CN115033760A - Big data software visualization method, system and storage medium - Google Patents
Big data software visualization method, system and storage medium Download PDFInfo
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Abstract
The invention discloses a big data software visualization method, a system and a storage medium, wherein the method is applied to a big data software visualization system and comprises the following steps: acquiring data parameter information of an object to be processed on multiple parameter dimensions, and integrating the data parameter information to obtain a big data parameter set; acquiring interface parameter information of the object to be processed on each parameter dimension, and processing each interface parameter information to obtain an interface parameter set; performing optimization analysis on the interface parameter set, and determining an interest network set from the interface parameter set; acquiring a selectable sequence space according to the big data parameter set and the interface parameter set; and carrying out optimized arrangement on the interest net set according to the data parameters in the big data parameter set and the selectable sequence space to generate a big data visualization parameter set.
Description
Technical Field
The invention relates to the field of big data visualization, in particular to a big data software visualization method, a big data software visualization system and a storage medium.
Background
Before the concept of big data visualization did not appear, people have been widely applied to data visualization, and big to population data and small to student performance statistics can be displayed and explored through visualization, rules therein can be visualized by various methods, information can be visualized by various methods at present, data visualization is scientific and technical research on data visual expression forms, wherein the data visual expression forms are defined as information extracted in a certain summary form, including various attributes and variables of corresponding information units, which is an evolving concept with continuously expanded boundaries, mainly referring to technically more advanced technical methods which allow the use of graphics, image processing, computer vision and user interfaces, the data is visually interpreted through expression, modeling and display of stereo, surface, attribute and animation, and the technical method covered by the data visualization is much wider than the special technical method such as stereo modeling.
In the big data era, when a plurality of big data visualization services process data, the data visualization services firstly determine what is expressed after the data visualization, and then provide guidance data required by work for clients through the data after the data visualization services are determined, so that the clients can correctly grasp key points or know industry dynamics and the like, and the clients can automatically select and adjust the content of the data visualization services through various graphs and data.
The current big data service website displays all the data acquired by the big data service website as far as possible according to the general direction of the acquired data, but the data required by different departments are different, and a part of the data is useless, so that all the departments can be used in daily work only by performing secondary processing after acquiring the data by the big data service website, particularly in the work related to municipal environmental protection, the current urban data acquisition usually acquires and displays all parameters such as traffic, pollution, accidents, capacity and the like in a correlated manner, and for the environmental protection department, a part of useless parameters related to the accident capacity and the like occupy the display space, and at the moment, secondary processing is needed, and for the traffic management department, the pollution and the discharge section are useless data.
Disclosure of Invention
The application provides a big data software visualization method, a big data software visualization system and a storage medium, which are used for solving the technical problem that big data display is not intelligent enough in the prior art.
In view of the foregoing, the present application provides a big data software visualization method, system and storage medium.
In a first aspect of the present application, a big data software visualization method is provided, where the method is applied to a big data software visualization system, and the method includes: acquiring data parameter information of an object to be processed on multiple parameter dimensions, and integrating the data parameter information to obtain a big data parameter set; acquiring interface parameter information of the object to be processed on each parameter dimension, and processing each interface parameter information to obtain an interface parameter set; performing optimization analysis on the interface parameter set, and determining an interest network set from the interface parameter set; acquiring a selectable sequence space according to the big data parameter set and the interface parameter set; optimizing and arranging the interest net set according to the data parameters in the big data parameter set and the selectable sequence space to generate a big data visualization parameter set; and taking the big data visualization parameter set as a parameter calling index, calling a specific numerical value from the big data parameter set, and generating a big data visualization page for displaying in the visualization system.
In a second aspect of the present application, there is provided a big data software visualization apparatus, wherein the system includes: the device comprises a first obtaining unit, a second obtaining unit and a processing unit, wherein the first obtaining unit is used for obtaining data parameter information of an object to be processed on multiple parameter dimensions and integrating the data parameter information to obtain a big data parameter set; a second obtaining unit, configured to obtain interface parameter information of the object to be processed in each of the multiple parameter dimensions, and process each of the interface parameter information to obtain an interface parameter set; the first processing unit is used for carrying out optimization analysis on the interface parameter set and determining an interest network set from the interface parameter set; the second processing unit is used for obtaining a selectable sequence space according to the big data parameter set and the interface parameter set; the third processing unit is used for carrying out optimized arrangement on the interest net set according to the data parameters in the big data parameter set and the selectable sequence space to generate a big data visualization parameter set; and the fourth processing unit is used for taking the big data visualization parameter set as a parameter calling index, calling a materialization numerical value from the big data parameter set, and generating a big data visualization page for displaying in the visualization system.
In a third aspect of the present application, a big data software visualization system is provided, including: a processor coupled to a memory for storing a program that, when executed by the processor, causes a system to perform the functions of the method of the first aspect.
In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the functions of the method according to the first aspect.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps of obtaining data parameter information of an object to be processed on multiple parameter dimensions, and integrating the data parameter information to obtain a big data parameter set; acquiring interface parameter information of the object to be processed on each parameter dimension, and processing each interface parameter information to obtain an interface parameter set; performing optimization analysis on the interface parameter set, and determining an interest network set from the interface parameter set; acquiring a selectable sequence space according to the big data parameter set and the interface parameter set; optimizing and arranging the interest net set according to the data parameters in the big data parameter set and the selectable sequence space to generate a big data visualization parameter set; and taking the big data visualization parameter set as a parameter calling index, calling a specific numerical value from the big data parameter set, and generating a big data visualization page for displaying in the visualization system, so as to solve the technical problem that the big data display in the prior art is not intelligent enough.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a big data software visualization method provided by the present application;
fig. 2 is a schematic flowchart illustrating a process of generating a big data visualization parameter set in a big data software visualization method provided by the present application;
fig. 3 is a schematic flowchart illustrating optimization of the initial display frame parameters in a big data software visualization method provided in the present application;
FIG. 4 is a system structure diagram of a big data software visualization method provided by the present application;
fig. 5 is a schematic structural diagram of an exemplary electronic device of the present application.
In the figure: 11. a first obtaining unit; 12. a second obtaining unit; 13. a first processing unit; 14. a second processing unit; 15. a third processing unit; 16. a fourth processing unit; 300. an electronic device; 301. a memory; 302. a processor; 303. a communication interface; 304. a bus architecture.
Detailed Description
The application provides a big data software visualization method and system, which are used for solving the technical problem that big data display is not intelligent enough in the prior art.
Summary of the application
The current big data service website displays all the data acquired by the big data service website as far as possible according to the general direction of the acquired data, but the data required by different departments are different, and a part of the data is useless, so that all the departments can be used in daily work only by performing secondary processing after acquiring the data by the big data service website, particularly in the work related to municipal environmental protection, the current urban data acquisition usually acquires and displays all parameters such as traffic, pollution, accidents, capacity and the like in a correlated manner, and for the environmental protection department, a part of useless parameters related to the accident capacity and the like occupy the display space, and at the moment, secondary processing is needed, and similarly, for the traffic management part, the pollution and the discharge section are useless data.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the method comprises the steps of obtaining data parameter information of an object to be processed on multiple parameter dimensions, and integrating the data parameter information to obtain a big data parameter set; acquiring interface parameter information of the object to be processed on each parameter dimension, and processing each interface parameter information to obtain an interface parameter set; performing optimization analysis on the interface parameter set, and determining an interest network set from the interface parameter set; acquiring a selectable sequence space according to the big data parameter set and the interface parameter set; optimizing and arranging the interest net set according to the data parameters in the big data parameter set and the selectable sequence space to generate a big data visualization parameter set; and taking the big data visualization parameter set as a parameter calling index, calling a specific numerical value from the big data parameter set, and generating a big data visualization page for displaying in the visualization system, so as to solve the technical problem that big data display in the prior art is not intelligent enough.
Having described the basic principles of the present application, the following detailed description will be made in a clear and complete manner with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present application, and not all embodiments of the present application, and that the present application is not limited by the exemplary embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. It should be further noted that, for the convenience of description, only some but not all of the elements relevant to the present application are shown in the drawings.
Example one
As shown in fig. 1, the present application provides a big data software visualization method, which is applied to a big data software visualization system, and the method includes:
s100: acquiring data parameter information of an object to be processed on multiple parameter dimensions, and integrating the data parameter information to obtain a big data parameter set;
in the embodiment of the present application, the data object refers to a called data service block. Specifically, after the user performs targeted keyword retrieval on the big data website, the user matches a corresponding index in a preset matching database, and calls a related data block according to the index.
Optionally, when the keyword of the user corresponds to the multiple matched indexes, the indexes are presented in a list manner, and the user is guided to perform confirmation selection in the list.
Step S100 in the method provided in the embodiment of the present application includes:
s110: acquiring a characteristic label of a data object to obtain a first big data parameter;
s120: acquiring a data value of the acquired data object to obtain a second big data parameter;
s130: acquiring image parameters of a data object to obtain a third big data parameter;
s140: collecting and acquiring classification parameters of the data object to obtain a fourth big data parameter;
s150: and taking the first big data parameter, the second big data parameter, the third big data parameter and the fourth big data parameter as the big data parameter set.
In the embodiment of the application, the feature tag refers to an index tag of multiple data in a data block, and is usually a large title of a group of data, the data value refers to a specific parameter value of each data in the data group, which may be a specific value, or a percentage or other form of digital parameter, the image parameter refers to a background plate commonly used by the group of parameters, a part of data may be displayed in a preset linkage with the background plate when being called, such a function is common in the background plate of a map or a three-dimensional scene, and the classification parameter refers to how many different sub-classifications exist in the group of data, where the sub-classification refers to the endmost sub-classification with synchronous digital data.
S200: acquiring interface parameter information of the object to be processed on each parameter dimension, and processing each interface parameter information to obtain an interface parameter set;
the calling object refers to a user seeking service on a big data visualization service website, when the user logs in and uses the big data visualization service, the user fills in own equipment parameters and common data labels in a user information center, the user can be conveniently used for matching proper big data in the follow-up process to provide the big data, and optionally, for the big data calling and cooperation among partial government departments, government characteristics can be called as various information parameters of the user.
Step S200 in the method provided in the embodiment of the present application includes:
s210: acquiring a label parameter of a calling object to obtain a first interface parameter;
s220: acquiring and obtaining display equipment parameters of a supply object to obtain second interface parameters;
s230: acquiring the characteristic call rate of a supply object to obtain a third interface parameter;
s240: and taking the first interface parameter, the second interface parameter and the third interface parameter as the interface parameter set.
In the embodiment of the application, the tag parameter refers to a user-defined common data tag or a data tag called by a user on a big data platform, the tag can reflect the data demand direction and the common data type of the user, the display device parameter refers to parameters such as the resolution ratio and the screen size filled by the user when the user calls data, the parameter reflects the data display space of the user, the characteristic calling rate refers to the proportion of the data tag called by the user in the total calling times, and the core degree of the data to the user can be reflected.
Optionally, when the data filled in a certain data call by the user is missing more, or only a very small amount of data selection is performed or no data is selected, the big data visualization service is ended, the request is regarded as an abnormal request, and no follow-up request is added.
S300: performing optimization analysis on the interface parameter set, and determining an interest network set from the interface parameter set;
in the embodiment of the application, the data tags of the users are analyzed, and the interest network with the preferred sequence is summarized. Specifically, in the long-term data call of the user, a lot of extremely low frequency or data tags generated by input errors are generated, the tags are accumulated over time, useless data occupy a lot of space when all the tags are displayed, and the data visualization service party has a lot of data in a database, so that the direction of centralized provision needs to be integrated by an interest network.
Step S300 in the method provided in the embodiment of the present application includes:
s310: generating a wide area interest parameter according to the first interface parameter;
s320: analyzing the wide area interest parameters according to the third interface parameters to generate a core interest network and selectable interest points;
s330: and taking the core interest network and the optional interest points as the interest network set.
Since the quantity of past data parameters contained in each user information is different, all the past records of the user need to be integrated, and the wide area interest parameters are extracted according to the corresponding records, wherein the wide area interest parameters include all the past parameters including the abnormal request. All available records are used as analysis references because there is not enough data to support core interest network analysis for users with few past records.
For users with a certain amount of past parameters, the total past parameter amount is too large, and full coverage can cause disorder of big data visualization services, so that various parameters in wide area interest parameters are analyzed according to the characteristic call rate, and the part of parameters which are correlated with each other and have high synchronous call rate are selected as a core interest network, so that the intelligent selection function of the big data visualization services can be greatly improved.
For example, a user is a certain environmental protection department, and when the user is used for urban environment assessment, parameters such as regional pollution big data, motor vehicle emission big data and industrial emission big data are called each time, and when the user is used for vehicle emission pollution analysis, the parameters such as the regional pollution big data, the urban congestion regional big data and the motor vehicle emission big data are usually called, and then an interest network which takes the regional pollution big data and the motor vehicle emission big data as core interest networks and extends other big data such as the industrial emission big data as selectable interest points is generated.
S400: acquiring a selectable sequence space according to the big data parameter set and the interface parameter set;
the selectable sequence space refers to a space which can be shared for visually displaying the big data based on the third big data parameter, specifically, different background pictures can be collocated for different data types, the background pictures are divided into a central display area and a free area for filling a visual big data chart, and the free areas for displaying the visual big data are different in different sizes and picture contents.
Step S400 in the method provided in the embodiment of the present application includes:
s410: analyzing the third big data parameter to obtain an initial sequence space;
s420: setting a basic parameter space;
s430: judging whether the initial sequence space meets the basic parameter space;
s440: if the initial sequence space meets the basic parameter space, taking the initial sequence space as the selectable sequence space; and if the initial sequence space meets the basic parameter space, extracting the edge color parameter of the initial sequence space, expanding the third big data parameter according to the edge color parameter, and generating the selectable sequence space.
The basic parameter space refers to the proportion of continuous free areas in the whole background picture, the free areas of partial background pictures are too large and appear to be spacious, and the partial background pictures can not meet the space required by data visualization.
The AI picture algorithm can adopt anti color optimization to distinguish color blocks of the picture, divide a large area with soft color change into a display area, and further adopt any image edge detection division to select the edges of the picture for random extension drawing until a space enough for data visualization display is formed.
Illustratively, when urban pollution analysis is carried out, a map which can include a partition where data is located is selected as a background picture, a color image is adopted in an area which is carried in the picture and can be used for data visual display, gray level adjustment is adopted in the rest of the area, so that all non-data display areas are black, white and gray fuzzy areas with similar colors, the black, white and gray fuzzy areas are defined as spaces which can be used for data visual display through an AI (artificial intelligence) picture algorithm, and the preset 30% display areas cannot be met after calculation, at the moment, the edges of the picture are selected by adopting any image edge detection and division to carry out random extension drawing, so that new black, white and gray fuzzy areas are generated at the edges of the picture until the total black, white and gray fuzzy areas reach the preset 30% in the ratio of the whole picture.
S500: optimizing and arranging the interest net set according to the data parameters in the big data parameter set and the selectable sequence space to generate a big data visualization parameter set;
in the embodiment of the application, the interest network sets are only chain network structures which are mutually associated and distinguish primary and secondary, a data group corresponding to the interest network set needs to be matched in the big data parameter set, the data group needs to be screened and a proper visual chart needs to be matched before the data group is used for generating the visual big data, different charts are suitable for displaying different data types, and the clarity and the display requirement size of the different charts are different.
Step S500 in the method provided in the embodiment of the present application includes:
s510: generating an interest network set according to the big data parameter set and the interest network set, wherein the interest network set comprises necessary data and common data;
s520: generating initial display frame parameters according to the selectable sequence space and the interest net set;
s530: and optimizing the initial display frame parameters according to the fourth big data parameters to obtain the big data visualization parameter set.
The data in the big data parameter set is compared and matched with the interest network set, a data set which accords with the user can be screened out, the data is divided into necessary data and common data according to whether the data set corresponds to a core interest point or a selectable interest point in the interest network, the data is adjusted according to a third interface parameter, the necessary data is used as core display data to be displayed at the moment, the effective rate of visual data can be improved, the rest data is used as selectable options for manual calling of a client, a frame is formed on the basis of the core interest points, the frame is subjected to icon matching, the parameters of the core interest points are visually displayed in an optimal chart form to the greatest extent, and the user experience can be improved.
For example, when a certain environmental protection department is used for urban environment assessment, parameters such as regional pollution big data, motor vehicle emission big data and industrial emission big data are called each time, optimal chart format matching is preferentially performed on the regional pollution big data, the motor vehicle emission big data and the industrial emission big data in the selectable sequence space, and the rest selectable sequence space is filled with common data according to the sequence and is subjected to chart visualization processing.
S600: and taking the big data visualization parameter set as a parameter calling index, calling a specific numerical value from the big data parameter set, and generating a big data visualization page for displaying in the visualization system.
And carrying out data calling according to the chart format and the data position layout selected by the big data visualization parameter set, calling specific numerical values in the big data parameter set to the chart at a required position, forming visualization data in the chart, and displaying the visualization data as a big data visualization page to a user after all the charts are filled with data. Optionally, an auto-refresh data function may be added according to the user parameter characteristics, wherein the auto-refresh function may be a timed refresh or a detected refresh. Illustratively, for part of data tags with real-time performance, detectable refreshing can be matched, and a big data visual page is automatically refreshed after big data collected in a database is changed, wherein the refreshing can set a minimum interval, so that the phenomenon that the refreshing frequency is too high to cause service period burden or the page enters the next refreshing state for complete refreshing is avoided.
Step S530 in the method provided in the embodiment of the present application includes:
s531: generating a plurality of groups of data selectable chart parameter sets which are arranged in sequence according to the fourth big data parameter, wherein the plurality of groups of data selectable chart parameter sets which are arranged in sequence comprise a plurality of groups of mutually independent chart parameters and occupation parameters;
s532: judging whether the optional sequence space can be met when necessary data in the initial display frame parameters adopt the data optional diagram parameter set of the first sequence;
s533: if necessary data in the initial display frame parameters can meet the optional sequence space when adopting the data selectable diagram parameter sets of the first sequence, combining and filling the necessary data and the data selectable diagram parameter sets of the first sequence to the initial display frame parameters, calling the common data to fill the residual space of the initial display frame parameters, and generating the big data visualization parameter set; if the necessary data in the initial display frame parameters cannot meet the optional sequence space when the data selectable diagram parameter sets of the first sequence are adopted, sequentially adopting the subsequent data selectable diagram parameter sets to be combined with the necessary data to generate a plurality of groups of combination results until the combination results meet the optional sequence space, and taking the combination results as the big data visualization parameter set.
For different data, the optimal diagram collocation is different, the data group can be presented best by adopting the optimal diagram, but because the space occupied by the optimal diagram is possibly overlarge, in some cases, the optimal diagram needs to be eliminated, and secondly, the diagram which is optimal as much as possible is selected under the condition that the space allows.
Example two
Based on the same inventive concept as the big data software visualization method in the foregoing embodiments, as shown in fig. 4 and 5, the present application provides a big data software visualization apparatus, wherein the big data software visualization apparatus includes:
the first obtaining unit 11 is configured to obtain data parameter information of an object to be processed in multiple parameter dimensions, and integrate the data parameter information to obtain a big data parameter set;
a second obtaining unit 12, configured to obtain interface parameter information of the object to be processed in each of the multiple parameter dimensions, and process each of the interface parameter information to obtain an interface parameter set;
the first processing unit 13 is configured to perform optimization analysis on the interface parameter set, and determine an interest network set from the interface parameter set;
the second processing unit 14 is configured to obtain a selectable sequence space according to the big data parameter set and the interface parameter set;
the third processing unit 15 is configured to perform optimized arrangement on the interest net set according to the data parameters in the big data parameter set and the selectable sequence space, and generate a big data visualization parameter set;
the fourth processing unit 16 is configured to use the big data visualization parameter set as a parameter invocation indicator, invoke a materialization numerical value from the big data parameter set, and generate a big data visualization page for displaying in the visualization system.
Further, the system further comprises:
the third obtaining unit is used for acquiring the feature tags of the obtained data objects to obtain a first big data parameter;
the fourth obtaining unit is used for acquiring data values of the obtained data objects to obtain second big data parameters;
the fifth obtaining unit is used for acquiring the image parameters of the obtained data object to obtain a third big data parameter;
the sixth obtaining unit is used for collecting and obtaining the classification parameters of the data object to obtain a fourth big data parameter;
and the fifth processing unit is used for taking the first big data parameter, the second big data parameter, the third big data parameter and the fourth big data parameter as the big data parameter set.
Further, the system further comprises:
a seventh obtaining unit, configured to acquire and obtain a tag parameter of the call object, so as to obtain a first interface parameter;
the eighth obtaining unit is used for acquiring and obtaining the display equipment parameters of the supply object to obtain second interface parameters;
a ninth obtaining unit, configured to acquire and obtain a characteristic call rate of a supply object to obtain a third interface parameter;
a sixth processing unit, configured to use the first interface parameter, the second interface parameter, and the third interface parameter as the interface parameter set.
Further, the system further comprises:
the seventh processing unit is used for generating a wide area interest parameter according to the first interface parameter;
a tenth obtaining unit, configured to analyze the wide area interest parameter according to the third interface parameter, and generate a core interest network and an optional interest point;
an eighth processing unit, configured to use the core interest network and the optional interest points as the interest network set.
Further, the system further comprises:
the ninth processing unit is used for analyzing the third big data parameter to obtain an initial sequence space;
a tenth processing unit for setting a basic parameter space;
an eleventh processing unit, configured to determine whether an initial sequence space satisfies the basic parameter space;
a twelfth processing unit, configured to take an initial sequence space as the selectable sequence space if the initial sequence space meets the basic parameter space; and if the initial sequence space meets the basic parameter space, extracting the edge color parameter of the initial sequence space, expanding the third big data parameter according to the edge color parameter, and generating the selectable sequence space.
Further, the system further comprises:
a thirteenth processing unit, configured to generate an interest network set according to the big data parameter set and the interest network set, where the interest network set includes necessary data and common data;
a fourteenth processing unit, configured to generate an initial display frame parameter according to the selectable sequence space and the interest net set;
and the fifteenth processing unit is configured to optimize the initial display frame parameter according to the fourth big data parameter, and obtain the big data visualization parameter set.
Further, the system further comprises:
a sixteenth processing unit, configured to generate multiple sets of data selectable graph parameter sets arranged in sequence according to the fourth big data parameter, where the multiple sets of data selectable graph parameter sets arranged in sequence include multiple sets of mutually independent graph parameters and occupancy parameters;
a seventeenth processing unit, configured to determine whether the optional sequence space can be satisfied when necessary data in the initial display frame parameters adopts the data optional graph parameter set of the first sequence;
an eighteenth processing unit, configured to, if necessary data in the initial display frame parameters meets the selectable sequence space when the data selectable diagram parameter sets of the first sequence are adopted, combine and fill the necessary data and the data selectable diagram parameter sets of the first sequence to the initial display frame parameters, and call the common data to fill a remaining space of the initial display frame parameters, so as to generate the big data visualization parameter set; if the necessary data in the initial display frame parameters cannot meet the optional sequence space when the data selectable diagram parameter sets of the first sequence are adopted, sequentially adopting the subsequent data selectable diagram parameter sets to be combined with the necessary data to generate a plurality of groups of combination results until the combination results meet the optional sequence space, and taking the combination results as the big data visualization parameter set.
EXAMPLE III
Based on the same inventive concept as the big data software visualization method in the foregoing embodiment, the present application further provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method as in the first embodiment.
Exemplary electronic device
The electronic device of the present application is described below with reference to figure 5,
based on the same inventive concept as the big data software visualization method in the foregoing embodiment, the present application further provides a system, including: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes the system to perform the steps of the method of embodiment one.
The electronic device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
The communication interface 303 may be any device, such as a transceiver, for communicating with other devices or communication networks, such as an ethernet, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), a wired access network, and the like.
The memory 301 may be, but is not limited to, ROM or other type of static storage device that can store static information and instructions, RAM or other type of dynamic storage device that can store information and instructions, EEPROM, CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integral to the processor.
The memory 301 is used for storing computer-executable instructions for implementing the present application, and is controlled by the processor 302 to execute. The processor 302 is configured to execute the computer-executable instructions stored in the memory 301, so as to implement a big data software visualization method provided by the above-mentioned embodiments of the present application.
Those of ordinary skill in the art will understand that: the various numbers of the first, second, etc. mentioned in this application are for convenience of description and are not intended to limit the scope of this application nor to indicate the order of precedence. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the present application are generated in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer finger
The instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The various illustrative logical units and circuits described in this application may be implemented or operated upon by design of a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in this application may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside in different components within the terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations may be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and its equivalent technology, it is intended that the present application include such modifications and variations.
Claims (10)
1. A big data software visualization method is applied to a big data software visualization system, and the method comprises the following steps:
acquiring data parameter information of an object to be processed on multiple parameter dimensions, and integrating the data parameter information to obtain a big data parameter set;
acquiring interface parameter information of the object to be processed on each parameter dimension, and processing each interface parameter information to obtain an interface parameter set;
performing optimization analysis on the interface parameter set, and determining an interest network set from the interface parameter set;
acquiring a selectable sequence space according to the big data parameter set and the interface parameter set;
optimizing and arranging the interest net set according to the data parameters in the big data parameter set and the selectable sequence space to generate a big data visualization parameter set;
and taking the big data visualization parameter set as a parameter calling index, calling a specific numerical value from the big data parameter set, and generating a big data visualization page for displaying in the visualization system.
2. The method according to claim 1, wherein the obtaining data parameter information of the object to be processed in multiple parameter dimensions and integrating the data parameter information to obtain a big data parameter set comprises:
acquiring data parameter information of the object to be processed on a feature tag dimension to obtain feature tag information, wherein the feature tag is an index tag of the object to be processed in a data block; and the number of the first and second groups,
acquiring data parameter information of the object to be processed on a data value dimension to obtain data value information, wherein the data value is a value of the data in a data group; and (c) a second step of,
acquiring data parameter information of the object to be processed on an image parameter dimension to obtain image parameter information, wherein the image parameter is a background plate value corresponding to the object to be processed; and the number of the first and second groups,
acquiring data parameter information of the object to be processed on a classification parameter dimension to obtain classification parameter information, wherein the classification parameter is the classification number of the object to be processed in the tail-end sub-classification with synchronous digital data; and the number of the first and second groups,
and integrating the feature label information, the data numerical value information, the image parameter information and the classification parameter information to obtain a big data parameter set.
3. The method of claim 2, wherein the plurality of parameter dimensions comprises: at least two of a tag parameter dimension, a display device parameter dimension, and a feature invocation rate parameter dimension;
the label parameters refer to common data labels defined by a user or data labels called by a user on a big data platform, the display equipment parameters refer to parameters such as resolution and screen size filled by the user when the user calls data, and the characteristic calling rate refers to the proportion of the data labels called by the user in the past in the total calling times.
4. The method of claim 3, wherein the performing optimization analysis on the interface parameter set to determine a set of interest nets from the interface parameter set comprises:
generating a wide area interest parameter according to the first interface parameter;
analyzing the wide area interest parameters according to the third interface parameters to generate a core interest network and selectable interest points;
and taking the core interest network and the optional interest points as the interest network set.
5. The method of claim 4, wherein obtaining the selectable sequence space according to the big data parameter set and the interface parameter set comprises:
analyzing the third big data parameter to obtain an initial sequence space;
setting a basic parameter space;
judging whether the initial sequence space meets the basic parameter space or not;
if the initial sequence space meets the basic parameter space, taking the initial sequence space as the selectable sequence space; if the initial sequence space meets the basic parameter space, extracting the edge color parameter of the initial sequence space, expanding the third big data parameter according to the edge color parameter, and generating the selectable sequence space.
6. The method of claim 5, wherein the optimally arranging the interest net sets according to the data parameters in the big data parameter set and the selectable sequence space to generate a big data visualization parameter set comprises:
generating a network of interest set according to the big data parameter set, wherein the network of interest set comprises necessary data and common data;
generating initial display frame parameters according to the selectable sequence space and the interest net set;
and optimizing the initial display frame parameters according to the fourth big data parameters to obtain the big data visualization parameter set.
7. The method of claim 6, wherein said optimizing said initial display frame parameter according to said fourth big data parameter to obtain said big data visualization parameter set comprises:
generating a plurality of groups of data selectable chart parameter sets which are arranged in sequence according to the fourth big data parameter, wherein the plurality of groups of data selectable chart parameter sets which are arranged in sequence comprise a plurality of groups of mutually independent chart parameters and occupation parameters;
judging whether the optional sequence space can be met when the necessary data in the initial display frame parameters adopt the data optional diagram parameter set of the first sequence;
if necessary data in the initial display frame parameters can meet the optional sequence space when adopting the data selectable diagram parameter sets of the first sequence, combining and filling the necessary data and the data selectable diagram parameter sets of the first sequence to the initial display frame parameters, calling the common data to fill the residual space of the initial display frame parameters, and generating the big data visualization parameter set; if the necessary data in the initial display frame parameters cannot meet the optional sequence space when the data selectable diagram parameter sets of the first sequence are adopted, sequentially adopting the subsequent data selectable diagram parameter sets to be combined with the necessary data to generate a plurality of groups of combination results until the combination results meet the optional sequence space, and taking the combination results as the big data visualization parameter set.
8. A big data software visualization apparatus, the apparatus comprising:
the device comprises a first obtaining unit (11) and a second obtaining unit, wherein the first obtaining unit is used for obtaining data parameter information of an object to be processed on multiple parameter dimensions and integrating the data parameter information to obtain a big data parameter set;
a second obtaining unit (12) configured to obtain interface parameter information of the object to be processed in each of the multiple parameter dimensions, and process each of the interface parameter information to obtain an interface parameter set;
the first processing unit (13) is used for carrying out optimization analysis on the interface parameter set and determining an interest network set from the interface parameter set;
the second processing unit (14) is used for obtaining a selectable sequence space according to the big data parameter set and the interface parameter set;
the third processing unit (15) is used for carrying out optimized arrangement on the interest net set according to the data parameters in the big data parameter set and the selectable sequence space to generate a big data visualization parameter set;
and the fourth processing unit (16) is used for taking the big data visualization parameter set as a parameter calling index, calling a materialization numerical value from the big data parameter set, and generating a big data visualization page for displaying in the visualization system.
9. A big data software visualization system, comprising: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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