is arranged, when resampling frequency is year
When, obtaining target data is the expenditure 1500 in 250,2018 of expenditure in 2017.Be understood that when, resampling frequency can basis
Actual conditions are reasonably selected.
S140: being based on Bayes Method, classify to the data of having cleaned, and obtains classification data.
Bayes Method (Bayesian classifier) is a kind of statistical classification method, it is a kind of utilization
The algorithm that probability statistics knowledge is classified.In the present embodiment, it is carried out by Bayes's classification classification to data have been cleaned
Type of transaction is the friendship of clothes for example, the transaction data that type of transaction is snacks is classified as life kind transaction data by classification
Easy data classification is dress ornament class transaction data, and the transaction data that type of transaction is insurance is classified as investment type transaction data etc..
By the statistic of classification to data have been cleaned, classification data is obtained.It is non-essential, it can first be carried out using sample data based on shellfish
The classification based training of this classification of leaf obtains the classification function based on Bayes Method, herein, calls directly based on Bayes
The classification function of classification is classified to data have been cleaned, and obtains classification data.
S150: carrying out visualization mapping for the target data and the classification data respectively, generates visualization Chart Of Account
Table.
The target data obtained by the sampling of different time dimension is subjected to visualization mapping, generates target data visualization
Account chart.Such as when sampling time dimension is the moon, the transaction of 12 each months in the middle of the month in 1 year is obtained as unit of year
Income or transaction expenditure, by visualization mapping, generate histogram, curve graph, the sector diagram of intuitive monthly income or expenditure
Etc. monthly incomes or the moon expenditure visualization account chart.The classification data that will be classified by Bayes Method, it is raw
Constituent class data visualization account chart, for example, type of transaction is divided into life kind, amusement class, body-building class, clothing, investment
The type transactions data such as class and traffic class generate the visualization account chart of different type of transaction data by visualization mapping,
Intuitively understand the total expenditure or total income of each type transactions
Above-mentioned reconciliation chart generation method removes noise data, reduces subsequent by carrying out segmented cleaning to transaction data
Data processing amount samples the data after cleaning using different time dimension, the target data after obtaining dilatation, it is ensured that mesh
Marking data more comprehensively correct can characterize transaction data, then by accurately being divided based on Bayes Method target data
The target data and the classification data are carried out visualization mapping by class respectively, and generation carries different time dimension and class
The reconciliation chart of type, is convenient for transaction data reconciliation, can significantly improve transaction data reconciliation efficiency and accuracy.
In one of the embodiments, before acquisition transaction data, comprising: identification user identifier；According to user identifier, match
Set the corresponding administration authority of user；Obtaining transaction data includes: response user believes according to the acquisition bill that administration authority is carried out
Breath operation, obtains transaction data.Wherein, user carries out identity logs, inputs user identity account, and server identifies user's body
Part, the corresponding unique identification of user identity is obtained, according to the unique identification of user identity, configures the power that user logs in and operates
Limit, user are based on own right, into operation interface, click in operation interface and obtain bill information operation button, server is rung
Bill information operation should be obtained, the corresponding transaction data of user identity permission is obtained.For example, when user A is ordinary consumer,
Login account password is inputted, server identifies user A ordinary consumer identity, the identity of user A ordinary consumer is obtained,
It responds user A and obtains bill expenditure information operation, into personal identification environment, the personal trade order of load extracts transaction data,
Transaction data includes exchange hour, products transactions, payment method, the transaction data informations such as shop and amount paid.Work as user
When B is merchant identity, User Identity is identified, log in environment into businessman, give businessman's B businessman's administration authority, businessman's B point
It hits interface and obtains bill income information button, information operation is taken according to the acquisition bill of businessman B, loads businessman B personal information,
Merchant transaction data are extracted, merchant transaction data include: payment method, payment client, payment time, payment product, Yi Jishou
Money amount of money etc..When user C is platform management person, the login account password of user C is obtained, the identity of identification user C is
Manager gives user's C administration authority according to the identity of user C, into user management environment, loads user C manager
Identity information, response user C obtain the operation of bill management information, and the transaction data of extraction includes: consumer and Business Information, friendship
Easy time, products transactions, payment method and merchant profit etc..Not according to consumer, businessman and manager's identity
Together, different login environment are created, different rights are configured according to user identity, the corresponding transaction data of identity authority is obtained, improves
Data security.
In one of the embodiments, as shown in figure 3, S120 includes:
S121: noise data analysis is carried out to each transaction data section respectively.
S122: according to the corresponding noise data analysis of each transaction data section as a result, obtaining each transaction data section
In field to be cleaned.
S123: searching in field to be cleaned and be augmented field, is augmented, is opened to that can be augmented field progress high order tensor
Measure sets of fields.
S124: cleaning field to be cleaned using tensor field relevant to field to be cleaned in tensor sets of fields,
Data have been cleaned in acquisition.
Field can be augmented to refer to, the field of more information is obtained after being augmented to field.Tensor field: field warp can be augmented
After high order tensor is augmented, the factor matrix in multiple dimensions that resolves into.Further, it obtains in designated time period, it is to be cleaned
The probability P that noise data value occurs in any one section in data, P=m/n, wherein designated time period can be with year, the moon or day etc.
For unit, m is the number that noise data value occurs in designated time period, and n is the sum of data record in designated time period, is determined
When the value of probability P is greater than preset threshold P0, the label affiliated field of noise data is field to be cleaned.By the word in data to be cleaned
Section is matched with the field that is augmented that can be augmented in field storehouse is preset, and acquisition can be augmented field, using tensor resolution algorithm according to
It is secondary to can be augmented field carry out high order tensor be augmented, obtain multiple tensor fields, and according to field semantics similarity by multiple
Amount field is classified as M tensor sets of fields.Semantic analysis is carried out to field to be cleaned, obtains tensor sets of fields according to field classification
In tensor sets of fields corresponding with field to be cleaned, and further obtain tensor sets of fields in tensor corresponding with field to be cleaned
Sets of fields cleans data to be cleaned, specifically, relevant tensor field can in tensor sets of fields with word to be cleaned
Duan Yuyi almost the same tensor field or the tensor field that the same attribute is indicated with field to be cleaned, or with data to be cleaned
The tensor field of field existence function dependence.Using almost the same with field semantics to be cleaned tensor sets of fields filling to
It cleans the vacancy value of field and repairs error value, and utilize the tensor field reparation with field to be cleaned with functional dependencies
Inconsistent value.In the present embodiment, by taking day is chronomere as an example, the noise data of daylight trading data is searched, calculating is made an uproar
Sound data proportion in whole transaction data, when noise data proportion is greater than preset threshold, to noise data institute
Category field is field to be cleaned, and the field that is augmented in data to be cleaned can be Merchant name, carries out high-order to Merchant name
Amount is augmented, and obtains tensor field, for example obtain the merchant product place of production, merchant product individual quantity purchase, product sales volume and production
The information such as product market.The range of information such as the place of production, quantity and selling spot and field to be cleaned are subjected to semantic analysis, are obtained
The corresponding tensor sets of fields of field to be cleaned is taken, in transaction data, on December 12nd, 2018, the product number of A purchase volume businessman B
Measure data and value data to lose, can be augmented according to businessman's B information, to purchase product quantity of the A in businessman B and
The amount of money is filled and repairs.It is understood that the transaction data when user is businessman and manager loses or mistake
It mistakes, can be augmented by field and data are filled and are repaired.By guaranteeing transaction to data cleansing in the present embodiment
The accuracy of data.
In one of the embodiments, to the sampling processing cleaned data and carried out different time dimension, mesh is obtained
Mark data include: to call resample function setup first time dimension sample frequency, are sampled according to the first time dimension
Frequency carries out sampling processing to the data of having cleaned, and obtains first time dimension target data；Resample function is called to set
The second time dimension sample frequency is set, the data of having cleaned are carried out at sampling according to the second time dimension sample frequency
Reason, obtains the second time dimension target data；Combine the first time dimension target data and the second time dimension mesh
Data are marked, target data is obtained.
It calls resample function to carry out the sampling processing of different time dimension, obtains target data.Wherein resample
Function is the function for adjusting sampling time dimension.In the present embodiment, transaction data can be understood as original sample, be based on transaction
The time of generation and to the transaction record data that are recorded of transaction.For example, transaction data are as follows: 4 when 25 days 13 December in 2017
Point, insurance, expenditure 3000；38 divide when 29 months 18 December in 2017, stock, expenditure 10000；58 divide on September 27,12 2018,
House property, expenditure 1,000,000；16 divide when 22 days 15 December in 2018, fund, expenditure 800,000, call resample function, setting sampling
When time dimension is year, target data is obtained are as follows: 2017, pay expenditure 1,800,000 in 13000,2018.The resampling time is set
When dimension is the moon, obtain paying the expenditure of in September, 13000,2,018 1,000,000 December in target data in 2017, December pays 800,000.
It is understood that the sampling of different time dimension can be carried out to businessman's income data when user is businessman, when user is pipe
When reason person, businessman can be taken in and the consumer spending carries out different time dimension sampling simultaneously.In the present embodiment, time dimension
Degree is not uniquely to limit, and can be reasonably adjusted according to user demand.By time resampling transaction data, use can be understood
The expenditure or income of family in a certain period of time, process is simple, and result is accurate.
It is based on Bayes Method in one of the embodiments, classifies to data have been cleaned, obtains classification data
It include: to obtain historical sample to have cleaned data；Using Naive Bayes Classification Algorithm to the historical sample cleaned data into
Row training, the Naive Bayes Classification function trained；By the Naive Bayes Classification function trained, to institute
It states and has cleaned data and classify, obtain classification data.Wherein NB Algorithm (Naive Bayes Classifier,
NBC classical mathematics theory) is risen in, is the classification method independently assumed based on Bayes' theorem and characteristic condition, according to research
Certain features of object mainly pass through training study connection to infer the research object belongs to which classification of the research field
Probability distribution is closed, specifically study prior probability and conditional probability.Further, item to be sorted, X=(a are set first1,
a2...an), in the present embodiment, it is a feature category of X that a, which can be the type of transaction fields such as traffic, amusement, education, investment,
Property, and it is mutually indepedent between each characteristic attribute.If C={ Y1,Y2...YnIt is a collection class, it can be in transaction data and hand over
Easy product type, such as books, clothes, shoes, stock, fund etc..Calculate prior probability P (Y1/X),P(Y2/X)....P(Yn/
X), the conditional probability P (a of each characteristic attribute is counted1/Y1),P(a2/Y2)..P(an/Yn), acquire p (YK/ X)=max { P (Y1/X),
P(Y2/X)...P(Yn/ X) }, then X belongs to YK, i.e., under conditions of X, acquires P (Yi/ X) (i=0,1,2...n) obtain it is maximum
Probability Y, it can be understood as, if products transactions are stock, the maximum probability that stock is investment field is obtained, then in transaction data
Stock exchange data is classified as investing, if products transactions are books, when obtaining the maximum probability that books are education sector, to transaction
Books transaction data is classified as educational data in data, and counts to every a kind of data.Divide according to transaction data
Class statistics, can be visually seen businessman in the income in each field.Consumer can see in the expenditure in each field and manager to disappear
Fei Zheyu businessman's balance data, it is convenient and simple, as a result accurately.
In one of the embodiments, as shown in figure 3, S150 includes:
S151: by ring than calculation formula, ring ratio is carried out respectively to target data and classification data.
S152: according to the ring of target data and classification data ratio as a result, obtaining target data and the respective list of classification data
Turnover increases and decreases amplitude in the time of position.
S153: increasing and decreasing amplitude and ring according to turnover and carry out visualization mapping than result, generates classification data and visualizes account
Mesh chart and target data visualize account chart.
Wherein, ring ratio refers to continuous two unit periods, such as continuous two months, the variation ratio of the amount in the times such as 2 years,
Ring can be with than calculation formula are as follows: sequential growth rate=(this issue-goes up issue)/upper issue × 100%, reflection current period increase than last
Grow how many.Further, in the present embodiment, by taking user is businessman as an example, when again businessman is to set the moon to be adopted as unit
When the target data that sample obtains, it is the moon that ring, which can be set, than the period, by ring ratio, is compared to income of adjacent month, e.g., when
5000,2 months income 9000, income in March 150,000,000 are taken in January, by ring than obtaining 2 months rings obtained compared with January
Ratio, that is, transaction amount increasing degree is (9000-5000)/5000 × 100%=80%, non-essential faster than development according to ring
Formula is spent, the sequential growth rate that can obtain 2 months is 9000/5000 × 100%=180%, for the ring ratio in March and 2 months, March
Sequential growth rate, that is, transaction amount increasing degree=(18000-9000)/9000 × 100%=100%, it is non-essential according to ring
Than development speed formula, the sequential growth rate that can obtain March is 18000/9000 × 100%=200%, according to ring than result and
Transaction amount increasing degree during carrying out visualization mapping, carries out highlighted mark when increasing degree is greater than preset threshold
Know prominent processing, generates target data and visualize account chart.It is understood that target data visualization account chart can be with
For charts such as curve graph and histograms.By taking histogram as an example, using X-axis as the unit time, Y-axis is transaction amount, is intuitively seen
Every monthly benefits and when monthly benefits relatively transaction amount in upper January be greater than preset threshold when highlight data.For number of classifying
According to by taking user is consumer as an example, in classification data, working as December, recreation data expenditure is 200, educational transaction pays and is
500, when investment type expenditure is 10,000, dress ornament class expenditure is 300, amusement in November expenditure is 100, spending for education 0, investment spending
It is 20,000, when dress ornament expenditure is 500, by ring than calculation formula, December is compared with the homogeneous data in November, when
When transaction amount increasing degree is greater than preset threshold, generates classification data visualization account chart and be highlighted, classified
Data visualization account chart may include the accounts chart such as sector diagram, radar map and curve graph.When classification data Chart Of Account
It is clear intuitively to see that of that month every class transaction data accounts for the specific gravity of total expenditure when table is sector diagram, and can see every a kind of data
Compared with similar transaction data in upper January expenditure increase and decrease be highlighted data.It is non-essential, in sector diagram, when the user clicks certain
When a kind of transaction data such as amusement transaction data, amusement transaction data can be unfolded, be showed each in amusement class data
Particular transactions situation.It in the present embodiment, can be according to practical feelings to the diagrammatic form and without limitation of visualization account chart
Condition is selected.The overall variation and different classes of income of expenditure and income can be intuitively grasped by visualizing account chart
With the variation of expenditure, as a result accurately, reconciliation is simple.
In one of the embodiments, target data and classification data are subjected to visualization mapping respectively, generate number of targets
After visualization account chart and classification data visualization account chart, further includes: visualize account chart to target data
It is analyzed with classification data visualization account chart, target data is visualized into account chart and classification data visualizes account
Chart and analysis result push to user.Wherein, after generating target data Visual Chart and classification data Visual Chart,
Chart is analyzed, for example, being analyzed for consumption data of consumers, being given pleasure in consumption data when user is consumer
Happy type expenditure accounting is great when 45%, it is proposed that and future reduces abstract the recreational consumption, and the types such as body-building, education is recommended to consume, or
Recommend consumption plan, the balancing user consumption expenditure for user.When user is businessman, increase when a certain product sales volume income is presented
Trend or the decline of a certain Sales Volume of Commodity income, it is proposed that two kinds of commodity bundle sales etc..When user is manager, push is monthly
The highest businessman of sales volume, is ranked up Merchant sales amount, the highest product of of that month sales volume is pushed, to each product by sale
Amount income is ranked up.It is non-essential, when user is consumer, account chart is visualized by target data, can be set
Spending Limit data in subsequent time period, such as next year or next month or second day period are right when carrying out the consumption expenditure
Consumer reminds.By push Visual Chart and to the analysis of Visual Chart as a result, allow users to it is objective directly
See data increase and decrease situation and obtain reply data increase and decrease situation and the suggestion made, process is simple, easy to use.
In one of the embodiments, as shown in figure 4, providing a kind of reconciliation chart generating means, comprise the following modules:
Transaction data obtains module 410, obtains transaction data, and the transaction data is divided into multiple transaction data
Section；
Data acquisition module 420 is cleaned, for carrying out noise data analysis, and root to each transaction data section respectively
It is analyzed according to noise data as a result, being cleaned to the transaction data, data have been cleaned in acquisition；
Target data obtains module 430, for obtaining to the sampling processing cleaned data and carried out different time dimension
Take target data；
Classification data obtains module 440, for being based on Bayes Method, classifies to the data of having cleaned, obtains
Take classification data；
Chart generating module 450, it is raw for the target data and the classification data to be carried out visualization mapping respectively
At visualization account chart.
Above-mentioned reconciliation chart generating means remove noise data, reduce subsequent by carrying out segmented cleaning to transaction data
Data processing amount samples the data after cleaning using different time dimension, the target data after obtaining dilatation, it is ensured that mesh
Marking data more comprehensively correct can characterize transaction data, then by accurately being divided based on Bayes Method target data
The target data and the classification data are carried out visualization mapping by class respectively, and generation carries different time dimension and class
The reconciliation chart of type, is convenient for transaction data reconciliation, can significantly improve transaction data reconciliation efficiency and accuracy.
Above-mentioned reconciliation chart generating means further include subscriber identification module in one of the embodiments, are used for identification
Family mark；According to user identifier, the corresponding administration authority of user is configured；Obtaining transaction data includes: user is according to management for response
The acquisition bill information operation that permission is carried out, obtains transaction data.
Data acquisition module 420 is cleaned in one of the embodiments, is also used to respectively to each transaction data section
Carry out noise data analysis；According to the corresponding noise data analysis of each transaction data section as a result, obtaining each number of deals
According to field to be cleaned in section；It searches in field to be cleaned and is augmented field, be augmented, obtain to field progress high order tensor can be augmented
Obtain tensor sets of fields；Field to be cleaned is cleaned using tensor field relevant to field to be cleaned in tensor sets of fields,
Data have been cleaned in acquisition.
Target data obtains module 430 in one of the embodiments, is also used to call resample function setup first
Time dimension sample frequency carries out sampling processing to the data of having cleaned according to the first time dimension sample frequency, obtains
To first time dimension target data；The second time dimension of resample function setup sample frequency is called, according to described second
Time dimension sample frequency carries out sampling processing to the data of having cleaned, and obtains the second time dimension target data；Combination institute
First time dimension target data and the second time dimension target data are stated, target data is obtained.
Classification data obtains module 440 in one of the embodiments, is also used to obtain historical sample and has cleaned data；
It has cleaned data to the historical sample using Naive Bayes Classification Algorithm to be trained, the naive Bayesian trained
Classification function；By the Naive Bayes Classification function trained, classify to the data of having cleaned, obtains classification
Data.
Chart generating module 450 in one of the embodiments, are also used to through ring than calculation formula, to target data
Carry out ring ratio respectively with classification data；According to the ring of target data and classification data ratio as a result, obtaining target data and classification number
Increase and decrease amplitude according to turnover in the respective unit time；Increase and decrease amplitude and ring according to turnover and carry out visualization mapping than result,
It generates classification data visualization account chart and target data visualizes account chart.
Above-mentioned reconciliation chart generating means further include analysis module in one of the embodiments, for target data
Visualization account chart and classification data visualization account chart are analyzed, and target data is visualized account chart and classification
Data visualization account chart and analysis result push to user.
Specific about reconciliation chart generating means limits the limit that may refer to above for reconciliation chart generation method
Fixed, details are not described herein.Modules in above-mentioned reconciliation chart generating means can fully or partially through software, hardware and its
Combination is to realize.Above-mentioned each module can be embedded in the form of hardware or independently of in the processor in computer equipment, can also be with
Software form is stored in the memory in computer equipment, executes the corresponding behaviour of the above modules in order to which processor calls
Make.
A kind of computer equipment is provided in one of the embodiments, which can be server, in
Portion's structure chart can be as shown in Figure 5.The computer equipment includes that the processor, memory, network connected by system bus connects
Mouth and database.Wherein, the processor of the computer equipment is for providing calculating and control ability.The storage of the computer equipment
Device includes non-volatile memory medium, built-in storage.The non-volatile memory medium be stored with operating system, computer program and
Database.The built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.It should
The database of computer equipment generates data for depositing reconciliation chart.The network interface of the computer equipment is used for and external end
End passes through network connection communication.The computer program realizes a kind of reconciliation chart generation method when being executed by processor.
It will be understood by those skilled in the art that structure shown in Fig. 5, only part relevant to application scheme is tied
The block diagram of structure does not constitute the restriction for the computer equipment being applied thereon to application scheme, specific computer equipment
It may include perhaps combining certain components or with different component layouts than more or fewer components as shown in the figure.
A kind of computer equipment, including memory and processor are provided in one of the embodiments, are deposited in memory
Computer program is contained, which performs the steps of when executing computer program
Transaction data is obtained, and the transaction data is divided into multiple transaction data sections；
Noise data analysis is carried out to each transaction data section respectively, and is analyzed according to noise data as a result, to institute
It states transaction data to be cleaned, data have been cleaned in acquisition；
To the sampling processing cleaned data and carried out different time dimension, target data is obtained；
Based on Bayes Method, classify to the data of having cleaned, obtains classification data；
The target data and the classification data are subjected to visualization mapping respectively, generate visualization account chart.
It is also performed the steps of when processor executes computer program in one of the embodiments,
Noise data analysis is carried out to each transaction data section respectively；It described makes an uproar according to each transaction data section is corresponding
Sound data analysis result obtains field to be cleaned in each transaction data section；Search being augmented in the field to be cleaned
Field is augmented the field progress high order tensor that is augmented, obtains tensor sets of fields；By in the tensor sets of fields with institute
It states the relevant tensor field of field to be cleaned to clean the field to be cleaned, data have been cleaned in acquisition.
It is also performed the steps of when processor executes computer program in one of the embodiments,
Resample function setup first time dimension sample frequency is called, according to the first time dimension sample frequency
Sampling processing is carried out to the data of having cleaned, obtains first time dimension target data；Call resample function setup the
Two time dimension sample frequencys carry out sampling processing to the data of having cleaned according to the second time dimension sample frequency,
Obtain the second time dimension target data；Combine the first time dimension target data and the second time dimension number of targets
According to obtaining target data.
It is also performed the steps of when processor executes computer program in one of the embodiments,
It obtains historical sample and has cleaned data；Data have been cleaned to the historical sample using Naive Bayes Classification Algorithm
It is trained, the Naive Bayes Classification function trained；It is right by the Naive Bayes Classification function trained
The data of having cleaned are classified, and classification data is obtained.
It is also performed the steps of when processor executes computer program in one of the embodiments,
By ring than calculation formula, ring ratio is carried out respectively to the target data and the classification data；According to the mesh
The ring ratio of data and the classification data is marked as a result, obtaining in the target data and the classification data respective unit time
Turnover increases and decreases amplitude；Increase and decrease amplitude and the ring according to the turnover and carry out visualization mapping than result, generates described point
Class data visualization account chart and the target data visualize account chart.
It is also performed the steps of when processor executes computer program in one of the embodiments,
Account chart is visualized to the classification data and target data visualization account chart is analyzed, by institute
It states classification data visualization account chart and target data visualization account chart and analysis result pushes to user.
A kind of computer readable storage medium is provided in one of the embodiments, is stored thereon with computer program,
It is performed the steps of when computer program execution processed
Transaction data is obtained, and the transaction data is divided into multiple transaction data sections；
Noise data analysis is carried out to each transaction data section respectively, and is analyzed according to noise data as a result, to institute
It states transaction data to be cleaned, data have been cleaned in acquisition；
To the sampling processing cleaned data and carried out different time dimension, target data is obtained；
Based on Bayes Method, classify to the data of having cleaned, obtains classification data；
The target data and the classification data are subjected to visualization mapping respectively, generate visualization account chart.
It is also performed the steps of when computer program is executed by processor in one of the embodiments,
Noise data analysis is carried out to each transaction data section respectively；It described makes an uproar according to each transaction data section is corresponding
Sound data analysis result obtains field to be cleaned in each transaction data section；Search being augmented in the field to be cleaned
Field is augmented the field progress high order tensor that is augmented, obtains tensor sets of fields；By in the tensor sets of fields with institute
It states the relevant tensor field of field to be cleaned to clean the field to be cleaned, data have been cleaned in acquisition.
It is also performed the steps of when computer program is executed by processor in one of the embodiments,
Resample function setup first time dimension sample frequency is called, according to the first time dimension sample frequency
Sampling processing is carried out to the data of having cleaned, obtains first time dimension target data；Call resample function setup the
Two time dimension sample frequencys carry out sampling processing to the data of having cleaned according to the second time dimension sample frequency,
Obtain the second time dimension target data；Combine the first time dimension target data and the second time dimension number of targets
According to obtaining target data.
It is also performed the steps of when computer program is executed by processor in one of the embodiments,
It obtains historical sample and has cleaned data；Data have been cleaned to the historical sample using Naive Bayes Classification Algorithm
It is trained, the Naive Bayes Classification function trained；It is right by the Naive Bayes Classification function trained
The data of having cleaned are classified, and classification data is obtained.
It is also performed the steps of when computer program is executed by processor in one of the embodiments,
By ring than calculation formula, ring ratio is carried out respectively to the target data and the classification data；According to the mesh
The ring ratio of data and the classification data is marked as a result, obtaining in the target data and the classification data respective unit time
Turnover increases and decreases amplitude；Increase and decrease amplitude and the ring according to the turnover and carry out visualization mapping than result, generates described point
Class data visualization account chart and the target data visualize account chart.
It is also performed the steps of when computer program is executed by processor in one of the embodiments,
Account chart is visualized to the classification data and target data visualization account chart is analyzed, by institute
It states classification data visualization account chart and target data visualization account chart and analysis result pushes to user.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, not to above-mentioned reality
It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention
Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (10)
1. a kind of reconciliation chart generation method, which is characterized in that the described method includes:
Transaction data is obtained, and the transaction data is divided into multiple transaction data sections；
Noise data analysis is carried out to each transaction data section respectively, and is analyzed according to noise data as a result, to the friendship
Easy data are cleaned, and data have been cleaned in acquisition；
To the sampling processing cleaned data and carried out different time dimension, target data is obtained；
Based on Bayes Method, classify to the data of having cleaned, obtains classification data；
The target data and the classification data are subjected to visualization mapping respectively, generate visualization account chart.
2. reconciliation chart generation method according to claim 1, which is characterized in that described respectively to each transaction data
The analysis of Duan Jinhang noise data, and analyzed according to noise data as a result, being cleaned to the transaction data, number has been cleaned in acquisition
According to including:
Noise data analysis is carried out to each transaction data section respectively；
According to the corresponding noise data analysis of each transaction data section as a result, obtaining word to be cleaned in each transaction data section
Section；
It searches in the field to be cleaned and is augmented field, the field progress high order tensor that is augmented is augmented, is opened
Measure sets of fields；
The field to be cleaned is carried out by tensor field relevant to the field to be cleaned in the tensor sets of fields clear
It washes, data have been cleaned in acquisition.
3. reconciliation chart generation method according to claim 1, which is characterized in that described to be carried out not to the data of having cleaned
With the sampling processing of time dimension, obtaining target data includes:
Resample function setup first time dimension sample frequency is called, according to the first time dimension sample frequency to institute
It states and has cleaned data progress sampling processing, obtain first time dimension target data；
The second time dimension of resample function setup sample frequency is called, according to the second time dimension sample frequency to institute
It states and has cleaned data progress sampling processing, obtain the second time dimension target data；
The first time dimension target data and the second time dimension target data are combined, target data is obtained.
4. reconciliation chart generation method according to claim 1, which is characterized in that it is described to be based on Bayes Method, to institute
It states and has cleaned data and classify, obtaining classification data includes:
It obtains historical sample and has cleaned data；
It has cleaned data to the historical sample using Naive Bayes Classification Algorithm to be trained, the simple shellfish trained
This classification function of leaf；
By the Naive Bayes Classification function trained, classify to the data of having cleaned, obtains classification data.
5. reconciliation chart generation method according to claim 1, which is characterized in that described by the target data and described point
Class data carry out visualization mapping respectively, generate target data visualization account chart and classification data visualizes account chart packet
It includes:
By ring than calculation formula, ring ratio is carried out respectively to the target data and the classification data；
According to the ring of the target data and classification data ratio as a result, obtaining the target data and the classification data is each
From unit time in turnover increase and decrease amplitude；
Increase and decrease amplitude and the ring according to the turnover and carry out visualization mapping than result, generates the classification data visualization
Account chart and the target data visualize account chart.
6. reconciliation chart generation method according to claim 1, which is characterized in that described by the target data and described point
Class data carry out visualization mapping respectively, generate target data visualization account chart and classification data visualization account chart it
Afterwards, further includes:
Account chart is visualized to the classification data and target data visualization account chart is analyzed, it will be described point
Class data visualization account chart and target data visualization account chart and analysis result push to user.
7. a kind of reconciliation chart generating means, which is characterized in that described device includes:
Transaction data obtains module, obtains transaction data, and the transaction data is divided into multiple transaction data sections；
Data acquisition module is cleaned, for carrying out noise data analysis to each transaction data section respectively, and according to noise
Data analysis result cleans the transaction data, and data have been cleaned in acquisition；
Target data obtains module, for obtaining target to the sampling processing cleaned data and carried out different time dimension
Data；
Classification data obtains module, for being based on Bayes Method, classifies to the data of having cleaned, obtains classification number
According to；
Chart generating module generates visual for the target data and the classification data to be carried out visualization mapping respectively
Change account chart.
8. device according to claim 7, which is characterized in that the cleaning data acquisition module is also used to respectively to each
The transaction data section carries out noise data analysis；According to the corresponding noise data analysis of each transaction data section as a result, obtaining
Take field to be cleaned in each transaction data section；It searches in the field to be cleaned and is augmented field, be augmented to described
Field carries out high order tensor and is augmented, and obtains tensor sets of fields；By related to the field to be cleaned in the tensor sets of fields
Tensor field the field to be cleaned is cleaned, acquisition cleaned data.
9. a kind of computer equipment, including memory and processor, the memory are stored with computer program, feature exists
In the step of processor realizes any one of claims 1 to 6 the method when executing the computer program.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of method described in any one of claims 1 to 6 is realized when being executed by processor.
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| CN109669933A (en) * | 2018-12-10 | 2019-04-23 | 平安科技（深圳）有限公司 | Transaction data intelligent processing method, device and computer readable storage medium |
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