CN109669992A - A kind of implementation method of data visualization conversion - Google Patents
A kind of implementation method of data visualization conversion Download PDFInfo
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- CN109669992A CN109669992A CN201811562579.5A CN201811562579A CN109669992A CN 109669992 A CN109669992 A CN 109669992A CN 201811562579 A CN201811562579 A CN 201811562579A CN 109669992 A CN109669992 A CN 109669992A
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Abstract
This application discloses a kind of implementation methods of data visualization conversion, it include: disposition data source, configure logic entity, configuration attribute, configuration association table, configuration visualization rendering engine, the rule of allocation models and model, collocation task scheduling, so that the task of the model publication is periodically executed, when executing the task schedule, regulation engine parsing is carried out to the model, data are extracted by the rule of the model, caching, and it is grouped in the buffer based on correlation rule, statistics and convergence, finally it is converted into target data, visualization rendering engine is rendered according to configuration information, it exports to browser engine.The implementation method of data visualization provided by the present application conversion by using Information Technology Methods in the collection of reliability operational indicator data, arrangement, statistics, the links such as analyze and show and realize automation, intelligent and visualization, data statistics in traditional approach can be eliminated, summarize, analyze the technical issues of lagging.
Description
Technical field
This application involves the implementation methods that information technology field more particularly to a kind of data visualization are converted.
Background technique
Management of electric power dependability is the safety management of electric system and the total quality control of equipment and overall process, is suitable
One of the scientific management methods of modern power industry industry characteristic are an important composition portions of power industry modern management
Point.
The content of power supply reliability system index notification is more at present, the frequency is high, and existing statistical indicator still relies on office table
Lattice production template is issued to constituent parts and is made a report on, and is collected and is summarized by correlation specific responsibility, counted using excel function,
Analysis is shown.
But since data collection, analysis workload are huge and human factor mistake is more, be unfavorable for data preservation,
It inquires and shared, index generates sluggish at least production reality 1 week, causes to summarize management of electric power dependability index analysis in industry
Lag, can not timely and effectively analyze the problem, be handled.
Summary of the invention
This application provides a kind of implementation methods of data visualization conversion, to solve electric reliability pipe in the prior art
The technical issues of reason index analysis summarizes lag.
In order to solve the above-mentioned technical problem, the embodiment of the present application discloses following technical solution:
The embodiment of the present application discloses a kind of implementation method of data visualization conversion, comprising:
Disposition data source: the basic information of data source, test data source connectivity, it is ensured that data source and source database are filled in
Information communication；
Configuration logic entity: the ontology of target database, and the object in the logic entity and the source database are configured
It is corresponding to manage table；
Configuration attribute: configuring the essential attribute of the ontology of target database, in the essential attribute and the source database
The column of physical table are corresponding；
Configuration association table: the contingency table includes logic entity contingency table and Attribute Association table, the logic entity association
Table is the mapping relations for establishing physical table in the logic entity and the source database；The Attribute Association table is described in foundation
Essential attribute and column of physical table in the source database, column polymerize the mapping relations summarized；
Configuration visualization rendering engine, the table taken various forms to the data in the source database, chart carry out
Show；
The rule of allocation models and model: the logic entity for meeting a certain rule is configured using certain rule, and is
Logic entity establishes correlation rule, verifies after correlation rule test passes through, the model is issued into independent task；
Collocation task scheduling, so that the task of model publication is periodically executed；
Regulation engine parsing, when executing the task schedule, parses the model, by the model
Rule extracts data, is cached, and it is rule-based in the buffer be grouped, statistics and convergence, be finally converted into number of targets
According to being rendered according to configuration information, output is to browser engine.
Optionally, the disposition data source, the configuration logic entity, the allocation models and model rule, described match
It sets task schedule and regulation engine parsing independently encapsulates.
Optionally, the data source supports multiple mainstream relevant database Oracle, Mysql, postgresql data
Source configuration.
Optionally, the basic information includes the username and password for driving title, data source URL, target database.
Optionally, the essential attribute includes the dimension visualized, classification, numerical value.
Optionally, the place when collocation task scheduling includes frequency, the implementation strategy, execution failure that configuration task executes
Reason strategy.
Optionally, the rule of the model includes global value variable bound rule, the logic entity and the source data
The constraint relationship rule, the classified statistic polymerization analysis rule of physical table in library.
Optionally, the target database is Neo4j chart database.
Compared with prior art, the application has the beneficial effect that
This application provides a kind of implementation methods of data visualization conversion, comprising:
Disposition data source: the basic information of data source, test data source connectivity, it is ensured that data source and source database are filled in
Information communication；
Configuration logic entity: the ontology of target database, and the object in the logic entity and the source database are configured
It is corresponding to manage table；
Configuration attribute: configuring the essential attribute of the ontology of target database, in the essential attribute and the source database
The column of physical table are corresponding；
Configuration association table: the contingency table includes logic entity contingency table and Attribute Association table, the logic entity association
Table is the mapping relations for establishing physical table in the logic entity and the source database；The Attribute Association table is described in foundation
Essential attribute and column of physical table in the source database, column polymerize the mapping relations summarized；
Configuration visualization rendering engine, the table taken various forms to the data in the source database, chart carry out
Show；
The rule of allocation models and model: the logic entity for meeting a certain rule is configured using certain rule, and is
Logic entity establishes correlation rule, verifies after correlation rule test passes through, the model is issued into independent task；
Collocation task scheduling, so that the task of model publication is periodically executed；
Regulation engine parsing, when executing the task schedule, parses the model, by the model
Rule extracts data, is cached, and is grouped in the buffer based on correlation rule, statistics and convergence, is finally converted into mesh
Data are marked, visualization rendering engine is rendered according to configuration information, output to browser engine.Data provided by the present application can
Shown depending on changing the implementation method converted with offer data acquisition, model configuration, data pick-up, task schedule, self-service combination,
Various dimensions Dynamic Display, the function of graph visualization analysis, according to actual job demand, by using Information Technology Methods can
By the collection of property operational indicator data, arrangement, statistics, the links such as analyzes and show and realize automation, intelligent and visualization, energy
Data statistics in traditional approach is enough eliminated, summarizes, analyze the technical issues of lag.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not
The application can be limited.
Specific embodiment
In order to make those skilled in the art better understand the technical solutions in the application, will implement below to the application
Technical solution in example is clearly and completely described, it is clear that described embodiment is only that the application a part is implemented
Example, rather than whole embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not making creative labor
Every other embodiment obtained under the premise of dynamic, shall fall within the protection scope of the present application.
The embodiment of the present application provides a kind of implementation method of data visualization conversion, comprising:
Disposition data source: the basic information of data source, test data source connectivity, it is ensured that data source and source database are filled in
Information communication.The basic information includes the username and password for driving title, data source URL, target database.The number
Multiple mainstream relevant database Oracle, Mysql, postgresql data source configurations are supported according to source, are met reliable in power supply
Property achievement data data analysis field, user is to the analysis of diversified data source requirement.For example stop in relational database
Telegram in reply achievement data obtains the data of log analysis again, also to add the data of message event.
Configuration logic entity: the ontology of target database, and the object in the logic entity and the source database are configured
It is corresponding to manage table.The logic entity is specifically included in the letter such as some peculiar descriptions, data type, title in target database
Breath.
Configuration attribute: configuring the essential attribute of the ontology of target database, in the essential attribute and the source database
The column of physical table are corresponding.Configuration attribute described herein can also be in the configuration essential attribute and the source database
The polymerization of the column of physical table summarizes corresponding.The essential attribute includes the dimension visualized, classification, numerical value.
Configuration association table: the contingency table includes logic entity contingency table and Attribute Association table, the logic entity association
Table is the mapping relations for establishing physical table in the logic entity and the source database；The Attribute Association table is described in foundation
The column of physical table in essential attribute and the source database, are also possible to physical table in the essential attribute and the source database
Column the mapping relations that summarize of polymerization；
Configuration visualization rendering engine, the table taken various forms to the data in the source database, chart carry out
Show.Data visualization is to select suitable visualization rendering engine to be shown in front end according to data structure, visualizes wash with watercolours
Dye engine has just specified data structure when persistence architecture, and has been realized in very more visualization wash with watercolours
Contaminate template, it is only necessary to select the visualization for supporting the data structure to render template according to data structure.The embodiment of the present application
Selection visualization rendering engine, therefore do not need to carry out data structure identification again when the displaying of front end, it does not need to be counted again
According to conversion operation, does not need to carry out the operations such as visualization rendering engine identification again yet, improve the performance of front end visual presentation.
The rule of allocation models and model: the logic entity for meeting a certain rule is configured using certain rule, and is
Logic entity establishes correlation rule, verifies after correlation rule test passes through, the model is issued into independent task.
The model is the implementation model of data pick-up, conversion, calculating and storage etc., executes reality by scheduler timing
The extraction of existing data, conversion are calculated and its are stored.Data with same rule are divided into a major class, that is, one
Model, such as power failure index analysis model configure annual power failure index by hierarchical relationship from organization down step by step and summarize, the moon
Degree power failure achievement data summarizes, and day degree power failure index, which summarizes, constitutes a power failure achievement data analysis model.
The rule of the model includes the object in global value variable bound rule, the logic entity and the source database
The constraint relationship rule, the classified statistic polymerization analysis rule for managing table, are configured, backstage executes in a manner of freeMark template engine
The rule parsing of model is carried out data analysis conversion at the instruction that computer can be performed by the rule parsing of model.
Collocation task scheduling, so that the task of model publication is periodically executed.The collocation task scheduling includes matching
The processing strategie when frequency, implementation strategy, execution failure of task execution is set, grid operation data is interim, lasting generates
, the task based on model configuration publication needs to be periodically executed data pick-up, conversion, more new data.
Regulation engine parsing, when executing the task schedule, parses the model, by the model
Rule extracts data, is cached, and is grouped in the buffer based on correlation rule, statistics and convergence, is finally converted into mesh
Data are marked, realize the recombination of data multidimensional degree, self-service configuration in real time, target data is stored in target database.Front end is visual
It only needs to carry out simple data base querying when change, finds the data of the condition of satisfaction, it is included in target data
The information such as the rendering mode of data visualization, data, data for meeting condition that the page can inquire automatically according to this and
Its corresponding visualization rendering mode carries out the visual presentation of data, output to browser engine.
The target database provided in the embodiment of the present application is Neo4j chart database, and the map of complex relationship is supported to deposit
It stores up, two most basic concepts are node and side in Neo4j.Node presentation-entity, the side then relationship between presentation-entity.Section
Point and side can have the attribute of oneself.Different entities are got up by a variety of different relationships, form complicated object diagram.
Neo4j provides the function of being searched and traversed in object diagram simultaneously, will be with postponing parsed rule, dimension, visual
Change data structure, visualization rendering mode etc. to be respectively stored on node and the attribute on side, visualization only needs when showing
Adapter is adapted to visualization renderer and the polymorphic of data can be realized shows.
User when wanting to show some report, necessarily should clearly be shown as what pattern (be line chart, histogram,
Or pie chart etc.), and the data structure of these different ways of presentation is often discrepant, it is therefore desirable to it is different data
Ways of presentation provides the data structure met the requirements.
Power supply reliability index and widget can be one-to-many, be also possible to one-one relationship, each achievement data source
At least one corresponding widget.For example, the monthly data for stopping telegram in reply indicator-specific statistics can both be shown as line chart or show dimension
Pie chart.In the embodiment of the present application when configuration visualization rendering engine, the adaptation that is correspondingly configured between achievement data and widget
Relationship, in each data source, the widget supported for it respectively provides the fitting relation between one, to look into notebook data source
The original JSON ask is converted to the data structure that different widgets can be adapted to.
Disposition data source described in above embodiments, the configuration logic entity, the allocation models and model rule, institute
It states collocation task scheduling and regulation engine parsing independently encapsulates.User only needs to configure data source, logic entity, mould
Data visualization conversion can be completed in type, the rule of model, visual means, scheduler task and executing rule engine, improves
Efficiency of research and development and performance of front end.
This application provides a kind of implementation methods of data visualization conversion, comprising: disposition data source, configuration logic are real
Body, configuration attribute, configuration association table, rule, the collocation task scheduling for configuring visualization rendering engine, allocation models and model,
So that the task of the model publication is periodically executed, when executing the task schedule, rule is carried out to the model
Engine parsing is extracted data by the rule of the model, is cached, and be grouped in the buffer based on correlation rule,
Statistics and convergence is finally converted into target data, and visualization rendering engine is rendered according to configuration information, and output to browser is drawn
It holds up.The implementation method of data visualization conversion provided by the present application, which has, to be provided data acquisition, model configuration, data pick-up, appoints
The function of business scheduling, self-service combination displaying, the analysis of various dimensions Dynamic Display, graph visualization is led to according to actual job demand
Cross using Information Technology Methods the collection of reliability operational indicator data, arrangement, statistics, analyze and show etc. links realize from
Dynamicization, intelligent and visualization can eliminate data statistics in traditional approach, summarize, analyze the technical issues of lag.
It should be noted that the terms "include", "comprise" or its any other variant are intended in the present specification
Non-exclusive inclusion, so that the circuit structure, article or the equipment that include a series of elements not only include those elements,
It but also including other elements that are not explicitly listed, or further include solid by this circuit structure, article or equipment
Some elements.In the absence of more restrictions, the element for thering is sentence "including a ..." to limit, it is not excluded that including
There is also other identical elements in the circuit structure of the element, article or equipment.
Those skilled in the art will readily occur to its of the application after considering specification and practicing the disclosure invented here
His embodiment.This application is intended to cover any variations, uses, or adaptations of the invention, these modifications, purposes or
Person's adaptive change follows the general principle of the application and including the undocumented common knowledge in the art of the application
Or conventional techniques.The description and examples are only to be considered as illustrative, and the true scope and spirit of the application are wanted by right
The content asked is pointed out.
Above-described the application embodiment does not constitute the restriction to the application protection scope.
Claims (8)
1. a kind of implementation method of data visualization conversion characterized by comprising
Disposition data source: the basic information of data source, test data source connectivity, it is ensured that the letter of data source and source database are filled in
Breath connection；
Configuration logic entity: the ontology of target database, and the physical table in the logic entity and the source database are configured
It is corresponding；
Configuration attribute: the essential attribute of the ontology of target database, physics in the essential attribute and the source database are configured
The column of table are corresponding；
Configuration association table: the contingency table includes logic entity contingency table and Attribute Association table, and the logic entity contingency table is
Establish the mapping relations of physical table in the logic entity and the source database；The Attribute Association table is described basic to establish
Attribute and column of physical table in the source database, column polymerize the mapping relations summarized；
Configuration visualization rendering engine, the table taken various forms to the data in the source database, chart show；
The rule of allocation models and model: the logic entity for meeting a certain rule is configured using certain rule, and is logic
Entity establishes correlation rule, verifies after correlation rule test passes through, the model is issued into independent task；
Collocation task scheduling, so that the task of model publication is periodically executed；
Regulation engine parsing, when executing the task schedule, parses the model, by the rule of the model
Data are extracted, are cached, and are grouped in the buffer based on correlation rule, statistics and convergence, number of targets is finally converted into
According to being rendered according to configuration information, output is to browser engine.
2. the implementation method of data visualization conversion according to claim 1, which is characterized in that the disposition data source,
The configuration logic entity, the allocation models and model rule, the collocation task scheduling and regulation engine parsing point
Other individual packages.
3. the implementation method of data visualization conversion according to claim 1, which is characterized in that the data source is supported more
A mainstream relevant database Oracle, Mysql, postgresql data source configuration.
4. the implementation method of data visualization according to claim 1 conversion, which is characterized in that the basic information includes
Drive the username and password of title, data source URL, target database.
5. the implementation method of data visualization according to claim 1 conversion, which is characterized in that the essential attribute includes
The dimension of visual presentation, classification, numerical value.
6. the implementation method of data visualization conversion according to claim 1, which is characterized in that the collocation task scheduling
Including configuration task execute frequency, implementation strategy, execute failure when processing strategie.
7. the implementation method of data visualization conversion according to claim 1, which is characterized in that the rule packet of the model
Include global value variable bound rule, the logic entity and the constraint relationship rule of the physical table in the source database, grouping
Statistics and convergence analysis rule.
8. the implementation method of data visualization according to claim 1 conversion, which is characterized in that the target database is
Neo4j chart database.
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