CN114595272A - Method and device for obtaining recommended chart type, electronic equipment and storage medium - Google Patents

Method and device for obtaining recommended chart type, electronic equipment and storage medium Download PDF

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CN114595272A
CN114595272A CN202210193415.XA CN202210193415A CN114595272A CN 114595272 A CN114595272 A CN 114595272A CN 202210193415 A CN202210193415 A CN 202210193415A CN 114595272 A CN114595272 A CN 114595272A
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data
chart
type
chart type
alternative
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江雪
胡娟
钟松
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Beijing Kingsoft Office Software Inc
Zhuhai Kingsoft Office Software Co Ltd
Wuhan Kingsoft Office Software Co Ltd
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Beijing Kingsoft Office Software Inc
Zhuhai Kingsoft Office Software Co Ltd
Wuhan Kingsoft Office Software Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2465—Query processing support for facilitating data mining operations in structured databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/26—Visual data mining; Browsing structured data

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  • General Engineering & Computer Science (AREA)
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  • Data Mining & Analysis (AREA)
  • Fuzzy Systems (AREA)
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Abstract

The application relates to the technical field of computers, and discloses a method for acquiring a recommended chart type, which comprises the following steps: identifying a data structure of user analysis data; determining a chart type range according to the data structure; the chart type range comprises at least one alternative chart type; identifying field semantics in user analysis data, and determining a first recommended chart type from a chart type range; recommending the determined first recommendation chart type. By performing semantic recognition on the user analysis data, the chart type meeting the user requirement can be determined from the chart type range, so that the chart type recommended to the user can more accurately meet the user requirement. The application also discloses a device for obtaining the recommended chart type, electronic equipment and a storage medium.

Description

Method and device for obtaining recommended chart type, electronic equipment and storage medium
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for obtaining a recommended chart type, an electronic device, and a storage medium.
Background
In order to facilitate a user to view, mine and display user analysis data in a table, data interpretation is usually performed on the user analysis data to obtain a visual chart, the user analysis data in the table includes two types, namely measurement and dimension, the measurement is numerical data, and the dimension is text data. Related art typically recommend visual charts based on a prescribed data structure (i.e., number and type of metrics, number and type of dimensions); for example, when the data selected by the user is a dimension and a non-time type measurement, "pie charts" are recommended to the user.
In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art:
in the related art, when user analysis data in a table is decoded, only the matching degree of a data structure of the user analysis data and a chart type is considered, and the situation that one data structure corresponds to multiple chart types exists, so that the chart type meeting the user requirement is difficult to be accurately recommended to a user.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.
The embodiment of the disclosure provides a method and a device for obtaining a recommended chart type, electronic equipment and a storage medium, so that the chart type meeting the requirements of a user can be recommended to the user more accurately.
In some embodiments, the method for obtaining a recommended chart type includes: identifying a data structure of user analysis data; determining a chart type range according to the data structure; the chart type range comprises at least one alternative chart type; identifying field semantics in the user analysis data, and determining a first recommended chart type from the chart type range; recommending the determined first recommendation chart type.
In some embodiments, the data structure includes a number and type of metrics, a number and type of dimensions, and determining a chart type range from the data structure includes: matching at least one alternative chart type corresponding to the number and the type of the measurement and the number and the type of the dimensionality from a preset chart type database; the chart type database stores the corresponding relation between the quantity and the type of the measurement and the alternative chart type, and the corresponding relation between the quantity and the type of the dimension and the alternative chart type; and determining each matched alternative chart type as the chart type range.
In some embodiments, determining a first recommended chart type from the range of chart types includes: and under the condition that the preset semantic content exists in the user analysis data, determining an alternative chart type corresponding to the semantic content in the chart type range as a first recommended chart type.
In some embodiments, recommending the determined first recommended chart type further comprises: acquiring data characteristics corresponding to the user analysis data; generating a first data comment according to the first recommended chart type and the data characteristics; and generating a first output chart according to the first data comment.
In some embodiments, obtaining data characteristics corresponding to the user analysis data includes: generating a first alternative output chart corresponding to the user analysis data according to the first recommended chart type; carrying out graph analysis processing on the first alternative output graph to obtain data characteristics corresponding to the first alternative output graph; or matching data characteristics corresponding to the first alternative output chart from a preset characteristic database, wherein the characteristic database stores the corresponding relation between the first alternative output chart and the data characteristics.
In some embodiments, generating a first output chart from the first data comment comprises: under the condition that a preset first keyword exists in the first data comment, acquiring a first adjusting operation corresponding to the first keyword; and adjusting the first alternative output chart according to the first adjusting operation to obtain a first output chart.
In some embodiments, generating a first data review based on the first recommended chart type and the data characteristics comprises:
matching a first alternative comment statement corresponding to the first recommended chart type and the data characteristics from a preset corpus; the corpus is stored with the corresponding relation between the first recommendation chart type and the first alternative comment sentence, and the corresponding relation between the data characteristics and the first alternative comment sentence; and filling the first alternative comment statement by using the data in the first alternative output chart to obtain the first data comment.
In some embodiments, recommending the determined first recommended chart type further comprises: carrying out data mining on the user analysis data by using a preset data mining method; under the condition of obtaining a data mining result, obtaining a second recommended chart type corresponding to the data mining method; generating a second data comment according to the second recommended chart type and the data mining result; and generating a second output chart according to the second data comment.
In some embodiments, generating a second output chart from the second data comment comprises: generating a second alternative output chart corresponding to the user analysis data according to the second recommended chart type; under the condition that a preset second keyword exists in the second data comment, acquiring a second adjustment operation corresponding to the second keyword; and adjusting the second alternative output chart according to the second adjusting operation to obtain a second output chart.
In some embodiments, generating a second data review based on the second recommended graph type and the data mining results comprises: matching a second alternative comment statement corresponding to the second recommended chart type and the data mining result from a preset corpus; the corpus is stored with the corresponding relation between the second recommendation chart type and the second alternative comment sentence and the corresponding relation between the data mining result and the second alternative comment sentence; and filling the second alternative comment statement by using the data in the second alternative output chart to obtain a second data comment.
In some embodiments, the means for obtaining a recommended chart type comprises: a first identification module configured to identify a data structure of user analysis data; a determination module configured to determine a chart type range from the data structure; the chart type range comprises at least one alternative chart type; the second identification module is configured to identify field semantics in the user analysis data and determine a first recommended chart type from the chart type range; a recommendation module configured to recommend the determined first recommendation chart type.
In some embodiments, the data structure includes a number and type of metrics, a number and type of dimensions, and the determination module determines the chart type range from the data structure by: matching at least one alternative chart type corresponding to the number and the type of the measurement and the number and the type of the dimension from a preset chart type database; the chart type database stores the corresponding relation between the quantity and the type of the measurement and the alternative chart type, and the corresponding relation between the quantity and the type of the dimensionality and the alternative chart type; and determining each matched alternative chart type as a chart type range.
In some embodiments, the second identification module determines the first recommended chart type from a range of chart types by: and under the condition that the preset semantic content exists in the user analysis data, determining an alternative chart type corresponding to the semantic content in the chart type range as a first recommended chart type.
In some embodiments, the means for obtaining a recommended chart type further comprises: the generating module is configured to acquire data characteristics corresponding to the user analysis data after recommending the determined first recommendation chart type; generating a first data comment according to the first recommended chart type and the data characteristics; generating a first output chart according to the first data comment.
In some embodiments, the generation module obtains data characteristics corresponding to the user analysis data by: generating a first alternative output chart corresponding to the user analysis data according to the first recommended chart type; carrying out graph analysis processing on the first alternative output graph to obtain data characteristics corresponding to the first alternative output graph; or, matching out the data characteristics corresponding to the first alternative output chart from a preset characteristic database, wherein the characteristic database stores the corresponding relation between the first alternative output chart and the data characteristics.
In some embodiments, the generation module generates the first output chart from the first data comment by: under the condition that a preset first keyword exists in the first data comment, acquiring a first adjusting operation corresponding to the first keyword; and adjusting the first alternative output chart according to the first adjusting operation to obtain a first output chart.
In some embodiments, the generation module generates the first data comment based on the first recommended chart type and the data characteristic by: matching a first alternative comment statement corresponding to the first recommended chart type and the data characteristics from a preset corpus; the corresponding relation between the first recommendation chart type and the first alternative comment sentence, the corresponding relation between the data characteristics and the first alternative comment sentence are stored in the corpus; and filling the first alternative comment statement by using the data in the first alternative output chart to obtain a first data comment.
In some embodiments, the generating module, after recommending the determined first recommendation chart type, further comprises: carrying out data mining on user analysis data by using a preset data mining method; under the condition of obtaining a data mining result, obtaining a second recommended chart type corresponding to the data mining method; generating a second data comment according to the second recommended chart type and the data mining result; and generating a second output chart according to the second data comment.
In some embodiments, the generation module generates the second output chart from the second data comment by: generating a second alternative output chart corresponding to the user analysis data according to the second recommended chart type; under the condition that a preset second keyword exists in the second data comment, acquiring a second adjustment operation corresponding to the second keyword; and adjusting the second alternative output chart according to the second adjusting operation to obtain a second output chart.
In some embodiments, the generation module generates the second data comment based on the second recommended graph type and the data mining result by: matching a second alternative comment sentence which corresponds to the second recommended chart type and corresponds to the data mining result from a preset corpus; the corpus is stored with the corresponding relation between the second recommendation chart type and the second alternative comment sentence and the corresponding relation between the data mining result and the second alternative comment sentence; and filling the second alternative comment statement by using the data in the second alternative output chart to obtain a second data comment.
In some embodiments, the electronic device includes a processor and a memory storing program instructions, and the processor is configured to execute the method for obtaining the recommended chart type when executing the program instructions.
In some embodiments, the storage medium stores program instructions that, when executed, perform the above-described method of obtaining a recommended chart type.
The method and the device for obtaining the recommended chart type, the electronic device and the storage medium provided by the embodiment of the disclosure can achieve the following technical effects: analyzing the data structure of the data by identifying the user; determining a chart type range according to the data structure; the chart type range comprises at least one alternative chart type; identifying field semantics in user analysis data, and determining a first recommended chart type from a chart type range; recommending the determined first recommendation chart type. By performing semantic recognition on the user analysis data, the chart type meeting the user requirement can be determined from the chart type range, so that the chart type recommended to the user can more accurately meet the user requirement.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
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One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are shown as like elements and not in limitation thereof, and wherein:
FIG. 1 is a schematic diagram of a method for obtaining recommended chart types according to an embodiment of the disclosure;
FIG. 2 is an application diagram of a correspondence between a data feature, a first alternative review statement, and a first recommendation chart type in accordance with an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an application of the embodiments of the present disclosure to obtain a first output graph;
FIG. 4 is a schematic diagram of another application of the disclosed embodiment for obtaining a first output graph;
FIG. 5 is a schematic diagram of a method for obtaining recommended chart types according to an embodiment of the disclosure;
FIG. 6 is a schematic diagram of an application of a method for obtaining a recommended chart type according to an embodiment of the present disclosure;
FIG. 7 is a schematic diagram of an apparatus for obtaining recommended chart types according to an embodiment of the disclosure;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
The terms "first," "second," and the like in the description and in the claims, and the above-described drawings of embodiments of the present disclosure, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the present disclosure described herein may be made. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions.
The term "plurality" means two or more unless otherwise specified.
In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
The term "correspond" may refer to an association or binding relationship, and a corresponds to B refers to an association or binding relationship between a and B.
The application can be applied to software such as online forms and forms.
It should be noted that the execution subject of the embodiment of the present invention may be an application running on a browser, and the application is a Web page program (Web App) of the browser on the terminal.
In addition, the electronic device according to the embodiment of the present invention may include, but is not limited to, a mobile phone, a Personal Digital Assistant (PDA), a wireless handheld device, a Tablet Computer (Tablet Computer), a Personal Computer (PC), a palm Computer (PDA, Personal Digital Assistants), a wearable device (such as smart glasses, a smart watch, and the like), and the like.
With reference to fig. 1, an embodiment of the present disclosure provides a method for obtaining a recommended chart type, including:
step S101, identifying a data structure of user analysis data;
step S102, determining a chart type range according to a data structure; the chart type range comprises at least one alternative chart type;
step S103, identifying field semantics in the user analysis data, and determining a first recommended chart type from the chart type range;
and step S104, recommending the determined first recommended chart type.
By adopting the method for acquiring the recommended chart type provided by the embodiment of the disclosure, the data structure of the user analysis data is identified; determining a chart type range according to the data structure; the chart type range comprises at least one alternative chart type; identifying field semantics in user analysis data, and determining a first recommended chart type from a chart type range; recommending the determined first recommendation chart type. By performing semantic recognition on the user analysis data, the chart type meeting the user requirement can be determined from the chart type range, so that the chart type recommended to the user can more accurately meet the user requirement.
Optionally, the user analysis data includes data in cells in a data table input by the user.
Optionally, the data structure of the user analysis data includes two types of metrics and dimensions; wherein, the measurement is numerical data, and the dimensionality is text data.
In some embodiments, the types of metrics include a time type metric, a number type metric, an amount type metric, the types of dimensions include a geographic dimension, a people dimension, an affiliated dimension, and the like; wherein the time type metric comprises numerical data representing time; the number type metric includes numerical data representing a number; the amount type metric includes numeric data representing currency; geographic dimensions include city, province, district or county, etc.; the character dimensions include names of people, etc.; the belonging dimension comprises a department to which the person belongs or a class to which the person belongs, and the like.
In some embodiments, the user analysis data is data in each cell in a data table input by a user, and a data structure of the user analysis data comprises two types including measurement and dimension; wherein, the measurement is numerical data, and the dimensionality is text data. Recommendation of visual charts, such as pie charts, bar charts, line charts, and continuous line charts, can be made based on the data structure. For example, pie charts require at least 1 dimension, 1 or 2 non-temporal types of metrics; continuous line graphs require at least 1 time-type metric and at least 1 non-time-type metric; in the case where the user analysis data is 1 dimension and 1 non-temporal type of metric, then the pie chart is recommended to the user. In some embodiments, the non-temporal type of metrics includes metrics other than temporal types, such as: number type metrics, amount type metrics, and the like.
Optionally, identifying a data structure of the user analysis data comprises: and counting the quantity and the type of the measurement and the quantity and the type of the dimensionality in the user analysis data to obtain a data structure of the user analysis data.
Optionally, the data structure includes the number and type of metrics and the number and type of dimensions, and the determining the chart type range according to the data structure includes: matching at least one alternative chart type corresponding to the number and the type of the measurement and the number and the type of the dimensionality from a preset chart type database; the chart type database stores the corresponding relation between the quantity and the type of the measurement and the alternative chart type, and the corresponding relation between the quantity and the type of the dimension and the alternative chart type; and determining each matched alternative chart type as a chart type range. In this way, the alternative chart types and the data structures are correspondingly stored in the chart type database, and the corresponding data structures can be obtained after semantic recognition is carried out on the user analysis data, so that various alternative chart types corresponding to the data structures can be directly matched from the chart type database, the chart type range is obtained, the chart type range corresponding to the user analysis data is narrowed, and the chart types meeting the user requirements can be conveniently recommended to the user.
In some embodiments, the preset chart type database stores the corresponding relationship between the number and type of the metrics and the candidate chart types, and the corresponding relationship between the number and type of the dimensions and the candidate chart types, for example: the alternative chart types stored in the chart type database comprise a grouping histogram, a stacking histogram, a percentage histogram, a grouping bar chart, a stacking bar chart, a percentage stacking bar chart, an embedded annular chart, a pie chart and the like, wherein the types of the dimensionalities corresponding to the grouping histogram comprise 2 types including dimensionality 1 and dimensionality 2, the number of the dimensionalities 1 is 1-12, and the number of the dimensionalities 2 is 1-6; the types of metrics corresponding to the grouping histogram include 1 metric type, and the number of metrics includes at least 1. The type of the dimension corresponding to the stacked histogram comprises dimension 1 and dimension 2, the number of the dimension 1 is 1-12, and the number of the dimension 2 is 1-6; the types of metrics corresponding to the stacked histogram include 1 metric type, and the number of metrics includes at least 1. The types of the dimensions corresponding to the percentage histogram comprise dimension 1 and dimension 2, the number of the dimension 1 is 1-12, and the number of the dimension 2 is 1-6; the type of metric corresponding to the percentage histogram includes 1 metric type, and the number of metrics includes at least 1. The type of the dimension corresponding to the grouping bar graph comprises dimension 1 and dimension 2, the number of the dimension 1 is 1-30, and the number of the dimension 2 is 1-6; the types of metrics corresponding to the grouped bar graph include 1 metric type, and the number of metrics includes at least 1. The type of the dimension corresponding to the stacking bar graph comprises dimension 1 and dimension 2, the number of the dimension 1 is 1-30, and the number of the dimension 2 is 1-6; the types of metrics corresponding to the stacked bar graph include 1 metric type, and the number of metrics includes at least 1. The type of the dimension corresponding to the percentage bar chart comprises dimension 1 and dimension 2, the number of the dimension 1 is 1-30, and the number of the dimension 2 is 1-6; the type of metric corresponding to the percentage bar graph includes 1 metric type, and the number of metrics includes at least 1. The number and type of measures corresponding to the pie chart are: 1 or 2 non-temporal types of metrics; the number and type of dimensions corresponding to the pie chart are: at least 1 dimension, and the type is not limited. The number and type of metrics for the histogram are: at least 2 metrics, of unlimited type; the number and type of dimensions corresponding to the histogram are: at least 1 dimension, and the type is not limited.
In some embodiments, in the case that the data structure of the user analysis data is 2 non-temporal type metrics and 1 dimension, the alternative chart types matched from the chart type database according to the data structure are: and determining the pie chart and the bar chart as a chart type range.
Optionally, determining a first recommended chart type from the chart type range includes: and under the condition that the preset semantic content exists in the user analysis data, determining an alternative chart type corresponding to the semantic content in the chart type range as a first recommended chart type. In this way, by introducing semantic recognition into the recommended chart type, the accuracy of hitting the user requirements can be improved, and the usability of the interpretation scheme provided for the user is higher.
Optionally, the preset semantic content includes: the term used to characterize the opposite semantics, the term used to characterize the implementation target semantics, the term used to characterize the proportional semantics, the term used to characterize the statistical semantics, the term used to characterize the temporal semantics, the term used to characterize the sequencing semantics, the term used to characterize the entity and its corresponding data, etc.
In some embodiments, the vocabulary for characterizing opposite semantics includes: the size, the number, the height, the front and the back, the upper and the lower terms and the like; the vocabulary for characterizing implementation target semantics includes: completion, task, achievement, goal, completion rate, etc.; the vocabulary for characterizing the scale semantics includes: the same ratio, the ring ratio and the like; the vocabulary for characterizing statistical semantics includes: total, subtotal, and the like; the vocabulary for characterizing the ranking semantics includes: ranking, ordering, etc.; the vocabulary for characterizing temporal semantics includes: time, date, etc.; the vocabulary for characterizing entities and their corresponding data includes: the performance of employees, the sales of the company in each month, the performance of departments, the performance of personnel, etc.
Optionally, the alternative chart types in the chart type range include: bi-directional bar charts, pictograms, histograms, composition charts, line charts, pie charts, bar charts, and the like.
In some embodiments, the semantic content to which the bi-directional bar graph and pictogram correspond includes "vocabulary for characterizing opposite semantics" or the like; semantic content corresponding to the histogram comprises 'vocabulary used for representing and realizing target semantics' and the like; the semantic content corresponding to the combined graph comprises 'vocabulary for representing and realizing target semantics'; semantic contents corresponding to the line graph comprise 'words for representing time semantics', 'words for representing statistical semantics' and the like; the semantic content corresponding to the pie chart comprises 'vocabulary used for representing statistical semantics' and the like; the semantic content corresponding to the bar graph includes "vocabulary for characterizing ordering semantics," "vocabulary for characterizing entities and their corresponding data," and so on.
In some embodiments, in the case that the preset semantic content includes "words for representing opposite semantics", in order to better embody a comparison relationship of data between the opposite semantics, a bidirectional bar graph or pictogram is determined as the first recommended chart type, and the bidirectional bar graph or pictogram enables data comparison of the opposite semantic words by using numerical comparison between forward and reverse pillar display categories; under the condition that the preset semantic content comprises 'words for representing the semantics of the implementation targets', in order to clearly and simply show the relation between the implementation targets, the histogram is determined to be of a first recommended chart type, on one hand, a user can conveniently understand a large amount of data and the relation between the data through the histogram, and on the other hand, the user can more quickly and intuitively interpret the original data through visual symbols; under the condition that the preset semantic content comprises 'words used for representing time semantics' and 'words used for representing proportion semantics', the line graph is determined to be a first recommended graph type, and changes among different data in each time node can be found visually through the line graph; under the condition that the preset semantic content comprises 'words used for representing statistical semantics', determining the pie chart as a first recommended chart type, and intuitively finding the proportion of the size of each item in the user analysis data to the sum of each item through the pie chart; under the condition that the preset semantic content comprises 'words used for representing sequencing semantics' or 'words used for representing entities and corresponding data thereof', the bar graph is determined to be of the first recommendation chart type, a user can intuitively know the size of each data through the bar graph, and meanwhile, the difference between the data is easy to compare.
In some embodiments, the semantic recognition is performed on the user analysis data in the data table through AI (Artificial Intelligence), in case that a preset semantic content exists in a field in which the user analysis data is recognized, for example: and completing, tasks, targets, achieving and the like, and determining a histogram of alternative chart types corresponding to the words for representing the target semantic realization as a first recommended chart type.
Optionally, determining a first recommended chart type from the chart type range includes: under the condition that the preset semantic content exists in the user analysis data, carrying out intention labeling on the identified semantic content; screening out an alternative chart type range from the chart type range according to the intention; and determining the alternative chart type corresponding to the semantic content in the alternative chart type range as a first recommended chart type. In this way, the intention corresponding to the identified semantic content is determined, a larger chart type range of the chart types corresponding to the user analysis data can be determined according to the intention, and then the alternative chart types corresponding to the semantic content are matched from the larger chart type range, so that the determination of the first recommended chart type is facilitated.
Optionally, in a case that there is no alternative chart type corresponding to the identified semantic content, one alternative chart type is randomly selected from the range of alternative chart types to be determined as the first recommended chart type. In this way, even when the alternative chart type corresponding to the semantic content does not exist in the large chart type range, one alternative chart type can be determined and recommended to the user.
Optionally, the chart type range includes one or more of a bar chart class, a pie chart class, or a line chart class, among others. Optionally, the bar graph class includes bar graphs, bi-directional bar graphs, and the like; the pie chart class comprises an embedded annular chart, a pie chart and the like; line graphs include line graphs, continuous line graphs, and the like.
Optionally, the intention corresponding to each semantic content includes: comparison intents, time series, ordering intents, and proportion intents, among others.
Optionally, performing intent annotation on the identified semantic content, including: matching an intention corresponding to semantic content from a preset intention database; the intention database stores the corresponding relation between the semantic content and the intention.
Optionally, the range of the alternative chart types corresponding to the comparison intention is a bar chart class; the range of the alternative chart types corresponding to the time sequence is a line graph type; the alternative chart type range corresponding to the proportion intention is a pie chart type; the range of alternative chart types corresponding to the sorting intention is a histogram class.
In some embodiments, the semantic content identified from the user analysis data is "a vocabulary for representing opposite semantics", the semantic content is subjected to intention labeling, an intention corresponding to the "the vocabulary for representing the opposite semantics" is obtained as a comparison intention, an alternative chart type range is screened from a chart type range according to the comparison intention and is a bar graph, and a first recommended chart type corresponding to the "the vocabulary for representing the opposite semantics" is matched from the alternative chart type corresponding to the bar graph type and is a bidirectional bar graph; in some embodiments, the semantic content identified from the user analysis data is "a vocabulary for representing statistical semantics", the semantic content is subjected to intent tagging, an intent corresponding to the "the vocabulary for representing statistical semantics" is obtained as a proportion intent, an alternative chart type range is screened from a chart type range according to the comparison intent as a pie chart class, and a first recommended chart type, which is the vocabulary for representing the opposite semantics ", is matched from the alternative chart type corresponding to the bar chart class as a pie chart.
Optionally, after recommending the determined first recommended chart type, the method further includes: acquiring data characteristics corresponding to user analysis data; generating a first data comment according to the first recommended chart type and the data characteristics; and generating a first output chart according to the first data comment.
Optionally, obtaining data characteristics corresponding to the user analysis data includes: generating a first alternative output chart corresponding to the user analysis data according to the first recommended chart type; carrying out graph analysis processing on the first alternative output graph to obtain data characteristics corresponding to the first alternative output graph; or, matching out the data characteristics corresponding to the first alternative output chart from a preset characteristic database, wherein the characteristic database stores the corresponding relation between the first alternative output chart and the data characteristics. In this way, the first data comment is generated through the first recommended chart type and the data characteristics, the data interpretation of the first alternative output chart can be performed, and the user can understand the first alternative output chart conveniently.
Optionally, the first data reviews descriptive textual conclusions characterizing the first alternative output chart.
Optionally, generating a first alternative output chart corresponding to the user analysis data according to the first recommended chart type includes: determining the first recommended chart type as the chart type of the first alternative output chart; and carrying out data processing on the user analysis data to obtain a first alternative output chart.
Optionally, generating the first data comment according to the first recommended chart type and the data characteristic includes: matching a first alternative comment statement corresponding to the first recommended chart type and the data characteristics from a preset corpus; the corresponding relation between the first recommendation chart type and the first alternative comment sentence, the corresponding relation between the data characteristics and the first alternative comment sentence are stored in the corpus; and filling the first alternative comment statement by using the data in the first alternative output chart to obtain a first data comment.
Optionally, the first alternative comment sentence includes: "XX changes over time, overall up/down/constant"; "rise/fall over the whole of X years"; "the acceleration rate of A is better than that of B"; "first ranked of XX"; "rank of XX last"; "A is greater than B"; "the minimum and maximum differ by X times", etc.
Referring to fig. 2, fig. 2 is an application diagram of a corresponding relationship between a data feature, a first alternative comment statement, and a first recommendation chart type provided by the embodiment of the present disclosure; in some embodiments, legend 1 through legend 5 are all example charts corresponding to the first recommended chart type; legend 1 is a time-varying line graph, and by performing graph analysis processing on legend 1, the data characteristics corresponding to legend 1 are obtained as follows: "a smooth trend in the whole, no particularly prominent data"; the first alternative comment sentence corresponding to the graph type of the legend 1 and corresponding to the data characteristics is matched from a preset corpus as "XX changes with time, overall rising/falling/unchanged"; the legend 2 is a histogram of sales amount changing with time, and the data characteristics corresponding to the legend 2 are obtained by performing chart analysis processing on the legend 2: "there are some time nodes that do not fit the overall trend"; the first alternative comment sentence corresponding to the graph type of the legend 2 and corresponding to the data characteristics is matched from a preset corpus as "increase/decrease in whole except for X years"; the legend 3 is a sales rate increase comparison graph which changes along with time, and the data characteristics corresponding to the legend 3 are obtained by carrying out graph analysis processing on the legend 3: "there are two fold lines for contrast acceleration"; a first alternative comment statement corresponding to the graph type of the legend 3 and corresponding to the data characteristics is matched from a preset corpus, and the acceleration rate of the first alternative comment statement is "a is better than that of the second alternative comment statement; the legend 4 is a bar graph for sales revenue comparison, and the data characteristics corresponding to the legend 4 are obtained by performing chart analysis processing on the legend 4: "all bar/histogram apply"; matching out a first alternative comment sentence which corresponds to the graph type of the legend 4 and corresponds to the data characteristics from a preset corpus, wherein the first alternative comment sentence is 'first ranking of XX', 'last ranking of XX', 'A is larger than B'; legend 5 is a bar chart of company sales comparisons, and by performing chart analysis processing on legend 5, the data characteristics corresponding to legend 5 are obtained: "large gap in data"; the first alternative comment sentence corresponding to the graph type of legend 4 and corresponding to the data feature is matched from the preset corpus as "X times the difference between the minimum and maximum".
Optionally, the first alternative comment statement is filled with data in the first alternative output chart, so as to obtain a first data comment.
In some embodiments, as shown in fig. 2, in the case that the first alternative output chart is legend 2, legend 2 is a bar chart of sales over time, and the first alternative comment statement corresponding to the chart type of legend 2 and corresponding to the data feature is "increase/decrease in total except for X years"; the first alternative comment statement is filled in according to the data in the legend 2, and the first data comment is obtained as "decline except 2004, overall rise"; in the case where the first alternative output chart is a graph 3, the graph 3 is a comparison graph of the sales increase rate over time, and the first alternative comment sentence corresponding to the graph type of the graph 3 and corresponding to the data feature is "the increase rate of a is better than B"; the first alternative comment sentence is filled according to the data in the legend 3, and the first data comment that "the sales fee acceleration rate is better than the net sales value" is obtained; in the case where the first alternative output chart is the legend 4, the legend 4 is a bar chart of sales revenue comparison, and the first alternative comment sentences corresponding to the chart type of the legend 4 and corresponding to the data characteristics are "first ranked of XX", "last ranked of XX", "a is greater than B"; the first candidate comment sentence is filled in according to the data in legend 4, the first data comment is obtained as "competitor D ranked first", "competitor C ranked last", "competitor D is larger than competitor B", etc.
Optionally, generating a first output chart from the first data comment comprises: under the condition that a preset first keyword exists in the first data comment, acquiring a first adjusting operation corresponding to the first keyword; and adjusting the first alternative output chart according to the first adjusting operation to obtain a first output chart.
Optionally, the first keyword includes: vocabulary for characterizing trend semantics, vocabulary for characterizing extreme semantics, vocabulary for characterizing comparison semantics, etc.
In some embodiments, the vocabulary for characterizing trend semantics includes: gradually increasing, gradually decreasing, and the like; the vocabulary for characterizing extremum semantics includes: maximum, minimum, highest, lowest, longest, shortest, etc.; the vocabulary for characterizing comparison semantics includes: compare, etc.
Optionally, the first adjusting operation comprises: adding trend lines, highlighting extreme values, presenting graphs with comparative relationships in different display modes, and the like.
Optionally, the first adjusting operation corresponding to the vocabulary for characterizing the trend semantics includes: adding a trend line; the first adjustment operation for lexical correspondence characterizing extremum semantics comprises: highlighting extrema, and/or highlighting specific numerical values; the first adjusting operation corresponding to the vocabulary used for characterizing the comparison semantics comprises: and presenting the graphs with the comparison relation in different display modes.
As shown in fig. 3 and 4, fig. 3 and 4 are schematic diagrams for obtaining a first output chart according to an embodiment of the disclosure; in some embodiments, there is a preset first keyword in the first data comment: under the condition of gradual increase, adding a trend line on the basis of the first alternative output chart to obtain a first output chart; there is a preset first keyword in the first data comment: under the condition of maximum, highlighting the extreme value part by color on the basis of the first alternative output chart, and marking the extreme value on the first alternative output chart to obtain a first output chart; therefore, the adjustment of the first alternative output chart according to the first data comment is realized, the generated first output chart can be better matched with the first data comment, and the user analysis data can be better expressed and explained, so that the user can understand conveniently.
Optionally, after recommending the determined first recommendation chart type, the method further includes: carrying out data mining on user analysis data by using a preset data mining method; under the condition of obtaining a data mining result, obtaining a second recommended chart type corresponding to the data mining method; generating a second data comment according to the second recommended chart type and the data mining result; and generating a second output chart according to the second data comment. Therefore, hidden information contained in the user analysis data can be mined through the data mining method, and the hidden information is difficult to obtain through browsing the user analysis data, so that deeper information hidden in the user analysis data can be effectively mined through the data mining method, a user can be effectively helped to analyze the data, and better data interpretation can be conveniently carried out on the user analysis data.
Optionally, the preset data mining method includes: centralized trend analysis, decentralised trend analysis, correlation analysis and the like; wherein, the centralized trend analysis comprises average number analysis calculation, median analysis calculation or mode analysis calculation; the analysis of the trend in the distance comprises calculation of a full-range analysis, calculation of a four-quadrant difference analysis, calculation of a mean-square difference analysis, calculation of an analysis of variance or calculation of a standard deviation analysis.
Optionally, obtaining a second recommended chart type corresponding to the data mining method includes: matching a second recommended chart type corresponding to the data mining method from a preset chart type database; the chart type database stores the corresponding relation between the data mining method and the second recommended chart type.
Optionally, generating a second data review according to the second recommended chart type and the data mining result includes: matching a second alternative comment sentence which corresponds to the second recommended chart type and the data mining result from a preset corpus; the corpus is stored with the corresponding relation between the second recommendation chart type and the second alternative comment sentence and the corresponding relation between the data mining result and the second alternative comment sentence; and filling the second alternative comment sentence by using the data in the second alternative output chart to obtain a second data comment.
In some embodiments, the second alternative output chart is an example table of sales in the areas of east and west of china, and the evaluation of the second alternative data obtained by performing data mining on the user analysis data corresponding to the second alternative output chart through standard difference analysis in the departure trend analysis includes: "the sales between XXX is greater than the difference in sales between XX", the second alternative comment sentence is filled with data in the second alternative output chart, and the second data comment is obtained as "the sales between east china is greater than the difference in sales between west china". Therefore, the standard of the dispersion degree of data distribution can be obtained through the data mining method, so that the degree of deviation of the data value from the calculated number average value can be measured, and the analysis data of a user can be better interpreted conveniently.
Optionally, generating a second output chart according to the second data comment includes: generating a second alternative output chart corresponding to the user analysis data according to the second recommended chart type; under the condition that a preset second keyword exists in the second data comment, acquiring a second adjustment operation corresponding to the second keyword; and adjusting the second alternative output chart according to the second adjusting operation to obtain a second output chart.
Optionally, the second keyword comprises: mean, median, mode, full range, quarter mean, variance, or standard deviation, and the like.
Optionally, the second adjusting operation comprises: highlighting means mean, mode, full range, quarter mean, variance, standard deviation, or the like, and/or highlighting specific numerical values.
In some embodiments, the second adjusting operation for the average number comprises: the highlights represent the mean; the second adjustment operation corresponding to the average difference comprises: the highlight indicates the average difference.
In some embodiments, there is a preset second keyword in the second data comment: under the condition of standard deviation, marking the standard deviation on the second alternative output chart on the basis of the second alternative output chart to obtain a second output chart; therefore, the adjustment of the second alternative output chart according to the second data comment is realized, the generated second output chart can be better matched with the second data comment, the user analysis data can be better expressed and explained, and the user can understand the second output chart conveniently.
With reference to fig. 5, an embodiment of the present disclosure provides a method for obtaining a first recommended chart type, including:
step S501, identifying a data structure of user analysis data; the data structure comprises the number and type of the measurement and the number and type of the dimension;
step S502, at least one alternative chart type corresponding to the number and the type of the measurement and the number and the type of the dimensionality is matched from a preset chart type database; the chart type database stores the corresponding relation between the quantity and the type of the measurement and the alternative chart type, and the corresponding relation between the quantity and the type of the dimension and the alternative chart type;
step S503, determining each matched alternative chart type as a chart type range;
step S504, field semantics in the user analysis data are identified, and under the condition that preset semantic content exists in the user analysis data, an alternative chart type corresponding to the semantic content in the chart type range is determined as a first recommended chart type;
and step S505, recommending the determined first recommended chart type.
By adopting the method for acquiring the recommended chart type provided by the embodiment of the disclosure, the data structure of the user analysis data is identified; determining a chart type range according to the data structure; the chart type range comprises at least one alternative chart type; identifying field semantics in user analysis data, and determining a first recommended chart type from a chart type range; recommending the determined first recommendation chart type. The data structure is obtained by performing semantic recognition on the user analysis data, a general chart type range can be determined according to the data structure of the user analysis data, so that the range of the first recommended chart type can be narrowed, and the chart type meeting the user requirement can be determined from the chart type range according to the recognized semantic content, so that the chart type recommended to the user can more accurately meet the user requirement.
With reference to fig. 6, an embodiment of the present disclosure provides a method for obtaining a recommended chart type, including:
step S601, identifying a data structure of user analysis data;
step S602, determining a chart type range according to a data structure; the chart type range comprises at least one alternative chart type;
step S603, field semantics in the user analysis data are identified, and a first recommended chart type is determined from the chart type range;
step S604, recommending the determined first recommended chart type;
step S605, acquiring data characteristics corresponding to the user analysis data;
step S606, generating a first data comment according to the type and the data characteristics of the first recommended chart;
step S607, generating a first output chart according to the first data comment;
step S608, data mining is carried out on the user analysis data by using a preset data mining method;
step S609, under the condition of obtaining a data mining result, obtaining a second recommended chart type corresponding to the data mining method;
step S610, generating a second data comment according to the second recommended chart type and the data mining result;
in step S611, a second output chart is generated according to the second data comment.
By adopting the method for acquiring the recommended chart type provided by the embodiment of the disclosure, the data structure of the user analysis data is identified; determining a chart type range according to the data structure; the chart type range comprises at least one alternative chart type; identifying field semantics in user analysis data, and determining a first recommended chart type from a chart type range; recommending the determined first recommendation chart type. By performing semantic recognition on the user analysis data, the chart type meeting the user requirements can be determined from the chart type range, so that the chart type recommended to the user can more accurately hit the user requirements; meanwhile, the first data comment and the second data comment are generated, and the first output chart and the second output chart are respectively obtained according to the first data comment and the second data comment, so that the analysis data of the user can be better interpreted, the generated first output chart and the second output chart are better matched with the first data comment and the second data comment, and the usability of the overall data interpretation is improved.
With reference to fig. 7, an apparatus for obtaining a recommended chart type according to an embodiment of the present disclosure includes: a first identification module 701, a determination module 702, a second identification module 703 and a recommendation module 704; the first recognition module 701 is configured to recognize a data structure of the user analysis data and to generate the data structure to the determination module; the determining module 702 is configured to receive the data structure sent by the first identifying module, determine a chart type range according to the data structure, and send the chart type range to the second identifying module; the chart type range comprises at least one alternative chart type; the second recognition module 703 is configured to receive the chart type range sent by the determination module, recognize field semantics in the user analysis data, determine a first recommended chart type from the chart type range, and send the first recommended chart type to the recommendation module; the recommending module 704 is configured to receive the first recommended chart type sent by the second identifying module and recommend the determined first recommended chart type.
By adopting the device for acquiring the recommended chart type, which is provided by the embodiment of the disclosure, the data structure of the user analysis data is identified through the first identification module; the determining module determines the chart type range according to the data structure; the chart type range comprises at least one alternative chart type; the second identification module identifies field semantics in the user analysis data and determines a first recommended chart type from the chart type range; the recommendation module recommends the determined first recommendation chart type. By performing semantic recognition on the user analysis data, the chart type meeting the user requirement can be determined from the chart type range, so that the chart type recommended to the user can more accurately meet the user requirement.
Optionally, the data structure includes a number and type of metrics, a number and type of dimensions, and the determination module determines the chart type range from the data structure by: matching at least one alternative chart type corresponding to the number and the type of the measurement and the number and the type of the dimensionality from a preset chart type database; the chart type database stores the corresponding relation between the quantity and the type of the measurement and the alternative chart type, and the corresponding relation between the quantity and the type of the dimension and the alternative chart type; and determining each matched alternative chart type as a chart type range.
Optionally, the second identifying module determines the first recommended chart type from the chart type range by: and under the condition that the preset semantic content exists in the user analysis data, determining an alternative chart type corresponding to the semantic content in the chart type range as a first recommended chart type.
Optionally, the apparatus for obtaining a recommended chart type further includes: the generating module is configured to acquire data characteristics corresponding to the user analysis data after recommending the determined first recommendation chart type; generating a first data comment according to the first recommended chart type and the data characteristics; generating a first output chart according to the first data comment.
Optionally, the generating module obtains data characteristics corresponding to the user analysis data by the following method, including: generating a first alternative output chart corresponding to the user analysis data according to the first recommended chart type; carrying out graph analysis processing on the first alternative output graph to obtain data characteristics corresponding to the first alternative output graph; or, matching out the data characteristics corresponding to the first alternative output chart from a preset characteristic database, wherein the characteristic database stores the corresponding relation between the first alternative output chart and the data characteristics.
Optionally, the generating module generates the first output chart according to the first data comment by: under the condition that a preset first keyword exists in the first data comment, acquiring a first adjusting operation corresponding to the first keyword; and adjusting the first alternative output chart according to the first adjusting operation to obtain a first output chart.
Optionally, the generating module generates the first data comment according to the first recommended chart type and the data characteristic by: matching a first alternative comment statement corresponding to the first recommended chart type and the data characteristics from a preset corpus; the corresponding relation between the first recommended chart type and the first alternative comment sentence and the corresponding relation between the data characteristics and the first alternative comment sentence are stored in the corpus; and filling the first alternative comment statement by using the data in the first alternative output chart to obtain a first data comment.
Optionally, after generating the first output chart according to the first data comment, the generating module further includes: carrying out data mining on user analysis data by using a preset data mining method; under the condition of obtaining a data mining result, obtaining a second recommended chart type corresponding to the data mining method; generating a second data comment according to the second recommended chart type and the data mining result; and generating a second output chart according to the second data comment.
Optionally, the generating module generates the second output chart according to the second data comment by: generating a second alternative output chart corresponding to the user analysis data according to the second recommended chart type; under the condition that a preset second keyword exists in the second data comment, acquiring a second adjustment operation corresponding to the second keyword; and adjusting the second alternative output chart according to the second adjusting operation to obtain a second output chart.
Optionally, the generating module generates a second data comment according to the second recommended graph type and the data mining result by: matching a second alternative comment sentence which corresponds to the second recommended chart type and corresponds to the data mining result from a preset corpus; the corpus is stored with the corresponding relation between the second recommended chart type and the second alternative comment sentence and the corresponding relation between the data mining result and the second alternative comment sentence; and filling the second alternative comment sentence by using the data in the second alternative output chart to obtain a second data comment.
As shown in fig. 8, an embodiment of the present disclosure provides an electronic device including a Processor (Processor)800 and a Memory (Memory)801 storing program instructions. Optionally, the electronic device may further include a Communication Interface (Communication Interface)802 and a bus 803. The processor 800, the communication interface 802, and the memory 801 may communicate with each other via a bus 803. Communication interface 802 may be used for information transfer. The processor 800 may call logic instructions in the memory 801 to perform the method of obtaining the recommended chart type of the above embodiment.
Further, the program instructions in the above-mentioned memory 801 may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product.
The memory 801 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 800 executes functional applications and data processing, i.e., implements the method of obtaining the recommended chart type in the above-described embodiments, by executing program instructions/modules stored in the memory 801.
The memory 801 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. In addition, the memory 801 may include a high-speed random access memory, and may also include a nonvolatile memory.
By adopting the electronic equipment provided by the embodiment of the disclosure, the data structure of the user analysis data is identified; determining a chart type range according to the data structure; the chart type range comprises at least one alternative chart type; identifying field semantics in user analysis data, and determining a first recommended chart type from a chart type range; recommending the determined first recommendation chart type. By performing semantic recognition on the user analysis data, the chart type meeting the user requirement can be determined from the chart type range, so that the chart type recommended to the user can more accurately meet the user requirement.
Optionally, the electronic device comprises: computers, servers, etc.
The embodiment of the disclosure provides a storage medium, and program instructions execute the method for obtaining the recommended chart type when running.
The disclosed embodiments provide a computer program product comprising a computer program stored on a computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the above-described method of obtaining a recommended chart type.
The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.
The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes one or more instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes, and may also be a transient storage medium.
The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps 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 may depend upon the particular application and design constraints imposed on the solution. 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 disclosed embodiments. It can be clearly understood by the skilled person that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Claims (13)

1. A method of obtaining a recommended chart type, comprising:
identifying a data structure of user analysis data;
determining a chart type range according to the data structure; the chart type range comprises at least one alternative chart type;
identifying field semantics in the user analysis data, and determining a first recommended chart type from the chart type range;
recommending the determined first recommendation chart type.
2. The method of claim 1, wherein the data structure includes a number and type of metrics and a number and type of dimensions, and wherein determining a chart type range from the data structure includes:
matching at least one alternative chart type corresponding to the number and the type of the measurement and the number and the type of the dimensionality from a preset chart type database; the chart type database stores the corresponding relation between the quantity and the type of the measurement and the alternative chart type, and the corresponding relation between the quantity and the type of the dimension and the alternative chart type;
and determining each matched alternative chart type as the chart type range.
3. The method of claim 1, wherein determining a first recommended chart type from the range of chart types comprises:
and under the condition that the preset semantic content exists in the user analysis data, determining an alternative chart type corresponding to the semantic content in the chart type range as a first recommended chart type.
4. The method of any of claims 1 to 3, wherein recommending the determined first recommended chart type further comprises:
acquiring data characteristics corresponding to the user analysis data;
generating a first data comment according to the first recommended chart type and the data characteristics;
and generating a first output chart according to the first data comment.
5. The method of claim 4, wherein obtaining data characteristics corresponding to the user analysis data comprises:
generating a first alternative output chart corresponding to the user analysis data according to the first recommended chart type;
carrying out graph analysis processing on the first alternative output graph to obtain data characteristics corresponding to the first alternative output graph; or matching data characteristics corresponding to the first alternative output chart from a preset characteristic database, wherein the characteristic database stores the corresponding relation between the first alternative output chart and the data characteristics.
6. The method of claim 5, wherein generating a first output chart from the first data comment comprises:
under the condition that a preset first keyword exists in the first data comment, acquiring a first adjusting operation corresponding to the first keyword;
and adjusting the first alternative output chart according to the first adjusting operation to obtain a first output chart.
7. The method of claim 5, wherein generating a first data review based on the first recommended chart type and the data trait comprises:
matching a first alternative comment statement corresponding to the first recommended chart type and the data characteristics from a preset corpus; the corpus is stored with the corresponding relation between the first recommendation chart type and the first alternative comment sentence, and the corresponding relation between the data characteristics and the first alternative comment sentence;
and filling the first alternative comment statement by using the data in the first alternative output chart to obtain the first data comment.
8. The method of claim 1, wherein recommending the determined first recommended chart type further comprises:
carrying out data mining on the user analysis data by using a preset data mining method;
under the condition of obtaining a data mining result, obtaining a second recommended chart type corresponding to the data mining method;
generating a second data comment according to the second recommended chart type and the data mining result;
and generating a second output chart according to the second data comment.
9. The method of claim 8, wherein generating a second output chart from the second data comment comprises:
generating a second alternative output chart corresponding to the user analysis data according to the second recommended chart type;
under the condition that a preset second keyword exists in the second data comment, acquiring a second adjustment operation corresponding to the second keyword;
and adjusting the second alternative output chart according to the second adjusting operation to obtain a second output chart.
10. The method of claim 9, wherein generating a second data review based on the second recommended graph type and the data mining results comprises:
matching a second alternative comment statement corresponding to the second recommended chart type and the data mining result from a preset corpus; the corpus is stored with the corresponding relation between the second recommendation chart type and the second alternative comment sentence and the corresponding relation between the data mining result and the second alternative comment sentence;
and filling the second alternative comment sentence by using the data in the second alternative output chart to obtain a second data comment.
11. An apparatus for obtaining a recommended chart type, comprising:
a first identification module configured to identify a data structure of user analysis data;
a determination module configured to determine a chart type range from the data structure; the chart type range comprises at least one alternative chart type;
the second identification module is configured to identify field semantics in the user analysis data and determine a first recommended chart type from the chart type range;
a recommendation module configured to recommend the determined first recommendation chart type.
12. An electronic device comprising a processor and a memory storing program instructions, wherein the processor is configured to execute the method of obtaining a recommended chart type according to any one of claims 1 to 10 when executing the program instructions.
13. A storage medium storing program instructions which, when executed, perform a method of obtaining a recommended chart type according to any one of claims 1 to 10.
CN202210193415.XA 2022-02-28 2022-02-28 Method and device for obtaining recommended chart type, electronic equipment and storage medium Pending CN114595272A (en)

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CN116186331A (en) * 2023-04-27 2023-05-30 北京亿信华辰软件有限责任公司 Graph interpretation method and system
CN117573847A (en) * 2024-01-16 2024-02-20 浙江同花顺智能科技有限公司 Visualized answer generation method, device, equipment and storage medium

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Publication number Priority date Publication date Assignee Title
CN116186331A (en) * 2023-04-27 2023-05-30 北京亿信华辰软件有限责任公司 Graph interpretation method and system
CN116186331B (en) * 2023-04-27 2023-08-04 北京亿信华辰软件有限责任公司 Graph interpretation method and system
CN117573847A (en) * 2024-01-16 2024-02-20 浙江同花顺智能科技有限公司 Visualized answer generation method, device, equipment and storage medium
CN117573847B (en) * 2024-01-16 2024-05-07 浙江同花顺智能科技有限公司 Visualized answer generation method, device, equipment and storage medium

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