CN116167864A

CN116167864A - Data processing method, apparatus, device, storage medium, and program product

Info

Publication number: CN116167864A
Application number: CN202310199222.XA
Authority: CN
Inventors: 舒杨; 陈宝山; 何晨熠; 夏成扬
Original assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Current assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Priority date: 2023-02-27
Filing date: 2023-02-27
Publication date: 2023-05-26

Abstract

The embodiment of the disclosure provides a data processing method, a device, equipment, a storage medium and a program product. In some embodiments of the present disclosure, national debt yield data for a set period is obtained; determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data; according to the time span of each data complement interval and the data change condition of the set frequency dimension of the interval end point, carrying out automatic complement on null data in each data complement interval to obtain national debt income data after complement is completed; the national debt income data after the completion of the supplementary recording is subjected to visual processing to obtain a national debt income ratio fitting curve graph, and the method automatically carries out data supplementary recording on the national debt income ratio data to generate a relatively accurate national debt income ratio fitting curve graph, so that the data processing efficiency is improved.

Description

Data processing method, apparatus, device, storage medium, and program product

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a data processing method, apparatus, device, storage medium, and program product.

Background

In the current environment, the national debt yield is limited by the data acquisition precision, the expertise and the accuracy of manual calibration in the data transmission process, and a national debt yield fitting curve conforming to the expected result cannot be obtained. Meanwhile, the method is influenced by epidemic situation limitation and holiday downtime and production stoppage, so that a plurality of report time points have no corresponding data, and a plurality of break points exist in the national debt yield fitting curve.

At present, the breakpoint data of the national debt yield fitting curve is manually subjected to supplementary recording, and the data processing efficiency is low.

Disclosure of Invention

The disclosure provides a data processing method, a device, equipment, a storage medium and a program product, which are used for improving the data processing efficiency in a mode of automatically supplementing national debt yield. The technical scheme of the present disclosure is as follows:

the embodiment of the disclosure provides a data processing method, which comprises the following steps:

acquiring national debt yield data of a set period;

determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data;

performing supplementary recording on null data in each data supplementary recording interval according to the time span of each data supplementary recording interval and the data change condition of the interval end point under the set frequency dimension, so as to obtain national debt income data after the supplementary recording is completed;

And carrying out visual processing on the national debt income data after the completion of the supplementary recording to obtain a national debt income ratio fitting curve graph.

An embodiment of the present disclosure provides a data processing apparatus, including:

the acquisition module is used for acquiring national debt yield data of a set period;

the determining module is used for determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data;

the supplementary record module is used for carrying out supplementary record on null data in each data supplementary record interval according to the time span of each data supplementary record interval and the data change condition of the interval end point under the set frequency dimension to obtain national debt income data after the supplementary record is completed;

and the visualization module is used for carrying out visualization processing on the national debt income data after the completion of the supplementary record to obtain a national debt income ratio fitting curve graph.

Optionally, the determining module is configured to, when determining, according to the null condition of the national debt yield data, a data complement interval corresponding to the national debt yield data:

taking the initial time point data in the national debt yield data as the initial end point of an initial interval, and taking the timing point closest to the initial time point data as the end point of the initial interval; and taking the other end point of the initial interval as the initial end point of the next interval, and dividing the subsequent interval in turn until the data complement interval is obtained, wherein null data exists in each data complement interval.

A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method described above.

A computer program product comprising a computer program/instruction, characterized in that the computer program/instruction, when executed by a processor, implements the method described above.

In some embodiments of the present disclosure, national debt yield data for a set period is obtained; determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data; according to the time span of each data complement interval and the data change condition of the set frequency dimension of the interval end point, carrying out automatic complement on null data in each data complement interval to obtain national debt income data after complement is completed; the national debt income data after the completion of the supplementary recording is subjected to visual processing to obtain a national debt income ratio fitting curve graph, and the method automatically carries out data supplementary recording on the national debt income ratio data to generate a relatively accurate national debt income ratio fitting curve graph, so that the data processing efficiency is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure and do not constitute an undue limitation on the disclosure.

Fig. 1 is a flow chart of a data processing method according to a first embodiment of the disclosure;

fig. 2 is a flow chart of another data processing method according to the second embodiment of the disclosure;

FIG. 3 is a flow chart of another data processing method according to the third embodiment of the disclosure;

FIG. 4 is a schematic diagram of a data processing apparatus according to an exemplary embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present disclosure.

Detailed Description

In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.

At present, the breakpoint data of the national debt yield fitting curve is manually complemented, the existing data are manually calibrated, the labor cost is high, and the accuracy is low; the curve has a plurality of break points affected by epidemic situation and holiday; and the complexity and difficulty of the review are high when the result is not in line with the expectation.

In view of the above-mentioned technical problems, in some embodiments of the present disclosure, national debt yield data for a set period is obtained; determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data; according to the time span of each data complement interval and the data change condition of the set frequency dimension of the interval end point, carrying out automatic complement on null data in each data complement interval to obtain national debt income data after complement is completed; the national debt income data after the completion of the supplementary recording is subjected to visual processing to obtain a national debt income ratio fitting curve graph, and the method automatically carries out data supplementary recording on the national debt income ratio data to generate a relatively accurate national debt income ratio fitting curve graph, so that the data processing efficiency is improved.

The following describes in detail the technical solutions provided by the embodiments of the present disclosure with reference to the accompanying drawings.

Fig. 1 is a flowchart of a data processing method according to a first embodiment of the disclosure. As shown in fig. 1, the method includes:

S101: acquiring national debt yield data of a set period;

s102: determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data;

s103: performing supplementary recording on null data in each data supplementary recording interval according to the time span of each data supplementary recording interval and the data change condition of the interval end point under the set frequency dimension, so as to obtain the national debt income data after the supplementary recording is completed;

s104: and carrying out visual processing on the national debt income data after the completion of the supplementary recording to obtain a national debt income ratio fitting curve graph.

In this embodiment, the execution body of the above method may be a server or a terminal device.

When the execution subject of the method is a server, the implementation form of the server is not limited. For example, the server may be a conventional server, a cloud host, a virtual center, or the like server device. The server mainly comprises a processor, a hard disk, a memory, a system bus and the like, and a general computer architecture type.

In this embodiment, national debt yield data of a set period is acquired; determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data; according to the time span of each data complement interval and the data change condition of the set frequency dimension of the interval end point, carrying out automatic complement on null data in each data complement interval to obtain national debt income data after complement is completed; the national debt income data after the completion of the supplementary recording is subjected to visual processing to obtain a national debt income ratio fitting curve graph, and the method automatically carries out data supplementary recording on the national debt income ratio data to generate a relatively accurate national debt income ratio fitting curve graph, so that the data processing efficiency is improved.

It should be noted that, the lagrangian interpolation formula refers to an interpolation polynomial that gives a node basis function on a node, and then makes a linear combination of the basis functions, where the combination coefficient is a node function value. Linear interpolation, also called two-point interpolation, is known as the value y0=f (x 0) for a given point of dissimilarity x0, x1, y1=f (x 1) linear interpolation being the construction of a first order polynomial: p1 (x) =ax+b, so that it satisfies the condition: p1 (x 0) =y0, P1 (x 1) =y1.

The national debt yield fitting curve needs to meet several characteristics:

scientificity: the absolute value of the yield is in an expected interval, and the extreme points meet objective scene conditions.

Continuity: at the reporting time point of the daily frequency, the curve is continuous without break points.

Waviness: i.e. both rising and falling, non-monotonic curves.

Interpretability: for the reporting time point of reaching the extremum, there is a macroscopic economic event that is interpreted accordingly.

In the embodiment of the disclosure, national debt yield data of a set period is acquired. It should be noted that the setting period is not limited in the embodiments of the present disclosure, and the setting period is typically 1 year, and may be one quarter, two years, or more.

Fitting the national debt yield curve, and performing data verification on the national debt yield data. The accuracy of part of data in the national debt yield data reaches the expectation, and the data verification sequence is that the initial time point of the pre-fitted national debt yield curve is verified sequentially from the initial time point to the final time point. The data verification includes, but is not limited to, the following two verification modes:

and checking an absolute value in a first checking mode. For each reporting time point national debt yield data, an absolute value interval is set, for example, to [0, 10% ], and for reporting time point data whose absolute value exceeds this interval, it is discarded and emptied.

And checking a second checking mode, namely checking relative numerical values. For data passing absolute numerical verification, we adopt the manner shown in Table 1 below, with four frequency dimensions, daily, weekly, monthly and yearly; and setting a change threshold value for each dimension, and respectively checking whether the change condition of each report time point under the four frequency dimensions meets the threshold value or not for each report time point, wherein the threshold value is set to be an absolute value and comprises a positive upper limit and a negative upper limit, carrying out the emptying processing on report time point data which does not meet the threshold value, and skipping the frequency dimension checking if the comparison time point data is empty under a certain frequency dimension. Wherein, table 1 is as follows:

In some embodiments of the present disclosure, a data complement interval corresponding to the national debt yield data is determined according to a null condition of the national debt yield data. One way of realizing this is that the starting point data in the national debt yield data is used as the starting end point of the starting interval, and the valued time closest to the starting point data is used as the ending end point of the starting interval; and taking the other end point of the initial interval as the initial end point of the next interval, and dividing the subsequent interval in turn until a data complement interval is obtained, wherein null data exists in each data complement interval.

In some embodiments of the present disclosure, data verification may be performed first, and then the complement of null data in the data complement interval may be performed; and the blank value data in the data complement interval can be subjected to complement, and then the data verification is performed. Including but not limited to the following two ways of data entry.

Data complement mode one: and (3) performing data verification in advance, regarding any one valued time point data, if the comparison time point data is empty in four frequency dimensions, considering the data to be reasonable, finally obtaining a group of data with empty values, starting from the initial time point A, taking the time point B closest to the time point and having the value, performing data complement recording by adopting a corresponding data complement recording mode, and after complement recording is completed, taking the time point B as the initial time point A, taking the time point B closest to the right and the valued time point as the new time point B, and completing data complement recording from left to right. The data complement mode has better interpretability, and ensures that all complement data is obtained according to real data.

And a second data complement mode: for any one of the valued time point data, if the comparison time point data is empty in four frequency dimensions, starting from the starting time A, taking the data which is closest to the starting time and the valued time point B to carry out data complement by adopting a corresponding data complement mode, and carrying out data verification in the mode after all the data are not empty from left to right until the time point A to the current time point. The data complement mode has continuity, and ensures that the change condition of the data at all time points is within an expected range.

In this embodiment, according to the time span of each data complement interval and the data change condition under the set frequency dimension of the interval end point, the blank value data in each data complement interval is complemented, so as to obtain the national debt profit data after complement is completed. Any one of the target data complement sections is taken as an example for explanation. Including but not limited to the following:

case one: and if the time span of the target data complement interval is less than one week, performing complement on null data in the target data complement interval by adopting a Lagrange interpolation method.

And a second case: if the time span of the target data complement interval is less than one month and more than one week, and the original month change condition of the interval ending endpoint corresponding to the target data complement interval is not null; quantifying the monthly cumulative change into a daily average change amount; the target data complement interval is any one of the data complement intervals; starting from the end point of the interval to the left, determining whether real non-complement data exists at a first time point of a month forward pushing a first time point of the data to be complemented; if true non-complement data exists at the first time point, adding the data in the first time point and the average daily variation to obtain complement data of the first data point to be complemented; if the first time point does not have the real non-complement data, searching a second time point to be complement data in the target data complement interval until all time points in the target data complement interval are traversed; aiming at the remaining time point of the null value in the target data complement interval, according to the overall change trend of the interval starting end point and the interval ending end point of the target data complement interval, the complement is started from the interval starting end point or the interval ending end point according to the same average daily change trend; if the interval starting end point data is larger than the interval ending end point data and the average change is negative, the interval starting end point is complemented according to the average change; if the interval starting end point data is smaller than the interval ending end point data and the average change is negative, the interval ending end point is complemented according to the average change; if the interval starting end point data is smaller than the interval ending end point data and the average change is a positive value, the interval starting end point is complemented according to the average change; if the interval starting end point data is larger than the interval ending end point data and the average change is positive, the interval ending end point is complemented according to the average change.

Case three: if the time span of the target data complement interval is less than one month and more than one week, and the original month change condition of the interval ending endpoint corresponding to the target data complement interval is null and the original year change condition is not null; quantifying the annual cumulative change into a daily average change amount; the target data complement interval is any one of the data complement intervals; starting from the end point of the interval to the left, determining whether real non-complement data exists at a first time point of a month forward pushing a first time point of the data to be complemented; if true non-complement data exists at the first time point, adding the data in the first time point and the average daily variation to obtain complement data of the first data point to be complemented; if the first time point does not have the real non-complement data, searching a second time point to be complement data in the target data complement interval until all time points in the target data complement interval are traversed; aiming at the remaining time point of the null value in the target data complement interval, according to the overall change trend of the interval starting end point and the interval ending end point of the target data complement interval, the complement is started from the interval starting end point or the interval ending end point according to the same average daily change trend; if the interval starting end point data is larger than the interval ending end point data and the average change is negative, the interval starting end point is complemented according to the average change; if the interval starting end point data is smaller than the interval ending end point data and the average change is negative, the interval ending end point is complemented according to the average change; if the interval starting end point data is smaller than the interval ending end point data and the average change is a positive value, the interval starting end point is complemented according to the average change; if the interval starting end point data is larger than the interval ending end point data and the average change is positive, the interval ending end point is complemented according to the average change.

Case four: and if the time span of the target data complement interval is less than one month and more than one week and the original month change condition and the original year change condition of the end point of the interval are null values, performing complement on null data in the target data complement interval by adopting a Lagrange interpolation method.

Case five: if the time span of the target data complement interval is more than one month and less than one year and the original annual change condition of the interval end endpoint corresponding to the target data complement interval is not null, quantifying the annual accumulated change into daily average change quantity; the target data complement interval is any one of the data complement intervals; starting from the end point of the interval to the left, determining whether real non-complement data exists at a second time point of one year forward of a third time point of the data to be complemented; if the second time point has real non-complement data, adding the data in the second time point and the average daily variation to obtain complement data of the first data time point to be complemented; if the second time point does not have the real non-complement data, searching a second time point to be complement data in the target data complement interval until all time points in the target data complement interval are traversed; aiming at the remaining time point of the null value in the target data complement interval, according to the overall change trend of the interval starting end point and the interval ending end point of the target data complement interval, the complement is started from the interval starting end point or the interval ending end point according to the same average daily change trend; if the interval starting end point data is larger than the interval ending end point data and the average change is negative, the interval starting end point is complemented according to the average change; if the interval starting end point data is smaller than the interval ending end point data and the average change is negative, the interval ending end point is complemented according to the average change; if the interval starting end point data is smaller than the interval ending end point data and the average change is a positive value, the interval starting end point is complemented according to the average change; if the interval starting end point data is larger than the interval ending end point data and the average change is positive, the interval ending end point is complemented according to the average change.

Case six: and if the time span of the target data complement interval is more than one month and less than one year and the original annual change condition of the end point of the interval is null, performing complement on null data in the target data complement interval by adopting a Lagrange interpolation method.

With reference to the above descriptions of the embodiments, fig. 2 is a schematic flow chart of another data processing method according to the second embodiment of the disclosure. As shown in fig. 2, the method includes:

s201: acquiring national debt yield data of a set period;

s202: performing data verification on national debt yield data of a set period;

s203: determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data;

s204: performing supplementary recording on null data in each data supplementary recording interval according to the time span of each data supplementary recording interval and the data change condition of the interval end point under the set frequency dimension, so as to obtain the national debt income data after the supplementary recording is completed;

s205: and carrying out visual processing on the national debt income data after the completion of the supplementary recording to obtain a national debt income ratio fitting curve graph.

In conjunction with the above descriptions of the embodiments, fig. 3 is a schematic flow chart of another data processing method according to the third embodiment of the disclosure. As shown in fig. 3, the method includes:

S301: acquiring national debt yield data of a set period;

s302: determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data;

s303: performing supplementary recording on null data in each data supplementary recording interval according to the time span of each data supplementary recording interval and the data change condition of the interval end point under the set frequency dimension, so as to obtain the national debt income data after the supplementary recording is completed;

s304: performing data verification on the national debt income data after the completion of the supplementary record;

s305: and carrying out visual processing on the national debt income data after the completion of the supplementary recording to obtain a national debt income ratio fitting curve graph.

In the embodiment of the method disclosed by the invention, national debt yield data of a set period is obtained; determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data; according to the time span of each data complement interval and the data change condition of the set frequency dimension of the interval end point, carrying out automatic complement on null data in each data complement interval to obtain national debt income data after complement is completed; the national debt income data after the completion of the supplementary recording is subjected to visual processing to obtain a national debt income ratio fitting curve graph, and the method automatically carries out data supplementary recording on the national debt income ratio data to generate a relatively accurate national debt income ratio fitting curve graph, so that the data processing efficiency is improved.

Fig. 4 is a schematic structural diagram of a data processing apparatus 40 according to an exemplary embodiment of the present disclosure. As shown in fig. 4, the data processing apparatus 40 includes: the system comprises an acquisition module 41, a determination module 42, a complement module 43 and a visualization module 44.

Wherein, the obtaining module 41 is configured to obtain national debt yield data of a set period;

the determining module 42 is configured to determine a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data;

the supplementary recording module 43 is configured to supplement the null data in each data supplementary recording interval according to the time span of each data supplementary recording interval and the data change condition under the set frequency dimension of the interval end point, so as to obtain the national debt profit data after the supplementary recording is completed;

and the visualization module 44 is configured to perform visualization processing on the national debt yield data after the completion of the supplementary recording, so as to obtain a national debt yield fitting curve graph.

Optionally, the determining module 42 is configured to, when determining the data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data:

taking the initial time point data in the national debt yield data as the initial end point of the initial interval, and taking the valued time point nearest to the initial time point data as the end point of the initial interval; and taking the other end point of the initial interval as the initial end point of the next interval, and dividing the subsequent interval in turn until a data complement interval is obtained, wherein null data exists in each data complement interval.

Optionally, the complement module 43 performs complement on null data in each data complement interval according to the time span of each data complement interval and the data change condition of the set frequency dimension of the interval end point, so as to obtain the national debt profit data after complement is completed, and the data are used for:

aiming at the target data complement interval, if the time span of the target data complement interval is less than one week, performing complement on null data in the target data complement interval by adopting a Lagrange interpolation method; or alternatively, the process may be performed,

if the time span of the target data complement interval is less than one month and more than one week, and the original month change condition and the original year change condition of the end point of the interval are null values, performing complement on null value data in the target data complement interval by adopting a Lagrange interpolation method; or alternatively, the process may be performed,

if the time span of the target data complement interval is more than one month and less than one year and the original annual change condition of the end point of the interval is null, performing complement on null data in the target data complement interval by adopting a Lagrange interpolation method;

the target data complement section is any one of the data complement sections.

aiming at the target data complement interval, if the time span of the target data complement interval is less than one month and more than one week, and the original month change condition of the interval ending endpoint corresponding to the target data complement interval is not null; or if the time span of the target data complement section is less than one month and more than one week, and the original month change condition of the section ending endpoint corresponding to the target data complement section is null and the original year change condition is not null; quantifying the monthly cumulative change into a daily average change amount; the target data complement interval is any one of the data complement intervals;

starting from the end point of the interval to the left, determining whether real non-complement data exists at a first time point of a month forward pushing a first time point of the data to be complemented; if true non-complement data exists at the first time point, adding the data in the first time point and the average daily variation to obtain complement data of the first data point to be complemented; if the first time point does not have the real non-complement data, searching a second time point to be complement data in the target data complement interval until all time points in the target data complement interval are traversed;

Aiming at the remaining time point of the null value in the target data complement interval, according to the overall change trend of the interval starting end point and the interval ending end point of the target data complement interval, the complement is started from the interval starting end point or the interval ending end point according to the same average daily change trend; if the interval starting end point data is larger than the interval ending end point data and the average change is negative, the interval starting end point is complemented according to the average change; if the interval starting end point data is smaller than the interval ending end point data and the average change is negative, the interval ending end point is complemented according to the average change; if the interval starting end point data is smaller than the interval ending end point data and the average change is a positive value, the interval starting end point is complemented according to the average change; if the interval starting end point data is larger than the interval ending end point data and the average change is positive, the interval ending end point is complemented according to the average change.

If the time span of the target data complement interval is less than one month and more than one week, and the original month change condition of the interval ending endpoint corresponding to the target data complement interval is null and the original year change condition is not null; quantifying the annual cumulative change into a daily average change amount; the target data complement interval is any one of the data complement intervals;

for the target data complement interval, if the time span of the target data complement interval is more than one month and less than one year and the original annual change condition of an interval end point corresponding to the target data complement interval is not null, quantifying the annual accumulated change into a daily average change amount; the target data complement interval is any one of the data complement intervals;

starting from the end point of the interval to the left, determining whether real non-complement data exists at a second time point of one year forward of a third time point of the data to be complemented; if the second time point has real non-complement data, adding the data in the second time point and the average daily variation to obtain complement data of the first data time point to be complemented; if the second time point does not have the real non-complement data, searching a second time point to be complement data in the target data complement interval until all time points in the target data complement interval are traversed;

Fig. 5 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present disclosure. As shown in fig. 5, the electronic device includes: a memory 501 and a processor 502. In addition, the electronic device further includes necessary components such as a power supply component 503 and a communication component 504.

Memory 501 is used to store computer programs and may be configured to store various other data to support operations on the electronic device. Examples of such data include instructions for any application or method operating on an electronic device.

The memory 501 may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

A communication component 504 for data transmission with other devices.

A processor 502, executable computer instructions stored in memory 501, for: acquiring national debt yield data of a set period; determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data; performing supplementary recording on null data in each data supplementary recording interval according to the time span of each data supplementary recording interval and the data change condition of the interval end point under the set frequency dimension, so as to obtain the national debt income data after the supplementary recording is completed; and carrying out visual processing on the national debt income data after the completion of the supplementary recording to obtain a national debt income ratio fitting curve graph.

Accordingly, the disclosed embodiments also provide a computer-readable storage medium storing a computer program. The computer-readable storage medium stores a computer program that, when executed by one or more processors, causes the one or more processors to perform the steps in the method embodiment of fig. 1.

Accordingly, the disclosed embodiments also provide a computer program product comprising a computer program/instructions for executing the steps of the method embodiment of fig. 1 by a processor.

The communication assembly of fig. 5 is configured to facilitate wired or wireless communication between the device in which the communication assembly is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as a mobile communication network of WiFi,2G, 3G, 4G/LTE, 5G, etc., or a combination thereof. In one exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

The power supply assembly shown in fig. 5 provides power for various components of the device in which the power supply assembly is located. The power components may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the devices in which the power components are located.

The display in fig. 5 described above includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or sliding action, but also the duration and pressure associated with the touch or sliding operation.

An audio component may also be included.

An audio component, which may be configured to output and/or input an audio signal. For example, the audio component includes a Microphone (MIC) configured to receive external audio signals when the device in which the audio component is located is in an operational mode, such as a call mode, a recording mode, and a speech recognition mode. The received audio signal may be further stored in a memory or transmitted via a communication component. In some embodiments, the audio assembly further comprises a speaker for outputting audio signals.

In the embodiments of the apparatus, device, storage medium and program product described above, national debt yield data for a set period is obtained; determining a data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data; according to the time span of each data complement interval and the data change condition of the set frequency dimension of the interval end point, carrying out automatic complement on null data in each data complement interval to obtain national debt income data after complement is completed; the national debt income data after the completion of the supplementary recording is subjected to visual processing to obtain a national debt income ratio fitting curve graph, and the method automatically carries out data supplementary recording on the national debt income ratio data to generate a relatively accurate national debt income ratio fitting curve graph, so that the data processing efficiency is improved.

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

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

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

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

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

It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.

The above is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of data processing, the method comprising:

acquiring national debt yield data of a set period;

2. The method according to claim 1, wherein the determining the data complement interval corresponding to the national debt rate of return data according to the null condition of the national debt rate of return data includes:

3. The method according to claim 1, wherein the performing the supplementary recording on the null data in each data supplementary recording interval according to the time span of each data supplementary recording interval and the data change condition in the set frequency dimension of the interval end point to obtain the national debt profit data after the supplementary recording is completed includes:

aiming at a target data complement interval, if the time span of the target data complement interval is less than one week, performing complement on null data in the target data complement interval by adopting a Lagrange interpolation method; or alternatively, the process may be performed,

if the time span of the target data complement interval is less than one month and greater than one week, and the original month change condition and the original year change condition of the end point of the interval are null values, performing complement on null value data in the target data complement interval by adopting a Lagrange interpolation method; or alternatively, the process may be performed,

The target data complement interval is any one of the data complement intervals.

4. The method according to claim 1, wherein the performing the supplementary recording on the null data in each data supplementary recording interval according to the time span of each data supplementary recording interval and the data change condition in the set frequency dimension of the interval end point to obtain the national debt profit data after the supplementary recording is completed includes:

aiming at a target data complement interval, if the time span of the target data complement interval is less than one month and more than one week, and the original month change condition of an interval end point corresponding to the target data complement interval is not null; quantifying the monthly cumulative change into a daily average change amount; the target data complement interval is any one of the data complement intervals;

starting from the end point of the interval to the left, determining whether real non-supplementary data exists at a first time point of a month forward from a first time point of the data to be supplementary; if true non-complement data exists at the first time point, adding the data in the first time point and the average daily variation to obtain complement data of a first data point to be complemented; if the first time point does not have the real non-complement data, searching a second time point to be complement data in the target data complement interval until all time points in the target data complement interval are traversed;

Aiming at the remaining time point with the null value in the target data complement interval, according to the overall change trend of the interval starting end point and the interval ending end point of the target data complement interval, starting the complement according to the same average daily change trend from the interval starting end point or the interval ending end point; if the interval starting end point data is larger than the interval ending end point data and the average change is negative, the interval starting end point is complemented according to the average change; if the interval starting end point data is smaller than the interval ending end point data and the average change is negative, the interval ending end point is complemented according to the average change; if the interval starting end point data is smaller than the interval ending end point data and the average change is a positive value, the interval starting end point is supplemented according to the average change; and if the interval starting end point data is larger than the interval ending end point data and the average change is a positive value, the interval ending end point is supplemented according to the average change.

5. The method according to claim 1, wherein the performing the supplementary recording on the null data in each data supplementary recording interval according to the time span of each data supplementary recording interval and the data change condition in the set frequency dimension of the interval end point to obtain the national debt profit data after the supplementary recording is completed includes:

6. The method according to claim 1, wherein the performing the supplementary recording on the null data in each data supplementary recording interval according to the time span of each data supplementary recording interval and the data change condition in the set frequency dimension of the interval end point to obtain the national debt profit data after the supplementary recording is completed includes:

for a target data complement interval, if the time span of the target data complement interval is more than one month and less than one year, and the original annual change condition of an interval end point corresponding to the target data complement interval is not null, quantifying the annual accumulated change as a daily average change amount; the target data complement interval is any one of the data complement intervals;

7. A data processing apparatus, comprising:

8. The apparatus of claim 7, wherein the determining module, when determining the data complement interval corresponding to the national debt yield data according to the null condition of the national debt yield data, is configured to:

9. The apparatus of claim 7, wherein the complement module performs complement on null data in each data complement interval according to a time span of each data complement interval and a data change condition of a set frequency dimension of an interval end point, so as to obtain the national debt and profit data after complement is completed, and the data complement module is configured to:

10. The apparatus of claim 7, wherein the complement module performs complement on null data in each data complement interval according to a time span of each data complement interval and a data change condition of a set frequency dimension of an interval end point, so as to obtain the national debt and profit data after complement is completed, and the data complement module is configured to:

Aiming at a target data complement interval, if the time span of the target data complement interval is less than one month and more than one week, and the original month change condition of an interval end point corresponding to the target data complement interval is not null; or if the time span of the target data complement interval is less than one month and greater than one week, and the original month change condition of the interval ending endpoint corresponding to the target data complement interval is null and the original year change condition is not null; quantifying the monthly cumulative change into a daily average change amount; the target data complement interval is any one of the data complement intervals;

11. The apparatus of claim 7, wherein the complement module performs complement on null data in each data complement interval according to a time span of each data complement interval and a data change condition of a set frequency dimension of an interval end point, so as to obtain the national debt and profit data after complement is completed, and the data complement module is configured to:

12. The apparatus of claim 7, wherein the complement module performs complement on null data in each data complement interval according to a time span of each data complement interval and a data change condition of a set frequency dimension of an interval end point, so as to obtain the national debt and profit data after complement is completed, and the data complement module is configured to:

13. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1-6.

14. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the method of any of claims 1-6.