CN117633318A - Intelligent data visualization system - Google Patents
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- 238000013079 data visualisation Methods 0.000 title claims abstract description 164
- 238000000034 method Methods 0.000 claims abstract description 71
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Abstract
The invention provides an intelligent data visualization system, which relates to the field of data visualization and comprises the following components: and a data processing module: the method comprises the steps of performing data framing on target business intelligent data, and performing data refining and data processing to obtain first data; and a data analysis module: the data visualization method comprises the steps of extracting key data in first data to obtain second data for data analysis, and determining a data visualization mode based on data analysis results; and a data conversion module: the data processing method comprises the steps of matching corresponding data conversion modes based on a data visualization mode, and carrying out data conversion on second data to obtain third data; and a data visualization module: and the data visualization unit is used for carrying out visualization mapping on the third data and carrying out data visualization based on the mapping result. Through carrying out data refining and processing to commercial intelligent data, obtain more accurate data visualization mode to match more appropriate data conversion mode, can make data visualization result more accurate, also can match data visualization accuracy demand more.
Description
Technical Field
The invention relates to the field of data visualization, in particular to an intelligent data visualization system.
Background
At present, the communication quality of the connecting network is better and the speed is faster and faster due to the development of technology, so the connecting network is widely applied to the commercial field.
However, precisely grasping the movements and demands of users from a large amount of business data and precisely displaying them is still one of the biggest difficulties faced by each manufacturer. The existing commercial data visualization method generally directly visualizes all data, so that the visualization result is inaccurate and the requirements of users cannot be well met.
Accordingly, the present invention provides an intelligent data visualization system.
Disclosure of Invention
The invention provides an intelligent data visualization system which is used for obtaining a more accurate data visualization mode by carrying out data refining and processing on business intelligent data and matching with a more proper data conversion mode, so that a data visualization result is more accurate and the requirement of the data visualization accuracy can be more matched.
The invention provides an intelligent data visualization system, which comprises:
and a data processing module: the method comprises the steps of performing data framing on target business intelligent data, and performing data refining and data processing based on a data framing result to obtain first data;
And a data analysis module: the data visualization method comprises the steps of extracting key data in first data to obtain second data, carrying out data analysis, and determining a data visualization mode of the first data based on a data analysis result;
and a data conversion module: the data processing method comprises the steps of matching corresponding data conversion modes based on the data visualization modes, and carrying out data conversion on second data to obtain third data;
and a data visualization module: and the system is used for carrying out visual mapping on the third data and carrying out target business intelligent data visualization based on the mapping result.
In one possible implementation, a data processing module includes:
a data frame unit: the method comprises the steps of acquiring business intelligent data, and framing effective data in a visual range in target business intelligent data to obtain first initial data;
a first adjusting unit: the method comprises the steps of analyzing a framed first initial data format and carrying out format adjustment to obtain first adjustment data;
a second adjusting unit: the method comprises the steps of performing data refining on first adjustment data, and removing repeated data to obtain second adjustment data;
and a data screening unit: and the data processing unit is used for carrying out data screening on the data with the sub-data length exceeding the preset length in the second adjustment data, eliminating the data with the correlation degree smaller than the preset correlation degree in the sub-data, obtaining first sub-data, and taking all the first sub-data as the first data.
In one possible implementation, a data screening unit includes:
data dividing subunit: the second adjustment data are divided into a plurality of sub-data according to the data acquisition mode and the difference of the acquisition time;
a data acquisition subunit: the data length of each sub data in the second adjustment data is acquired to obtain a data length set corresponding to the sequence of the sub data;
a data comparison subunit: the data length set corresponding to the current second adjustment data is used for comparing with the preset maximum length of the sub data;
extracting the length of the sub data with the length larger than the maximum length of the preset sub data to obtain a second data length set;
data corresponding subunit: the method comprises the steps of obtaining sub data in second adjustment data corresponding to each sub data length in a second data length set to obtain a first sub data set;
parameter acquisition subunit: the method comprises the steps of acquiring the data type of each single data in each piece of sub data in a first sub data set and the influence degree of each single data in each piece of sub data on the corresponding sub data currently;
correlation determination subunit: the method comprises the steps of obtaining the correlation degree between each single data and corresponding sub data based on the data type of the single data in each sub data and the influence degree of the single data on the corresponding sub data;
Correlation comparison subunit: the method comprises the steps of removing single data with a correlation degree smaller than a preset degree, and reordering the rest single data in the same sub data according to the sequence of initial single data to obtain first ordered sub data;
a first inspection subunit: the method comprises the steps of capturing sensitive data in each piece of sub data in a first sub data set, and carrying out first inspection on corresponding first ordered sub data based on the sensitive data to inspect whether the first ordered sub data contains complete sensitive data or not;
a second inspection subunit: the data meaning detection module is used for carrying out second detection on the data meaning of the first ordered sub-data and detecting whether the first ordered sub-data can express the data meaning or not;
data refining subunit: and the first data is obtained by combining the first ordered sub-data which is qualified by the first inspection and the second inspection with the rest sub-data in the second adjustment data.
In one possible implementation, the data analysis module includes:
data classification unit: the method comprises the steps of acquiring the purpose of each piece of sub-data in first data, and classifying the first data based on the purpose of each piece of sub-data;
a data extraction unit: the method comprises the steps of obtaining key sub-data in first data based on a classification result, and obtaining second data based on all the key sub-data;
A data analysis unit: the data visualization method comprises the steps of carrying out data analysis on second data and determining a data visualization mode corresponding to the current second data;
the data visualization mode corresponding to the second data is the data visualization mode corresponding to the first data.
In one possible implementation, the data analysis unit includes:
a first analysis subunit: the data analysis tool is used for carrying out data analysis on each piece of sub data in the second data by using the data analysis tool in the business intelligent field to obtain a first analysis result of each piece of sub data;
a second analysis subunit: the system is used for integrating all data resources in a business intelligent database by using a local and big data intelligent linkage mechanism, arranging the data resources, and carrying out simulation judgment on each piece of sub data in the second data based on the data resource arrangement result to obtain a second analysis result corresponding to each piece of sub data;
mode determination subunit: and the data visualization method is used for acquiring the overlapping part of the first analysis result and the second analysis result of the same sub data, determining the data visualization mode corresponding to the current sub data based on the overlapping part of the results, and obtaining all the data visualization modes of the second data.
In one possible implementation, the data conversion module includes:
A data matching unit: the data conversion method comprises the steps of obtaining each piece of sub data in second data and a data visualization mode corresponding to each piece of sub data, and matching corresponding data conversion modes based on the data visualization modes;
a data conversion unit: the data conversion method comprises the steps of carrying out data conversion on each piece of sub data in second data based on a corresponding data conversion mode to obtain third initial data;
a data sorting unit: and the method is used for sequencing the third initial data so that the sub-data sequencing of the third initial data is matched with the sub-data sequencing of the first data, thereby obtaining ordered third data.
In one possible implementation, a data visualization module includes:
mapping unit: the method comprises the steps of classifying third data based on different data visualization modes, performing visualization mapping on the third data based on each classification, and obtaining a first visualization set based on sub-data in the same visualization mode;
the same data visualization mode corresponds to sub-data of a plurality of third data, and each sub-data can only correspond to one data visualization mode;
and a visualization unit: the method comprises the steps of respectively visualizing sub-data in each first visual set;
the visualization result of the target business intelligent data is that the target business intelligent data and the sub-data in the visualization mode are located in the same visualization position.
In one possible implementation, after the data visualization module, the method further includes: the result examination module specifically comprises:
standard acquisition unit: the method comprises the steps of obtaining data visualization precision of target business intelligent data, and obtaining a corresponding data visualization standard range based on the data visualization precision;
and a visual judgment unit: the data visualization method comprises the steps of comparing a data visualization result of target business intelligent data with a data visualization standard range;
if the data visualization result is within the data visualization standard range, judging that the target business intelligent data visualization is successful;
otherwise, judging that the target business intelligent data visualization result fails, and carrying out early warning and data framing, processing and visualization again on the current visualization result.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of an intelligent data visualization system in an embodiment of the present invention;
FIG. 2 is a block diagram of a data processing module in an embodiment of the invention;
fig. 3 is a block diagram of a data analysis unit according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1
An embodiment of the present invention provides an intelligent data visualization system, as shown in fig. 1, including:
and a data processing module: the method comprises the steps of performing data framing on target business intelligent data, and performing data refining and data processing based on a data framing result to obtain first data;
and a data analysis module: the data visualization method comprises the steps of extracting key data in first data to obtain second data, carrying out data analysis, and determining a data visualization mode of the first data based on a data analysis result;
And a data conversion module: the data processing method comprises the steps of matching corresponding data conversion modes based on the data visualization modes, and carrying out data conversion on second data to obtain third data;
and a data visualization module: and the system is used for carrying out visual mapping on the third data and carrying out target business intelligent data visualization based on the mapping result.
In this embodiment, the data framing refers to framing and selecting valid data in the visual range in the target business intelligence data, for example, valid data in the visual range includes numerical data, text data, time sequence data, geographic data, classification data, network data and relationship data.
In this embodiment, data refinement refers to removing duplicate data from data obtained by framing data, and removing single data from sub-data with an excessively long data length.
In this embodiment, the single data refers to a single data constituting each sub-data, for example, if the current sub-data is ABCDE, the corresponding single data is A, B, C, D, E.
In this embodiment, the data processing refers to adjusting the data format of the target business intelligent data, so as to make the target business intelligent data more standardized, thereby enabling fast processing, improving the system working performance, and simultaneously increasing the data reliability and security.
In this embodiment, the first data is data obtained by performing data framing on the target business intelligent data, and then performing data refining and data processing on the obtained effective data.
In this embodiment, the second data refers to a data set obtained after extracting key sub-data in the first data.
In this embodiment, the data analysis refers to performing data analysis on the second data by using a data analysis tool and a local and big data intelligent linkage mechanism, so as to obtain single data capable of performing data visualization on each piece of sub data in the second adjustment data and a data visualization mode of each piece of sub data.
In this embodiment, the data visualization means includes: serpentine, nested doughnuts, shells, mountain ranges, sausage patterns, and the like.
In this embodiment, data transformation refers to transforming the data form of the current data into a data form that can be visualized.
In this embodiment, the third data refers to the third data obtained by performing data conversion on the second data and then ordering the conversion results.
In this embodiment, the visual mapping refers to mapping the third data with different data visualization manners, so that the third data is displayed according to the corresponding visualization manners.
The beneficial effects of the technical scheme are as follows: through carrying out data refining and processing to commercial intelligent data, obtain more accurate data visualization mode to match more appropriate data conversion mode, can make data visualization result more accurate, also can match data visualization accuracy demand more.
Example 2
Based on the embodiment 1, the data processing module, as shown in fig. 2, includes:
a data frame unit: the method comprises the steps of acquiring business intelligent data, and framing effective data in a visual range in target business intelligent data to obtain first initial data;
a first adjusting unit: the method comprises the steps of analyzing a framed first initial data format and carrying out format adjustment to obtain first adjustment data;
a second adjusting unit: the method comprises the steps of performing data refining on first adjustment data, and removing repeated data to obtain second adjustment data;
and a data screening unit: and the data processing unit is used for carrying out data screening on the data with the sub-data length exceeding the preset length in the second adjustment data, eliminating the data with the correlation degree smaller than the preset correlation degree in the sub-data, obtaining first sub-data, and taking all the first sub-data as the first data.
In this embodiment, the valid data refers to data in the target business intelligent data, which has the characteristics of reliable data source, complete data, correlation with the target, correct data and the like.
In this embodiment, the first initial data refers to framing data obtained after data framing of the valid data in the target business intelligence data.
In this embodiment, the data format refers to the arrangement format in which the target business intelligence data is stored in a file or record, and is determined comprehensively by the data type and the data length.
In this embodiment, the format adjustment refers to adjusting the first adjustment data according to the data type and the data length.
In this embodiment, the second adjustment data refers to adjustment data obtained by eliminating repeated data in the first adjustment data.
In this embodiment, the data length refers to the data length of each sub-data in the second adjustment data, and the longer the data length, the more bytes occupied and the more data that can be stored.
In this embodiment, the correlation refers to the influence degree of each single data on the corresponding sub-data, where the value of the correlation is (0, 1).
In this embodiment, the first sub data refers to sub data obtained by processing sub data with a sub data length exceeding a preset length in the second adjustment data, and removing part of single data in the sub data, for example, the second adjustment data is abcdefghij, and is divided into a plurality of sub data, which are ab, cdefgh, ij respectively, and if the preset length is 5 bits, the length of cdefgh exceeds the preset length, where h is the data with the minimum correlation, and then h is removed to obtain cdefg, which is the first sub data.
In this embodiment, the first data refers to data obtained by performing data framing on the target business intelligent data, performing data refining and data processing on the obtained effective data, and meanwhile, the first data also refers to a data set formed by sub-data with a sub-data length exceeding a preset length in the second adjustment data.
The beneficial effects of the technical scheme are as follows: through carrying out data refining and processing on business intelligent data, a more accurate data visualization mode is obtained, so that a more proper data conversion mode is matched, and a data visualization result can be more accurate.
Example 3
Based on example 2, the data screening unit comprises:
data dividing subunit: the second adjustment data are divided into a plurality of sub-data according to the data acquisition mode and the difference of the acquisition time;
a data acquisition subunit: the data length of each sub data in the second adjustment data is acquired to obtain a data length set corresponding to the sequence of the sub data;
a data comparison subunit: the data length set corresponding to the current second adjustment data is used for comparing with the preset maximum length of the sub data;
extracting the length of the sub data with the length larger than the maximum length of the preset sub data to obtain a second data length set;
Data corresponding subunit: the method comprises the steps of obtaining sub data in second adjustment data corresponding to each sub data length in a second data length set to obtain a first sub data set;
parameter acquisition subunit: the method comprises the steps of acquiring the data type of each single data in each piece of sub data in a first sub data set and the influence degree of each single data in each piece of sub data on the corresponding sub data currently;
correlation determination subunit: the method comprises the steps of obtaining the correlation degree between each single data and corresponding sub data based on the data type of the single data in each sub data and the influence degree of the single data on the corresponding sub data;
correlation comparison subunit: the method comprises the steps of removing single data with a correlation degree smaller than a preset degree, and reordering the rest single data in the same sub data according to the sequence of initial single data to obtain first ordered sub data;
a first inspection subunit: the method comprises the steps of capturing sensitive data in each piece of sub data in a first sub data set, and carrying out first inspection on corresponding first ordered sub data based on the sensitive data to inspect whether the first ordered sub data contains complete sensitive data or not;
a second inspection subunit: the data meaning detection module is used for carrying out second detection on the data meaning of the first ordered sub-data and detecting whether the first ordered sub-data can express the data meaning or not;
Data refining subunit: and the first data is obtained by combining the first ordered sub-data which is qualified by the first inspection and the second inspection with the rest sub-data in the second adjustment data.
In this embodiment, the data acquisition method includes: sensor acquisition, crawler capturing, inputting and the like.
In this embodiment, for example, the second adjustment data is abcde, where based on the data acquired by the sensor being a, the data captured by the crawler being bc, and the data being de being entered, the second adjustment data may be divided into three sub-data, which are a, bc, and de, respectively.
In this embodiment, the data length set refers to a set of lengths of each sub-data in the second adjustment data, for example, the data length set may be {18bit, 19bit, 4bit }.
In this embodiment, the preset maximum length of the sub-data refers to a maximum length of the data predetermined based on the data refining degree, the data accuracy and the total length of the data, for example, the preset maximum length of the sub-data may be 16 bits, and sub-data of the second adjustment data corresponding to the length data exceeding 16 bits in the data length set may be selected to obtain the first sub-data set.
In this embodiment, the second data length set refers to a data set obtained by extracting a length of sub data having a length greater than a maximum length of preset sub data.
In this embodiment, the first sub-data set refers to a sub-data set obtained by acquiring sub-data corresponding to each sub-data length in the second data length set.
In this embodiment, the single data refers to the single data constituting each sub-data, for example, the current sub-data is ABCDE, and the corresponding single data is A, B, C, D, E.
In this embodiment, the influence degree refers to the influence degree of each single data in each piece of current sub data on the corresponding sub data, where the influence degree of the single data is determined based on the ratio of the sum of squares of deviations of the single data to the sum of squares of deviations of the whole data, and the sum of squares of deviations is the square of the data value of the single data minus the average data value of the current sub data. For example, if the single data is specific numerical data, the influence degree on the corresponding sub-data is higher.
In this embodiment, the data types refer to data types obtained by classifying data according to a metering hierarchy, where the data types include: class data, sequencing data, distance data, ratio data, and the like.
In this embodiment, the correlation between each single data and the corresponding sub-data is T;
wherein T is the correlation degree between each single data and the corresponding sub data; s is the occurrence number of the data type of the ith single data in the corresponding sub data; sz is the total occurrence number of single data containing different data types in the corresponding sub data; A data type that is the i-th single data; />A data type-corresponding value conversion coefficient for the i-th single data;a conversion value for the i-th single data; />The number of single data contained in the current sub data; />The influence weights of the data types corresponding to the single data and the data influence degrees corresponding to the single data on the correlation degree of the sub-data are respectively given, wherein +.>。
In this embodiment, the preset degree refers to a minimum correlation degree that is predetermined based on the degree of data refinement and the accuracy of data, where the preset degree has a value of (0, 1), for example, the preset degree may be 0.6, and data corresponding to a correlation degree greater than 0.6 may be retained.
In this embodiment, the first ordered sub-data refers to ordered sub-data obtained by eliminating single data whose phase Guan Du is smaller than a preset level, and reordering the remaining single data in the same sub-data according to the order of the initial single data.
In this embodiment, the sensitive data refers to data in each sub-data that may have an irreversible effect on the sub-data, for example, a specific sequenced data may have an irreversible effect on the current sub-data.
In this embodiment, the first test refers to comparing the captured sensitive data with the corresponding first ordered sub-data, and comparing to determine whether the first ordered sub-data includes all complete sensitive data, if so, determining that the first test of the first ordered sub-data is qualified.
In this embodiment, the second test refers to combining data meanings of the first ordered sub-data, checking whether the combined result can express the data meanings within the data accuracy range, and if so, determining that the second test of the first ordered sub-data is qualified.
The beneficial effects of the technical scheme are as follows: through carrying out data refining and processing on business intelligent data, a more accurate data visualization mode is obtained, so that a more proper data conversion mode is matched, and a data visualization result can be more accurate.
Example 4:
based on embodiment 2, the data analysis module includes:
data classification unit: the method comprises the steps of acquiring the purpose of each piece of sub-data in first data, and classifying the first data based on the purpose of each piece of sub-data;
a data extraction unit: the method comprises the steps of obtaining key sub-data in first data based on a classification result, and obtaining second data based on all the key sub-data;
a data analysis unit: the data visualization method comprises the steps of carrying out data analysis on second data and determining a data visualization mode corresponding to the current second data;
the data visualization mode corresponding to the second data is the data visualization mode corresponding to the first data.
In this embodiment, the first data is data obtained by performing data framing on the target business intelligent data, and then performing data refining and data processing on the obtained effective data.
In this embodiment, the uses of the sub data include: support type data, decision type data, development type data, sales type data, relationship type data, risk type data, and the like.
In this embodiment, the key sub-data refers to all sub-data included in the classification result that the sub-data uses affect the first data after classifying the first data based on the sub-data uses.
In this embodiment, the second data refers to a data set obtained after extracting key sub-data in the first data.
In this embodiment, the data analysis refers to performing data analysis on the second data by using a data analysis tool and a local and big data intelligent linkage mechanism.
In this embodiment, the data visualization means includes serpentine patterns, nested donuts, shells, mountain patterns, sausage patterns, and the like.
In this embodiment, the data visualization mode corresponding to the second data is the data visualization mode corresponding to the first data.
The beneficial effects of the technical scheme are as follows: through analyzing business intelligent data, a more accurate data visualization mode is obtained, so that a more proper data conversion mode can be matched, and a data visualization result can be more accurate.
Example 5:
based on the embodiment 4, the data analysis unit, as shown in fig. 3, includes:
a first analysis subunit: the data analysis tool is used for carrying out data analysis on each piece of sub data in the second data by using the data analysis tool in the business intelligent field to obtain a first analysis result of each piece of sub data;
a second analysis subunit: the system is used for integrating all data resources in a business intelligent database by using a local and big data intelligent linkage mechanism, arranging the data resources, and carrying out simulation judgment on each piece of sub data in the second data based on the data resource arrangement result to obtain a second analysis result corresponding to each piece of sub data;
mode determination subunit: and the data visualization method is used for acquiring the overlapping part of the first analysis result and the second analysis result of the same sub data, determining the data visualization mode corresponding to the current sub data based on the overlapping part of the results, and obtaining all the data visualization modes of the second data.
In this embodiment, the data analysis tool includes: SAS business analysis, qlikView, board, etc.
In this embodiment, the second data refers to a data set obtained after extracting key sub-data in the first data.
In this embodiment, the first analysis result refers to an analysis result obtained by performing data analysis on the second data by a data analysis tool in the business intelligence field.
In this embodiment, the local and big data intelligent linkage mechanism is to analyze the business intelligent data by combining the analysis method of the local system on the business data and the analysis method of the big data cloud on the business data.
In this embodiment, the data resource refers to a data resource in the business intelligence database that is related to the target business intelligence data.
In this embodiment, the simulation judgment refers to simulating the data visualization result of the target business intelligence data based on the data resources, for example, the sorted data resources are in a serpentine pattern mode, and the target business intelligence data can also be visualized in a serpentine pattern mode.
In this embodiment, the second analysis result refers to an analysis result obtained after performing a simulation judgment on each sub-data in the second data according to the arrangement result of the data resource.
In this embodiment, the data visualization means includes serpentine patterns, nested donuts, shells, mountain patterns, sausage patterns, and the like.
The beneficial effects of the technical scheme are as follows: through adopting two different modes to carry out data analysis to commercial intelligent data to obtain more accurate data visualization mode, make data visualization result more accurate, also can match data visualization accuracy demand more.
Example 6:
based on embodiment 5, the data conversion module includes:
a data matching unit: the data conversion method comprises the steps of obtaining each piece of sub data in second data and a data visualization mode corresponding to each piece of sub data, and matching corresponding data conversion modes based on the data visualization modes;
a data conversion unit: the data conversion method comprises the steps of carrying out data conversion on each piece of sub data in second data based on a corresponding data conversion mode to obtain third initial data;
a data sorting unit: and the method is used for sequencing the third initial data so that the sub-data sequencing of the third initial data is matched with the sub-data sequencing of the first data, thereby obtaining ordered third data.
In this embodiment, the data conversion means a conversion means for converting the current data form into a data form capable of visualization, and the data conversion means includes: the index value is patterned, the index relationship is patterned, the time and space are visualized, etc.
In this embodiment, data transformation refers to transforming the data form of the current data into a data form that can be visualized.
In this embodiment, the third initial data refers to data obtained after data conversion based on each sub-data in the second data.
In this embodiment, the third data refers to ordering the third initial data, so that the ordered result is consistent with the ordering of the sub-data in the first data, and the obtained ordered third initial data is the third data.
The beneficial effects of the technical scheme are as follows: by matching the proper data conversion mode based on the data visualization mode and performing data conversion, the data conversion result can be more accurate, so that the data visualization result is more accurate.
Example 7:
based on embodiment 6, the data visualization module includes:
mapping unit: the method comprises the steps of classifying third data based on different data visualization modes, performing visualization mapping on the third data based on each classification, and obtaining a first visualization set based on sub-data in the same visualization mode;
the same data visualization mode corresponds to sub-data of a plurality of third data, and each sub-data can only correspond to one data visualization mode;
and a visualization unit: the method comprises the steps of respectively visualizing sub-data in each first visual set;
the visualization result of the target business intelligent data is that the target business intelligent data and the sub-data in the visualization mode are located in the same visualization position.
In this embodiment, the visualization mapping refers to mapping the third data with a different data visualization.
In this embodiment, the first visualization set means that after the third data is mapped in a visualization manner, the data belonging to the same data visualization manner forms a first visualization set.
In this embodiment, the same data visualization mode corresponds to sub-data of the plurality of third data, and each sub-data can only correspond to one data visualization mode.
In this embodiment, the visualization result of the target business intelligence data is the same visualization location as the sub-data of the visualization mode.
The beneficial effects of the technical scheme are as follows: different data visualization modes are adopted for different data, so that the data visualization result is more accurate, and the data visualization accuracy requirement can be matched more.
Example 8:
based on the embodiment 1, the data visualization module further includes: the result examination module specifically comprises:
standard acquisition unit: the method comprises the steps of obtaining data visualization precision of target business intelligent data, and obtaining a corresponding data visualization standard range based on the data visualization precision;
and a visual judgment unit: the data visualization method comprises the steps of comparing a data visualization result of target business intelligent data with a data visualization standard range;
If the data visualization result is within the data visualization standard range, judging that the target business intelligent data visualization is successful;
otherwise, judging that the target business intelligent data visualization result fails, and carrying out early warning and data framing, processing and visualization again on the current visualization result.
In this embodiment, the data visualization accuracy refers to the accuracy of performing data visualization on the target business intelligent data, for example, a complete section of business data abcdefghij, where the data visualization accuracy is 80%, and the data that needs to be visualized may be bcdefghi finally.
In this embodiment, the data visualization standard range refers to a scope of visualization of each data corresponding to the intelligent data of the same data type and the same data visualization accuracy as the target business intelligent data when performing data visualization.
In this embodiment, early warning refers to early warning of business intelligent data whose data visualization results do not meet the scope of the visualization standard.
The beneficial effects of the technical scheme are as follows: by examining the data visualization result of the business intelligent data, the data visualization result can be more accurate, and the data visualization accuracy requirement can be matched more.
In this embodiment, the visual judgment unit includes:
according to a business semantic mechanism, dividing the target business intelligent data into data blocks, and attaching a first importance to each data block;
wherein,representing the distance value between the semantics U of the corresponding data block and the business intelligent center V; />A furthest distance value from the business intelligence center V representing historical existence; />Representing a first importance of the corresponding data block;
according to the data position and the data meaning of each first data in each data block, determining the association meaning between each first data and the rest of each data in the same data block and establishing an association map of each first data based on the meaning distance of the association meaning;
according to the association map, adding a second importance to the corresponding first data;
wherein Z2 represents a second importance corresponding to the first data;representing the total number of map branch lines in the associated map corresponding to the first data; />Representing that the line length of the map branch line in the associated map corresponding to the first data is larger than the total line number of the preset length; />Meaning distance of a u 1-th map branch line in the corresponding association map is represented; />Representing the association meaning of the u 1-th map branch line in the corresponding association map; / >The representation is based on->And->The value range is (0, 1); ln represents the sign of the logarithmic function;
calculating the final importance of each first data according to the first importance and the second importance;
wherein Z3 is the final importance of the corresponding first data;
counting the first data with the final importance larger than the preset importance, and determining the counted number;
when the ratio of the statistical quantity to the total quantity of the target business intelligent data is smaller than or equal to the visualization precision, if the standard data in the data visualization standard range fully cover the statistical first data, judging that the data visualization result is in the data visualization standard range;
if the standard data in the data visualization standard range does not fully cover the first data, determining a visualization occupied block of each uncovered data based on an importance-occupied position mapping table according to the final importance of the uncovered part data;
acquiring a first occupied block of a visual result of covered data, and performing block contradiction elimination with all the visual occupied blocks to perform visual flow deployment on the visual occupied blocks and contents to be displayed on all the first occupied blocks;
Performing visual display on the target business intelligent data according to the deployed visual flow;
when the ratio of the statistical quantity to the total quantity of the target business intelligent data is larger than the visualization precision, if the statistical data fully covers the data corresponding to the visualization precision, judging that the visualization result is in the data visualization standard range;
and if the counted data does not fully cover the data corresponding to the visualization precision, judging that the visualization result of the target business intelligent data fails.
In this embodiment, for example, a1, a2, and a3 exist in the data block, where the association graph of a1 is that a line connecting a1 and a2 and a line connecting a1 and a3, each line may be regarded as a branch of the graph, and the line length is determined according to the association meaning and the meaning distance, that is, the closer the corresponding meaning distance is, the greater the finally determined association value is, the association meaning is to determine the similarity between the two.
In this embodiment, the preset importance is preset, and the value is generally 0.3.
In this embodiment, for example, there are 10 pieces of target business intelligent data, and the visualization accuracy is 80%, then it is indicated that at least 8 pieces of data need to be visualized for reasonable display, however, in the process of visualization, some pieces of data need to be displayed, that is, the data with a final importance greater than the preset importance are data that need to be displayed, in the process, there may be no related data need to be displayed within the scope of the visualization standard, and thus incomplete display is caused, so full coverage analysis is needed, if full coverage is needed, it is determined that visualization can be effectively performed, and if full coverage is not available, a visualized occupied block is allocated to the data according to the importance of the uncovered portion of data, so as to ensure that the requirement of the visualization accuracy is met.
In this embodiment, the importance-occupying position mapping table is set in advance, including different final importance and matched visualization positions, so as to facilitate effective visualization of uncovered data.
In this embodiment, the purpose of the conflict elimination is to avoid that the data to be displayed cannot be effectively displayed due to time conflict or conflict of display positions in the process of visualization, and the visualization flow deployment is a flow after the conflict elimination, for example, the first data a1 and a2 are displayed in the 1 st second, and the first data a3 is displayed in the second.
The final importance of each data is respectively obtained according to the first importance and the second importance by carrying out block division on the total data and carrying out map establishment on each data of the data blocks, and the data to be displayed is reasonably analyzed under the two conditions of smaller than the visual precision and not smaller than the visual precision, so that the effective display and complete display of the important data are ensured, the accuracy displayed by the data to be displayed is indirectly ensured, and the visual precision requirement is met.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. An intelligent data visualization system, comprising:
and a data processing module: the method comprises the steps of performing data framing on target business intelligent data, and performing data refining and data processing based on a data framing result to obtain first data;
and a data analysis module: the data visualization method comprises the steps of extracting key data in first data to obtain second data, carrying out data analysis, and determining a data visualization mode of the first data based on a data analysis result;
and a data conversion module: the data processing method comprises the steps of matching corresponding data conversion modes based on the data visualization modes, and carrying out data conversion on second data to obtain third data;
and a data visualization module: and the system is used for carrying out visual mapping on the third data and carrying out target business intelligent data visualization based on the mapping result.
2. An intelligent data visualization system as in claim 1, wherein the data processing module comprises:
a data frame unit: the method comprises the steps of acquiring business intelligent data, and framing effective data in a visual range in target business intelligent data to obtain first initial data;
a first adjusting unit: the method comprises the steps of analyzing a framed first initial data format and carrying out format adjustment to obtain first adjustment data;
A second adjusting unit: the method comprises the steps of performing data refining on first adjustment data, and removing repeated data to obtain second adjustment data;
and a data screening unit: and the data processing unit is used for carrying out data screening on the data with the sub-data length exceeding the preset length in the second adjustment data, eliminating the data with the correlation degree smaller than the preset correlation degree in the sub-data, obtaining first sub-data, and taking all the first sub-data as the first data.
3. An intelligent data visualization system as recited in claim 2, wherein the data screening unit comprises:
data dividing subunit: the second adjustment data are divided into a plurality of sub-data according to the data acquisition mode and the difference of the acquisition time;
a data acquisition subunit: the data length of each sub data in the second adjustment data is acquired to obtain a data length set corresponding to the sequence of the sub data;
a data comparison subunit: the data length set corresponding to the current second adjustment data is used for comparing with the preset maximum length of the sub data;
extracting the length of the sub data with the length larger than the maximum length of the preset sub data to obtain a second data length set;
data corresponding subunit: the method comprises the steps of obtaining sub data in second adjustment data corresponding to each sub data length in a second data length set to obtain a first sub data set;
Parameter acquisition subunit: the method comprises the steps of acquiring the data type of each single data in each piece of sub data in a first sub data set and the influence degree of each single data in each piece of sub data on the corresponding sub data currently;
correlation determination subunit: the method comprises the steps of obtaining the correlation degree between each single data and corresponding sub data based on the data type of the single data in each sub data and the influence degree of the single data on the corresponding sub data;
correlation comparison subunit: the method comprises the steps of removing single data with a correlation degree smaller than a preset degree, and reordering the rest single data in the same sub data according to the sequence of initial single data to obtain first ordered sub data;
a first inspection subunit: the method comprises the steps of capturing sensitive data in each piece of sub data in a first sub data set, and carrying out first inspection on corresponding first ordered sub data based on the sensitive data to inspect whether the first ordered sub data contains complete sensitive data or not;
a second inspection subunit: the data meaning detection module is used for carrying out second detection on the data meaning of the first ordered sub-data and detecting whether the first ordered sub-data can express the data meaning or not;
data refining subunit: and the first data is obtained by combining the first ordered sub-data which is qualified by the first inspection and the second inspection with the rest sub-data in the second adjustment data.
4. The intelligent data visualization system of claim 2, wherein the data analysis module comprises:
data classification unit: the method comprises the steps of acquiring the purpose of each piece of sub-data in first data, and classifying the first data based on the purpose of each piece of sub-data;
a data extraction unit: the method comprises the steps of obtaining key sub-data in first data based on a classification result, and obtaining second data based on all the key sub-data;
a data analysis unit: the data visualization method comprises the steps of carrying out data analysis on second data and determining a data visualization mode corresponding to the current second data;
the data visualization mode corresponding to the second data is the data visualization mode corresponding to the first data.
5. The intelligent data visualization system of claim 4, wherein the data analysis unit comprises:
a first analysis subunit: the data analysis tool is used for carrying out data analysis on each piece of sub data in the second data by using the data analysis tool in the business intelligent field to obtain a first analysis result of each piece of sub data;
a second analysis subunit: the system is used for integrating all data resources in a business intelligent database by using a local and big data intelligent linkage mechanism, arranging the data resources, and carrying out simulation judgment on each piece of sub data in the second data based on the data resource arrangement result to obtain a second analysis result corresponding to each piece of sub data;
Mode determination subunit: and the data visualization method is used for acquiring the overlapping part of the first analysis result and the second analysis result of the same sub data, determining the data visualization mode corresponding to the current sub data based on the overlapping part of the results, and obtaining all the data visualization modes of the second data.
6. The intelligent data visualization system of claim 5, wherein the data transformation module comprises:
a data matching unit: the data conversion method comprises the steps of obtaining each piece of sub data in second data and a data visualization mode corresponding to each piece of sub data, and matching corresponding data conversion modes based on the data visualization modes;
a data conversion unit: the data conversion method comprises the steps of carrying out data conversion on each piece of sub data in second data based on a corresponding data conversion mode to obtain third initial data;
a data sorting unit: and the method is used for sequencing the third initial data so that the sub-data sequencing of the third initial data is matched with the sub-data sequencing of the first data, thereby obtaining ordered third data.
7. The intelligent data visualization system of claim 6, wherein the data visualization module comprises:
mapping unit: the method comprises the steps of classifying third data based on different data visualization modes, performing visualization mapping on the third data based on each classification, and obtaining a first visualization set based on sub-data in the same visualization mode;
The same data visualization mode corresponds to sub-data of a plurality of third data, and each sub-data can only correspond to one data visualization mode;
and a visualization unit: the method comprises the steps of respectively visualizing sub-data in each first visual set;
the visualization result of the target business intelligent data is that the target business intelligent data and the sub-data in the visualization mode are located in the same visualization position.
8. The intelligent data visualization system of claim 1, further comprising, after the data visualization module: the result examination module specifically comprises:
standard acquisition unit: the method comprises the steps of obtaining data visualization precision of target business intelligent data, and obtaining a corresponding data visualization standard range based on the data visualization precision;
and a visual judgment unit: the data visualization method comprises the steps of comparing a data visualization result of target business intelligent data with a data visualization standard range;
if the data visualization result is within the data visualization standard range, judging that the target business intelligent data visualization is successful;
otherwise, judging that the target business intelligent data visualization result fails, and carrying out early warning and data framing, processing and visualization again on the current visualization result.
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