CN111539162B - Web multi-target pneumatic data analysis system - Google Patents
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Abstract
The invention discloses a Web multi-target pneumatic data analysis system, which relates to the technical field of pneumatic data analysis, and can ensure that a single Web application developed can take account of multiple analysis methods of multiple types of pneumatic data by dynamically generating an analysis field component group, different interfaces or applications do not need to be developed respectively aiming at different pneumatic data types, and different interfaces or applications do not need to be developed respectively aiming at different analysis targets; the method comprises the steps that pneumatic data sources are stored in a classified mode according to objects and pneumatic data types, target pneumatic data are flattened, and deep related data of the pneumatic data can be analyzed together; by dynamically generating an analysis field component group and a filtering field component group, data analysis with inconsistent pneumatic data dimensions can be considered; the EChats are used as drawing middleware in a combined mode, the real-time interaction characteristics of the interfaces of the EChats can be used for observing analysis results, and multiple types of multi-target analysis graphs can be generated simultaneously for the analysis of the same set of target pneumatic data.
Description
Technical Field
The invention relates to the technical field of pneumatic data analysis, in particular to a Web multi-target pneumatic data analysis system.
Background
The method can generate more aerodynamic data in the aerodynamic design process and the aerodynamic test process of aerospace and rapid vehicles, and the aerodynamic performance of a design target, including performance data in the aspects of aerodynamic force, aerodynamic heat, aerodynamic pressure and the like, can be estimated through analysis of the aerodynamic data.
At present, scientific researchers generally adopt Matlab programming tools to perform pneumatic analysis on a single machine, and two-dimensional coordinate series curves or other curves under pneumatic data dimension selection and condition filtering are realized. For the purpose of improving network sharing, cooperation and reuse of pneumatic data, networking, centralized management and client-side Web of pneumatic data are important trends.
At present, the analysis of the pneumatic data on the Web needs to download the data first, analyze the data in a Matlab mode, and upload the data to a Web server for sharing and cooperation, and the operation is very inconvenient. The traditional online analysis Web tool is difficult to directly use, and pneumatic data analysis is professional, so that the pneumatic data analysis not only comprises single-component analysis of pneumatic data, but also comprises pneumatic error analysis, pneumatic statistical analysis and pneumatic uncertainty analysis, and a professional algorithm is required for processing. In addition, for general analysis visualization, the analysis mainly includes selection of analysis items (dimensions) and data filtering before analysis for some dimensions (data fields). The pneumatic data is also the same, the pneumatic data field model of the pneumatic data has specificity, the pneumatic data comprises various pneumatic data (referred to as pneumatic data types) such as pneumatic force, pneumatic heat, pneumatic pressure, hinge moment and the like, and the analysis dimensions of the pneumatic data to be analyzed are different according to different pneumatic data types; meanwhile, in the process of acquiring or reprocessing the aerodynamic data, the aerodynamic data of the same data type can have different data items (some data items can be obtained by conversion according to other data items, for example, a drag coefficient Cx, a lift coefficient Cy and a force measurement coefficient Cz in the aerodynamic force can obtain a roll moment coefficient Cmx, a yaw moment coefficient Cmy and a pitch moment coefficient Cmz), and the data items sometimes need to be analysis items and sometimes need to be data filter items, namely need to be taken as a taken option of a UI pull-down assembly for Web application, so that a user can conveniently select and analyze the data items.
In the analysis of pneumatic data, there are generally multiple viewing angles of the same data, such as by making multiple views, viewing the data from various angles, and grasping the data. In general analysis, the type of generated analysis chart needs to be reselected for different observation sides, which is complicated in operation.
Disclosure of Invention
The invention aims to provide a Web multi-target pneumatic data analysis system which can alleviate the problems.
In order to alleviate the above problems, the technical scheme adopted by the invention is as follows:
a Web multi-target pneumatic data analysis system comprises:
a front end portion and a rear end portion;
the front end part comprises a DOM rendering layer and a data processing layer;
the DOM rendering layer comprises: the data set selection front window is used for selecting an analysis method, an analysis object and a pneumatic data type; the analysis main form comprises an analysis field component group, a filtration field component group and an ECharts drawing area multi-target component group, wherein the analysis field component group and the filtration field component group are used for selecting multi-target analysis conditions, and the ECharts drawing area multi-target component group is used for selecting and drawing a multi-target analysis graph;
the data processing layer is used for dynamically generating an analysis field component group, a filtration field component group and an ECharts drawing area multi-target component group, obtaining subject data according to the flattened data, the selected analysis method and the multi-target analysis condition, drawing a multi-target analysis chart according to the flattened data, the subject data and the selected analysis method, and generating multiple types of multi-target analysis charts simultaneously for the analysis of the same set of target pneumatic data;
the rear end portion includes: the data persistence layer is used for storing the pneumatic data sources according to the object and the pneumatic data type in a classified mode; and the service layer is used for acquiring a plurality of target pneumatic data from a pneumatic data source according to the selected analysis object and the type of the pneumatic data, and carrying out flattening processing on the target pneumatic data to obtain double-layer flattened data comprising simple data and complex data.
The technical effect of the technical scheme is as follows: by dynamically generating the analysis field component group, a single developed Web application can take into account multiple analysis methods of multiple types of pneumatic data, different interfaces or applications do not need to be developed respectively aiming at different pneumatic data types, and different interfaces or applications do not need to be developed respectively aiming at different analysis targets; the method comprises the steps that pneumatic data sources are stored in a classified mode according to objects and pneumatic data types, target pneumatic data are flattened, and deep related data of the pneumatic data can be analyzed together; by dynamically generating an analysis field component group and a filtering field component group, data analysis with inconsistent pneumatic data dimensions can be considered; the EChats are used as the drawing middleware in a combined mode, the real-time interaction characteristics of the interfaces of the EChats can be used for observing analysis results, and for the analysis of the same group of target pneumatic data, multiple types of multi-target analysis graphs can be generated at the same time for different observation sides, so that the operation is convenient.
Further, the analysis method includes single component analysis among multi-component analysis, error analysis, statistical analysis, binary analysis, multivariate analysis, uncertainty analysis, and pneumatic analysis;
the analysis object comprises an analyzer corresponding to the pneumatic data and a model/theoretical appearance of a vehicle corresponding to the pneumatic data;
the aerodynamic data types include aerodynamic force, aerodynamic heat, and aerodynamic pressure.
The technical effect of the technical scheme is as follows: the scheme can meet the requirement that an analysis method can dynamically generate an interface component group aiming at common dimensionality and specific dimensionality and generate subsequent theme data to generate an analysis chart.
Further, the data set selects a front window and a control thereof, and is compiled by a static method or generated by a dynamic method.
Furthermore, the service layer is provided with a large-class data query module and a data flattening module, the large-class data query module is used for querying data in the pneumatic data source according to the analysis object and the type of the pneumatic data, and the data flattening module is used for flattening the target pneumatic data.
The technical effect of the technical scheme is as follows: the flattened field object has only two layers including a numerical value member and a simple object member, so that the JSON data converted into the JSON data has only two layers, the data transmitted to the front end is greatly simplified, the data volume is also reduced sharply, and the front end only analyzes the numerical value field and the embedded simple object when extracting the analysis field, and does not need circulating recursion deep processing.
Furthermore, when the data flattening module processes data, the data flattening module is a JPA mode based on Java EE, and the data flattening module directly uses a Getter function to flatten the data.
The technical effect of the technical scheme is as follows: the Getter function only acquires an analyzed id field value and a description field value used for visual viewing by a user aiming at a deep object embedded in pneumatic data and uniformly puts the values into a numerical layer, so that the Getter function has the advantages that the Getter function is possessed by a field object (including a deep object), a deep object data query is not required to be specially written, the object id field and the object description field of each deep object can be easily acquired by recursion to extract an object field and a field value more clearly, and the Getter function of a top-level field object is formed.
Further, the data persistence layer stores the pneumatic data source by adopting a relational database table.
Further, the multi-target analysis condition comprises an analysis field, a grouping field, a comparison value and a filter value; the analysis field component group is used for selecting an analysis field, a grouping field, a comparison field and a comparison value and generating first subject data; the filtering field component group is used for selecting a filtering value and generating second theme data. .
The technical effect of the technical scheme is as follows: the method can generate required component groups according to different analysis targets or multiple targets at the same time, analyze field component groups for selection of analysis fields, and then form projection data under the fields, namely first theme data; the filtered field component group is automatically generated after subtracting the analysis field from all fields of the data under flattening, and provides second subject data formed after filtering by the user; the requirements of dynamically generating the analysis field group and the filtering field group can be met, the requirement of simultaneously generating the second theme data is met, and the generation of subsequent analysis graphs is facilitated.
Further, the analysis field component group is dynamically generated by an analysis field component assembly engine of the data processing layer according to the flattened data and the selected analysis method.
The technical effect of the technical scheme is as follows: the method comprises the steps of generating required component groups according to different analysis targets or multiple targets at the same time, analyzing field component groups for selection of analysis fields, and forming projection data under the fields, namely first theme data.
Further, the filtered field component group is dynamically generated by a filtered field component assembly engine of the data processing layer according to the flattened data.
The technical effect of the technical scheme is as follows: the filtering field component group is automatically generated after subtracting the analysis field from all fields of the data under the flattening, meets the requirements of dynamically generating the filtering field and the filtering value list, and meets the requirements of forming second subject data after filtering by a user.
Still further, the multi-target analysis graph includes an original line graph, an absolute error line graph, and a relative error line graph.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a framework diagram of a Web-enabled multi-target pneumatic data analysis system in an embodiment;
FIG. 2 is a schematic diagram of the pneumatic data of the multi-level association structure stored in the backend database in the embodiment;
FIG. 3 is a diagram illustrating a method for dynamically generating an analysis field component group according to an embodiment;
FIG. 4 is a diagram illustrating a dynamic generation method of a filtered field component group according to an embodiment;
FIG. 5 is a schematic diagram of an automatic matching method of the present system when one or more analysis charts are provided for different pneumatic analysis methods in an embodiment;
FIG. 6 is a schematic diagram of construction of data packets and options in the multi-target pneumatic analysis method in the embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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 invention.
Examples
Referring to fig. 1, the present embodiment provides a Web-based multi-target pneumatic data analysis system, which includes a front-end portion and a back-end portion;
the front end part comprises a DOM rendering layer and a data processing layer;
the DOM rendering layer includes: the data set selection front window is used for selecting an analysis method, an analysis object and a pneumatic data type; the analysis main window comprises an analysis field component group, a filtration field component group and an ECharts drawing area multi-target component group, wherein the analysis field component group and the filtration field component group are used for selecting multi-target analysis conditions, and the ECharts drawing area multi-target component group is used for selecting and drawing a multi-target analysis graph;
the data processing layer is used for dynamically generating an analysis field component group, a filter field component group and an ECharts drawing area multi-target component group, obtaining subject data according to the flattened data, the selected analysis method and the multi-target analysis conditions, drawing a multi-target analysis graph according to the flattened data, the subject data and the selected analysis method, and generating multiple types of multi-target analysis graphs simultaneously for the analysis of the same set of target pneumatic data, wherein the multi-target analysis graph comprises an original line graph, an absolute error line graph and a relative error line graph.
The rear end portion includes: the data persistence layer is used for storing the pneumatic data sources according to the object and the pneumatic data type in a classified mode; and the service layer is used for acquiring a plurality of target pneumatic data from a pneumatic data source according to the selected analysis object and the type of the pneumatic data, and carrying out flattening processing on the target pneumatic data to obtain double-layer flattened data comprising simple data and complex data.
In this embodiment, the data set selection pre-form includes analysis method selection, analysis object selection, and pneumatic data type selection; the analysis method comprises single component analysis in multi-component analysis, error analysis, statistical analysis, binary analysis, multivariate analysis, uncertainty analysis and pneumatic analysis; the analysis object comprises an analyzer corresponding to the pneumatic data and a model/theoretical appearance of the vehicle corresponding to the pneumatic data, and the analysis object information and the pneumatic data have relevance; the aerodynamic data types include aerodynamic force, aerodynamic heat, and aerodynamic pressure.
In this embodiment, the control of the data set selection front window is a drop-down list, and the post-selection value of the drop-down list box is given by querying the corresponding dimension of the rear end, so that the data set selection front window and the control thereof are compiled by a static method or generated by a dynamic method.
In this embodiment, the service layer is provided with a large data query module and a data flattening module.
After the user interaction selects an analysis method, an analysis object and a pneumatic data type on the preposed data set selection window, a request (such as an http protocol) is submitted to the back end to obtain target pneumatic data, and a large-scale data query module at the back end queries data in a pneumatic data source according to the analysis object and the pneumatic data type.
The back-end then needs to return data suitable for front-end analysis.
The back end contains various types of pneumatic data, and in this embodiment, the data persistence layer stores pneumatic data sources in a relational database table, as shown in fig. 2.
The data flattening module is used for flattening the target pneumatic data, is a JPA mode based on Java EE, and directly uses a Getter function to flatten the data. The flattened data can be changed into a data format required by the front end by adopting message mapping, the embodiment uses a JSON mode for expression, the depth of the expressed flattened data is two layers, including simple data and complex data, wherein the complex data can be two layers of data. Simple data such as Cx in the data is a member of a piece of data, and complex data such as Fas is an embedded object which is reduced to only two attributes of id and name (multiple data can be expanded according to needs).
In the present embodiment, the multi-target analysis condition includes an analysis field, a grouping field, a comparison value, and a filter value; the analysis field component group is used for selecting an analysis field, a grouping field, a comparison field and a comparison value and generating first theme data; the field component group is used for selecting a filtering value and generating second theme data.
The first subject data is smaller dimension data under analysis fields, grouping fields and comparison field values corresponding to the flattened data, namely projection data; the second topic data is data under the control of the filtering field value under the first topic data.
In this embodiment, the analysis field component group is dynamically generated by an analysis field component assembly engine of the data processing layer according to the flattened data and the selected analysis method, and the process includes analysis field dimension extraction.
As shown in fig. 3. The number and content of the analysis field components are closely related to the analysis method, for example, the unit analysis needs to include three components, namely, an x-axis single-selection drop-down list component, a y-axis drop-down list component and a grouping field drop-down list component, the multi-component analysis includes a multi-selection comparison field and a grouping field, and the error analysis includes a grouping value single-selection drop-down list component in addition to the x-axis single-selection drop-down list component, the y-axis drop-down list component and the grouping field.
Expression phiaThe pneumatic analysis method determines the component group phiaThe number and type of middle components, and the component group phiaConstructing a mapping II of the analysis method A on the DOM;
expression of phi'aF (ad) indicates that the flattened pneumatic data and the analysis method together dynamically determine the centralized reflection of the contents of the drop-down list of components in the analysis component group.
The parse field component cluster with the specific drop down lists (field list and value list) is determined by both the parsing method and the flattened data to form the specific content on the DOM.
In this embodiment, for different analysis methods, the analysis field assembly engine generates different component groups on the DOM, and for different fields provided by different flattened data, extracts the flattened data field first, and then matches different drop-down list fields in corresponding components in a redundant analysis field matching manner. In order to prevent field conflict and selection of specific drop-down list values, a selection monitor can be registered in the component, and the monitor can check whether the values selected by the corresponding controls conflict or not according to the conflict field names registered on the component by processing the conflict through the monitor.
Similarly, the same operation mechanism is adopted for the change of the grouping reference value list due to the change of the drop-down list, such as the change of the grouping field, so as to ensure the real-time reaction of the interface. As mentioned above, the data corresponding to each field in the flattened data may be simple data or complex data, some need to change the machine-recognized data such as Cx into the Chinese resistance coefficient that is customary for human eye observation, some need, and some simple values need to satisfy the Option of the drop-down list box such as the select element of HTML (as shown in the following code blocks). When the drop-down list displays pneumatic data values, the values of general machine parts are mv (machine value), and the display part is hv (human vision) for human eye viewing for friendliness, and the expression is
Is to process the flattened data into a translation relationship of the component group and the drop-down component candidate list item. The metadata (v) function is used for judging whether the data is pneumatic data or the field name of the pneumatic data, the field name is the field name when the data is true, the field name such as Cx at the time needs to be subjected to table lookup by using a function theta (v), and the Chinese name (the resistance coefficient at this time) corresponding to the Cx is obtained and displayed; when the specific numerical value or data corresponding to the field is not the field, two cases are classified, the first case is a basic data type which is judged by typeof (v), but is a basic data type when primary, mv and hv are both filled by v values, and when object is object, id and name of the object are filled, so that man-machine data separation and organic combination are achieved.
In the embodiment, the filtering field component group is dynamically generated by a filtering field component assembly engine of the data processing layer according to the flattened data, and the process comprises filtering field dimension extraction and arrangement of discrete values of a filtering range. As shown in fig. 4, in the pneumatic data analysis process, the dynamic generation process of the filtering field component group in different pneumatic analysis main forms is similar to the analysis field component group, and mainly includes the number of dynamic filtering components and the list of screening candidates corresponding to each pneumatic field. Because the filtering items of the pneumatic data analysis are basically uniform, and only a plurality of problems exist, the filtering fields and the pneumatic analysis method are not hooked, and only are hooked with the candidate fields of the pneumatic data.
In this embodiment, after the analysis main form has the field component group and the field component group, the analysis main form creates an ECharts drawing area parent DOM, and introduces an ECharts js component in an HTML page where the DOM is located. The ECharts plot area supports multiple plots, multiple plots under the same pneumatic analysis can be plotted, and different plots can be provided with different plot types. And analyzing the main form and automatically generating components required by analysis post-processing, such as a save button, a drawing description text input box and the like.
In this embodiment, the user can select the analysis dimension and the filter field value in the analysis main form. The analysis field selection to determine the analysis coordinate type and coordinate axis of the analysis graph, and the filter value selection to select a specific filter word value for each corresponding filter field. And entering a specific multi-target analysis chart generation process after the analysis dimension and the filtering value are determined.
Firstly, screening in flattened data according to a screening field of a user, and then extracting each analysis field value according to an analysis field to form structured data; on the basis of structured data, according to a currently selected analysis method, obtaining an analysis algorithm under the method to further process the data, wherein the analysis algorithm is written by js under the conditions that a single component and other general analyses do not need a specific analysis algorithm, but under the specific analysis conditions of error analysis, statistical analysis, binary association analysis and the like, the analysis algorithm needs to be reused for further processing; then, sorting a certain dimension under the condition that data field sorting is needed; and finally, assembling the ECharts drawing data Option by combining the analysis field, the series illustration content and the coordinate axis content which form the ECharts. And finally calling an API (application programming interface) of the ECharts to draw an analysis graph. In this process, there may be multiple graphs, and multiple graphs may have different graph types, depending on the pneumatic analysis method.
FIG. 5 shows an automatic matching method of a system when different analysis methods have one or more analysis graphs, wherein an Options array template of each method is preset and constructed in each method, and each option has a graph type and a place of a title, a series and series data to be filled. The analysis algorithm and the type and the number of the analysis diagrams of a specific pneumatic analysis method are determined by a predefined pneumatic analysis template, on the pneumatic data analysis template, a pre-customized algorithm function is in the template space of each pneumatic analysis method, and simultaneously, Options array data for analyzing the type and the number of the EChats diagrams to be formed exist, and the data information in each array member Option corresponds to the type of the diagram. The selection of the corresponding pneumatic analysis method before analysis allows the analysis subject program to select the corresponding Options and specific analysis algorithms for data reprocessing and Option data preparation. Then the Option data can be passed to ECharts middleware for analysis mapping after the Option data is subsequently completed.
As shown in fig. 6, when drawing data is submitted for final drawing, each Option in a plurality of Options is iterated, each single Option preparation data comprises a query template for analyzing the specific type of a diagram, namely a broken line diagram or a radar diagram, data preparation is carried out according to the type of the diagram, the title of the diagram and the coordinate axis related information of the diagram are filled in the Option, then preparation of the diagram data is carried out according to the analysis field of an analysis main interface, and finally a specific Option data object is formed; the specific Option object can be directly fed to carry out drawing on the ECharts, so that a DOM parent element in a drawing area is positioned firstly, then a DOM parent element added with a child drawing DOM element such as a div element is created, a chart caption of a sub-chart can be added if necessary, finally the API of the ECharts is called to carry out drawing of an analysis chart, and the whole analysis work is completed after all iterations of Options. The post-processing after the analysis includes the post-processing operations such as the ECharts interaction of the analysis graph, the comment editing of the analysis subgraph, the storage of the analysis structure, and the like, which belong to the prior art and are not described herein again.
The Web multi-target pneumatic data analysis system has the following advantages:
1) the dynamic construction of the dynamic analysis parameter component group can enable the developed single Web application to be compatible with single-component, multi-component, error analysis, statistical analysis, binary analysis and multivariate analysis for processing aerodynamic force, aerodynamic heat and aerodynamic pressure data, different interfaces or applications do not need to be developed respectively aiming at different aerodynamic data types, and different interfaces or applications do not need to be developed respectively aiming at different analysis targets;
2) classifying and storing the pneumatic data by using a relational data structure corresponding to an object model, flattening the provided pneumatic data to enable the depth-related data of the pneumatic data to be analyzed together, wherein for example, the model, the train number, the wind tunnel and numerical calculation software can be used as a grouping dimension and a filtering dimension in pneumatic data analysis;
3) the dynamic analysis parameter component group construction method and the dynamic filtering component group construction method can give consideration to data analysis of inconsistent pneumatic data dimensionality, for example, a certain pneumatic force has physical rudder information, and a certain pneumatic force has numerical rudder information, and the analysis dimensionality and the filtering dimensionality change the component content or the component number of an analysis interface according to the fed pneumatic data condition;
4) the pneumatic analysis method template technology (the template comprises a specific analysis algorithm and an ECharts diagram Options template data array) provides a multi-target analysis solution, so that a single analysis can generate a plurality of analysis diagrams, for example, an error analysis can simultaneously generate an original line diagram, an absolute error line diagram and a relative error line diagram;
5) the combination of ECharts as the drawing middleware can also use the real-time interactive characteristics of ECharts to observe the analysis result, for example, only some series of data (such as Mach 3 curve) is convenient for the user to observe.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A Web multi-target pneumatic data analysis system is characterized by comprising:
a front end portion and a rear end portion;
the front end part comprises a DOM rendering layer and a data processing layer;
the DOM rendering layer comprises: the data set selection front window is used for selecting an analysis method, an analysis object and a pneumatic data type; the analysis main form comprises an analysis field component group, a filtration field component group and an ECharts drawing area multi-target component group, wherein the analysis field component group and the filtration field component group are used for selecting multi-target analysis conditions, and the ECharts drawing area multi-target component group is used for selecting and drawing a multi-target analysis graph;
the analysis method comprises single component analysis in multi-component analysis, error analysis, statistical analysis, binary analysis, multivariate analysis, uncertainty analysis and pneumatic analysis;
the analysis object comprises an analyzer corresponding to the pneumatic data and a model/theoretical appearance of a vehicle corresponding to the pneumatic data;
the pneumatic data types comprise pneumatic force, pneumatic heat and pneumatic pressure;
the multi-target analysis condition comprises an analysis field, a grouping field, a comparison value and a filtering value; the analysis field component group is used for selecting an analysis field, a grouping field, a comparison field and a comparison value and generating first subject data; the filtering field component group is used for selecting a filtering value and generating second theme data;
the first subject data is projection data under analysis fields, grouping fields and comparison field values corresponding to the flattened data; the second subject data is data under the control of the screening field value under the first subject data;
the data processing layer is used for dynamically generating an analysis field component group, a filtration field component group and an ECharts drawing area multi-target component group, obtaining subject data according to the flattened data, the selected analysis method and the multi-target analysis condition, drawing a multi-target analysis graph according to the subject data and the selected analysis method, and generating multiple types of multi-target analysis graphs simultaneously for the analysis of the same group of target pneumatic data;
the rear end portion includes: the data persistence layer is used for storing the pneumatic data sources according to the object and the pneumatic data type in a classified mode; and the service layer is used for acquiring a plurality of target pneumatic data from a pneumatic data source according to the selected analysis object and the type of the pneumatic data, and carrying out flattening processing on the target pneumatic data to obtain double-layer flattened data comprising simple data and complex data.
2. The Web multi-target pneumatic data analysis system according to claim 1, wherein the data set selects a front window and a control thereof, and is generated by a static method or a dynamic method.
3. The Web multi-target pneumatic data analysis system according to claim 1, wherein the service layer is provided with a large-class data query module and a data flattening module, the large-class data query module is used for querying data in a pneumatic data source according to an analysis object and a pneumatic data type, and the data flattening module is used for flattening target pneumatic data.
4. The Web multi-target pneumatic data analysis system according to claim 3, wherein the data flattening module is a JPA mode based on Java EE when processing data, and directly uses a Getter function to flatten the data.
5. The Web multi-objective pneumatic data analysis system of claim 1, wherein the data persistence layer stores pneumatic data sources using relational database tables.
6. The Web-enabled multi-objective pneumatic data analysis system of claim 1, wherein the analysis field component groups are dynamically generated by an analysis field component assembly engine of the data processing layer based on the flattened data and the selected analysis method.
7. The Web multi-objective pneumatic data analysis system of claim 1, wherein the filter field component groups are dynamically generated by a filter field component assembly engine of the data processing layer from flattened data.
8. The Web-enabled multi-target pneumatic data analysis system of claim 1, wherein the multi-target analysis graph comprises a raw line graph, an absolute error line graph, and a relative error line graph.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010012170A1 (en) * | 2008-07-28 | 2010-02-04 | 成都市华为赛门铁克科技有限公司 | Database security monitoring method, device and system |
| CN104991777A (en) * | 2015-07-14 | 2015-10-21 | 普元信息技术股份有限公司 | System and method for implementing view development of Web application automatic testing |
| CN106487594A (en) * | 2016-10-31 | 2017-03-08 | 中国人民解放军91655部队 | Network traffics collection based on micro services assembly and analysis system |
| CN107909238A (en) * | 2017-10-09 | 2018-04-13 | 中国电子科技集团公司第二十八研究所 | A kind of city collaboration processing and interlinked command system and command hall |
| CN108090240A (en) * | 2016-11-23 | 2018-05-29 | 北京空间技术研制试验中心 | The processing of pneumatic gauging test flight data and appraisal procedure |
| CN108108563A (en) * | 2017-12-28 | 2018-06-01 | 河南省水利勘测设计研究有限公司 | Cross-platform hydrodynamics modeling method based on BIM and GIS technology |
| CN108804513A (en) * | 2018-04-24 | 2018-11-13 | 华东计算技术研究所（中国电子科技集团公司第三十二研究所） | Automatic visual analysis method for big data platform |
| CN108961848A (en) * | 2018-07-06 | 2018-12-07 | 深圳点猫科技有限公司 | A kind of method and electronic equipment of the generation DOM element for intelligent tutoring |
| CN109284394A (en) * | 2018-09-12 | 2019-01-29 | 青岛大学 | A method of Company Knowledge map is constructed from multi-source data integration visual angle |
| CN110799997A (en) * | 2017-10-27 | 2020-02-14 | 埃森哲环球解决方案有限公司 | Industrial data service, data modeling, and data application platform |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102289540B (en) * | 2011-07-01 | 2013-05-15 | 中国航空工业集团公司科学技术委员会 | Workflow-driven genetic algorithm aviation optimization system orienting to service heterogeneous grid |
| WO2017084106A1 (en) * | 2015-11-20 | 2017-05-26 | 田川 | System and method for numerical simulation of aircraft flow field |
| CN106951225B (en) * | 2017-02-15 | 2020-06-12 | 中国银行保险信息技术管理有限公司 | Data processing method and device based on service configuration template |
| CN108710652A (en) * | 2018-05-09 | 2018-10-26 | 长城计算机软件与系统有限公司 | A kind of data analysing method and system, storage medium based on statistics |
-
2020
- 2020-04-26 CN CN202010339337.0A patent/CN111539162B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010012170A1 (en) * | 2008-07-28 | 2010-02-04 | 成都市华为赛门铁克科技有限公司 | Database security monitoring method, device and system |
| CN104991777A (en) * | 2015-07-14 | 2015-10-21 | 普元信息技术股份有限公司 | System and method for implementing view development of Web application automatic testing |
| CN106487594A (en) * | 2016-10-31 | 2017-03-08 | 中国人民解放军91655部队 | Network traffics collection based on micro services assembly and analysis system |
| CN108090240A (en) * | 2016-11-23 | 2018-05-29 | 北京空间技术研制试验中心 | The processing of pneumatic gauging test flight data and appraisal procedure |
| CN107909238A (en) * | 2017-10-09 | 2018-04-13 | 中国电子科技集团公司第二十八研究所 | A kind of city collaboration processing and interlinked command system and command hall |
| CN110799997A (en) * | 2017-10-27 | 2020-02-14 | 埃森哲环球解决方案有限公司 | Industrial data service, data modeling, and data application platform |
| CN108108563A (en) * | 2017-12-28 | 2018-06-01 | 河南省水利勘测设计研究有限公司 | Cross-platform hydrodynamics modeling method based on BIM and GIS technology |
| CN108804513A (en) * | 2018-04-24 | 2018-11-13 | 华东计算技术研究所（中国电子科技集团公司第三十二研究所） | Automatic visual analysis method for big data platform |
| CN108961848A (en) * | 2018-07-06 | 2018-12-07 | 深圳点猫科技有限公司 | A kind of method and electronic equipment of the generation DOM element for intelligent tutoring |
| CN109284394A (en) * | 2018-09-12 | 2019-01-29 | 青岛大学 | A method of Company Knowledge map is constructed from multi-source data integration visual angle |
Non-Patent Citations (3)
| Title |
|---|
| 一种基于数据驱动模型的VirtualDOM树的构建方法;校丽丽 等;《西南科技大学学报》;20180330;第33卷(第1期);第68-74页 * |
| 基于ECharts的科技统计数据可视化设计与实现;徐欣威;《天津科技》;20190331;第46卷(第3期);第66-70页 * |
| 基于Three.js的飞行仿真系统设计;冯姣;《计算机测量与控制》;20200228(第2期);第216-219页 * |
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