CN110008386B

CN110008386B - Data generation, processing and evaluation method, device, equipment and medium

Info

Publication number: CN110008386B
Application number: CN201910043615.5A
Authority: CN
Inventors: 皮龙娇
Original assignee: Advanced New Technologies Co Ltd
Current assignee: Advanced New Technologies Co Ltd; Advantageous New Technologies Co Ltd
Priority date: 2019-01-17
Filing date: 2019-01-17
Publication date: 2023-08-01
Anticipated expiration: 2039-01-17
Also published as: CN110008386A

Abstract

The embodiment of the specification discloses a data generation, processing and evaluation method, device, equipment and medium, wherein the data generation method comprises the following steps: determining influence factors of target data to be generated, and an actual weight value and a reference weight value of each influence factor; determining whether a target data generation condition is met according to the actual weight value and the reference weight value; if yes, generating target data according to one or more influence factors.

Description

Data generation, processing and evaluation method, device, equipment and medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a medium for generating, processing, and evaluating data.

Background

In the prior art, it is possible to determine whether the generated target data meets the requirements after the target data is generated. If the generated target data does not meet the requirements, the generation links, influence factors and the like of the target data need to be checked step by step. Along with the development of technology, the generation of target data often has a plurality of links and a plurality of influencing factors, and the investigation data link is long, and the investigation steps are identical, so that the investigation workload is large and the efficiency is low.

In view of this, there is a need for more efficient and effective data generation methods.

Disclosure of Invention

The embodiment of the specification provides a data generation, processing and evaluation method, device, equipment and medium, which are used for solving the technical problem of how to more effectively or efficiently generate and process data.

In order to solve the above technical problems, the embodiments of the present specification are implemented as follows:

the embodiment of the specification provides a data generation method, which comprises the following steps:

determining influence factors of target data to be generated, and an actual weight value and a reference weight value of each influence factor;

determining whether a target data generation condition is met according to the actual weight value and the reference weight value;

if yes, generating target data according to one or more influence factors.

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

determining influence factors of the data to be processed, and an actual weight value and a reference weight value of each influence factor;

determining whether a data processing condition is met or not according to the actual weight value and the reference weight value;

if yes, determining a processing rule of the data to be processed according to one or more influence factors, and/or determining a processing result of the data to be processed according to one or more influence factors.

The embodiment of the specification provides a data evaluation method, which comprises the following steps:

determining influence factors of data to be evaluated, and an actual weight value and a reference weight value of each influence factor;

for any influence factor, judging whether the influence factor is qualified or not according to the actual weight value and the reference weight value of the influence factor;

and determining an evaluation result of the data according to the qualification judgment result of the influence factor.

The present specification provides a data generation apparatus including:

the factor determining module is used for determining influence factors of target data to be generated, and an actual weight value and a reference weight value of each influence factor;

the generation judging module is used for determining whether the target data generation condition is met or not according to the actual weight value and the reference weight value;

and the data generation module is used for generating target data according to one or more influence factors if the target data generation condition is determined to be met.

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

the factor determining module is used for determining influence factors of the data to be processed, and an actual weight value and a reference weight value of each influence factor;

the processing judging module is used for determining whether the data processing condition is met or not according to the actual weight value and the reference weight value;

And the processing module is used for determining a processing rule of the data to be processed according to one or more influence factors and/or determining a processing result of the data to be processed according to one or more influence factors if the target data is determined to be processed.

An embodiment of the present specification provides a data evaluation device including:

the factor determining module is used for determining influence factors of the data to be evaluated, and an actual weight value and a reference weight value of each influence factor;

the factor evaluation module is used for judging whether any influence factor is qualified or not according to the actual weight value and the reference weight value of the influence factor;

and the data evaluation module is used for determining the evaluation result of the data according to the qualification judgment result of the influence factor.

An embodiment of the present specification provides a data generating apparatus including:

at least one processor;

the method comprises the steps of,

a memory communicatively coupled to the at least one processor;

wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to:

if yes, generating target data according to one or more influence factors.

The embodiment of the present specification provides a data processing apparatus including:

at least one processor;

the method comprises the steps of,

a memory communicatively coupled to the at least one processor;

wherein,,

An embodiment of the present specification provides a data evaluation apparatus including:

at least one processor;

the method comprises the steps of,

a memory communicatively coupled to the at least one processor;

wherein,,

The present description provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, perform the steps of:

if yes, generating target data according to one or more influence factors.

The embodiment of the present specification provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions when executed by a processor implement the steps of:

The above-mentioned at least one technical scheme that this description embodiment adopted can reach following beneficial effect:

the actual weight value of the influence factor is evaluated by utilizing the reference weight value of the influence factor, and the evaluation result is accurate and reasonable; by evaluating the influence factors, whether the influence factors are normal or have errors or not can be determined before data generation and/or processing and/or evaluation, and the applicable or inapplicable influence factors can be rapidly and accurately positioned; the evaluation result of the influence factors can accurately, finely and comprehensively reflect the generation process and the generation quality of the data, and whether the data is generated or not, whether the data is processed or not, a processing mode, a processing result, an evaluation result and the like can be reasonably determined before the data is generated and/or processed and/or evaluated, so that the generation, processing and evaluation efficiency of the data is improved.

Drawings

In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the following description will briefly explain the embodiments of the present description or the drawings needed in the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments described in the present description, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic diagram of the operation of a data generation system in the first embodiment of the present specification.

Fig. 2 is a flow chart of a data generating method in a second embodiment of the present specification.

Fig. 3 is a schematic diagram of a data generation process in the second embodiment of the present specification.

FIG. 4 is a schematic diagram illustrating the operation of a data processing system in a third embodiment of the present disclosure.

Fig. 5 is a flowchart of a data processing method in a fourth embodiment of the present specification.

Fig. 6 is a schematic diagram of a data processing procedure in a fourth embodiment of the present specification.

Fig. 7 is a schematic operation diagram of a data evaluation system in a fifth embodiment of the present specification.

Fig. 8 is a flowchart of a data evaluation method in the sixth embodiment of the present specification.

Fig. 9 is a schematic diagram of a data evaluation process in the sixth embodiment of the present specification.

Fig. 10 is a schematic structural diagram of a data generating apparatus in an eighth embodiment of the present specification.

Fig. 11 is a schematic structural diagram of another data generating apparatus in the eighth embodiment of the present specification.

Fig. 12 is a schematic structural diagram of another data generating apparatus in the eighth embodiment of the present specification.

Fig. 13 is a schematic structural view of a data processing apparatus in a ninth embodiment of the present specification.

Fig. 14 is a schematic structural diagram of a data evaluation device in a tenth embodiment of the present specification.

Detailed Description

In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.

As shown in fig. 1, a first embodiment of the present specification provides a data generating system, specifically, the data generating system determines influence factors of target data to be generated, and actual weight values and reference weight values of the respective influence factors; the data generation system determines whether a target data generation condition is met according to the actual weight value and the reference weight value; if yes, the data generation system generates target data according to one or more influence factors.

In the present embodiment, each influence factor or data of the influence factor, history weight data, or the like may be used as inputs to the above-described data generating system (inputs are not limited to these items), and the data generating system outputs the actual weight value, the reference weight value, the difference value between the actual weight value and the reference weight value (whether or not the data difference value is out of range may also be output), the weight value fluctuation data (including history fluctuation data), whether or not the target data generating condition is satisfied, and the generated target data or the like (outputs are not limited to these items) according to the above-described steps.

In the embodiment, the actual weight value of the influence factor is evaluated by utilizing the reference weight value of the influence factor, and the evaluation result is accurate and reasonable; by evaluating the influence factors, whether the influence factors are normal or have errors or not can be determined before target data are generated and/or processed and/or evaluated, and the applicable or inapplicable influence factors can be rapidly and accurately positioned; the evaluation result of the influence factors can accurately, finely and comprehensively reflect the generation process and the generation quality of the data, and whether the target data is generated or not, whether the target data is processed or not, a processing mode, a processing result, an evaluation result and the like can be reasonably determined before the target data is generated and/or processed and/or evaluated, so that the generation, processing and evaluation efficiency of the data is improved.

From the program perspective, the execution subject of the above-described flow may be a computer, a server, a corresponding data generation system, or the like. In addition, the third party application client may assist the execution body in executing the above-mentioned flow.

Fig. 2 is a flowchart of a data generation method in the second embodiment of the present specification, and fig. 3 is a flowchart of a data generation process in the second embodiment of the present specification. Referring to fig. 2 and 3, the data generating method in this embodiment includes:

s101: and determining the influence factors of the target data to be generated, and the actual weight value and the reference weight value of each influence factor.

In the present embodiment, the target data may be any data without necessarily distinguishing the data type, the data generation time, the data size, and other conditions. Any data can be designated as target data, and conditions which are required to be met by the target data can also be designated; for example, one or more types of data may be designated as target data. The "target data to be generated" may have one or more generation phases, for example, may be generated at one or more time points or time periods, and the time points or time periods may be fixed between the time points or time periods, so that the time points or time periods may be used as the generation phases, and the data generated by the generation phases may be unified as the target data. In summary, the target data in the present embodiment is not limited to "continuously generated".

In the present embodiment, an influence factor of target data to be generated may be determined. The "influencing factor" may be a factor that can exert an effect or influence on the generation of the target data, including, but not limited to, a parameter of the target data, a generation condition or a generation rule of the target data, and the like. Such "source data" may also be used as an influencing factor if the target data is processed or manipulated from the source data. For example, the target data is obtained by calculating the A data and the B data, and then the A data, the B data and the calculation rule can be used as influencing factors of the target data; if the a data is defined to be acquired within a certain period of time, the constraint and the period of time can also be used as the influencing factors of the target data. It can be seen that the number or type of influencing factors, etc. may be plural or plural. In this embodiment, the specific content of the influence factors may be defined or specified according to the situation or need, including but not limited to the name, number, and type of the influence factors. The determined influence factor of the target data to be generated is hereinafter referred to as "corresponding influence factor" of the target data to be generated. It should be noted that, the influence factors corresponding to the target data to be generated determined each time may be the same or different.

In this embodiment, an actual weight value and a reference weight value of each influence factor may be determined, and the weight values may reflect the contribution degree of the influence factors to the target data. The following describes the available ways of determining the reference weight value and the actual weight value of the influence factor (the present embodiment is not limited to the following ways):

1.1 determining a reference weight value of an influence factor

1.1.1 for any influence factor, determining one or more historical weight values for the influence factor

Any influence factor corresponding to the target data to be generated may be referred to as an influence factor C, and one or more historical weight values thereof may be determined. As for the influence factor C, it may also play a role in some time or several times of target data generation in the past, and the weight value of the influence factor C may be determined in the past target data generation process of each time of the role, and the weight value of the influence factor C in some time or several times of target data generation process may be determined as the historical weight value of the influence factor C. The weight value of the influence factor C in the target data generation process of all the influence factors C in the past can be used as the historical weight value, and one or more weight values of the influence factors C can be selected from the historical weight values as the historical weight value.

1.1.2 determining a reference weight value for the influencing factor based on the historical weight value for the influencing factor

The reference weight value of the influence factor C can be determined by the historical weight value of the influence factor C, for example, a median value or a mean value (such as a weighted average value) of the historical weight value is used as the reference weight value of the influence factor C.

1.2 determining the actual weight value of the influencing factor

1.2.1 for any influence factor, determining the number of target events associated with the influence factor

In this embodiment, the target event may be any event, without necessarily distinguishing the event type, event generation time, and other conditions. Any event can be designated as a target event, or a condition which needs to be met by the target event is designated; for example, one or more types of events may be designated as target events, such as all eligible transaction events over a period of time may be designated as target events, including but not limited to transactants, transaction types, transaction platforms, etc. In the present embodiment, a target event may be defined or specified according to a scenario or need.

Taking the influence factor C as an example, the target event associated with the influence factor C is the target event on which the influence factor C acts or affects. For example, if the target event refers to all transaction events on a certain transaction platform within a period of time, the influencing factor C is a certain transactor, the transaction event participated by the transactor in the target event is the target event associated with the influencing factor C. It can be seen that the influence factor C can be regarded as both the influence factor of the target event associated therewith and the influence factor of the target data to be generated.

1.2.2 determining the actual weight value of the influence factor according to the number of target events and the total number of target events associated with the influence factor

According to the content in 1.2.1, the total number of target events and the number of target events associated with the influence factor C can be determined on the basis of determining the target events, and then the actual weight value of the influence factor C can be determined according to the number of target events associated with the influence factor C and the total number of target events, for example, the ratio of the number of target events associated with the influence factor C to the total number of target events is used as the actual weight value of the influence factor C.

In the past target data generation process, the weight value of the influence factor C may be determined in the above manner, so as to obtain the historical weight value of the influence data C.

S102: determining whether the target data generation condition is met according to the actual weight value and the reference weight value

For the target data to be generated, after determining (or specifying) the respective influence factors of the target data to be generated, for each influence factor, the actual weight value and the reference weight value thereof may be determined. Then, it may be determined whether the target data generation condition is satisfied based on the actual weight value and the reference weight value of each influence factor. Specifically, it may be determined whether the target data generation condition is satisfied in the following manner (the present embodiment is not limited to the following manner):

2.1, for any influence factor, determining whether the difference value between the actual weight value and the reference weight value of the influence factor exceeds the preset range corresponding to the influence factor

Taking the influence factor C as an example, if the actual weight value of the influence factor C is C1 and the reference weight value is C2, it can be determined whether the difference between C1 and C2 exceeds the preset range corresponding to the influence factor C (simply referred to as whether the influence factor C exceeds the range, which is equivalent to the evaluation of the influence factor). If the preset range corresponding to the influence factor C is C2+/-C (C is more than or equal to 0), C1 < C2-C or C1 > C2+c can be used as the preset range exceeding the influence factor C; or C1 is less than or equal to C2-C or C1 is more than or equal to C2+c and is used as a preset range corresponding to the influence factor C.

The preset ranges corresponding to the influence factors may be the same or different for different influence factors. Through the step, whether each influence factor corresponding to the target data to be generated exceeds the range or not can be determined.

2.2, if the difference value of the actual weight value and the reference weight value is not beyond the range, the influence factor number is equal to or larger than a first threshold value, and then the target data generation condition is met; and/or if the number of the influence factors of which the difference value between the actual weight value and the reference weight value exceeds the range is equal to or smaller than a second threshold value, the target data generation condition is met; and/or if the difference value between the actual weight value and the reference weight value is not beyond the range, the number of the influence factors is equal to or smaller than a third threshold value, and the target data generation condition is not met; and/or if the number of influencing factors in which the difference between the actual weight value and the reference weight value exceeds the range is equal to or greater than the fourth threshold value, the target data generation condition is not satisfied

The target data generation condition may specifically be: in one or more influence factors corresponding to target data to be generated, if the number of influence factors not exceeding the range is equal to or greater than a first threshold value, the target data generation condition is met; and/or if the number of the influence factors exceeding the range is equal to or smaller than a second threshold value, the target data generation condition is met; and/or if the number of influence factors not exceeding the range is equal to or smaller than a third threshold value, the target data generation condition is not satisfied; and/or if the number of the influence factors exceeding the range is equal to or greater than the fourth threshold value, the target data generation condition is not satisfied.

In particular, the first threshold, the second threshold, the third threshold, or the fourth threshold may take all the influence factor numbers corresponding to the target data to be generated, and of course, the above conditions may be combined, but no conflict may occur, for example, if the influence factor number not exceeding the range is equal to or greater than the first threshold, the target data generation condition is satisfied, and if the influence factor number not exceeding the range is equal to or less than the third threshold, the first threshold and the third threshold do not take all the influence factor numbers corresponding to the target data to be generated at the same time when both the target data generation condition is not satisfied.

S103: if so (i.e., if it is determined that the target data generation condition is satisfied), generating target data according to one or more impact factors

In the present embodiment, in the case where it is determined that the target data generation condition is satisfied, the target data is generated. Wherein the target data is generated from one or more influencing factors. Although the influence factors corresponding to the target data to be generated may be a plurality of numbers or a plurality of types, each of the corresponding influence factors is not necessarily required to be utilized in generating the target data. For example, if the target data is obtained by calculating a type of data and a type of B type of data, wherein the a type of data and the B type of data are limited to be obtained within a certain period of time, the influence factor corresponding to the target data can be determined as a type of data (including actual data values), B type of data (including actual data values), and calculation rules and time periods; and if the target data generation condition is met, obtaining the target data by utilizing the A data and the B data through an operation rule. In this example, the actual weight value and the reference weight value of the influence factor of the time period may be used to determine whether the target data generation condition is satisfied, but when the target data is actually generated, the influence factor of the time period does not participate, but the influence factors of the a data (including the actual data value), the B data (including the actual data value), and the operation rule are used to generate the target data.

In the present embodiment, the influence factors actually involved in generating the target data may be defined or specified according to the scenario or need.

In this embodiment, if one or more influence factors are out of range while it is determined that the target data generation condition is not satisfied, it may be determined whether the out-of-range influence factors need to be adjusted (or corrected, hereinafter, referred to as "out-of-range"), including but not limited to:

3.1, for any influence factor out of range, if the influence factor is adjusted, determining an actual weight value of the adjusted influence factor, and then determining whether a target data generation condition is met according to an actual weight value and a reference weight value of an unadjusted influence factor and the actual weight value and the reference weight value of the adjusted influence factor (the target data generation condition can be the same as S102); and if the target data generation condition is met, generating target data according to one or more influence factors. If the influence factor involved in the generation of the target data is adjusted, the adjusted state, value, or the like is used to generate the target data.

And 3.2, if all the out-of-range influence factors do not need to be adjusted, generating target data according to one or more influence factors. In this embodiment, an out-of-range influence factor does not necessarily mean that it is erroneous. For example, for a transaction scenario, if the target data is the total amount of transactions on a certain transaction platform from 0 to 24 days, the target event is a transaction event occurring on the transaction platform from 0 to 24 days, wherein the merchant H is both a transaction participant of the transaction platform and an influencing factor of the target data; if the amount of transactions and transaction events engaged by merchant H on a certain day increases significantly, the actual weight value of merchant H on that day may increase significantly, and the difference from its reference weight value may be out of range, but this may be due to normal promotional activity performed by merchant H on that day, so that even if merchant H is out of range, its actual weight value may still be correctly available.

Of course, in the case where the target data generation condition is satisfied, it may be determined whether or not the out-of-range influence factor needs to be adjusted.

In this embodiment, it may also be determined whether to process the target data according to the actual weight value and the reference weight value, including but not limited to: in one or more influence factors corresponding to the target data to be generated, if the number of influence factors not exceeding the range is equal to or greater than a fifth threshold value, the data processing condition is met; and/or if the number of the influence factors exceeding the range is equal to or smaller than a sixth threshold value, the data processing condition is met; and/or if the number of the influence factors not exceeding the range is equal to or smaller than a seventh threshold value, the data processing condition is not satisfied; and/or if the number of out-of-range influencing factors is equal to or greater than the eighth threshold, the data processing condition is not satisfied.

In the case where the target data generation condition is satisfied and the data processing condition is satisfied, the generated target data may be processed. With the generation of the target data, all the influence factors corresponding to the target data are not necessarily needed to participate in the data processing, namely, the processing rule of the target data can be determined according to one or more influence factors, and/or the processing result of the target data can be determined according to one or more influence factors.

The determination of whether to process the target data may be made before or after the target data is generated. In addition, as described above, whether the data processing condition is satisfied may be determined based on the actual weight value and the reference weight value of the unadjusted influence factor, and the actual weight value and the reference weight value of the adjusted influence factor.

In this embodiment, the actual weight value of the influence factor is evaluated by using the reference weight value of the influence factor, and the evaluation result is accurate and reasonable (for any influence factor, the more the used historical weight value is, the more accurate the reference weight value is, so that the more accurate the evaluation of the actual weight value is; by evaluating the influence factors, whether the influence factors are normal or have errors can be determined before the target data is generated and/or processed, and the influence factors which are applicable or not can be rapidly and accurately positioned, namely whether the influence factors are abnormal can be determined before the target data is generated and/or processed; the influence factors reflect the contribution degree, and the evaluation results of the influence factors can accurately, finely and comprehensively reflect the generation process and the generation quality of the data, so that whether the target data are generated or not can be determined before the target data are generated, whether the target data are processed or not, the processing mode, the processing result and the like can be determined, and the generation and processing efficiency of the data are improved.

As shown in fig. 4, a third embodiment of the present disclosure provides a data processing system, specifically, the data processing system determines an impact factor of data to be processed, and an actual weight value and a reference weight value of each impact factor; the data processing system determines whether the data processing condition is met according to the actual weight value and the reference weight value; if yes, the data processing system determines a processing rule of the data to be processed according to one or more influence factors, and/or the data processing system determines a processing result of the data to be processed according to one or more influence factors.

In this embodiment, each influence factor or data of the influence factor, history data of the influence factor or history weight data, or the like may be used as input to the data generating system, and the data processing system outputs the actual weight value, the reference weight value, the difference value between the actual weight value and the reference weight value (whether the data difference value is out of range or not may also be output), the weight value fluctuation data (including history fluctuation data), whether the data processing condition, the data processing rule, the data processing result, or the like of each influence factor is satisfied according to the above steps.

In the embodiment, the actual weight value of the influence factor is evaluated by utilizing the reference weight value of the influence factor, and the evaluation result is accurate and reasonable; by evaluating the influence factors, whether the influence factors are normal or have errors or not can be determined before data generation and/or processing and/or evaluation, and the applicable or inapplicable influence factors can be rapidly and accurately positioned; the evaluation result of the influence factors can accurately, finely and comprehensively reflect the generation process and the generation quality of the data, and whether the data is generated or not, whether the data is processed or not, a processing mode, a processing result, an evaluation result and the like can be reasonably determined before the data is generated and/or processed and/or evaluated, so that the generation, processing and evaluation efficiency of the data is improved.

From a program perspective, the execution subject of the above-mentioned flow may be a computer or a server or a corresponding data processing system, or the like. In addition, the third party application client may assist the execution body in executing the above-mentioned flow.

Fig. 5 is a flowchart of a data processing method in the fourth embodiment of the present specification, and fig. 6 is a flowchart of a data processing process in the fourth embodiment of the present specification. Referring to fig. 5 and 6, the data processing method in this embodiment includes:

s201: determining influence factors of data to be processed, and actual weight values and reference weight values of the influence factors

Like S101, the target data to be generated in S101 may be replaced with the data to be processed in this step (here, may be conceptual replacement). In particular, since target data to be generated can be generated as data to be processed after generation, the influence factors of the data in the generation stage and the processing stage may be different; the influence factors of the data in the processing stage can be the influence factors of the generating stage, can also comprise part or all of the influence factors of the generating stage, and can also be different from the influence factors of the generating stage. For example, the I data and the J data are calculated to generate C data, and then the I data and the J data are influence factors of the generation stage of the C data; if the C data is subjected to the deduplication process, the deduplication process may be independent of the I data and the J data, so that the I data and the J data are not necessarily influencing factors of the C data processing stage. The impact factor of the data to be processed may be referred to as the "corresponding impact factor" of the data to be processed.

S202: determining whether the data processing condition is satisfied according to the actual weight value and the reference weight value

For the data to be processed, after determining (or specifying) the respective impact factors of the data to be processed, for each impact factor, the actual weight value and the reference weight value thereof may be determined. Then, it may be determined whether the data processing condition is satisfied based on the actual weight value and the reference weight value of each influence factor. Specifically, it may be determined whether the data processing condition is satisfied in the following manner (the present embodiment is not limited to the following manner):

4.1, for any influence factor, determining whether the difference value between the actual weight value and the reference weight value of the influence factor exceeds the preset range corresponding to the influence factor

Taking the influence factor C as an example, if the actual weight value of the influence factor C is C1 and the reference weight value is C2, it can be determined whether the difference between C1 and C2 exceeds the preset range corresponding to the influence factor C (abbreviated as whether the influence factor C is out of range). If the preset range corresponding to the influence factor C is C2+/-C1 (C1 is more than or equal to 0), C1 < C2-C1 or C1 > C2+c1 can be used as the preset range exceeding the influence factor C; or C1 is less than or equal to C2-C1 or C1 is more than or equal to C2+ C1 and is used as a preset range corresponding to the influence factor C.

The preset ranges corresponding to the influence factors may be the same or different for different influence factors. Through the step, whether each influence factor corresponding to the data to be processed exceeds the range or not can be determined.

4.2, if the difference value of the actual weight value and the reference weight value is not beyond the range, the influence factor number is equal to or larger than a ninth threshold value, and the data processing condition is met; and/or if the number of the influence factors of which the difference value between the actual weight value and the reference weight value exceeds the range is equal to or smaller than a tenth threshold value, the data processing condition is met; and/or if the difference value between the actual weight value and the reference weight value is not beyond the range, the number of the influence factors is equal to or smaller than an eleventh threshold value, and the data processing condition is not met; and/or if the number of influencing factors in which the difference between the actual weight value and the reference weight value exceeds the range is equal to or greater than the twelfth threshold value, the data processing condition is not satisfied

The data processing conditions may specifically be: in one or more influence factors corresponding to the data to be processed, if the number of influence factors not exceeding the range is equal to or greater than a ninth threshold value, the data processing condition is met; and/or if the number of the influence factors exceeding the range is equal to or smaller than a tenth threshold value, the data processing condition is met; and/or if the number of the influence factors not exceeding the range is equal to or smaller than an eleventh threshold value, the data processing condition is not satisfied; and/or if the number of out-of-range influencing factors is equal to or greater than a twelfth threshold, the data processing condition is not satisfied.

In particular, the ninth threshold, tenth threshold, eleventh threshold, or twelfth threshold may be used to take all the influence factor numbers corresponding to the data to be processed, and of course, the above conditions may be used in combination, but no conflict may occur, for example, if the influence factor number not exceeding the range is equal to or greater than the ninth threshold, the data processing condition is satisfied, and if the influence factor number not exceeding the range is equal to or less than the eleventh threshold, the data processing condition is not satisfied, and when both the ninth threshold and the eleventh threshold are not simultaneously used, all the influence factor numbers corresponding to the data to be processed are not taken.

S203: if yes (i.e. if it is determined that the data processing conditions are met), determining a processing rule of the data to be processed according to one or more influence factors, and/or determining a processing result of the data to be processed according to one or more influence factors

In the same way as S103, the data processing does not necessarily need to participate in all the influence factors corresponding to the data to be processed, i.e. the processing rule of the data to be processed can be determined according to one or more influence factors, and/or the processing result of the data to be processed can be determined according to one or more influence factors. For example, the data is sorted by time, only a time factor may be needed to participate.

The determination of whether to process the data may be made before or after the data to be processed is generated.

In the embodiment, the actual weight value of the influence factor is evaluated by utilizing the reference weight value of the influence factor, and the evaluation result is accurate and reasonable; by evaluating the influence factors, whether the influence factors are normal or have errors can be determined before data generation and/or processing, and the applicable or inapplicable influence factors can be rapidly and accurately positioned, namely whether the influence factors are abnormal or not can be determined before data generation and/or processing; the influence factors reflect the contribution degree, and the evaluation results of the influence factors can accurately, finely and comprehensively reflect the generation process and the generation quality of the data, so that whether the data are generated or not can be determined before the data are generated, whether the data are processed or not, the processing mode, the processing result and the like can be determined, and the generation and processing efficiency of the data are improved.

As shown in fig. 7, a fifth embodiment of the present disclosure provides a data evaluation system, specifically, a data processing system determines influence factors of data to be evaluated, and actual weight values and reference weight values of the respective influence factors; the data processing system judges whether the influence factor is qualified or not according to the actual weight value and the reference weight value of the influence factor; the data processing system determines an evaluation result of the data based on the qualification determination result of the impact factor.

In this embodiment, each influence factor or data of the influence factor, history weight data, or the like may be used as input to the data generating system, and the data generating system outputs the actual weight value, the reference weight value, the difference value between the actual weight value and the reference weight value (whether the data difference value is out of range or not may also be output), the weight value fluctuation data (including the history fluctuation data), the data evaluation result, and the like of each influence factor according to the above steps.

From the program perspective, the execution subject of the above-described flow may be a computer, a server, a corresponding data evaluation system, or the like. In addition, the third party application client may assist the execution body in executing the above-mentioned flow.

Fig. 8 is a flowchart of a data evaluation method in the sixth embodiment of the present specification, and fig. 9 is a flowchart of a data evaluation process in the sixth embodiment of the present specification. Referring to fig. 8 and 9, the data evaluation method in the present embodiment includes:

s301: determining influence factors of data to be evaluated, and actual weight values and reference weight values of the influence factors

As in S101, the target data to be generated in S101 may be replaced with the data to be evaluated in this step (here, may be conceptual replacement). In particular, since target data to be generated can be used as data to be evaluated after generation, the influence factors of the data in the generation stage and the evaluation stage may be different; the influence factors of the data in the evaluation stage may be the influence factors of the generation stage, may include part or all of the influence factors of the generation stage, or may be different from the influence factors of the generation stage. For example, the I data and the J data are calculated to generate C data, and then the I data and the J data are influence factors of the generation stage of the C data; the evaluation of the C data may be independent of the I data and the J data, so that both are not necessarily factors affecting the evaluation stage of the C data. The influence factor of the data to be evaluated may be referred to as "corresponding influence factor" of the data to be evaluated.

S302: for any influence factor, judging whether the influence factor is qualified or not according to the actual weight value and the reference weight value of the influence factor

For the data to be evaluated, when each influence factor of the data to be evaluated is determined (or specified), the actual weight value and the reference weight value thereof can be determined for each influence factor. Then, it may be determined whether the data evaluation condition is satisfied based on the actual weight value and the reference weight value of each influence factor. Specifically, whether the data evaluation condition is satisfied may be determined in the following manner (the present embodiment is not limited to the following manner):

5.1, for any influence factor, determining whether the difference value between the actual weight value and the reference weight value of the influence factor exceeds the preset range corresponding to the influence factor

Taking the influence factor C as an example, if the actual weight value of the influence factor C is C1 and the reference weight value is C2, it can be determined whether the difference between C1 and C2 exceeds the preset range corresponding to the influence factor C (abbreviated as whether the influence factor C is out of range). If the preset range corresponding to the influence factor C is C2+/-C2 (c2 is more than or equal to 0), C1 < C2-C2 or C1 > C2+c2 can be used as the preset range exceeding the influence factor C; or C1 is less than or equal to C2-C2 or C1 is more than or equal to C2+c2 and is used as a preset range corresponding to the influence factor C. And if the influence factor is out of the range, the influence factor is qualified, and if the influence factor is out of the range, the influence factor is unqualified.

The preset ranges corresponding to the influence factors may be the same or different for different influence factors. Through the step, whether each influence factor corresponding to the data to be evaluated is qualified or not can be determined.

5.2, if the number of the influence factors qualified in the difference value between the actual weight value and the reference weight value is equal to or greater than a thirteenth threshold value, meeting the data evaluation condition; and/or if the number of the unqualified influence factors of the difference value between the actual weight value and the reference weight value is equal to or smaller than a fourteenth threshold value, the data evaluation condition is met; and/or if the number of the influence factors qualified by the difference value between the actual weight value and the reference weight value is equal to or smaller than a fifteenth threshold value, the data evaluation condition is not satisfied; and/or if the number of influencing factors failing to pass the difference between the actual weight value and the reference weight value is equal to or greater than a sixteenth threshold value, the data evaluation condition is not satisfied

The data evaluation conditions may specifically be: in one or more influence factors corresponding to the data to be evaluated, if the number of the qualified influence factors is equal to or greater than a thirteenth threshold value, the data evaluation condition is met; and/or if the number of unqualified influence factors is equal to or smaller than a fourteenth threshold, meeting the data evaluation condition; and/or if the number of the qualified influence factors is equal to or smaller than a fifteenth threshold value, the data evaluation condition is not satisfied; and/or if the number of unqualified influence factors is equal to or greater than a sixteenth threshold, the data evaluation condition is not satisfied.

In particular, the thirteenth threshold, the fourteenth threshold, the fifteenth threshold, or the sixteenth threshold may be used to obtain all the influence factor numbers corresponding to the data to be evaluated, and of course, the above conditions may be used in combination, but no conflict may occur, for example, if the qualified influence factor number is equal to or greater than the thirteenth threshold, the data evaluation condition is satisfied, and if the qualified influence factor number is equal to or less than the fifteenth threshold, the thirteenth threshold and the fifteenth threshold are not used simultaneously, and all the influence factor numbers corresponding to the data to be evaluated are not obtained simultaneously.

S303, determining the evaluation result of the data according to the qualification determination result of the influence factor

In the present embodiment, data evaluation criteria that can be employed are: the more the qualified influence factors are, the better the data evaluation result is. Wherein the evaluation result can be represented using a scale.

In the embodiment, the actual weight value of the influence factor is evaluated by utilizing the reference weight value of the influence factor, and the evaluation result is accurate and reasonable; by evaluating the influence factors, whether the influence factors are normal or have errors can be determined before data generation and/or processing and/or evaluation, and the influence factors which are applicable or not can be rapidly and accurately positioned, namely whether the influence factors are abnormal can be determined before data generation and/or processing and/or evaluation; the influence factors reflect the contribution degree, and the data generation process and the generation quality can be accurately, finely and comprehensively reflected through the evaluation results of the influence factors, so that whether the data are generated or not can be determined before the data are generated, whether the data are processed or not, the processing mode, the processing result, the evaluation result and the like can be determined, and the data generation, processing and evaluation efficiency is improved.

The seventh embodiment of the present disclosure provides a method for generating, processing, and evaluating data in a specific scenario, where the scenario is a commission return service scenario. In order to better stimulate the partner to promote the business, a series of commission policies are formulated for different business stages, different industries and different scenes; each commission policy explicitly defines rules for commission amount calculation, commission bills are calculated according to the rules, and calculation rules of the policies are different. The rebate bill refers to a department or unit that has a reconciliation or payment activity with the partner, and the accounting details provided to the partner are calculated based on a series of rules. Because the calculation of the return commission amount is involved, the data quality for calculating the return commission amount is particularly important, and the rules of the return commission amount are mutually dependent, so that the high-quality data is produced, the high-risk prevention and control requirements are high, the abnormality needs to be detected in time, the alarm needs to be given, and the data quality is ensured.

In this embodiment, the final commission amount is used as the target data to be generated, and before the final commission amount is generated, the influence factor corresponding to the target data to be generated can be determined first, and the influence factor corresponding to the target data is assumed to include sales amount and sales amount of the partner and the partner in a certain time; determining the actual weight value and the reference weight value of each influence factor (if a certain influence factor has no historical weight value, the reference weight value can be set to an initial value): taking the sales event within a certain time as a target event, for a certain partner, taking the sales event participated by the partner as the associated target event, and dividing the number of the sales event participated by the partner by the number of the target event, so as to calculate the actual weight value of the partner as an influence factor; the historical weight value can be obtained by dividing the sales event participated in the past time of the partner by the number of the sales event of the past time, and the reference weight value can be obtained by the historical weight value; taking the total sales amount in the certain time as a target event, taking the sales amount of a certain partner as a target event related to the partner, and dividing the target event by the sales amount to obtain an actual weight value taking the sales amount as an influence factor; the historical weight value can be obtained by dividing the sales amount of the past time of the partner by the sales total amount of the past time, and the reference weight value can be obtained by the historical weight value; taking the sales amount in the certain time as a target event, taking the sales amount of a certain partner as a target event related to the partner, and dividing the sales amount by the target event to obtain an actual weight value taking the sales amount as an influence factor; the historical weight value can be obtained by dividing the sales volume of the past time of the partner by the sales total volume of the past time, and the reference weight value can be obtained by the historical weight value.

And determining whether the target data generation condition is met or not according to the actual weight value and the reference weight value of each influence factor, and if so, determining the commission amount of the partner according to the calculation rule of the commission amount and the influence factors such as sales amount or sales quantity of the partner.

If the influence factor is out of range, such as the sales volume or sales volume of the partner is obviously increased or decreased, the data statistics error is possible, so that whether to adjust the influence factor can be determined; if the data statistics are correct, the commission amount of the partner can still be determined according to the sales amount or the influence factors such as sales quantity.

Whether the target data is processed or evaluated can be determined through the actual weight value and the reference weight value of the influence factor, for example, the target data is subjected to sorting processing, and if the influence factor of sales corresponding to a certain partner is out of range, the influence factor is not added into sorting; if the sales corresponding to a partner become larger or smaller, and this influence factor is out of range, the evaluation result of the return amount can be adjusted up or down.

The number of rules calculated by the commission is large and has interdependence, and a summary bill based on a service provider is produced every period (such as daily, weekly and monthly), the middle rule calculation process is difficult to fully embody in the bill, and if only the final total amount of the commission is seen, a certain middle rule calculation error or a certain business abnormality is difficult to find. According to the embodiment, the actual weight value of the influence factor is evaluated by utilizing the reference weight value of the influence factor, and the evaluation result is accurate and reasonable; the embodiment can evaluate each influence factor, determine whether the influence factor is normal or has errors before generating and/or processing and/or evaluating the target data, and quickly and accurately position the applicable or inapplicable influence factor, namely determine whether each influence factor has anomalies before generating and/or processing and/or evaluating the target data; the influence factors reflect the contribution degree, and the data generation process and the generation quality can be accurately, finely and comprehensively reflected through the evaluation results of the influence factors, so that whether the target data is generated or not can be determined before the target data is generated, whether the target data is processed or not, the processing mode, the processing results, the evaluation results and the like can be determined, and the data generation, processing and evaluation efficiency is improved.

As shown in fig. 10, an eighth embodiment of the present specification provides a data generating apparatus including:

a factor determining module 401, configured to determine an impact factor of the target data to be generated, and an actual weight value and a reference weight value of each impact factor;

a generation decision module 402, configured to determine whether a target data generation condition is satisfied according to the actual weight value and the reference weight value;

the data generating module 403 is configured to generate the target data according to one or more impact factors if it is determined that the target data generating condition is satisfied.

Optionally, determining the reference weight value of the influence factor includes:

for any influence factor, determining one or more historical weight values of the influence factor;

and determining a reference weight value of the influence factor according to the historical weight value of the influence factor.

Optionally, determining the actual weight value of the influence factor includes:

for any influence factor, determining the number of target events associated with the influence factor;

and determining the actual weight value of the influence factor according to the number of the target events and the total number of the target events which are associated with the influence factor.

Optionally, the ratio of the number of the target events associated with the influence factor in the preset time to the total number of the target events in the preset time is used as the actual weight value of the influence factor.

Optionally, determining whether the target data generation condition is met according to the actual weight value and the reference weight value includes:

for any influence factor, determining whether the difference value between the actual weight value of the influence factor and the reference weight value exceeds a preset range corresponding to the influence factor;

if the number of the influence factors of which the difference value between the actual weight value and the reference weight value is not beyond the range is equal to or larger than a first threshold value, the target data generation condition is met;

and/or the number of the groups of groups,

if the number of the influence factors of which the difference value between the actual weight value and the reference weight value exceeds the range is equal to or smaller than a second threshold value, the target data generation condition is met;

and/or the number of the groups of groups,

if the number of the influence factors of which the difference value between the actual weight value and the reference weight value is not beyond the range is equal to or smaller than a third threshold value, the target data generation condition is not met;

and/or the number of the groups of groups,

if the number of the influence factors of which the difference value between the actual weight value and the reference weight value exceeds the range is equal to or larger than a fourth threshold value, the target data generation condition is not satisfied.

Optionally, as shown in fig. 11, the apparatus further includes:

an adjustment module 404 for:

If the target data generation condition is not met, determining whether an influence factor of which the difference value between the actual weight value and the reference weight value exceeds the range needs to be adjusted;

if all the influence factors of which the difference values between the actual weight values and the reference weight values are out of the range do not need to be adjusted, the data generating module 403 generates target data according to one or more influence factors;

if the impact factors of which the differences between the one or more actual weight values and the reference weight value exceed the range need to be adjusted, the adjustment module 404 determines the actual weight value and the reference weight value of the adjusted impact factors;

the generation determination module 402 determines whether the target data generation condition is satisfied according to the unadjusted influence factor and the actual weight value and the reference weight value of the adjusted influence factor;

if the target data generation condition is satisfied, the data generation module 403 generates target data according to one or more impact factors.

Optionally, as shown in fig. 12, the apparatus further includes:

and a processing module 405, configured to determine whether to process the target data according to the actual weight value and the reference weight value.

Optionally, determining whether to process the target data according to the actual weight value and the reference weight value includes:

if yes, determining a processing rule of the target data according to one or more influence factors, and/or determining a processing result of the target data according to one or more influence factors.

As shown in fig. 13, a ninth embodiment of the present specification provides a data processing apparatus including:

the factor determining module 501 is configured to determine an impact factor of data to be processed, and an actual weight value and a reference weight value of each impact factor;

a processing determination module 502, configured to determine whether a data processing condition is satisfied according to the actual weight value and the reference weight value;

a processing module 503, configured to determine a processing rule of the data to be processed according to one or more impact factors, and/or determine a processing result of the data to be processed according to one or more impact factors, if it is determined to process the target data.

As shown in fig. 14, a tenth embodiment of the present specification provides a data evaluation apparatus including:

the factor determining module 601 is configured to determine an impact factor of data to be evaluated, and an actual weight value and a reference weight value of each impact factor;

The factor evaluation module 602 is configured to determine, for any influence factor, whether the influence factor is qualified according to an actual weight value and a reference weight value of the influence factor;

and the data evaluation module 603 is configured to determine an evaluation result of the data according to the qualification determination result of the influence factor.

An eleventh embodiment of the present specification provides a data generating apparatus including:

at least one processor;

the method comprises the steps of,

a memory communicatively coupled to the at least one processor;

wherein,,

if yes, generating target data according to one or more influence factors.

A twelfth embodiment of the present specification provides a data processing apparatus including:

at least one processor;

the method comprises the steps of,

a memory communicatively coupled to the at least one processor;

Wherein,,

A thirteenth embodiment of the present specification provides a data evaluation device including:

at least one processor;

the method comprises the steps of,

a memory communicatively coupled to the at least one processor;

wherein,,

A fourteenth embodiment of the present specification provides a computer-readable storage medium storing computer-executable instructions which, when executed by a processor, perform the steps of:

if yes, generating target data according to one or more influence factors.

A fifteenth embodiment of the present specification provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, perform the steps of:

A sixteenth embodiment of the present specification provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, perform the steps of:

The above embodiments may be used in combination.

The foregoing describes certain embodiments of the present disclosure, other embodiments being within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings do not necessarily have to be in the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-transitory computer readable storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to portions of the description of method embodiments being relevant.

The apparatus, the device, the nonvolatile computer readable storage medium and the method provided in the embodiments of the present disclosure correspond to each other, and therefore, the apparatus, the device, and the nonvolatile computer storage medium also have similar advantageous technical effects as those of the corresponding method, and since the advantageous technical effects of the method have been described in detail above, the advantageous technical effects of the corresponding apparatus, device, and nonvolatile computer storage medium are not described herein again.

In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming Language, which is called Hardware Description Language (HDL), but HDL is not only one, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Descr IP address extension), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Descr IP address extension), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Descr IP address extension), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Descr IP address extension) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Appl ication Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchIP address PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.

The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.

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

The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. 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 also be noted that 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 the element.

The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.

The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the present disclosure. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.

Claims

1. A data generation method is characterized in that,

if yes, generating target data according to one or more influence factors;

wherein determining the reference weight value of the influence factor comprises: for any influence factor, determining one or more historical weight values of the influence factor; determining a reference weight value of the influence factor according to the historical weight value of the influence factor;

Determining the actual weight value of the impact factor includes: for any influence factor, determining the number of target events associated with the influence factor; and determining the actual weight value of the influence factor according to the number of the target events and the total number of the target events which are associated with the influence factor.

2. The method of claim 1, wherein a ratio of a number of target events associated with the impact factor in a predetermined time to a total number of target events in the predetermined time is used as an actual weight value of the impact factor.

3. The method of claim 1, wherein determining whether a target data generation condition is met based on the actual weight value and a reference weight value comprises:

and/or the number of the groups of groups,

And/or the number of the groups of groups,

and/or the number of the groups of groups,

4. The method of claim 1, wherein the method further comprises:

if the influence factors of which the difference values of all the actual weight values and the reference weight values are out of the range do not need to be adjusted, generating target data according to one or more influence factors;

if the influence factors of which the differences between one or more actual weight values and the reference weight values exceed the range need to be adjusted, determining the actual weight values and the reference weight values of the adjusted influence factors;

determining whether the target data generation condition is met or not according to the unadjusted influence factors and the actual weight values and the reference weight values of the adjusted influence factors;

And if the target data generation condition is met, generating target data according to one or more influence factors.

5. The method of any one of claims 1 to 4, wherein the method further comprises:

and determining whether to process the target data according to the actual weight value and the reference weight value.

6. The method of claim 5, wherein determining whether to process the target data based on the actual weight value and a reference weight value comprises:

7. A data processing method is characterized in that,

if yes, determining a processing rule of the data to be processed according to one or more influence factors, and/or determining a processing result of the data to be processed according to one or more influence factors;

8. A data evaluation method is characterized in that,

determining an evaluation result of the data according to the qualification judgment result of the influence factors;

wherein determining the reference weight value of the influence factor comprises: determining one or more historical weight values of the influence factors for any of the influence factors; determining a reference weight value of the influence factor according to the historical weight value of the influence factor;

9. The method of claim 8, wherein,

the data includes transaction data.

10. A data generating apparatus, comprising:

the data generation module is used for generating target data according to one or more influence factors if the target data generation condition is determined to be met;

11. A data processing apparatus, comprising:

the processing module is used for determining a processing rule of the data to be processed according to one or more influence factors and/or determining a processing result of the data to be processed according to one or more influence factors if the data processing condition is determined to be met;

12. A data evaluation device, comprising:

the data evaluation module is used for determining an evaluation result of the data according to the qualification judgment result of the influence factors;

13. A data generating apparatus, characterized by comprising:

at least one processor;

the method comprises the steps of,

a memory communicatively coupled to the at least one processor;

wherein,,

if yes, generating target data according to one or more influence factors;

14. A data processing apparatus, comprising:

at least one processor;

the method comprises the steps of,

a memory communicatively coupled to the at least one processor;

wherein,,

15. A data evaluation apparatus, characterized by comprising:

at least one processor;

the method comprises the steps of,

a memory communicatively coupled to the at least one processor;

wherein,,

16. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions when executed by a processor perform the steps of:

if yes, generating target data according to one or more influence factors;

17. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions when executed by a processor perform the steps of:

18. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions when executed by a processor perform the steps of: