CN114387085A - Method and device for processing pipeline data, computer equipment and storage medium - Google Patents
Method and device for processing pipeline data, computer equipment and storage medium Download PDFInfo
- Publication number
- CN114387085A CN114387085A CN202210032636.9A CN202210032636A CN114387085A CN 114387085 A CN114387085 A CN 114387085A CN 202210032636 A CN202210032636 A CN 202210032636A CN 114387085 A CN114387085 A CN 114387085A
- Authority
- CN
- China
- Prior art keywords
- data
- processed
- pipeline
- running
- processing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012545 processing Methods 0.000 title claims abstract description 103
- 238000000034 method Methods 0.000 title claims abstract description 42
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 96
- 238000011156 evaluation Methods 0.000 claims abstract description 70
- 238000004590 computer program Methods 0.000 claims description 39
- 238000005259 measurement Methods 0.000 claims description 27
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000010586 diagram Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 8
- 230000008859 change Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 5
- 238000003672 processing method Methods 0.000 description 5
- 238000000692 Student's t-test Methods 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- 238000012353 t test Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 229910021389 graphene Inorganic materials 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
Abstract
The application relates to a method and a device for processing pipeline data, a computer device and a storage medium. The method comprises the following steps: acquiring to-be-processed flow data; acquiring pre-configured running data reference information corresponding to the running data to be processed, and reading processing logic corresponding to the running data reference information; processing the to-be-processed pipeline data according to the processing logic to obtain characteristic data; and calculating the authenticity evaluation index of the flowing water data to be processed according to the characteristic data. By adopting the method, the running water data to be processed can be accurately evaluated.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for processing pipeline data, a computer device, a storage medium, and a program product.
Background
The financial loan institution collects bank flow provided by the enterprise to be checked when checking the enterprise information, the electronic flow of the bank records a lot of information, and an applicant can use the information to achieve the purpose of the applicant, for example, cheat the checking of the financial loan institution by a software counterfeiting flow mode to obtain loan qualification. However, since the stream provided by the applicant is counterfeit, there is a high risk that the financial loan institution will provide the applicant with a loan.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device and a storage medium for processing pipeline data, which can accurately evaluate the pipeline data to be processed.
In a first aspect, the present application provides a method for processing pipelined data, the method comprising:
acquiring to-be-processed flow data;
acquiring pre-configured running data reference information corresponding to running data to be processed, and reading processing logic corresponding to the running data reference information;
processing the to-be-processed flow data according to the processing logic to obtain characteristic data;
and calculating the authenticity evaluation index of the flowing water data to be processed according to the characteristic data.
In one embodiment, after the obtaining of the pre-configured pipeline data reference information corresponding to the pipeline data to be processed includes:
and dividing the to-be-processed pipeline data according to a preset rule to obtain a first part of to-be-processed pipeline data and a second part of to-be-processed pipeline data.
In one embodiment, the processing the to-be-processed pipeline data according to the processing logic to obtain the feature data includes: acquiring field information corresponding to the reference information of the running data; inquiring whether field information exists in the second part of to-be-processed flow data or not to obtain an inquiry result; and obtaining the characteristic data of the to-be-processed flow data according to the query result.
In one embodiment, the processing the to-be-processed pipeline data according to the processing logic to obtain the feature data includes: calculating corresponding measurement indexes according to the first part of the to-be-processed pipeline data and the second part of the to-be-processed pipeline data; and obtaining the characteristic data of the flowing water data to be processed according to the measurement indexes.
In one embodiment, the obtaining the feature data of the to-be-processed pipeline data according to the metric includes: acquiring a preset threshold value; comparing the measurement index with a threshold value to obtain a comparison result; and obtaining the characteristic data of the flowing water data to be processed according to the comparison result.
In one embodiment, the calculating the authenticity evaluation index of the running water data to be processed according to the feature data includes: acquiring a preset evaluation grade; and obtaining the authenticity evaluation index of the flowing water data to be processed according to the characteristic data and the evaluation grade.
In a second aspect, the present application further provides a pipelined data processing apparatus, comprising:
the acquisition module is used for acquiring the to-be-processed flow data;
the processing logic determining module is used for acquiring pre-configured pipelined data reference information corresponding to the pipelined data to be processed and reading processing logic corresponding to the pipelined data reference information;
the characteristic data calculation module is used for processing the to-be-processed flow data according to the processing logic to obtain characteristic data;
and the evaluation module is used for calculating the authenticity evaluation index of the flowing water data to be processed according to the characteristic data.
In a third aspect, the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method in any one of the above embodiments when executing the computer program.
In a fourth aspect, the present application further provides a computer-readable storage medium. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method in any of the above-mentioned embodiments.
In a fifth aspect, the present application further provides a computer program product. Computer program product comprising a computer program which, when being executed by a processor, carries out the steps of the method of any of the above embodiments.
According to the method, the device, the computer equipment and the storage medium for processing the flowing water data, the flowing water data to be processed and the reference information which is configured in advance and corresponds to the flowing water data to be processed are obtained, the processing logic corresponding to the reference information of the flowing water data to be processed is read, different processing logics are adopted according to different reference information, so that the characteristic data of the flowing water data to be processed through the processing logics can be more real and accurate, and finally the authenticity evaluation index of the flowing water data to be processed can be obtained through calculation according to the characteristic data, so that the authenticity of the flowing water data to be processed can be accurately obtained.
Drawings
FIG. 1 is a diagram of an application environment of a method for pipelined data processing in one embodiment;
FIG. 2 is a flow diagram that illustrates a method for pipelined data processing in one embodiment;
FIG. 3 is a schematic flow chart diagram in one embodiment;
FIG. 4 is a schematic view of the evaluation scale in another embodiment;
FIG. 5 is a block diagram of a pipelined data processing apparatus in one embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The pipeline data processing method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104, or may be located on the cloud or other network server. Firstly, acquiring running water data to be processed; then, acquiring pre-configured running data reference information corresponding to the running data to be processed, and reading processing logic corresponding to the running data reference information; processing the to-be-processed flow data according to the processing logic to obtain characteristic data; and finally, calculating the authenticity evaluation index of the flowing water data to be processed according to the characteristic data. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart car-mounted devices, and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, and the like. The server 104 may be implemented as a stand-alone server or as a server cluster comprised of multiple servers.
In one embodiment, as shown in fig. 2, a method for processing pipeline data is provided, which is exemplified by the method applied to the server 104 in fig. 1, and includes the following steps:
s202, acquiring the to-be-processed pipeline data.
The to-be-processed flow data refers to bank flow data which needs to be subjected to authenticity evaluation, and the to-be-processed flow data can be bank flow data actively provided by a user.
Specifically, the server 104 obtains the to-be-processed flow data uploaded by the terminal, where the to-be-processed flow data is bank flow data actively provided by the client, and optionally, the server may select part or all of the to-be-processed flow data from the terminal as needed and upload the selected to the server, so that the server may process the to-be-processed flow data according to the instruction, thereby reducing workload of manual entry and checking, improving efficiency, and simultaneously being beneficial to ensuring security of the data.
S204, acquiring pre-configured pipeline data reference information corresponding to the pipeline data to be processed, and reading processing logic corresponding to the pipeline data reference information.
The pre-configured running data reference information corresponding to the running data to be processed refers to information that can be used for referencing the running data to be processed subsequently, such as the time of a suddenly-occurring common event. The processing logic refers to a processing mode of the to-be-processed pipeline data and is determined according to the reference information, namely different reference information corresponds to different processing logic.
Specifically, the server first obtains the to-be-processed pipeline data, and obtains reference information corresponding to the to-be-processed pipeline data, where the reference information is information that can be obtained from the outside and generates different processing modes for subsequent to-be-processed pipeline data, and optionally, the reference information may be a public event or a pre-paid amount, and then reads a corresponding processing logic according to the reference information. Optionally, if the reference information is a public event, the corresponding processing logic may be to query field information of the public event in the to-be-processed pipeline processing, and perform the next processing according to the query result.
And S206, processing the to-be-processed pipeline data according to the processing logic to obtain characteristic data.
The characteristic data is obtained by processing the to-be-processed pipeline data by using corresponding processing logic and can be used for judging the authenticity index of the to-be-processed pipeline data.
Specifically, the server processes the to-be-processed pipeline data according to the processing logic corresponding to the reference information, so as to obtain the characteristic data of the to-be-processed data. Optionally, if the reference information is a public event, the reference information may use the time of the public event as a boundary, divide the to-be-processed pipeline data into two parts, compare changes of the to-be-processed pipeline data before and after the public event or process the to-be-processed pipeline data after the public event occurs, obtain whether articles related to the public event exist in the to-be-processed pipeline data after the public event occurs or change of the pipeline data with corresponding measures is taken, and obtain feature data, where the feature data refers to a comparison result of the to-be-processed pipeline data before and after the public event and whether articles related to the public event exist in the to-be-processed pipeline data after the public event occurs or change of the pipeline data with corresponding measures is taken, for example, whether the expenditure or income of the content related to the public event increases rapidly and increases the magnitude. In other embodiments, where the public event is hygiene related, revenue from the sale of the cleaning composition may rise significantly in the flow data of the product manufacturer.
And S208, calculating the authenticity evaluation index of the flowing water data to be processed according to the characteristic data.
The authenticity rating index is a quantitative index for measuring the authenticity of the to-be-processed running water data, and the authenticity of the current enterprise running water or the authenticity grade can be judged after the authenticity rating index of the to-be-processed running water data is obtained. For example, one level is represented as false, five levels are represented as true, and two-four levels represent the degree of authenticity, i.e., the degree of doubt, of the running data to be processed.
Specifically, the server may calculate an authenticity evaluation index of the to-be-processed running water data according to the feature data, wherein optionally, the authenticity evaluation index of the to-be-processed running water data may be determined by a preset evaluation level.
According to the method for processing the running data, the running data to be processed and the reference information which is configured in advance and corresponds to the running data to be processed are obtained, the processing logic corresponding to the reference information of the running data to be processed is read, different processing logics are adopted according to different reference information, so that the characteristic data of the running data to be processed through the processing logics can be more real and accurate, and finally the authenticity evaluation index of the running data to be processed can be calculated according to the characteristic data, so that the authenticity of the running data to be processed can be accurately obtained.
In one embodiment, after acquiring the pre-configured pipeline data reference information corresponding to the pipeline data to be processed, the method includes: and dividing the to-be-processed pipeline data according to a preset rule to obtain a first part of to-be-processed pipeline data and a second part of to-be-processed pipeline data.
The preset rule refers to a preset rule for dividing the to-be-processed pipeline data, and may be set according to the reference information.
Specifically, after acquiring the pre-configured running data reference information corresponding to the running data to be processed, the server divides the running data to be processed according to a preset rule, and divides the running data to be processed into two parts to obtain a first part of running data to be processed and a second part of running data to be processed, wherein optionally, in other embodiments, when the reference information is a common event, the reference information is divided by using the time of the occurrence of the common event as a boundary to obtain the first part of running data to be processed before the occurrence of the common event and the second part of running data to be processed after the occurrence of the common event.
In the embodiment, the to-be-processed flowing water is divided according to the preset rule to obtain the first part of the to-be-processed flowing water data and the second part of the to-be-processed flowing water data, and the first part of the to-be-processed flowing water data and the second part of the to-be-processed flowing water data are processed respectively, so that the more accurate authenticity evaluation index of the to-be-processed flowing water data can be obtained subsequently.
In one embodiment, processing the to-be-processed pipeline data according to the processing logic to obtain the feature data includes: acquiring field information corresponding to the reference information of the running data; inquiring whether field information exists in the second part of to-be-processed flow data or not to obtain an inquiry result; and obtaining the characteristic data of the to-be-processed flow data according to the query result.
The field information refers to information related to the reference information, wherein optionally, when the reference information is a public event, the field information is content related to the public event, and a vocabulary related to the current public event can be found from the Baidu index, for example, the related vocabulary arranged in the top five is used as the field information. In other embodiments, when the reference information is a prepaid amount, the field information is running information related to the prepaid amount. And the query result refers to a result of querying the second part of the to-be-processed pipeline data.
Specifically, the server first obtains field information corresponding to the reference information of the pipeline data, then queries whether field information exists in the second part of to-be-processed pipeline data, queries whether field information exists, and obtains a related query result, and processes according to the query result to obtain the feature data of the to-be-processed pipeline data. In other embodiments, when the field information is content related to a public event, querying whether there is a payout or income of the content related to the public event in the second part of the to-be-processed pipeline data, and processing the payout of the content related to the public event to obtain the feature data of the to-be-processed pipeline data, wherein optionally, the feature data may be obtained by double-population t-test calculation. In other embodiments, when the field information is a running water related to the money amount, whether a running water related to the field information exists in the second part of the running water data to be processed is queried, if one or more running water in the query result does not correspond to the running water related to the money amount to be printed in advance, the characteristic data of the running water data to be processed is immediately determined to be 0%, otherwise, the characteristic data is determined to be 100%. In one embodiment, the amount of money to be paid in advance may be realized by the lending institution transferring the random number of times and the random amount of money to a target customer by using a new account which is known to the lending institution, as a cycle, wherein the account of the target customer may obtain an account number of an enterprise and a highly managed bank of the enterprise through account number data submitted by the target customer during auditing, and if the target customer does not submit a loan application to the lending institution, possible enterprise account number information may also be found from account numbers and names of counter parties in other applied customer running documents.
In the above embodiment, by querying whether field information exists in the second part of the to-be-processed pipeline data, a query result is obtained, and the feature data is obtained by calculation according to the query result, so as to determine authenticity evaluation of the to-be-processed pipeline data according to the feature data in the following.
In one embodiment, processing the to-be-processed pipeline data according to the processing logic to obtain the feature data includes: calculating corresponding measurement indexes according to the first part of the to-be-processed pipeline data and the second part of the to-be-processed pipeline data; and obtaining the characteristic data of the flowing water data to be processed according to the measurement indexes.
The measurement index is a numerical value used for quantifying variation fluctuation of the first part of the to-be-processed pipeline data and the second part of the to-be-processed pipeline data, and fluctuation of the first part of the to-be-processed pipeline data and fluctuation of the second part of the to-be-processed pipeline data can be obtained through the measurement index.
Specifically, the server may calculate, according to a preset rule, a metric of the first part of the to-be-processed pipeline data and the second part of the to-be-processed pipeline data, in an embodiment, the calculation rule of the metric is as follows:
wherein
Is the average of the first portion of the pipeline data to be processed,
is the mean, n, of the second part of the running water data to be processed1And n2Respectively identifying the number of the first part of the pipeline data to be processed and the second part of the pipeline data to be processed, for example when n1At 12, the running water data is expressed as S in the first 12 months1 2And
and respectively representing the variance of the first part of the to-be-processed pipeline data and the second part of the to-be-processed pipeline data, wherein df is a degree of freedom. Specifically, referring to fig. 3, fig. 3 is a schematic flow chart of an embodiment, where 2019/3-2020/2 are the first part of the to-be-processed flow data, and 2020/3-2021/2 are the second part of the to-be-processed flow data, and then the corresponding metric t is calculated to be 0.02.
Specifically, the measurement index is calculated to obtain the characteristic data of the to-be-processed running water data, wherein optionally, the measurement index may be compared with a preset threshold, and the characteristic data of the to-be-processed running water data is obtained by processing according to the measurement index and the comparison result.
In the above embodiment, the characteristic data of the to-be-processed pipeline data can be obtained by calculating the first part of to-be-processed pipeline data and the first part of to-be-processed pipeline data metric, so as to determine the authenticity evaluation of the to-be-processed pipeline data subsequently according to the characteristic data.
In one embodiment, obtaining the characteristic data of the pipeline data to be processed according to the metric includes: acquiring a preset threshold value; comparing the measurement index with a threshold value to obtain a comparison result; and obtaining the characteristic data of the flowing water data to be processed according to the comparison result.
The preset threshold is a threshold calculated according to a preset rule, and can be set according to an actual scene, for example, can be calculated according to a significance level and a degree of freedom; the comparison result may refer to a relationship between the metric and the threshold.
Specifically, the calculated measure index is compared with a threshold value to obtain a comparison result between the measure index and the threshold value, for example, the threshold value is greater than the measure index or the threshold value is less than or equal to the measure index, and then the feature data of the to-be-processed running water data is calculated according to the comparison result.
In the above embodiment, the authenticity probability of the to-be-processed running water data can be obtained by calculating the comparison result between the measurement index and the threshold value and obtaining the feature data of the to-be-processed running water data according to the comparison result.
In one embodiment, calculating the authenticity evaluation index of the running water data to be processed according to the characteristic data comprises the following steps: acquiring a preset evaluation grade; and obtaining the authenticity evaluation index of the flowing water data to be processed according to the characteristic data and the evaluation grade.
The evaluation level is an evaluation level set according to an application scenario, for example, the authenticity level of the data to be processed may be divided into five levels, where one level is false, two to four levels are in doubt, five levels are true, and each level has a corresponding authenticity probability, which is specifically shown in fig. 4, where fig. 4 is an evaluation level schematic diagram in one embodiment.
Specifically, the server obtains a preset evaluation level, and then obtains an authenticity evaluation index of the to-be-processed running water data according to the feature data and the preset evaluation level, in one embodiment, as shown in fig. 4, when the feature data is 0%, the to-be-processed running water data is determined to be false according to the preset evaluation level being one level, and in other embodiments, when the feature data is 30%, the to-be-processed running water data is determined to be two levels, which indicates that the to-be-processed running water information is most likely to be generated according to software.
In the embodiment, the authenticity evaluation index of the flowing water data to be processed can be obtained through the characteristic data and the preset evaluation level, and the authenticity of the flowing water data to be processed can be clearly and accurately known to be a true, false or in-doubt level according to the evaluation index.
In one embodiment, when the reference information is a public event, the time when the public event occurs may be used as a node, and the running data to be processed is divided into two parts before and after the occurrence of the public event, and compared. Specifically, the first five vocabularies related to the common event, that is, the field information corresponding to the reference information, can be found according to the Baidu index, and then whether the field information is included in the to-be-processed pipeline data is queried. For example, under the influence of public events, the demand for related supplies rises dramatically — this will be a new addition to the bank flow. Therefore, whether related material expenses and related reimbursement expenses are newly added in the bank running water of the enterprise after the public event occurs can be inquired. If the field information exists in the to-be-processed flowing water data, the flowing water related to the field information is derived to obtain a query result, processing is carried out according to the query result, such as dimension reduction, clustering and the like, so as to obtain characteristic data, and authenticity evaluation of the to-be-processed flowing water data is obtained according to the characteristic data and a preset evaluation grade.
In other embodiments, the first part of the running water data to be processed before and after the common event and the second part of the running water data can be compared to obtain the authenticity evaluation index of the running water data to be processed. In particular, many businesses are not as business as ever, and may even be forced to work under the influence of public events. (e.g., some casinos: KTV, mahjongg, etc.) in this case, the payroll of workers and expenditure of water and electricity costs should be reduced-this will be a variable in bank water, and the payroll is located to two years in the industry where the enterprise is located, based on the average payroll of the industries 2019 and 2020 published by the national statistics bureau, and is used as a reference. If there is a large change, the two-year payroll of the business should also have a corresponding change. By comparing the pay conditions of wages, social security, accumulation money and water and electricity charges in the flowing water of the enterprise bank after 2 months in 2020, the authenticity of the flowing water of the enterprise bank can be distinguished. For example: and (3) evaluating the monthly wage, social security, accumulation fund (if any) and monthly water and electricity charge characteristic data of one year before the epidemic situation occurs and one year after the epidemic situation occurs by using a double-population t-test (two-sample-t-test). (the time is not necessarily one year before or after the public event and can be adjusted according to the running time length of the bank of the enterprise as appropriate) according to the characteristic data and the preset evaluation level, and the authenticity evaluation of the running data to be processed is obtained.
In other embodiments, the activity level of an enterprise may be assessed by further processing the characteristic data, such as mathematical modeling, to evaluate the ability of the enterprise to respond to public events.
In the embodiment, the common event is used as the reference information, the to-be-processed running data is segmented, the second part of the to-be-processed running data after the common event occurs is inquired whether field information exists or not, an inquiry result is obtained, the expenditure of the cost of related materials can be obtained to obtain the characteristic data, and finally the authenticity evaluation index of the to-be-processed running data is obtained according to the characteristic data, so that the authenticity evaluation index of the to-be-processed running data can be really and effectively obtained.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the present application further provides a pipeline data processing apparatus for implementing the above-mentioned pipeline data processing method. The implementation scheme for solving the problem provided by the apparatus is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the pipelined data processing apparatus provided below can refer to the limitations in the pipelined data processing method above, and details are not described here.
In one embodiment, as shown in fig. 5, there is provided a pipelined data processing apparatus comprising: an acquisition module 100, a processing logic determination module 200, a feature data calculation module 300, and an evaluation module 400, wherein:
the acquiring module 100 is configured to acquire pipeline data to be processed.
And the processing logic determining module 200 is configured to acquire pre-configured pipeline data reference information corresponding to the to-be-processed pipeline data, and read the processing logic corresponding to the pipeline data reference information.
And the characteristic data calculation module 300 is configured to process the to-be-processed pipeline data according to the processing logic to obtain characteristic data.
And the evaluation module 400 is configured to calculate an authenticity evaluation index of the to-be-processed running water data according to the feature data.
In one embodiment, the pipelined data processing apparatus further includes:
and the pipeline dividing module is used for dividing the pipeline data to be processed according to a preset rule to obtain a first part of pipeline data to be processed and a second part of pipeline data to be processed.
In one embodiment, the above feature data calculating module 300 includes:
and the field information acquisition unit is used for acquiring the field information corresponding to the pipeline data reference information.
And the field information query unit is used for querying whether the second part of to-be-processed pipeline data has the field information or not to obtain a query result.
And the first characteristic calculation unit is used for obtaining the characteristic data of the to-be-processed running water data according to the query result.
In one embodiment, the above feature data calculating module 300 further includes:
and the measurement index calculation unit is used for calculating corresponding measurement indexes according to the first part of the to-be-processed pipeline data and the second part of the to-be-processed pipeline data.
And the second characteristic calculation unit is used for obtaining the characteristic data of the flowing water data to be processed according to the measuring index.
In one embodiment, the second feature calculating unit includes:
and the threshold acquiring subunit is used for acquiring a preset threshold.
And the comparison subunit is used for comparing the measurement index with the threshold value to obtain a comparison result.
And the characteristic data calculating subunit is used for obtaining the characteristic data of the to-be-processed running water data according to the comparison result.
In one embodiment, the above evaluation module 400 includes:
and the grade acquisition unit is used for acquiring the preset evaluation grade.
And the authenticity evaluation unit is used for calculating the authenticity evaluation index of the flowing water data to be processed according to the characteristic data.
The various modules in the pipelined data processing apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the flowing water data to be processed. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a pipelined data processing method.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a pipelined data processing method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: acquiring to-be-processed flow data; acquiring pre-configured running data reference information corresponding to running data to be processed, and reading processing logic corresponding to the running data reference information; processing the to-be-processed flow data according to the processing logic to obtain characteristic data; and calculating the authenticity evaluation index of the flowing water data to be processed according to the characteristic data.
In one embodiment, after obtaining the pre-configured pipeline data reference information corresponding to the pipeline data to be processed, which is implemented when the processor executes the computer program, the method includes:
and dividing the to-be-processed pipeline data according to a preset rule to obtain a first part of to-be-processed pipeline data and a second part of to-be-processed pipeline data.
In one embodiment, the processing of the to-be-processed pipeline data according to the processing logic to obtain the feature data when the processor executes the computer program includes: acquiring field information corresponding to the reference information of the running data; inquiring whether field information exists in the second part of to-be-processed flow data or not to obtain an inquiry result; and obtaining the characteristic data of the to-be-processed flow data according to the query result.
In one embodiment, the processing of the to-be-processed pipeline data according to the processing logic to obtain the feature data when the processor executes the computer program includes: calculating corresponding measurement indexes according to the first part of the to-be-processed pipeline data and the second part of the to-be-processed pipeline data; and obtaining the characteristic data of the flowing water data to be processed according to the measurement indexes.
In one embodiment, the obtaining of the feature data of the to-be-processed running water data according to the metric, which is implemented when the processor executes the computer program, includes: acquiring a preset threshold value; comparing the measurement index with a threshold value to obtain a comparison result; and obtaining the characteristic data of the flowing water data to be processed according to the comparison result.
In one embodiment, the calculating of the authenticity evaluation index of the running water data to be processed according to the characteristic data, which is realized when the processor executes the computer program, comprises: acquiring a preset evaluation grade; and obtaining the authenticity evaluation index of the flowing water data to be processed according to the characteristic data and the evaluation grade.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring to-be-processed flow data; acquiring pre-configured running data reference information corresponding to running data to be processed, and reading processing logic corresponding to the running data reference information; processing the to-be-processed flow data according to the processing logic to obtain characteristic data; and calculating the authenticity evaluation index of the flowing water data to be processed according to the characteristic data.
In one embodiment, the obtaining of the pre-configured pipeline data reference information corresponding to the pipeline data to be processed, which is implemented when the computer program is executed by the processor, includes: and dividing the to-be-processed pipeline data according to a preset rule to obtain a first part of to-be-processed pipeline data and a second part of to-be-processed pipeline data.
In one embodiment, the processing of the running data to be processed according to the processing logic to obtain the feature data when the computer program is executed by the processor includes: acquiring field information corresponding to the reference information of the running data; inquiring whether field information exists in the second part of to-be-processed flow data or not to obtain an inquiry result; and obtaining the characteristic data of the to-be-processed flow data according to the query result.
In one embodiment, the processing of the running data to be processed according to the processing logic to obtain the feature data when the computer program is executed by the processor includes: calculating corresponding measurement indexes according to the first part of the to-be-processed pipeline data and the second part of the to-be-processed pipeline data; and obtaining the characteristic data of the flowing water data to be processed according to the measurement indexes.
In one embodiment, the obtaining of the feature data of the to-be-processed running water data according to the measure implemented when the computer program is executed by the processor includes: acquiring a preset threshold value; comparing the measurement index with a threshold value to obtain a comparison result; and obtaining the characteristic data of the flowing water data to be processed according to the comparison result.
In one embodiment, the computer program, when executed by a processor, for calculating an authenticity evaluation index of the running water data to be processed according to the feature data, includes: acquiring a preset evaluation grade; and obtaining the authenticity evaluation index of the flowing water data to be processed according to the characteristic data and the evaluation grade.
In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of: acquiring to-be-processed flow data; acquiring pre-configured running data reference information corresponding to running data to be processed, and reading processing logic corresponding to the running data reference information; processing the to-be-processed flow data according to the processing logic to obtain characteristic data; and calculating the authenticity evaluation index of the flowing water data to be processed according to the characteristic data.
In one embodiment, the obtaining of the pre-configured pipeline data reference information corresponding to the pipeline data to be processed, which is implemented when the computer program is executed by the processor, includes:
and dividing the to-be-processed pipeline data according to a preset rule to obtain a first part of to-be-processed pipeline data and a second part of to-be-processed pipeline data.
In one embodiment, the processing of the running data to be processed according to the processing logic to obtain the feature data when the computer program is executed by the processor includes: acquiring field information corresponding to the reference information of the running data; inquiring whether field information exists in the second part of to-be-processed flow data or not to obtain an inquiry result; and obtaining the characteristic data of the to-be-processed flow data according to the query result.
In one embodiment, the processing of the running data to be processed according to the processing logic to obtain the feature data when the computer program is executed by the processor includes: calculating corresponding measurement indexes according to the first part of the to-be-processed pipeline data and the second part of the to-be-processed pipeline data; and obtaining the characteristic data of the flowing water data to be processed according to the measurement indexes.
In one embodiment, the obtaining of the feature data of the to-be-processed running water data according to the measure implemented when the computer program is executed by the processor includes: acquiring a preset threshold value; comparing the measurement index with a threshold value to obtain a comparison result; and obtaining the characteristic data of the flowing water data to be processed according to the comparison result.
In one embodiment, the computer program, when executed by a processor, for calculating an authenticity evaluation index of the running water data to be processed according to the feature data, includes: acquiring a preset evaluation grade; and obtaining the authenticity evaluation index of the flowing water data to be processed according to the characteristic data and the evaluation grade.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A method for processing pipelined data, the method comprising:
acquiring to-be-processed flow data;
acquiring pre-configured running data reference information corresponding to the running data to be processed, and reading processing logic corresponding to the running data reference information;
processing the to-be-processed pipeline data according to the processing logic to obtain characteristic data;
and calculating the authenticity evaluation index of the flowing water data to be processed according to the characteristic data.
2. The method according to claim 1, wherein after obtaining the pre-configured pipeline data reference information corresponding to the pipeline data to be processed, the method comprises:
and dividing the to-be-processed pipeline data according to a preset rule to obtain a first part of to-be-processed pipeline data and a second part of to-be-processed pipeline data.
3. The method of claim 1, wherein processing the to-be-processed pipelined data according to the processing logic to obtain feature data comprises:
acquiring field information corresponding to the pipelining data reference information;
inquiring whether the second part of to-be-processed pipeline data has the field information or not to obtain an inquiry result;
and obtaining the characteristic data of the to-be-processed flow data according to the query result.
4. The method of claim 1, wherein processing the to-be-processed pipelined data according to the processing logic to obtain feature data comprises:
calculating corresponding measurement indexes according to the first part of the to-be-processed pipeline data and the second part of the to-be-processed pipeline data;
and obtaining the characteristic data of the flowing water data to be processed according to the measurement index.
5. The method according to claim 4, wherein the obtaining the characteristic data of the running water data to be processed according to the metric comprises:
acquiring a preset threshold value;
comparing the measurement index with the threshold value to obtain a comparison result;
and obtaining the characteristic data of the flowing water data to be processed according to the comparison result.
6. The method according to claim 1, wherein the calculating the authenticity evaluation index of the running water data to be processed according to the feature data comprises:
acquiring a preset evaluation grade;
and obtaining the authenticity evaluation index of the flowing water data to be processed according to the characteristic data and the evaluation grade.
7. A pipelined data processing apparatus, the apparatus comprising:
the acquisition module is used for acquiring the to-be-processed flow data;
the processing logic determining module is used for acquiring pre-configured pipelined data reference information corresponding to the to-be-processed pipelined data and reading processing logic corresponding to the pipelined data reference information;
the characteristic data calculation module is used for processing the to-be-processed flow data according to the processing logic to obtain characteristic data;
and the evaluation module is used for calculating the authenticity evaluation index of the flowing water data to be processed according to the characteristic data.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210032636.9A CN114387085B (en) | 2022-01-12 | Method, device, computer equipment and storage medium for processing stream data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210032636.9A CN114387085B (en) | 2022-01-12 | Method, device, computer equipment and storage medium for processing stream data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114387085A true CN114387085A (en) | 2022-04-22 |
| CN114387085B CN114387085B (en) | 2024-04-16 |
Family
ID=
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116702024A (en) * | 2023-05-16 | 2023-09-05 | 见知数据科技(上海)有限公司 | Method, device, computer equipment and storage medium for identifying type of stream data |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2540793A1 (en) * | 1996-12-06 | 1998-06-06 | The Distribution Systems Research Institute | Integrated information communication system |
| CA2517874A1 (en) * | 2000-06-22 | 2001-12-27 | Claude Choquet | Electronic virtual certification by data processing method via a communication network |
| CN1977249A (en) * | 2004-07-15 | 2007-06-06 | 索尼株式会社 | Information processing device, information processing method, and computer program |
| AU2014274590A1 (en) * | 2012-02-20 | 2015-01-22 | Kl Data Security Pty Ltd | Cryptographic Method and System |
| CN105825553A (en) * | 2016-05-31 | 2016-08-03 | 广州势必可赢网络科技有限公司 | Voice print verifying method and system |
| CN109377059A (en) * | 2018-10-27 | 2019-02-22 | 平安医疗健康管理股份有限公司 | A kind of data processing method and equipment based on risk management and control |
| CN110765414A (en) * | 2019-09-11 | 2020-02-07 | 深信服科技股份有限公司 | Performance index data evaluation method, device, equipment and storage medium |
| CN111324716A (en) * | 2020-02-18 | 2020-06-23 | 深圳壹账通智能科技有限公司 | Index data acquisition method and device, computer equipment and storage medium |
| CN111352907A (en) * | 2020-03-30 | 2020-06-30 | 见知数据科技(上海)有限公司 | Method and device for analyzing pipeline file, computer equipment and storage medium |
| CN111476597A (en) * | 2020-03-19 | 2020-07-31 | 平安国际智慧城市科技股份有限公司 | Resource quantity estimation result processing method and related equipment |
| CN111970286A (en) * | 2020-08-21 | 2020-11-20 | 北京恒华伟业科技股份有限公司 | User login method and device and web server |
| CN112446312A (en) * | 2020-11-19 | 2021-03-05 | 深圳市中视典数字科技有限公司 | Three-dimensional model identification method and device, electronic equipment and storage medium |
| CN112818767A (en) * | 2021-01-18 | 2021-05-18 | 深圳市商汤科技有限公司 | Data set generation method, data set forgery detection device, electronic device, and storage medium |
| CN113095712A (en) * | 2021-04-25 | 2021-07-09 | 国家电网有限公司 | Enterprise credit granting score obtaining method and device and computer equipment |
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2540793A1 (en) * | 1996-12-06 | 1998-06-06 | The Distribution Systems Research Institute | Integrated information communication system |
| CA2517874A1 (en) * | 2000-06-22 | 2001-12-27 | Claude Choquet | Electronic virtual certification by data processing method via a communication network |
| CN1977249A (en) * | 2004-07-15 | 2007-06-06 | 索尼株式会社 | Information processing device, information processing method, and computer program |
| AU2014274590A1 (en) * | 2012-02-20 | 2015-01-22 | Kl Data Security Pty Ltd | Cryptographic Method and System |
| CN105825553A (en) * | 2016-05-31 | 2016-08-03 | 广州势必可赢网络科技有限公司 | Voice print verifying method and system |
| CN109377059A (en) * | 2018-10-27 | 2019-02-22 | 平安医疗健康管理股份有限公司 | A kind of data processing method and equipment based on risk management and control |
| CN110765414A (en) * | 2019-09-11 | 2020-02-07 | 深信服科技股份有限公司 | Performance index data evaluation method, device, equipment and storage medium |
| CN111324716A (en) * | 2020-02-18 | 2020-06-23 | 深圳壹账通智能科技有限公司 | Index data acquisition method and device, computer equipment and storage medium |
| CN111476597A (en) * | 2020-03-19 | 2020-07-31 | 平安国际智慧城市科技股份有限公司 | Resource quantity estimation result processing method and related equipment |
| CN111352907A (en) * | 2020-03-30 | 2020-06-30 | 见知数据科技(上海)有限公司 | Method and device for analyzing pipeline file, computer equipment and storage medium |
| CN111970286A (en) * | 2020-08-21 | 2020-11-20 | 北京恒华伟业科技股份有限公司 | User login method and device and web server |
| CN112446312A (en) * | 2020-11-19 | 2021-03-05 | 深圳市中视典数字科技有限公司 | Three-dimensional model identification method and device, electronic equipment and storage medium |
| CN112818767A (en) * | 2021-01-18 | 2021-05-18 | 深圳市商汤科技有限公司 | Data set generation method, data set forgery detection device, electronic device, and storage medium |
| CN113095712A (en) * | 2021-04-25 | 2021-07-09 | 国家电网有限公司 | Enterprise credit granting score obtaining method and device and computer equipment |
Non-Patent Citations (1)
| Title |
|---|
| 李晓伟: "XML驱动的政府财务集中支付系统开发和实现", 《中国优秀博硕士学位论文全文数据库 (硕士)信息科技辑》, no. 01, 15 March 2005 (2005-03-15), pages 138 - 314 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116702024A (en) * | 2023-05-16 | 2023-09-05 | 见知数据科技(上海)有限公司 | Method, device, computer equipment and storage medium for identifying type of stream data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Khandani et al. | Systemic risk and the refinancing ratchet effect | |
| US20100023379A1 (en) | Method and system for determining real estate market value changes | |
| TW201911179A (en) | Merchant evaluation method and system | |
| CN107622444A (en) | Business access method and device, electronic equipment | |
| KR20200007271A (en) | Evaluation methodology of cryptocurrency and the cryptocurrency index | |
| US20110320368A1 (en) | Determining environmental impact | |
| CN114240611A (en) | Method and device for accounting accuracy of service data, computer equipment and storage medium | |
| CN113159545A (en) | Method and device for determining value attribute value, electronic equipment and storage medium | |
| CN112241917A (en) | Intelligent financial institution pre-loan management method and system | |
| Thackham et al. | Exposure at default without conversion factors—evidence from Global Credit Data for large corporate revolving facilities | |
| CN114387085A (en) | Method and device for processing pipeline data, computer equipment and storage medium | |
| CN114387085B (en) | Method, device, computer equipment and storage medium for processing stream data | |
| CN110827137A (en) | Credit evaluation method and device | |
| JP2010522369A (en) | Processing device for normalizing bars representing weighted variables | |
| CN114493821B (en) | Data verification and cancellation method and device, computer equipment and storage medium | |
| CN113240472B (en) | Financial product recommendation method, electronic equipment and storage medium | |
| CN115729923A (en) | Customer data analysis method and device, storage medium and computer equipment | |
| CN115496363A (en) | Budget control method, budget control device, computer equipment and storage medium | |
| CN116362879A (en) | Transaction data simulation method, device, computer equipment, storage medium and product | |
| CN114565452A (en) | Transfer risk identification method and device, computer equipment and storage medium | |
| CN114638614A (en) | Enterprise credit line determination method and device based on multi-dimensional government affair data | |
| CN115482114A (en) | Multi-stage investment portfolio attribution method, device, computer equipment and storage medium | |
| CN114663206A (en) | Batch account-merging data processing method and device, computer equipment and storage medium | |
| CN114091715A (en) | Method and system for predicting future refund of account receivable | |
| CN116188178A (en) | Resource information processing method, device, equipment, storage medium and product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |