CN113722310A - Blood relationship information visual representation method - Google Patents
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- 239000008280 blood Substances 0.000 title claims abstract description 54
- 210000004369 blood Anatomy 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 title claims abstract description 48
- 230000000007 visual effect Effects 0.000 title claims abstract description 14
- 238000004458 analytical method Methods 0.000 claims abstract description 11
- 238000004140 cleaning Methods 0.000 claims abstract description 8
- 230000008569 process Effects 0.000 claims description 11
- 238000012800 visualization Methods 0.000 claims description 6
- 238000007405 data analysis Methods 0.000 claims description 5
- 238000010224 classification analysis Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 14
- 230000000694 effects Effects 0.000 abstract description 2
- 230000006872 improvement Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011010 flushing procedure Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000007639 printing Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000011960 computer-aided design Methods 0.000 description 1
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- 230000002452 interceptive effect Effects 0.000 description 1
- 210000001503 joint Anatomy 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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- G06F16/24—Querying
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Abstract
The application discloses a blood relationship information visual representation method, which comprises the steps of collecting initial data; analyzing the initial data; cleaning the analyzed data; analyzing the relationship of blood relationship of the cleaned data; storing the data blood relationship according to the hierarchical structure; and constructing a visual platform for collecting and visually displaying the relationship of the blood relationship. Through data integrated processing, to data blood relationship analysis, arrangement and storage, make the blood relationship more clear in the subsequent data is visual, can more audio-visual show data, and through improving data processing quality, guarantee the treatment effect of data, be convenient for trace to the source, the aassessment of data, through clear data blood relationship, be convenient for filing and destroying of data, the problem of the unclear transform show of data when the show is spent to current data blood relationship has been solved, the management and the application of data of being convenient for.
Description
Technical Field
The application relates to a visual representation method, in particular to a blood relationship information visual representation method.
Background
Visualization is a theory, a method and a technology for converting data into graphs or images to be displayed on a screen by utilizing computer graphics and image processing technologies and then performing interactive processing, and relates to a plurality of fields of computer graphics, image processing, computer vision, computer aided design and the like.
At present, many data blood relationship are displayed, and due to the fact that sources of the data are complicated and various, data circulation routes are unclear, data processing quality is low, or data processing in a certain link is improper, transformation of the data in a circulation process cannot be clearly seen. Therefore, a method for visually representing blood-related information is proposed to solve the above problems.
Disclosure of Invention
The embodiment provides a blood relationship information visual representation method which is used for solving the problem that in the prior art, the transformation display of data in the circulation process is not clear when the blood relationship of the data is displayed.
According to an aspect of the present application, there is provided a method for visually representing blood margin information, the method comprising the steps of:
(1) collecting initial data;
(2) analyzing the initial data;
(3) cleaning the analyzed data;
(4) analyzing the relationship of blood relationship of the cleaned data;
(5) storing the data blood relationship according to the hierarchical structure;
(6) and constructing a visual platform for collecting and visually displaying the relationship of the blood relationship.
Further, in the step (1), the relevant initial data is collected through big data, and the whole data is classified according to different data sources, such as official data, unofficial data, third-party transaction data, self data and the like.
Further, the classified data in the step (2) is subjected to classification analysis, the deep analysis is sequentially performed in an increasing manner according to the classification, the associated data and the like in the classified keywords, the associated data during the analysis is collected at the same time, the analyzed data is correspondingly classified and stored, and the mutual communication, transmission and access among different storage modules are ensured.
Further, during data analysis in step (2), data circulation paths can be collected by collecting data circulation lines, data circulation paths can be collected, data flow-in nodes can be converged towards main nodes, data flow-out from the main nodes can be diffused towards the data flow-out nodes, information of three dimensions is represented, the three dimensions are direction, data updating magnitude and data updating frequency, and meanwhile, the value of the data can be judged through data audiences, the updating magnitude and the updating frequency.
Further, the data cleansing, i.e. cleansing rule node in the step (3), is used to express screening standards in the data circulation process, the requirements of each place on the data quality are different, the data receiver will filter the accessed data according to the requirements of the data receiver, form data standards according to the requirements, and cleanse the data according to the standards.
Further, the step (4) of analyzing the blood margin comprises: three levels, a task level, a data level, and a field level, wherein the task level represents: data in a large data platform is often generated by tasks one by one, although different names exist in different application systems, the data are essentially the same, such as application in Yarn, Job in Oozie, and Job in Spark/MR/Hive, and higher-level information, such as a server, running time, waiting time, current task flow state, and the like, can be known by looking at the blood-related relationship of task levels.
Further, the data levels in step (4) are also called tables, directories and the like, and broadly include HDFS, HBase, relational database, Kafka, Ftp, local file and the like, and by looking at the relationship of the blood relationship of the data levels, it can be seen that: the table depends on the chain, the importance degree of the table (the number of subsequent users) and the basic information of the table, and can carry out data quality and influence analysis work.
Further, the field level in the step (4) can know how much the influence of changing the field is, how the field is generated, and the like, and the field level is divided into two types according to the definition in Hive: project (only affects a single output field) and Predicate (assert, affect all output fields).
Further, in the step (5), different types of storage structures are formed by different databases, tables and fields of the data consanguinity relationship, different hierarchical structures are formed by different storage structures, and the information description degrees of the data consanguinity relationship represented by different hierarchies are different.
Further, when performing the visualization display in the step (6), firstly, a display instruction is acquired, a data source node which needs to be visualized is determined, a corresponding blood-related data object is determined from the data object table according to the data source node and is used as a current blood-related data object, and then the data blood-related relationship is displayed according to the data hierarchy structure, or is archived or destroyed.
Through the above-mentioned embodiment of this application, adopted data analysis, data are clear and data analysis and other integrated processing, the circulation process of the collection data that can be clear for data blood relationship is clearer when visual show, great improvement the treatment quality of data, solved current data blood relationship when the show data the not clear problem of transform show in the circulation process, the management and the application of the data of being convenient for.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
Furthermore, the terms "mounted," "disposed," "provided," "connected," and "sleeved" are to be construed broadly. For example, it may be a fixed connection, a removable connection, or a unitary construction; can be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements or components. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The bloody cut edge information visual representation method in the embodiment can be suitable for a tax-controlled parking data acquisition terminal, for example, the following tax-controlled parking data acquisition terminal is provided in the embodiment, and the bloody cut edge information visual representation method in the embodiment can be used for the tax-controlled parking data acquisition terminal.
The system comprises a tax control module, a data processing module, a bill service module information query module and at least two data interfaces; the tax control module: downloading invoice source number information of corresponding parking lot operating units from a tax control parking invoice information center; a data processing module: processing invoice information collected from each parking charging subsystem, and locally storing and uploading the invoice information; a bill service module: providing invoice voiding and red flushing bills; the invoices which are not old are treated according to 'waste', and the invoices which are old are treated according to 'red flushing'; an information inquiry module: providing a local query of invoice offer information; one of the two data interfaces is a data interface which is networked with the tax control invoice information center, and the other data interface is a data interface which is in butt joint with the parking charging subsystem; the data processing module collects and stores parking invoice information, parking behavior information and charging system state information, the tax-control parking data collection terminal is respectively connected with the monthly payment receipt printer module and the temporary parking payment receipt printer module, and the monthly payment receipt printer module has the following flow: the method comprises the following steps of paying monthly payment, issuing/continuing card/system registration → a parking charging system sends a printing instruction to a printer → the printer applies for an invoice source number to a tax-controlled parking data acquisition terminal → the tax-controlled parking data acquisition terminal issues the invoice source number to the printer → prints monthly invoice → invoice issuing information uploading, and the process of the temporary parking invoice issuing printer module is as follows: the vehicle leaves the field, charges, pays → the parking charging system sends a printing instruction to the printer → the printer applies for an invoice source number to the tax-controlled parking data acquisition terminal → the tax-controlled parking data acquisition terminal sends the invoice source number to the printer → prints the temporary parking invoice → the invoice information is uploaded.
Of course, the embodiment can also be used for other data acquisition terminals. Here, details are not repeated, and the method for visually representing the blood relationship information according to the embodiment of the present application is described below.
Referring to fig. 1, a method for visually representing blood-related information includes the following steps:
(1) collecting initial data;
(2) analyzing the initial data;
(3) cleaning the analyzed data;
(4) analyzing the relationship of blood relationship of the cleaned data;
(5) storing the data blood relationship according to the hierarchical structure;
(6) and constructing a visual platform for collecting and visually displaying the relationship of the blood relationship.
Acquiring related initial data through big data in the step (1), and classifying the whole data according to different data sources, wherein the related initial data is acquired through the big data in the step (1), and the whole data is official data, unofficial data, third-party transaction data, self data and the like;
performing classification analysis on the classified data in the step (2), sequentially performing incremental deep analysis according to classification, associated data and the like in classified keywords, collecting associated data during analysis, performing corresponding classification storage on the analyzed data, and ensuring that different storage modules can be mutually communicated, transmitted and accessed;
during data analysis in the step (2), a data circulation line can be collected, namely a data circulation path can be collected, the data flow from a data inflow node is converged to a main node, the data flow from the main node is diffused to a data outflow node, three-dimensional information is represented, namely direction, data updating magnitude and data updating frequency, and meanwhile, the value of the data can be judged through data audience, the data updating magnitude and the data updating frequency;
the data cleaning, namely cleaning rule node in the step (3) is used for expressing the screening standard in the data circulation process, the requirements of each place on the data quality are different, the data receiver can filter the accessed data according to the requirements of the data receiver, form the data standard according to the requirements, and clean the data according to the standards;
the step (4) of analyzing the blood margin comprises the following steps: three levels, a task level, a data level, and a field level, wherein the task level represents: data in a big data platform is often generated by tasks one by one, although different names exist in different application systems, the data are essentially the same, such as application in Yarn, Job in Oozie, and Job in Spark/MR/Hive, and higher-level information, such as a server, running time, waiting time, current task flow state and the like, can be known by looking at the blood-related relationship of task levels;
the data levels in the step (4) are also called tables, directories and the like, and broadly include HDFS, HBase, relational databases, Kafka, Ftp, local files and the like, and by checking the relationship of the blood relationship of the data levels, it can be seen that: the table depends on a chain, the importance degree (the number of subsequent users) of the table and the basic information of the table, and can carry out data quality and influence analysis work;
the field level in step (4) can know how much the field is affected by changing, how the field is generated, and the like, and the field level is divided into two types according to the definition in Hive: project (only affects a single output field) and Predicate (assert, affect all output fields);
different databases, tables and fields of the data consanguinity relationship in the step (5) form different types of storage structures, different hierarchical structures are formed by the different storage structures, and the different levels of information description indicating the data consanguinity relationship are different;
when the visualization display is performed in the step (6), firstly, a display instruction is acquired, a data source node needing visualization is determined, a corresponding blood relationship data object is determined from the data object table according to the data source node and is used as a current blood relationship data object, and then the data blood relationship is displayed according to the data hierarchy structure, or is filed or destroyed.
The application has the advantages that: through data integrated processing, data blood relationship analysis, arrangement and storage for blood relationship is more clear in subsequent data visualization, can more audio-visual show data, and through improving data processing quality, guarantees the treatment effect of data, and the data of being convenient for trace to the source, aassessment through clear data blood relationship, is convenient for file and destroy of data.
It is well within the skill of those in the art to implement, without undue experimentation, the present application is not directed to software and process improvements, as they relate to circuits and electronic components and modules.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method for visually representing blood relationship information is characterized in that: the method for visually representing the blood margin information comprises the following steps:
(1) collecting initial data;
(2) analyzing the initial data;
(3) cleaning the analyzed data;
(4) analyzing the relationship of blood relationship of the cleaned data;
(5) storing the data blood relationship according to the hierarchical structure;
(6) and constructing a visual platform for collecting and visually displaying the relationship of the blood relationship.
2. A method as claimed in claim 1, wherein the method comprises: in the step (1), the relevant initial data is collected through big data, and the whole data is classified according to different data sources, such as official data, unofficial data, third-party transaction data, self data and the like.
3. A method as claimed in claim 1, wherein the method comprises: and (3) performing classification analysis on the classified data in the step (2), sequentially performing incremental deep analysis according to the classification, the associated data and the like in the classified keywords, collecting the associated data during analysis, performing corresponding classification storage on the analyzed data, and ensuring that different storage modules can be mutually communicated, transmitted and accessed.
4. A method as claimed in claim 1, wherein the method comprises: during data analysis in the step (2), data circulation paths can be collected, data flow-in nodes can be converged to the main nodes, data flow-out nodes can be diffused to the data flow-out nodes, information of three dimensions including direction, data updating magnitude and data updating frequency is represented, and meanwhile the value of the data can be judged through data audiences, the updating magnitude and the updating frequency.
5. A method as claimed in claim 1, wherein the method comprises: and (3) the data cleaning, namely cleaning rule nodes, are used for expressing the screening standards in the data circulation process, the requirements of each place on the data quality are different, the data receiver can filter the accessed data according to the requirements of the data receiver, form data standards according to the requirements, and clean the data according to the standards.
6. A method as claimed in claim 1, wherein the method comprises: the step (4) of analyzing the blood margin comprises the following steps: three levels, a task level, a data level, and a field level, wherein the task level represents: data in a large data platform is often generated by tasks one by one, although different names exist in different application systems, the data are essentially the same, such as application in Yarn, Job in Oozie, and Job in Spark/MR/Hive, and higher-level information, such as a server, running time, waiting time, current task flow state, and the like, can be known by looking at the blood-related relationship of task levels.
7. A method as claimed in claim 6, wherein the method comprises: the data levels in the step (4) are also called tables, directories and the like, and broadly include HDFS, HBase, relational databases, Kafka, Ftp, local files and the like, and by checking the relationship of the blood relationship of the data levels, it can be seen that: the table depends on the chain, the importance degree of the table (the number of subsequent users) and the basic information of the table, and can carry out data quality and influence analysis work.
8. A method as claimed in claim 6, wherein the method comprises: the field level in step (4) can know how much the field is affected by changing, how the field is generated, and the like, and the field level is divided into two types according to the definition in Hive: project (only affects a single output field) and Predicate (assert, affect all output fields).
9. A method as claimed in claim 1, wherein the method comprises: and (5) forming different types of storage structures by different databases, tables and fields of the data blood relationship in the step (5), wherein the different types of storage structures form different hierarchical structures, and the different hierarchical structures represent different information description degrees of the data blood relationship.
10. A method as claimed in claim 1, wherein the method comprises: when the visualization display is performed in the step (6), firstly, a display instruction is acquired, a data source node needing visualization is determined, a corresponding blood relationship data object is determined from the data object table according to the data source node and is used as a current blood relationship data object, and then the data blood relationship is displayed according to the data hierarchy structure, or is filed or destroyed.
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| CN114911785A (en) * | 2022-05-16 | 2022-08-16 | 北京航空航天大学 | Data blood reason management method and device and electronic equipment |
| CN115203179A (en) * | 2022-05-16 | 2022-10-18 | 北京航空航天大学 | Data cleaning method and device |
| CN116932831A (en) * | 2023-09-14 | 2023-10-24 | 北京滴普科技有限公司 | Method and device for constructing data blood-lineage diagram |
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Application publication date: 20211130 |