CN112800036A - Report analysis chart automatic generation and display method and system - Google Patents
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Abstract
The embodiment of the invention provides a method for automatically generating and displaying a report analysis chart, which comprises the following steps: the method comprises the following steps: extracting data from a database; step two: cleaning the extracted data; step three: when the cleaned data is abnormal data, providing a wind control warning, and displaying the reason of the abnormality to a user; step four: when the cleaned data is normal data, acquiring junk data and core data through data characteristic identification; step five: the embodiment of the invention realizes data visualization, automatically analyzes the data and provides wind control and warning prompts for abnormal data.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a method and a system for automatically generating and displaying a report analysis chart.
Background
The existing data visualization platform is only used for data display and does not have the effect of data analysis, so that the utilization rate of database resources is low, and meanwhile, business personnel cannot judge the value of data.
Summary of the invention
In order to overcome the defects of the prior art, the invention provides an automatic generation and display method of a report analysis chart, which is used for solving the problems that the prior art cannot automatically analyze data and cannot provide wind control and warning prompts for abnormal data.
The technical scheme adopted by the invention for solving the technical problems is as follows: the method for automatically generating and displaying the report analysis chart is characterized by comprising the following steps of:
the method comprises the following steps: extracting data from a database;
step two: cleaning the extracted data;
step three: when the cleaned data is abnormal data, providing a wind control warning, and displaying the reason of the abnormality to a user;
step four: when the cleaned data is normal data, acquiring junk data and core data through data characteristic identification;
step five: analyzing the core data and displaying the analysis result.
Specifically, the step of extracting data from the database comprises:
and extracting data from the function read _ SQL connection database packaged by the pandas library through an SQL statement.
Specifically, the step of extracting data from the function read _ SQL connection database encapsulated by the pandas library through the SQL statement comprises the following steps:
the extracted data is selected from the function read _ SQL join database encapsulated by the pandas library in Python by SQL statements.
Specifically, the step of cleaning the extracted data includes:
and cleaning data extracted from the function read _ sql link database packaged by the pandas library.
Specifically, the data extracted from the function read _ sql link database encapsulated in the pandas library is cleaned, and the steps include:
sorting and combining the data of multiple dimensions;
and removing the junk information and optimizing and arranging the core information.
Specifically, when the data after being cleaned is abnormal data, a wind control warning is provided, and the reason for the abnormality is displayed to a user, the steps include:
the data after being cleaned has fields which do not appear in history, fields without data and fields without data;
and distinguishing abnormal degrees by self-defining abnormal indexes, identifying mutation data, providing wind control warning, and displaying abnormal reasons to a user.
Specifically, when the cleaned data is normal data, the garbage data and the core data are obtained through data feature identification, and the steps include:
when the cleaned data is normal data, generating a random forest by using a decision tree algorithm to obtain the importance level of each field of the data;
and removing the junk data with lower grade, and reserving the core data.
Specifically, the core data is analyzed, and the steps include:
expanding the field with higher grade in a single dimension;
and feeding back the key interval trend of the data change to the user, and prompting the importance degree of the key interval trend of the data change.
Specifically, the core data is analyzed and the analysis result is displayed, and the steps include:
comparing the existing data with historical data, analyzing the difference of each dimension to obtain the trend of data evolution, and feeding back the trend to a user;
or
Identifying the field group with higher relevance by identifying the key data, and feeding back the field group to the user;
or
Inputting all key fields of historical data into a decision tree algorithm to generate a random forest, and training the random forest into a prediction model;
and predicting the current data by using the prediction model, and displaying the prediction result and the probability.
Specifically, the method is characterized by analyzing core data and displaying an analysis result, and the method comprises the following steps:
a statistical chart is drawn and displayed by utilizing matplotlib in python, and the displaying mode comprises a bar chart, a broken line chart, a pie chart and a combination chart.
An automatic generation and display system for report analysis chart comprises the following steps:
a data extraction unit for extracting data from a database;
the data cleaning unit is used for cleaning the extracted data;
the first data judgment unit is used for providing a wind control warning when the cleaned data is abnormal data and displaying the reason of the abnormality to a user;
the second data judgment unit is used for obtaining the junk data and the core data through data characteristic identification when the cleaned data are normal data;
and the data analysis display unit is used for analyzing the core data and displaying the analysis result.
The invention has the beneficial effects that: the method comprises the following steps: extracting data from a database; step two: cleaning the extracted data; step three: when the cleaned data is abnormal data, providing a wind control warning, and displaying the reason of the abnormality to a user; step four: when the cleaned data is normal data, acquiring junk data and core data through data characteristic identification; step five: the core data are analyzed, and the analysis result is displayed, so that data visualization is realized, automatic analysis is performed on the data, and wind control and warning prompts are provided for abnormal data.
Drawings
FIG. 1 is a flow chart of a method for automatically generating and displaying a report analysis chart.
FIG. 2 is a functional block diagram of an automatic report analysis chart generation and display system.
FIG. 3 is another flow chart of a method for automatically generating and displaying report analysis chart.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of specific implementations of the present invention is provided in conjunction with specific embodiments:
the first embodiment is as follows:
fig. 1 shows an implementation flow of a report analysis chart automatic generation and display method provided in an embodiment of the present invention, and for convenience of description, only the relevant parts related to the embodiment of the present invention are shown, which are detailed as follows:
in step S101, data is extracted from a database;
specifically, the step of extracting data from the database comprises:
and extracting data from the function read _ SQL connection database packaged by the pandas library through an SQL statement.
Specifically, the step of extracting data from the function read _ SQL connection database encapsulated by the pandas library through the SQL statement comprises the following steps:
the extracted data is selected from the function read _ SQL join database encapsulated by the pandas library in Python by SQL statements.
In step S102, the extracted data is cleaned;
specifically, the step of cleaning the extracted data includes:
and cleaning data extracted from the function read _ sql link database packaged by the pandas library.
Specifically, the data extracted from the function read _ sql link database encapsulated in the pandas library is cleaned, and the steps include:
sorting and combining the data of multiple dimensions;
and removing the junk information and optimizing and arranging the core information.
In step S103, when the data after being cleaned is abnormal data, providing a wind control warning, and displaying the reason of the abnormality to the user;
specifically, when the data after being cleaned is abnormal data, a wind control warning is provided, and the reason for the abnormality is displayed to a user, the steps include:
the data after being cleaned has fields which do not appear in history, fields without data and fields without data;
and distinguishing abnormal degrees by self-defining abnormal indexes, identifying mutation data, providing wind control warning, and displaying abnormal reasons to a user.
In step S104, when the cleaned data is normal data, the garbage data and the core data are obtained through data feature identification;
specifically, when the cleaned data is normal data, the garbage data and the core data are obtained through data feature identification, and the steps include:
when the cleaned data is normal data, generating a random forest by using a decision tree algorithm to obtain the importance level of each field of the data;
and removing the junk data with lower grade, and reserving the core data.
Specifically, the core data is analyzed, and the steps include:
expanding the field with higher grade in a single dimension;
and feeding back the key interval trend of the data change to the user, and prompting the importance degree of the key interval trend of the data change.
In step S105, the core data is analyzed and the analysis result is presented.
Specifically, the core data is analyzed and the analysis result is displayed, and the steps include:
comparing the existing data with historical data, analyzing the difference of each dimension to obtain the trend of data evolution, and feeding back the trend to a user;
or
Identifying the field group with higher relevance by identifying the key data, and feeding back the field group to the user;
or
Inputting all key fields of historical data into a decision tree algorithm to generate a random forest, and training the random forest into a prediction model;
and predicting the current data by using the prediction model, and displaying the prediction result and the probability.
Specifically, the core data is analyzed and the analysis result is displayed, and the steps include:
a statistical chart is drawn and displayed by utilizing matplotlib in python, and the displaying mode comprises a bar chart, a broken line chart, a pie chart and a combination chart.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
Example two:
fig. 2 shows a structure of an automatic report analysis chart generation and display system according to a second embodiment of the present invention, and for convenience of description, only the parts related to the second embodiment of the present invention are shown, which are detailed as follows:
a data extraction unit 201 for extracting data from a database;
a data cleaning unit 202 for cleaning the extracted data;
the first data judgment unit 203 is used for providing a wind control warning when the cleaned data is abnormal data and displaying the reason of the abnormality to a user;
a second data judgment unit 204, configured to obtain garbage data and core data through data feature identification when the cleaned data is normal data;
and the data analysis and presentation unit 205 is used for analyzing the core data and presenting the analysis result.
In the embodiment of the invention, the method comprises the following steps: extracting data from a database; step two: cleaning the extracted data; step three: when the cleaned data is abnormal data, providing a wind control warning, and displaying the reason of the abnormality to a user; step four: when the cleaned data is normal data, acquiring junk data and core data through data characteristic identification; step five: the core data are analyzed, and the analysis result is displayed, so that data visualization is realized, automatic analysis is performed on the data, and wind control and warning prompts are provided for abnormal data. The detailed implementation of each unit can refer to the description of the first embodiment, and is not repeated herein.
Example three:
fig. 3 is another schematic flow chart of a report analysis chart automatic generation and display method provided by the third embodiment of the present invention, and for convenience of description, only the parts related to the third embodiment of the present invention are shown, where the method includes:
the detailed implementation mode is as follows:
data extraction: the database is connected by the encapsulated function read _ SQL of the pandas library in python, and then the data to be extracted is selected in SQL statements, so that the data will be in Dataframe type format for the following operations.
Data cleaning: and sorting and combining the data of multiple dimensions, removing junk information, and optimally arranging core information. The sorting method is to extract effective fields through historical data and user settings, and then use merge function in the pandas library to splice data.
Abnormal data: the fields which do not appear in history, the fields which do not have data or the fields which lack data are defined as abnormal data, abnormal degrees are distinguished by setting abnormal indexes in a self-defined mode, meanwhile, historical data are compared, the data which are mutated are identified, wind control warning is provided, and the reason of the abnormal is displayed for a user.
Data feature identification: and generating a random forest by using a decision tree algorithm, wherein the algorithm can score each field, and the field with high score is a key field, so that the important grade of each field of the data can be obtained, the junk data with lower grade is removed, and the data with higher grade, namely the core data, is reserved.
Analyzing core data: after the data are subjected to feature recognition, the field with higher grade is subjected to single-dimension expansion, the change trend of the key interval of data change is fed back to a user, and the importance degree of the data is prompted.
Comparing historical data: and comparing the existing data with the historical data, and analyzing the difference of each dimension to obtain the trend of data evolution.
And (3) key data identification: and (3) fusing the characteristics by permutation and combination, wherein the fusion method is to separately input the fields of the combination into a decision tree model to obtain the probability distribution of the combination and the prediction result, and if the relevance is high, the relevance between the fields of the combination is high. Therefore, the relevance among all dimensions is obtained, the field group identifications with higher relevance are screened out and then fed back to the user.
And (3) displaying an analysis result: and drawing a statistical chart by utilizing matplotlib in python and displaying the statistical chart in a manner of bar chart, broken line chart, pie chart and combination chart, explaining each displayed picture, wherein the explained content comprises the importance level of each field and other fields with higher relevance to the field.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the embodiments described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation.
Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (11)
1. A report analysis chart automatic generation and display method is characterized by comprising the following steps:
the method comprises the following steps: extracting data from a database;
step two: cleaning the extracted data;
step three: when the cleaned data is abnormal data, providing a wind control warning, and displaying the reason of the abnormality to a user;
step four: when the cleaned data is normal data, acquiring junk data and core data through data characteristic identification;
step five: analyzing the core data and displaying the analysis result.
2. The method for automatically generating and displaying the report analysis chart according to claim 1, wherein the step of extracting data from the database comprises the following steps:
and extracting data from the function read _ SQL connection database packaged by the pandas library through an SQL statement.
3. The method for automatically generating and displaying report analysis graph according to claim 2, wherein the step of extracting data from the function read _ SQL link database encapsulated in the pandas library through SQL statements comprises the steps of:
the extracted data is selected from the function read _ SQL join database encapsulated by the pandas library in Python by SQL statements.
4. The method for automatically generating and displaying the report analysis chart according to claim 3, wherein the extracted data is cleaned, and the steps comprise:
and cleaning data extracted from the function read _ sql link database packaged by the pandas library.
5. The method as claimed in claim 4, wherein the step of cleaning the data extracted from the function read _ sql link database encapsulated in the pandas library comprises the steps of:
sorting and combining the data of multiple dimensions;
and removing the junk information and optimizing and arranging the core information.
6. The method for automatically generating and displaying the report analysis chart according to claim 5, wherein when the cleaned data is abnormal data, a wind control warning is provided, and the reason of the abnormality is displayed to a user, and the method comprises the following steps:
the data after being cleaned has fields which do not appear in history, fields without data and fields without data;
and distinguishing abnormal degrees by self-defining abnormal indexes, identifying mutation data, providing wind control warning, and displaying abnormal reasons to a user.
7. The method for automatically generating and displaying the report analysis chart according to claim 6, wherein when the cleaned data is normal data, the garbage data and the core data are obtained through data characteristic identification, and the steps comprise:
when the cleaned data is normal data, generating a random forest by using a decision tree algorithm to obtain the importance level of each field of the data;
and removing the junk data with lower grade, and reserving the core data.
8. The method for automatically generating and displaying the report analysis chart according to claim 7, wherein core data are analyzed, and the steps comprise:
expanding the field with higher grade in a single dimension;
and feeding back the key interval trend of the data change to the user, and prompting the importance degree of the key interval trend of the data change.
9. The method for automatically generating and displaying the report analysis chart according to claim 8, wherein core data are analyzed and an analysis result is displayed, and the steps comprise:
comparing the existing data with historical data, analyzing the difference of each dimension to obtain the trend of data evolution, and feeding back the trend to a user;
or
Identifying the field group with higher relevance by identifying the key data, and feeding back the field group to the user; or
Inputting all key fields of historical data into a decision tree algorithm to generate a random forest, and training the random forest into a prediction model;
and predicting the current data by using the prediction model, and displaying the prediction result and the probability.
10. The method for automatically generating and displaying the report analysis chart according to claim 9, wherein core data are analyzed and an analysis result is displayed, and the steps comprise:
a statistical chart is drawn and displayed by utilizing matplotlib in python, and the displaying mode comprises a bar chart, a broken line chart, a pie chart and a combination chart.
11. An automatic generation and display system for report analysis chart comprises the following steps:
a data extraction unit for extracting data from a database;
the data cleaning unit is used for cleaning the extracted data;
the first data judgment unit is used for providing a wind control warning when the cleaned data is abnormal data and displaying the reason of the abnormality to a user;
the second data judgment unit is used for obtaining the junk data and the core data through data characteristic identification when the cleaned data are normal data;
and the data analysis display unit is used for analyzing the core data and displaying the analysis result.
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| CN113283222B (en) * | 2021-06-11 | 2021-10-08 | 平安科技（深圳）有限公司 | Automatic report generation method and device, computer equipment and storage medium |
| CN113836132A (en) * | 2021-11-29 | 2021-12-24 | 中航金网(北京)电子商务有限公司 | Method and device for checking multi-end report forms |
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