CN115908033B - Data processing method, device and system applied to business ticket rating model - Google Patents
Data processing method, device and system applied to business ticket rating model Download PDFInfo
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Abstract
The invention discloses a data processing method, a device and a system applied to a business ticket rating model, which take the month of financial data as a reference section and the period value of the financial data as a data format standard, and convert bill disclosure information in a default state in a cumulative value form into new data in a non-default period value form in a month reference form through various data operations; and meanwhile, a correlation based on financial data and new data is established through the new data, so that early warning is realized, and an early warning function based on bill disclosure information is provided for a user.
Description
Technical Field
The invention relates to the technical field of computer processing, in particular to a data processing method, device and system applied to a business ticket rating model.
Background
The business bill is a business bill or a business acceptance bill for short, the business bill is issued by a ticket issuing person, and a payor is entrusted with unconditionally paying a determined amount to a bill of a payee or a ticket holder on a specified date. The commercial draft is classified into a commercial acceptance draft and a bank acceptance draft. The commercial acceptance draft is accepted by a payer other than the bank (the payer is an acceptance person), and the bank acceptance draft is accepted by the bank.
The bill information disclosure platform is a platform which is approved by China people's banks and is constructed and operated by Shanghai bill exchange to disclose information related to bill business. The bill information disclosure platform provides service support for the contractor to disclose the commercial draft and the credit information of the contractor and provides service for the financial institutions, ticket holding enterprises or other public to inquire about the related disclosure information.
The bill information disclosure platform records bill transaction events, wherein the bill transaction events of a bill generally comprise: ticket outlet event, acceptance event, redemption event, when acceptance event occurs, the acceptance amount of a single ticket is recorded, and when payment event occurs, the clearing amount of a single ticket is recorded.
The bill information disclosure platform records each bill transaction event (ticket issuing, acceptance and payment), and can provide bill information inquiry service for users at present, specifically, the user can provide a target enterprise name, and the bill information disclosure platform returns bill disclosure information (bill information) when the target enterprise recently discloses date. Since only a part of enterprises have single data, the data is not full, and a mode of summary disclosure is adopted, so that bill information released on a disclosure date is released for different enterprises in an irregular mode, the release date is generally the disclosure date, and the bill information provided by the bill information disclosure platform is generally the accumulated value of the bill at the last disclosure date, such as the accumulated value of the acceptance occurrence amount and the accumulated value of the acceptance balance. Therefore, such exposed bill information is not effective and intuitive for the user to obtain the bill credit status of the target enterprise, is very unfriendly for the user, and cannot play a role in revealing risk warning.
Financial data refers to content reflecting financial status and business outcome of an enterprise. Generally comprises: the business income, accounts receivable, income tax amount and the like of each month are generally recorded in financial account book data and report data, wherein the financial data is data which is calculated according to real business operation financial information statistics and then registered, and the report data mainly comprises: asset liability statement data, damage benefit statement data, cash flow statement data, etc., which pertains to the basic financial data of an enterprise.
In order to provide more scientific and reasonable bill transaction risk warning functions for users, the invention aims to discover the potential risk of enterprise operation by combining the public real-time financial data of the month report state with the real-time bill disclosure information so as to provide warning information for the users.
However, the disclosure of the real-time bill disclosure information is irregular (default condition exists) and the expression mode is the accumulated value, and the real-time financial data is the regular monthly report content and the expression mode is the period value, so that the technical problem faced by the present invention is to establish the consistency of the two types of data and solve the default problem of the data.
Disclosure of Invention
The invention aims to provide a data processing method, a device and a system for a business ticket rating model, which are used for converting ticket disclosure information in a default state in a cumulative value form into new data in a non-default period value form in a month-based form through various data operations by taking the month of financial data as a reference segment and taking the period value of the financial data as a data format standard; and meanwhile, a correlation based on financial data and new data is established through the new data, so that early warning is realized, and an early warning function based on bill disclosure information is provided for a user.
In one aspect, the present application provides a data processing method for a business ticket rating model, specifically including the following steps:
s1, receiving a rating request message of a user, wherein the rating request message comprises the following components: a target business name;
s2, forwarding a rating request message of the user to a business acceptance draft transaction registration data server;
s3, historical bill acceptance credit information responding to the name of the target enterprise from a business acceptance draft transaction registration data server is obtained;
s4, cutting historical bill acceptance credit information of the target enterprise into blocks according to month, screening and deleting the blocks of the same month to obtain valued month records, and obtaining a first data set of the valued month records which are sequentially arranged;
s5, inserting a value default month record in a month vacancy position of the first data set to obtain a second data set comprising the value month record and the value default month record;
s6, performing month period value conversion processing on the second data set to obtain a third data set which expresses a valued month record of the month period value and a default month record of the month period value;
s7, extracting a data set of the current query year from the third data set, and traversing whether a month record corresponding to the last year in the data set of the current query year is a value default month record or not; if not, the default month record of the value is complemented by adopting a history method, if yes, the default month record of the value is complemented by adopting an interpolation method, and after traversing is completed, the data set of the current query year is converted into a fourth data set which is all the valued month record;
s8, recombining the fourth data set into a rating request message, and forwarding the recombined rating request message to a business acceptance draft rating server.
Preferably, the method comprises the steps of,
the historical ticket redemption credit information includes: a plurality of disclosure records, each disclosure record comprising: the date of disclosure, the total amount of acceptance, the number of outstanding notes, the total amount outstanding when the date of disclosure is currently disclosed;
the specific process of S4 is as follows:
s41, dividing the disclosure records of the same month into a cut block according to the disclosure date;
s42, traversing each switch, reserving a disclosure record corresponding to the day closest to the next month, deleting disclosure records corresponding to the rest days, and recording one disclosure record screened from each cut block as a valued month record;
s43, sequentially ordering and combining the valued month records into a first data set.
Preferably, the method comprises the steps of,
the specific process of S6 is as follows:
converting the current accumulated value of the total amount of the acceptance, the number of the settled strokes, the total amount of the unconfined strokes and the total amount of the unconfined strokes in the second data set into a month period value;
if the current accumulated value of the current month or/and the current accumulated value of the last month is in a default state, the month period value of the current month is in the default state, and if the month record of the current month or the last month is in a value default month record, the current accumulated value of the current month or/and the current accumulated value of the last month is in the default state.
Preferably, the method comprises the steps of,
the process of adopting the history method to complement the default month record of the value is as follows:
s711, calculating a month-to-month growth rate of each valued month record in the current query year, wherein a month-to-month growth rate calculation formula is as follows: month-to-month growth rate = present month period value/present month period value of last year corresponding month;
s712, calculating an average value of the month equal ratio increase rate;
s713, taking the average value of the month equal rate of increase as a substitute value of the month equal rate of increase recorded by default month of each value in the current query year;
s714, calculating a month period value of the default month record according to the month equal ratio increasing rate of the default month record, wherein the calculation formula is as follows: the month period value of the value default month record=the month period value of the month corresponding to the month of the last year of the month of the value default month record;
the above steps S711 to S714 are performed once for the total number of credits, the total amount of credits, the number of paid out credits, and the total amount of paid out credits, respectively.
Preferably, the method comprises the steps of,
the process of adopting interpolation method to complement the default month record of the value is as follows:
s721, searching adjacent valued month records before and after a default month record in the median of the current inquiry year;
s722, obtaining an average value of the month period values based on the month period values recorded by the front and back adjacent valued months;
s723, replacing a month period value recorded by a default month by adopting an average value of month period values;
the above-described S721 to S723 are performed once for the total number of credits, the total amount of credits, the number of paid out credits, and the total amount of paid out credits, respectively.
Preferably, the method comprises the steps of,
the method also comprises the following steps:
calculating the month period value of the number of outstanding strokes according to the month period value of the total number of accepted strokes and the month period value of the number of outstanding strokes, wherein the calculation formula is as follows: month period value of number of outstanding strokes = month period value of total number of strokes accepted-month period value of number of outstanding strokes;
calculating the month period value of the undelivered amount according to the month period value of the acceptance total amount and the month period value of the settled total amount, wherein the calculation formula is as follows: the month period value of the outstanding amount=the month period value of the acceptance total amount-the month period value of the outstanding total amount.
Preferably, the method comprises the steps of,
and the starting month of the current query year is the month where the generation date of the rating request message is, and the ending month is the month after the starting month is pushed back for 12 months in the previous annual direction.
In another aspect, a data processing apparatus for use in a ticket rating model, includes:
one or more processors;
and the storage unit is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors can realize the data processing method applied to the business ticket rating model.
In another aspect, a data processing system for use in a business ticket rating model, comprising:
the client is used for generating a rating request message, and the rating request message comprises: a target business name;
the data completeness checking server is used for receiving the rating request message, forwarding the rating request message to the business acceptance draft transaction registration data server, receiving historical bill acceptance credit information responding to the target enterprise name from the business acceptance draft transaction registration data server, processing the completeness data of the historical bill acceptance credit information into a fourth data set, recombining the fourth data set into the rating request message, and forwarding the recombined rating request message to the business acceptance draft rating server;
and the business acceptance draft rating server is used for outputting a rating early warning message according to the fourth data set.
Preferably, the process of outputting the rating early warning message according to the fourth data set is as follows:
and calculating the ratio of the financial data of each month of the target enterprise to the total amount of acceptance, the number of settled strokes, the total amount of not settled strokes and the total amount of not settled strokes in the fourth data set, and outputting a rating early warning message if the ratio exceeds an early warning value.
The invention has the beneficial effects that:
according to the invention, the month of the financial data is taken as a reference section, the period value of the financial data is taken as a data format standard, and bill disclosure information in a default state in the form of an accumulated value is converted into new data in the form of a period value without default by various data operations; and meanwhile, a correlation based on financial data and new data is established through the new data, so that early warning is realized, and an early warning function based on bill disclosure information is provided for a user.
The invention can convert bill disclosure information into new data of the benchmarking financial data.
The invention can solve the problem of default of bill disclosure information and obtain new data with higher accuracy and complement.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a diagram showing the information content of ticket disclosure information (history ticket redemption credit information).
Fig. 3 is a data content of the ticket disclosure information (historical ticket redemption credit information) after the information content has been converted into the first data set.
Fig. 4 is the data content after the first data set is converted into the second data set.
Fig. 5 shows the data content after the second data set is converted into the third data set.
Fig. 6 shows the data content of the third data set after being converted into the fourth data set by using the history method.
Fig. 7 shows the data content of the third data set after interpolation to the fourth data set.
FIG. 8 is a diagram showing the data contents of the fourth dataset for the completion of the total outstanding pen number and the total outstanding monetary amount.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
In addition, descriptions of well-known structures, functions and configurations may be omitted for clarity and conciseness. Those of ordinary skill in the art will recognize that various changes and modifications of the examples described herein can be made without departing from the spirit and scope of the present disclosure.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values.
Example 1
As shown in fig. 2, the historical bill acceptance credit information refers to bill disclosure information issued by a bill information disclosure platform, and records, each record including a disclosure date, an acceptance total amount, a settled total amount, an unclosed total amount, and an unclosed total amount; the plurality of records at this time may be regarded as daily records, and the data values corresponding to the total number of credits, the total amount of credits already paid, the total amount of outstanding credits, and the total amount of outstanding credits are cumulative values, which represent cumulative amounts of the corresponding fields. The state of the bill disclosure information at this time is: there are multiple disclosure records for a single month, default state, cumulative value expression state.
In one aspect, the present application provides a data processing method for a business ticket rating model that enables conversion of historical ticket redemption credit information into financial data by the following steps.
Referring to fig. 1, the data processing method specifically includes the following steps:
s1, receiving a rating request message of a user, wherein the rating request message comprises the following components: a target business name;
s2, forwarding a rating request message of the user to a business acceptance draft transaction registration data server;
s3, historical bill acceptance credit information responding to the name of the target enterprise from a business acceptance draft transaction registration data server is obtained;
s4, cutting historical bill acceptance credit information of the target enterprise into blocks according to month, screening and deleting the blocks of the same month to obtain valued month records, and obtaining a first data set of the valued month records which are sequentially arranged; as shown in fig. 3, in the first data obtained by the processing of this step, there are a plurality of valued month records, where the valued month record refers to one line of data in the first data set, the one line of data includes: the data values corresponding to the monthly, the total amount of acceptance, the total amount of settled, the total amount of uncleaned and the total amount of uncleaned are still accumulated values;
s5, inserting a value default month record in a month vacancy position of the first data set to obtain a second data set comprising the value month record and the value default month record; as shown in fig. 4, since the first data set has a month default condition, but the financial data is a data structure according to months, in order to further supplement the first data set with the month marked with the financial data, the present invention provides an inserting operation of inserting, on the basis of the first data set, value default month records, each value default month record including: the month, the total amount of the acceptance, the total amount of the settled, the total amount of the uncleaned and the total amount of the uncleaned are all in a default state.
S6, performing month period value conversion processing on the second data set to obtain a third data set which expresses a valued month record of the month period value and a default month record of the month period value; as shown in fig. 5, since the second data set expresses still the cumulative value, and the financial data is generally a period value managed by the enterprise, in order to mark the financial data, the second data set needs to be further converted into a data set in a period value state, and therefore, whether the second data set has a value month record or a value default month record needs to be converted into a new period value, this operation is mainly conversion of the data value, that is, the numerical content in the second data set is converted from the cumulative value into the period value, where the period value is a data value expressing that a redemption event occurs in one month period, for example, expresses the total number of occurrence of the redemption event in one month period and is marked as the total number of redemption events.
S7, extracting a data set of the current query year from the third data set, and traversing whether a month record corresponding to the last year in the data set of the current query year is a value default month record or not; if not, the default month record of the value is complemented by adopting a history method, if yes, the default month record of the value is complemented by adopting an interpolation method, and after traversing is completed, the data set of the current query year is converted into a fourth data set which is all the valued month record;
as shown in fig. 6, the data set generally includes at least 2 years of data, and is generally based on the current query year, and the default month record of the value in the current query year is complemented by combining the data of the previous year; the objective of traversing whether the month record corresponding to the last year in the data set of the current query year is the value default month record or not is to complement the value default month record with the highest priority by using a historical method, namely, the complement value default month record is preferably complemented by using the data of the last year, so that any value is prevented from being inserted manually and subjectively, the data accords with the running characteristics of an enterprise, the data value of a bill of a stable enterprise generally follows the running inertia in continuous running activities, and the proportional relation between the data of the current year and the corresponding month record of the last year can be reasonably inserted by researching the proportional relation of the corresponding month record of the last year, and the data distortion is avoided. As shown in fig. 7, however, as in the present invention, there is a case where the month record corresponding to the previous year is not default, and therefore, in order to reduce the data distortion, the present invention also uses the data interpolation method of the present year to complement the data; compared with the prior art, the method has the advantages that the operation of degrading and supplementing by the historical method and the interpolation method is set, so that the supplemented data value can be close to the real state, and the risk of distortion is reduced.
S8, recombining the fourth data set into a rating request message, and forwarding the recombined rating request message to a business acceptance draft rating server. As shown in fig. 1, the obtained new fourth data set may be sent to a business receipt rating server, where the business receipt rating server performs risk prediction by combining the fourth data set with the financial data obtained in advance in a comparison manner, provides a two-dimensional data combined risk alarm function for the user, and solves the problem that the bill information disclosure platform only provides accumulated value information and is not friendly to the user.
Preferably, as shown in fig. 1 to 8, the above technical idea will be further explained in conjunction with specific processes.
As shown in fig. 2, the history ticket redemption credit information includes: a plurality of disclosure records, each disclosure record comprising: the date of disclosure, the total amount of acceptance, the number of outstanding notes, the total amount outstanding when the date of disclosure is currently disclosed; as shown in FIG. 2, the disclosure records refer to a line of data in FIG. 2, wherein the total amount accepted, the total amount settled, the total amount not settled express an accumulated value, and the accumulated value refers to an accumulated total amount of a certain field of the enterprise by a certain disclosure date.
The specific process of S4 is as follows:
s41, dividing the disclosure records of the same month into a cut block according to the disclosure date;
s42, traversing each switch, reserving a disclosure record corresponding to the day closest to the next month, deleting disclosure records corresponding to the rest days, and recording one disclosure record screened from each cut block as a valued month record;
s43, sequentially ordering and combining the valued month records into a first data set.
From the above, the bill information disclosure platform returns to the historical bill acceptance credit information of the target enterprise, where there may be more than one data record in the same month, for example, the frame area in fig. 2, and the record in one month only needs to be reserved for the last time so as to facilitate the subsequent period value conversion, so that redundancy removal processing, namely, deleting the redundant record, is needed, and the processing method is that: the last record of each month is reserved as the accumulated value of the current month bill data, and other redundant data records of the current month are deleted, so that a first data set with the valued month records ordered in sequence can be obtained as shown in figure 3.
Preferably, the method comprises the steps of,
the specific process of S6 is as follows:
converting the current accumulated value of the total amount of the acceptance, the number of the settled strokes, the total amount of the unconfined strokes and the total amount of the unconfined strokes in the second data set into a month period value;
the conversion formula is as follows: the month period value=the current accumulated value of the month-the current accumulated value of the last month, and the month period value is subjected to integer processing; if the current accumulated value of the current month or/and the current accumulated value of the last month is in a default state, the month period value of the current month is in the default state, and if the month record of the current month or the last month is in a value default month record, the current accumulated value of the current month or/and the current accumulated value of the last month is in the default state.
As shown in fig. 4, the first data set has a month missing, and thus, the record corresponding to the missing month may be inserted by an insertion method, and the inserted record may be inserted by default, thereby obtaining the second data set.
Since the second data set expresses the accumulated value, as shown in fig. 5, in order to scale the financial data, the present invention scales the accumulated value into the period value by the above method, as shown in fig. 5, taking the record of 201903 months as an example, and taking the total number of credits as a target, based on the formula: the month period value=the month current accumulated value-the last month current accumulated value, the corresponding data are substituted, and the period value of the total number of the acceptance strokes=494-390=104. And similarly, carrying out the method on the total amount of the acceptance, the number of the settled strokes, the total amount of the uncleaned strokes and the total amount of the uncleaned strokes once to obtain corresponding numerical values.
What needs to be specified is: if the current accumulated value of the current month or/and the current accumulated value of the last month is in a default state, the month period value of the current month is in the default state, and if the month record of the current month or the last month is in a value default month record, the current accumulated value of the current month or/and the current accumulated value of the last month is in the default state. As with the cumulative value, fig. 4, it can be seen that the record of the month 201910 is a default month record and the record of the month 201911 is a monthly record; when the period value is converted, the current calculation is 201911 months, and the record of 201910 months is the last month record, so that the output period value is the default month record although the present month is the valued month record, as shown in fig. 5, after the processing, the number of default records is increased, but the follow-up completion is performed, and the distortion degree affecting the data is not increased.
Preferably, in order to reduce the distortion degree of the data when the default month record is completed, the invention preferably adopts a historical method for completion and then adopts an interpolation method for completion. Therefore, the current value to be complemented needs to be judged to be complemented by the cooperation method applicable to the default month record, and therefore, one judgment is needed; and the rule for judging is to observe whether the month record corresponding to the month of the last year corresponding to the default month record to be completed currently is the default month record.
Taking fig. 5 as an example, the last year of the default month records of two values 202002 and 202003 corresponds to the month record of 201902 and 201903, and the two month records are all valued month records, so that a history method is adopted for the completion processing mode of the default month records of two values 202002 and 202003; the last year of the three-value default month records 202009, 202010 and 202011 corresponds to the 3-month records 201909, 201910 and 201911, and the three-month records are all the value default month records, so that interpolation is adopted for the complement processing mode of the three-value default month records 202009, 202010 and 202011.
Specifically, the process of adopting the history method to complement the default month record of the value is as follows:
s711, calculating a month-to-month growth rate of each valued month record in the current query year, wherein a month-to-month growth rate calculation formula is as follows: month-to-month growth rate = present month period value/present month period value of last year corresponding month;
s712, calculating an average value of the month equal ratio increase rate;
s713, taking the average value of the month equal rate of increase as a substitute value of the month equal rate of increase recorded by default month of each value in the current query year;
s714, calculating a month period value of the default month record according to the month equal ratio increasing rate of the default month record, wherein the calculation formula is as follows: the month period value of the value default month record=the month period value of the month corresponding to the month of the last year of the month of the value default month record;
the above steps S711 to S714 are performed once for the total number of credits, the total amount of credits, the number of paid out credits, and the total amount of paid out credits, respectively.
Specifically, taking fig. 6 as an example, two records 202002 and 202003 are filled in by using a history method. The business ticket is a credit ticket applied to commodity transaction, generally enterprises adopting commodities have normal management architecture and financial architecture and good stable business foundation, and in the economic stable period, the business level is stable, so that the month-to-month ratio increase rate of four indexes of total number of credits, total amount of credits, amount of credits and amount of credits is a basically stable value within one year, and the month-to-month ratio increase rate of the four indexes does not change greatly in a single month. By utilizing the characteristics, the method and the device set the same-ratio growth rate as the data characteristic value, so that data filling is carried out, and the fidelity of the data is greatly ensured. The following specifically describes the process flow of the history method by taking the total amount of the redemption:
first, the month-to-month growth rate of six months 202004, 202005, 202006, 202007, 202008, 202012 is calculated, and the month-to-month growth rate is calculated by the following formula: month-to-month growth rate = current month period value/last year month period value of 1.62, 1.58, 1.51, 1.94, 0.81, 0.77, respectively.
And then, the average value of the month equal rate of increase is calculated to be 1.37, and the average value is used as a month equal rate of increase substitution value of the vacant months 202002 and 202003.
Calculating an index value of the vacant month according to the month equal ratio increasing rate of the vacant month, wherein the calculation formula is as follows: month period value = month period value corresponding to one year from month to month.
Therefore, by means of the characteristic that the same-year month-to-month growth fluctuation is not large, the reasonable period value is calculated by creatively utilizing the month-to-month growth rate and the period value corresponding to the month of the last year, and therefore the default position is complemented. The error between the values processed in this way and the actual values is tested only about 5%. The error of about 5% indicates that the invention has higher fidelity.
Specifically, the process of using interpolation to complement the default month record of the value is as follows:
s721, searching adjacent valued month records before and after a default month record in the median of the current inquiry year;
s722, obtaining an average value of the month period values based on the month period values recorded by the front and back adjacent valued months;
s723, replacing a month period value recorded by a default month by adopting an average value of month period values;
the above-described S721 to S723 are performed once for the total number of credits, the total amount of credits, the number of paid out credits, and the total amount of paid out credits, respectively.
Specifically, as shown in fig. 7, three pieces of blank data 202009, 202010, 202011 are filled in by interpolation. The difference method is based on four indexes of total number of payouts, total amount of payouts, number of payouts and amount of payouts, wherein the period value of the four indexes is a basically stable value in a short period, and the great change cannot occur. The following specifically describes the process flow of the difference method by taking the total amount of the redemption:
three non-empty months 202008 and 202012 were first found, 202009, 202010, 202011, which were adjacent before and after recording, and then the average of the values during the corresponding non-empty months was calculated, and then rounded.
This average was used as a surrogate for the three records 202009, 202010, 202011.
Preferably, the method comprises the steps of,
as shown in fig. 8, the method further comprises the following steps:
calculating the month period value of the number of outstanding strokes according to the month period value of the total number of accepted strokes and the month period value of the number of outstanding strokes, wherein the calculation formula is as follows: month period value of number of outstanding strokes = month period value of total number of strokes accepted-month period value of number of outstanding strokes;
calculating the month period value of the undelivered amount according to the month period value of the acceptance total amount and the month period value of the settled total amount, wherein the calculation formula is as follows: the month period value of the outstanding amount=the month period value of the acceptance total amount-the month period value of the outstanding total amount.
Preferably, the method comprises the steps of,
and the starting month of the current query year is the month where the generation date of the rating request message is, and the ending month is the month after the starting month is pushed back for 12 months in the previous annual direction.
Example 2
As shown in fig. 1, a data processing apparatus applied to a ticket rating model, where the data processing apparatus may be understood as a data completeness check server in fig. 1, includes:
one or more processors;
and the storage unit is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors can realize the data processing method applied to the business ticket rating model.
Example 3
As shown in fig. 1, a data processing system applied to a ticket rating model, comprising:
the client is used for generating a rating request message, and the rating request message comprises: a target business name;
the data completeness checking server is used for receiving the rating request message, forwarding the rating request message to the business acceptance draft transaction registration data server, receiving historical bill acceptance credit information responding to the target enterprise name from the business acceptance draft transaction registration data server, processing the completeness data of the historical bill acceptance credit information into a fourth data set, recombining the fourth data set into the rating request message, and forwarding the recombined rating request message to the business acceptance draft rating server; it should be noted that, as shown in fig. 8, although the fourth data set is obtained, the fourth data set may be cut, and only the data content of the current query year needs to be retained, that is, the data content of the frame selection area in fig. 8, so as to reduce the size of the message data.
And the business acceptance draft rating server is used for outputting a rating early warning message according to the fourth data set.
Preferably, the process of outputting the rating early warning message according to the fourth data set is as follows:
and calculating the ratio of the financial data of each month of the target enterprise to the total amount of acceptance, the number of settled strokes, the total amount of not settled strokes and the total amount of not settled strokes in the fourth data set, and outputting a rating early warning message if the ratio exceeds an early warning value.
The contractor in the drawing is the target enterprise.
The financial data can be specifically business income, accounts receivable, income tax amount and the like, and the proportion between the data and the total amount of acceptance, total amount of settlement, total amount of outstanding and total amount of outstanding in the fourth data set is calculated, so that the corresponding financial data and bill disclosure data are placed in the same data format and in the same dimension for observation, and the financial data are public, real-time and monthly operation data from a target enterprise, so that the consistency between operation behavior and bill business behavior can be associated. Therefore, the method and the system realize the early warning after the observation of consistency, and realize the consistency observation of the bill disclosure data with accumulated value from the financial data by providing the data processing method and the lap joint association relation, thereby assisting the user in reading the bill disclosure data and having better friendliness.
In addition, the process of outputting the rating early warning message according to the fourth data set may further be: outputting a rating early warning message according to an early warning model, wherein input values of the early warning model comprise: and the output value of the early warning model is 0 or 1, wherein 0 represents no risk and no early warning is needed, and 1 represents risk and early warning is needed.
The sources of financial data are: and submitting financial reports and self-supplementing declarations by the enterprises.
Financial data category: repayment capability data, operation capability data, and profitability data.
The early warning model is a machine learning model, the input value of the model is divided into two types, one type is the financial data of a target enterprise, and the financial data comprises repayment capability indexes, operation capability indexes and profitability indexes; the other type is the enterprise ticket data (fourth data set) after the supplementation is completed, and the enterprise ticket data comprises a total amount of acceptance, a total amount of settlement, a total amount of unclean and a total amount of unclean; and after the two types of data are input into the early warning model, an output value of 0 or 1 is obtained after calculation, wherein 0 represents no risk and no early warning is needed, and 1 represents that the risk needs early warning. Early warning model mlf=f (chbz, cdb, cde, wqys, varws); where chbz, cdb, cde, wqys, varws are variables. chbz: the total amount/accounts receivable (fourth data set); cdb: the total number of strokes (fourth dataset); cde: the total amount (fourth data set); wqys wqe/ys, outstanding total amount (fourth dataset)/revenue (financial data); varws d (wqsd), the rate of change of the ratio of the total amount (fourth data set) to the resulting tax amount (financial data), i.e., the increment/raw value, is not established.
The early warning model is the prior art and will not be described in detail here.
The foregoing description of the preferred embodiment of the invention is not intended to limit the invention in any way, but rather to cover all modifications, equivalents, improvements and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The data processing method applied to the business ticket rating model is characterized by comprising the following steps of:
s1, receiving a rating request message of a user, wherein the rating request message comprises the following components: a target business name;
s2, forwarding a rating request message of the user to a business acceptance draft transaction registration data server;
s3, historical bill acceptance credit information responding to the name of the target enterprise from a business acceptance draft transaction registration data server is obtained;
s4, cutting historical bill acceptance credit information of the target enterprise into blocks according to month, screening and deleting the blocks of the same month to obtain valued month records, and obtaining a first data set of the valued month records which are sequentially arranged;
s5, inserting a value default month record in a month vacancy position of the first data set to obtain a second data set comprising the value month record and the value default month record;
s6, performing month period value conversion processing on the second data set to obtain a third data set which expresses a valued month record of the month period value and a default month record of the month period value;
s7, extracting a data set of the current query year from the third data set, and traversing whether a month record corresponding to the last year in the data set of the current query year is a value default month record or not; if not, the default month record of the value is complemented by adopting a history method, if yes, the default month record of the value is complemented by adopting an interpolation method, and after traversing is completed, the data set of the current query year is converted into a fourth data set which is all the valued month record;
s8, recombining the fourth data set into a rating request message, and forwarding the recombined rating request message to a business acceptance draft rating server.
2. A data processing method for use in a business ticket rating model as in claim 1,
the historical ticket redemption credit information includes: a plurality of disclosure records, each disclosure record comprising: the date of disclosure, the total amount of acceptance, the number of outstanding notes, the total amount outstanding when the date of disclosure is currently disclosed;
the specific process of S4 is as follows:
s41, dividing the disclosure records of the same month into a cut block according to the disclosure date;
s42, traversing each switch, reserving a disclosure record corresponding to the day closest to the next month, deleting disclosure records corresponding to the rest days, and recording one disclosure record screened from each cut block as a valued month record;
s43, sequentially ordering and combining the valued month records into a first data set.
3. A data processing method for use in a business ticket rating model as in claim 1,
the specific process of S6 is as follows:
converting the current accumulated value of the total amount of the acceptance, the number of the settled strokes, the total amount of the unconfined strokes and the total amount of the unconfined strokes in the second data set into a month period value;
the conversion formula is as follows: the month period value=the current accumulated value of the month-the current accumulated value of the last month, and the month period value is subjected to integer processing;
if the current accumulated value of the current month or/and the current accumulated value of the last month is in a default state, the month period value of the current month is in the default state, and if the month record of the current month or the last month is in a value default month record, the current accumulated value of the current month or/and the current accumulated value of the last month is in the default state.
4. A data processing method for a business ticket rating model as in claim 3,
the process of adopting the history method to complement the default month record of the value is as follows:
s711, calculating a month-to-month growth rate of each valued month record in the current query year, wherein a month-to-month growth rate calculation formula is as follows: month-to-month growth rate = present month period value/present month period value of last year corresponding month;
s712, calculating an average value of the month equal ratio increase rate;
s713, taking the average value of the month equal rate of increase as a substitute value of the month equal rate of increase recorded by default month of each value in the current query year;
s714, calculating a month period value of the default month record according to the month equal ratio increasing rate of the default month record, wherein the calculation formula is as follows: the month period value of the value default month record=the month period value of the month corresponding to the month of the last year of the month of the value default month record;
the above steps S711 to S714 are performed once for the total number of credits, the total amount of credits, the number of paid out credits, and the total amount of paid out credits, respectively.
5. A data processing method for a business ticket rating model as in claim 3,
the process of adopting interpolation method to complement the default month record of the value is as follows:
s721, searching adjacent valued month records before and after a default month record in the median of the current inquiry year;
s722, obtaining an average value of the month period values based on the month period values recorded by the front and back adjacent valued months;
s723, replacing a month period value recorded by a default month by adopting an average value of month period values;
the above-described S721 to S723 are performed once for the total number of credits, the total amount of credits, the number of paid out credits, and the total amount of paid out credits, respectively.
6. A data processing method for use in a ticket rating model as in claim 4 or 5,
the method also comprises the following steps:
calculating the month period value of the number of outstanding strokes according to the month period value of the total number of accepted strokes and the month period value of the number of outstanding strokes, wherein the calculation formula is as follows: month period value of number of outstanding strokes = month period value of total number of strokes accepted-month period value of number of outstanding strokes;
calculating the month period value of the undelivered amount according to the month period value of the acceptance total amount and the month period value of the settled total amount, wherein the calculation formula is as follows: the month period value of the outstanding amount=the month period value of the acceptance total amount-the month period value of the outstanding total amount.
7. A data processing method for use in a business ticket rating model as in claim 1,
and the starting month of the current query year is the month where the generation date of the rating request message is, and the ending month is the month after the starting month is pushed back for 12 months in the previous annual direction.
8. A data processing apparatus for use in a ticket rating model, comprising:
one or more processors;
a storage unit for storing one or more programs, which when executed by the one or more processors, enable the one or more processors to implement a data processing method as claimed in any one of claims 1-6 for application to a ticket rating model.
9. A data processing system for a business ticket rating model, comprising: comprising the following steps:
the client is used for generating a rating request message, and the rating request message comprises: a target business name;
a data integrity check server comprising:
one or more processors;
a storage unit configured to store one or more programs, which when executed by the one or more processors, enable the one or more processors to implement a data processing method according to any one of claims 1 to 6, applied to a ticket rating model;
and the business acceptance draft rating server is used for outputting a rating early warning message according to the fourth data set.
10. A data processing system for use in a business ticket rating model as in claim 9, wherein: the process of outputting the rating early warning message according to the fourth data set is as follows:
and calculating the ratio of the financial data of each month of the target enterprise to the total amount of acceptance, the number of settled strokes, the total amount of not settled strokes and the total amount of not settled strokes in the fourth data set, and outputting a rating early warning message if the ratio exceeds an early warning value.
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| CN110852855A (en) * | 2018-08-02 | 2020-02-28 | 上海宝信软件股份有限公司 | Intelligent ticket distribution method and system |
| CN113643115A (en) * | 2021-08-19 | 2021-11-12 | 四川川投云链科技有限公司 | Method and system for scoring business acceptance draft credit based on option pricing model |
| CN114331105A (en) * | 2021-12-27 | 2022-04-12 | 上海聚均科技有限公司 | Electronic draft processing system, method, electronic device and storage medium |
| CN115293913A (en) * | 2022-07-08 | 2022-11-04 | 中国银行股份有限公司 | Early warning method and device for payment refusal risk of commercial draft acceptance person |
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