Method and device for obtaining recommended chart types, electronic device, and storage medium

Abstract

The present application relates to the field of computer technology and discloses a method for obtaining recommended chart types, the method comprising: identifying the data structure of user analysis data; determining a chart type range based on the data structure; including at least one alternative chart type in the chart type range; identifying the semantics of fields in the user analysis data and determining a first recommended chart type from the chart type range; and recommending the determined first recommended chart type. By performing semantic recognition on the user analysis data, a chart type that meets the user's needs can be determined from the chart type range, thereby enabling the chart type recommended to the user to more accurately meet the user's needs. The present application also discloses a device for obtaining recommended chart types, an electronic device, and a storage medium.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and for example, to a method and device for obtaining recommended chart types, an electronic device, and a storage medium. Background Art

[0002] To facilitate viewing, mining, and displaying user analysis data in a table, it's often necessary to interpret the data to create visualizations. This data consists of two types: metrics and dimensions. Metrics are numerical data, while dimensions are textual data. Related technologies typically recommend visualizations based on a specified data structure (i.e., the number and type of metrics, and the number and type of dimensions). For example, if the user selects a single dimension and a non-time-based metric, a pie chart might be recommended.

[0003] During the implementation of the embodiments of the present disclosure, it was found that at least the following problems exist in the related art:

[0004] In related technologies, when interpreting user analysis data in a table, only the matching degree between the data structure of the user analysis data and the chart type is considered. There is a situation where one data structure corresponds to multiple chart types, which makes it difficult to accurately recommend chart types that meet user needs to users. Summary of the Invention

[0005] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical elements or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0006] The embodiments of the present disclosure provide a method and apparatus for obtaining recommended chart types, an electronic device, and a storage medium, so as to more accurately recommend chart types that meet user needs to users.

[0007] In some embodiments, the method for obtaining recommended chart types includes: identifying a data structure of user analysis data; determining a chart type range based on the data structure; the chart type range includes 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; and recommending the determined first recommended chart type.

[0008] In some embodiments, the data structure includes the number and type of metrics and the number and type of dimensions, and determining the chart type range based on the data structure includes: matching at least one alternative chart type corresponding to the number and type of metrics and the number and type of dimensions from a preset chart type database; the chart type database stores the correspondence between the number and type of metrics and the alternative chart types, and the correspondence between the number and type of dimensions and the alternative chart types; and determining each matched alternative chart type as the chart type range.

[0009] In some embodiments, determining a first recommended chart type from the chart type range includes: upon identifying that preset semantic content exists in the user analysis data, determining an alternative chart type in the chart type range that corresponds to the semantic content as the first recommended chart type.

[0010] In some embodiments, after recommending the determined first recommended chart type, the method further includes: obtaining data characteristics corresponding to the user analysis data; generating a first data review based on the first recommended chart type and the data characteristics; and generating a first output chart based on the first data review.

[0011] 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 based on the first recommended chart type; performing chart analysis processing on the first alternative output chart to obtain data characteristics corresponding to the first alternative output chart; or matching data characteristics corresponding to the first alternative output chart from a preset characteristics database, wherein the characteristics database stores the correspondence between the first alternative output chart and the data characteristics.

[0012] In some embodiments, generating a first output chart based on the first data review includes: when there is a preset first keyword in the first data review, obtaining a first adjustment operation corresponding to the first keyword; adjusting the first alternative output chart according to the first adjustment operation to obtain a first output chart.

[0013] In some embodiments, generating a first data review based on the first recommended chart type and the data characteristics includes:

[0014] A first alternative comment sentence corresponding to the first recommended chart type and the data characteristics is matched from a preset corpus; the corpus stores the correspondence between the first recommended chart type and the first alternative comment sentence, and the correspondence between the data characteristics and the first alternative comment sentence; the first alternative comment sentence is filled with the data in the first alternative output chart to obtain the first data comment.

[0015] In some embodiments, after recommending the determined first recommended chart type, the method further includes: performing data mining on the user analysis data using a preset data mining method; when obtaining the data mining results, obtaining a second recommended chart type corresponding to the data mining method; generating a second data review based on the second recommended chart type and the data mining results; and generating a second output chart based on the second data review.

[0016] In some embodiments, generating a second output chart based on the second data review includes: generating a second alternative output chart corresponding to the user analysis data based on the second recommended chart type; when there is a preset second keyword in the second data review, obtaining a second adjustment operation corresponding to the second keyword; adjusting the second alternative output chart according to the second adjustment operation to obtain a second output chart.

[0017] In some embodiments, generating a second data review based on the second recommended chart type and the data mining results includes: matching a second alternative comment sentence corresponding to the second recommended chart type and the data mining results from a preset corpus; storing the correspondence between the second recommended chart type and the second alternative comment sentence, and the correspondence between the data mining results and the second alternative comment sentence in the corpus; and filling the second alternative comment sentence with data in the second alternative output chart to obtain a second data review.

[0018] In some embodiments, the device for obtaining recommended chart types includes: a first identification module, configured to identify the data structure of user analysis data; a determination module, configured to determine a chart type range based on the data structure; the chart type range includes at least one alternative chart type; a second identification module, configured to identify field semantics in the user analysis data, and determine a first recommended chart type from the chart type range; and a recommendation module, configured to recommend the determined first recommended chart type.

[0019] In some embodiments, the data structure includes the number and type of metrics and the number and type of dimensions, and the determination module determines the chart type range based on the data structure in the following manner: matching at least one alternative chart type corresponding to the number and type of metrics and the number and type of dimensions from a preset chart type database; the chart type database stores the correspondence between the number and type of metrics and the alternative chart types, as well as the correspondence between the number and type of dimensions and the alternative chart types; and determining each matched alternative chart type as the chart type range.

[0020] In some embodiments, the second identification module determines the first recommended chart type from the chart type range in the following manner: when it is identified that preset semantic content exists in the user analysis data, the second identification module determines the candidate chart type corresponding to the semantic content in the chart type range as the first recommended chart type.

[0021] In some embodiments, the device for obtaining a recommended chart type also includes: a generation module, the generation module being configured to obtain data characteristics corresponding to the user analysis data after recommending the determined first recommended chart type; generate a first data comment based on the first recommended chart type and the data characteristics; and generate a first output chart based on the first data comment.

[0022] In some embodiments, the generation module obtains data characteristics corresponding to the user analysis data in the following ways, including: generating a first alternative output chart corresponding to the user analysis data based on a first recommended chart type; performing chart analysis processing on the first alternative output chart to obtain data characteristics corresponding to the first alternative output chart; or matching data characteristics corresponding to the first alternative output chart from a preset feature database, wherein the feature database stores the correspondence between the first alternative output chart and the data characteristics.

[0023] In some embodiments, the generation module generates a first output chart based on the first data review in the following manner, including: when there is a preset first keyword in the first data review, obtaining a first adjustment operation corresponding to the first keyword; adjusting the first alternative output chart according to the first adjustment operation to obtain the first output chart.

[0024] In some embodiments, the generation module generates a first data review based on the first recommended chart type and data characteristics in the following manner, including: matching a first alternative comment sentence corresponding to the first recommended chart type and the data characteristics from a preset corpus; the corpus stores the correspondence between the first recommended chart type and the first alternative comment sentence, and the correspondence between the data characteristics and the first alternative comment sentence; and filling the first alternative comment sentence with the data in the first alternative output chart to obtain the first data review.

[0025] In some embodiments, after the generation module recommends the determined first recommended chart type, it also includes: performing data mining on the user analysis data using a preset data mining method; when the data mining results are obtained, obtaining a second recommended chart type corresponding to the data mining method; generating a second data review based on the second recommended chart type and the data mining results; and generating a second output chart based on the second data review.

[0026] In some embodiments, the generation module generates a second output chart based on the second data review in the following manner, including: generating a second alternative output chart corresponding to the user analysis data based on the second recommended chart type; when there is a preset second keyword in the second data review, obtaining a second adjustment operation corresponding to the second keyword; adjusting the second alternative output chart according to the second adjustment operation to obtain a second output chart.

[0027] In some embodiments, the generation module generates a second data review based on the second recommended chart type and the data mining results in the following manner, including: matching a second alternative comment sentence corresponding to the second recommended chart type and the data mining results from a preset corpus; storing the correspondence between the second recommended chart type and the second alternative comment sentence, and the correspondence between the data mining results and the second alternative comment sentence in the corpus; and filling the second alternative comment sentence with data in the second alternative output chart to obtain a second data review.

[0028] In some embodiments, the electronic device includes a processor and a memory storing program instructions, and the processor is configured to perform the above-mentioned method for obtaining recommended chart types when executing the program instructions.

[0029] In some embodiments, the storage medium stores program instructions, and when the program instructions are executed, the above-mentioned method for obtaining recommended chart types is executed.

[0030] The method and apparatus, electronic device, and storage medium for obtaining recommended chart types provided by the embodiments of the present disclosure can achieve the following technical effects: identifying the data structure of user analysis data; determining a chart type range based on the data structure; including at least one candidate chart type in the chart type range; identifying the semantics of fields in the user analysis data, and determining a first recommended chart type from the chart type range; and recommending the determined first recommended chart type. By performing semantic recognition on the user analysis data, a chart type that meets the user's needs can be determined from the chart type range, thereby enabling the chart types recommended to the user to more accurately meet the user's needs.

[0031] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] One or more embodiments are exemplarily described by corresponding drawings. These exemplary descriptions and drawings do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a scale limitation. In addition,

[0033] Figure 1is a schematic diagram of a method for obtaining recommended chart types provided by an embodiment of the present disclosure;

[0034] Figure 2 is an application diagram of a corresponding relationship between a data feature, a first candidate comment statement, and a first recommended chart type according to an embodiment of the present disclosure;

[0035] Figure 3 is a schematic diagram of an application for obtaining a first output chart according to an embodiment of the present disclosure;

[0036] Figure 4 is another application schematic diagram of obtaining a first output chart according to an embodiment of the present disclosure;

[0037] Figure 5 is a schematic diagram of a method for obtaining recommended chart types provided by an embodiment of the present disclosure;

[0038] Figure 6 This is a schematic diagram of an application of a method for obtaining recommended chart types provided by an embodiment of the present disclosure;

[0039] Figure 7 is a schematic diagram of a device for obtaining recommended chart types provided by an embodiment of the present disclosure;

[0040] Figure 8 It is a structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0041] In order to be able to understand the features and technical content of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The accompanying drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the sake of convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0042] In the description and claims of the embodiments of the present disclosure, as well as in the accompanying drawings, the terms "first," "second," and the like are used to distinguish similar items and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate to describe the embodiments of the present disclosure herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions.

[0043] Unless otherwise stated, the term "plurality" means two or more.

[0044] 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 means: A or B.

[0045] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0046] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0047] This application can be applied to software such as online forms and tables.

[0048] 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.

[0049] In addition, the electronic devices involved in the embodiments of the present invention may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers (Tablet Computers), personal computers (PCs), PDAs (Personal Digital Assistants), wearable devices (such as smart glasses, smart watches, etc.), etc.

[0050] Combine Figure 1 As shown, an embodiment of the present disclosure provides a method for obtaining a recommended chart type, including:

[0051] Step S101, identifying the data structure of user analysis data;

[0052] Step S102, determining a chart type range according to the data structure; the chart type range includes at least one candidate chart type;

[0053] Step S103, identifying the semantics of the fields in the user analysis data, and determining a first recommended chart type from a range of chart types;

[0054] Step S104: recommending the determined first recommended chart type.

[0055] The method for obtaining recommended chart types provided in an embodiment of the present disclosure identifies the data structure of user analysis data; determines a chart type range based on the data structure; the chart type range includes at least one candidate chart type; identifies the semantic meaning of fields in the user analysis data, determines a first recommended chart type from the chart type range; and recommends the determined first recommended chart type. By performing semantic recognition on the user analysis data, a chart type that meets the user's needs can be determined from the chart type range, thereby enabling the chart types recommended to the user to more accurately meet the user's needs.

[0056] Optionally, the user analysis data includes data in each cell in a data table input by the user.

[0057] Optionally, the data structure of the user analysis data includes two types: metrics and dimensions; wherein metrics are numerical data and dimensions are text data.

[0058] In some embodiments, the types of measurements include time-type measurements, number-type measurements, and amount-type measurements, and the types of dimensions include geographic dimensions, person dimensions, and belonging dimensions, etc.; wherein, time-type measurements include numerical data representing time; number-type measurements include numerical data representing numbers; amount-type measurements include numerical data representing currency; geographic dimensions include cities, provinces, districts, or counties, etc.; person dimensions include the names of people, etc.; and belonging dimensions include the departments to which people belong or the classes to which people belong, etc.

[0059] In some embodiments, the user analysis data is the data in each cell of the data table input by the user, and the data structure of the user analysis data includes two types: metrics and dimensions; wherein the metrics are numerical data and the dimensions are text data. Based on the data structure, visual charts can be recommended, such as pie charts, bar charts, line charts, and continuous line charts. For example, a pie chart requires at least 1 dimension and 1 or 2 non-time type metrics; a continuous line chart requires 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-time type metric, a pie chart will be recommended to the user. In some embodiments, non-time type metrics include metrics other than time type, such as number type metrics, amount type metrics, etc.

[0060] Optionally, identifying the data structure of the user analysis data includes: performing statistics on the number and type of metrics and the number and type of dimensions in the user analysis data to obtain the data structure of the user analysis data.

[0061] Optionally, the data structure includes the number and type of metrics, and the number and type of dimensions. Determining the chart type range based on the data structure includes: matching at least one alternative chart type corresponding to the number and type of metrics and the number and type of dimensions from a preset chart type database; storing the correspondence between the number and type of metrics and the alternative chart types, and the correspondence between the number and type of dimensions and the alternative chart types in the chart type database; and determining each matched alternative chart type as the chart type range. In this way, by storing the alternative chart types and the data structure in the chart type database, the corresponding data structure can be obtained after semantic recognition of the user analysis data. In this way, various alternative chart types corresponding to the data structure can be matched directly from the chart type database, thereby obtaining the chart type range, narrowing the range of chart types corresponding to the user analysis data, and facilitating the recommendation of chart types that meet user needs to the user.

[0062] In some embodiments, a preset chart type database stores a correspondence between the number and type of metrics and candidate chart types, as well as a correspondence between the number and type of dimensions and candidate chart types. For example, the candidate chart types stored in the chart type database include grouped bar charts, stacked bar charts, percentage bar charts, grouped bar charts, stacked bar charts, percentage stacked bar charts, inline donut charts, and pie charts. The dimension types corresponding to grouped bar charts include two types: dimension 1 and dimension 2, with the number of dimension 1 ranging from 1 to 12 and the number of dimension 2 ranging from 1 to 6. The metric types corresponding to grouped bar charts include one metric type, with at least one metric. The dimension types corresponding to stacked bar charts include dimension 1 and dimension 2, with the number of dimension 1 ranging from 1 to 12 and the number of dimension 2 ranging from 1 to 6. The metric types corresponding to stacked bar charts include one metric type, with at least one metric. The dimension types corresponding to percentage bar charts include dimension 1 and dimension 2, with the number of dimension 1 ranging from 1 to 12 and the number of dimension 2 ranging from 1 to 6. The metric types corresponding to percentage bar charts include one metric type, with at least one metric. Grouped bar charts support two dimension types: Dimension 1 and Dimension 2. The number of Dimension 1 items is 1-30, and the number of Dimension 2 items is 1-6. Grouped bar charts support one metric type, and at least one metric type is required. Stacked bar charts support two dimension types: Dimension 1 and Dimension 2. The number of Dimension 1 items is 1-30, and the number of Dimension 2 items is 1-6. Stacked bar charts support one metric type, and at least one metric type is required. Percentage bar charts support two dimension types: Dimension 1 and Dimension 2. The number of Dimension 1 items is 1-30, and the number of Dimension 2 items is 1-6. Percentage bar charts support one metric type, and at least one metric type is required. Pie charts support one or two non-time metrics. Pie charts support at least one dimension, and any type is required. Column charts support at least two metrics, and any type is required. Column charts support at least one dimension, and any type is required.

[0063] In some embodiments, when the data structure of the user analysis data is two non-time type metrics and one dimension, the alternative chart types matched from the chart type database according to the data structure are: pie chart and bar chart, then the pie chart and bar chart are determined as the chart type range.

[0064] Optionally, determining the first recommended chart type from the range of chart types includes: upon identifying the presence of preset semantic content in the user analysis data, determining an alternative chart type within the range of chart types corresponding to the semantic content as the first recommended chart type. In this way, by introducing semantic recognition into the recommended chart types, the accuracy of matching user needs can be improved, and the usability of the interpretation solutions provided to users can be enhanced.

[0065] Optionally, the preset semantic content includes: words used to represent opposite semantics, words used to represent target semantics, words used to represent proportional semantics, words used to represent statistical semantics, words used to represent time semantics, words used to represent sorting semantics, words used to represent entities and their corresponding data, etc.

[0066] In some embodiments, the words used to represent opposite semantics include: size, how much, high and low, front and back, up and down, etc.; the words used to represent the semantics of achieving goals include: completion, task, achievement, target, completion rate, etc.; the words used to represent the semantics of proportion include: year-on-year, quarter-on-quarter, etc.; the words used to represent the semantics of statistics include: total, total, subtotal, etc.; the words used to represent the semantics of sorting include: ranking, sorting, etc.; the words used to represent the semantics of time include: time, date, etc.; the words used to represent entities and their corresponding data include: employee performance, company sales in each month, department performance, personnel achievements, etc.

[0067] Optionally, the candidate chart types in the chart type range include: a bidirectional bar chart, a pictogram, a column chart, a combination chart, a line chart, a pie chart, a bar chart, and the like.

[0068] In some embodiments, the semantic content corresponding to the bidirectional bar chart and pictogram includes "vocabulary used to represent opposite semantics" and the like; the semantic content corresponding to the bar chart includes "vocabulary used to represent the semantics of achieving the target" and the like; the semantic content corresponding to the combination chart includes "vocabulary used to represent the semantics of achieving the target"; the semantic content corresponding to the line chart includes "vocabulary used to represent time semantics", "vocabulary used to represent statistical semantics" and the like; the semantic content corresponding to the pie chart includes "vocabulary used to represent statistical semantics" and the like; the semantic content corresponding to the bar chart includes "vocabulary used to represent sorting semantics", "vocabulary used to represent entities and their corresponding data" and the like.

[0069] In some embodiments, when the preset semantic content includes "vocabulary for representing opposite semantics", in order to better reflect the comparative relationship between the data of opposite semantics, a bidirectional bar chart or pictogram is determined as the first recommended chart type. The bidirectional bar chart or pictogram can realize the data comparison of opposite semantic words by using positive and negative columns to display the numerical comparison between categories; when the preset semantic content includes "vocabulary for representing the semantics of achieving goals", in order to clearly and simply reflect the relationship between the various achieved goals, a bar chart is determined as the first recommended chart type. Figure 1 On the one hand, it makes it easier for users to understand large amounts of data and the relationships between the data; on the other hand, it allows users to interpret the original data more quickly and intuitively through visual symbols; when the preset semantic content includes "vocabulary used to represent time semantics" and "vocabulary used to represent proportion semantics", the line chart is determined as the first recommended chart type, and the line chart can be used to intuitively discover the changes between different data at each time node; when the preset semantic content includes "vocabulary used to represent statistical semantics", the pie chart is determined as the first recommended chart type, and the pie chart can be used to intuitively discover the ratio of the size of each item in the user's analysis data to the total amount; when the preset semantic content includes "vocabulary used to represent sorting semantics" or "vocabulary used to represent entities and their corresponding data", the bar chart is determined as the first recommended chart type, and the bar chart can enable users to intuitively understand the size of each data and easily compare the differences between the data.

[0070] In some embodiments, semantic recognition is performed on user analysis data in a data table through AI (Artificial Intelligence). When it is recognized that there is preset semantic content in the field of the user analysis data, such as completion, task, goal and achievement, the alternative chart type bar chart corresponding to the "vocabulary used to represent the semantics of achieving the goal" is determined as the first recommended chart type.

[0071] Optionally, determining the first recommended chart type from the chart type range includes: when pre-set semantic content is identified in the user analysis data, annotating the identified semantic content with intent; filtering a range of candidate chart types from the chart type range based on the intent; and determining the candidate chart type corresponding to the semantic content within the candidate chart type range as the first recommended chart type. Thus, by first determining the intent corresponding to the identified semantic content, a larger chart type range of chart types corresponding to the user analysis data can be determined based on the intent, and then matching the candidate chart type corresponding to the semantic content from the larger chart type range, thereby facilitating determination of the first recommended chart type.

[0072] Optionally, if no candidate chart type corresponds to the identified semantic content, a random candidate chart type is selected from the range of candidate chart types and determined as the first recommended chart type. In this way, even if no candidate chart type corresponds to the semantic content within a larger range of chart types, an alternative chart type can still be determined and recommended to the user.

[0073] Optionally, the chart type range includes one or more of a column chart, a pie chart, or a line chart. Optionally, the column chart includes a bar chart, a bar chart, a bidirectional bar chart, etc.; the pie chart includes an embedded ring chart, a pie chart, etc.; and the line chart includes a broken line chart, a continuous line chart, etc.

[0074] Optionally, the intent corresponding to each semantic content includes: comparison intent, time series, sorting intent, and proportion intent, etc.

[0075] Optionally, the identified semantic content is annotated with intent, including: matching the intent corresponding to the semantic content from a preset intent database; the intent database stores the correspondence between the semantic content and the intent.

[0076] Optionally, the alternative chart type range corresponding to the comparison intent is the bar chart class; the alternative chart type range corresponding to the time series intent is the line chart class; the alternative chart type range corresponding to the proportion intent is the pie chart class; the alternative chart type range corresponding to the sorting intent is the column chart class.

[0077] In some embodiments, the semantic content identified from the user analysis data is "vocabulary used to represent opposite semantics", the semantic content is annotated with intent, and the intent corresponding to the "vocabulary used to represent opposite semantics" is obtained as a comparison intent. According to the comparison intent, the alternative chart type range is screened out from the chart type range as a column chart class, and the first recommended chart type for "vocabulary used to represent opposite semantics" is matched as a two-way bar chart from the alternative chart types corresponding to the column chart class; in some embodiments, the semantic content identified from the user analysis data is "vocabulary used to represent statistical semantics", the semantic content is annotated with intent, and the intent corresponding to the "vocabulary used to represent statistical semantics" is obtained as a proportion intent. According to the comparison intent, the alternative chart type range is screened out from the chart type range as a pie chart class, and the first recommended chart type for "vocabulary used to represent opposite semantics" is matched as a pie chart from the alternative chart types corresponding to the column chart class.

[0078] Optionally, after recommending the determined first recommended chart type, the method further includes: obtaining data characteristics corresponding to the user analysis data; generating a first data review based on the first recommended chart type and the data characteristics; and generating a first output chart based on the first data review.

[0079] Optionally, obtaining data characteristics corresponding to the user analysis data includes: generating a first candidate output chart corresponding to the user analysis data based on a first recommended chart type; performing chart analysis on the first candidate output chart to obtain data characteristics corresponding to the first candidate output chart; or matching data characteristics corresponding to the first candidate output chart from a preset characteristics database, wherein the characteristics database stores correspondences between first candidate output charts and data characteristics. In this manner, generating a first data commentary based on the first recommended chart type and the data characteristics enables data interpretation of the first candidate output chart, facilitating user understanding of the first candidate output chart.

[0080] Optionally, the first data comment is used to characterize a descriptive text conclusion of the first candidate output chart.

[0081] Optionally, generating a first candidate 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 candidate output chart; and performing data processing on the user analysis data to obtain the first candidate output chart.

[0082] Optionally, a first data comment is generated based on the first recommended chart type and data characteristics, including: matching a first alternative comment sentence corresponding to the first recommended chart type and the data characteristics from a preset corpus; the corpus stores the correspondence between the first recommended chart type and the first alternative comment sentence, and the correspondence between the data characteristics and the first alternative comment sentence; and filling the first alternative comment sentence with the data in the first alternative output chart to obtain the first data comment.

[0083] Optionally, the first alternative commentary statements include: "XX has generally increased / decreased / remained unchanged over time"; "Except for an increase / decrease in X years, it has generally increased / decreased"; "A's growth rate is better than B"; "XX ranks first"; "XX ranks last"; "A is greater than B"; "The difference between the minimum and the maximum is X times", etc.

[0084] Combine Figure 2 As shown, Figure 2: This is an application diagram of the correspondence between a data feature, a first alternative comment statement and a first recommended chart type provided by an embodiment of the present disclosure; in some embodiments, Figures 1 to 5 are all example diagrams corresponding to the first recommended chart type; Figure 1 is a line chart that changes with time. By performing chart analysis on Figure 1, the data feature corresponding to Figure 1 is obtained as follows: "The overall trend is stable, and there is no particularly outstanding data"; the first alternative comment statement corresponding to the chart type of Figure 1 and the data feature is matched from the preset corpus as "XX rises / falls / remains unchanged over time"; Figure 2 is a bar chart of sales changing with time. By performing chart analysis on Figure 2, the data feature corresponding to Figure 2 is obtained as follows: "Some time nodes are inconsistent with the overall trend"; the first alternative comment statement corresponding to the chart type of Figure 2 and the data feature is matched from the preset corpus as "Except for the rise / fall in X years, the overall trend is rising / falling"; Figure 3 is a bar chart that changes with time. A graph comparing sales growth over time. By performing graph analysis on Figure 3, the corresponding data feature is obtained: "There are two broken lines for comparing growth rates"; the first alternative comment statement corresponding to the graph type of Figure 3 and the data feature is matched from the preset corpus: "A's growth rate is better than B"; Figure 4 is a bar graph comparing sales revenue. By performing graph analysis on Figure 4, the corresponding data feature is obtained: "All bar graphs / column graphs are applicable"; the first alternative comment statements corresponding to the graph type of Figure 4 and the data feature are matched from the preset corpus: "XX ranks first", "XX ranks last", "A is greater than B"; Figure 5 is a bar graph comparing company sales. By performing graph analysis on Figure 5, the corresponding data feature is obtained: "There is a large gap in the data"; the first alternative comment statement corresponding to the graph type of Figure 4 and the data feature is matched from the preset corpus: "The minimum and maximum differ by X times".

[0085] Optionally, the first candidate comment statement is filled with data in the first candidate output chart to obtain a first data comment.

[0086] Combine Figure 2As shown, in some embodiments, when the first alternative output chart is Example 2, Example 2 is a bar chart showing sales changes over time, and the first alternative comment sentence corresponding to the chart type of Example 2 and the data characteristics is "Except for the increase / decrease in X years, the overall trend is increase / decrease"; then the first alternative comment sentence is filled in according to the data in Example 2, and the first data comment is obtained as "Except for the decrease in 2004, the overall trend is increase"; when the first alternative output chart is Example 3, Example 3 is a comparison chart of sales growth rates over time, and the first alternative comment sentence corresponding to the chart type of Example 3 and the data characteristics is "A's The growth rate is better than B". The first alternative comment statement is filled in according to the data in Figure 3, and the first data comment is obtained as "The growth rate of sales expenses is better than net sales value". When the first alternative output chart is Figure 4, Figure 4 is a bar chart comparing sales revenue. The first alternative comment statements corresponding to the chart type of Figure 4 and the data characteristics are "XX ranks first", "XX ranks last", "A is greater than B". The first alternative comment statement is filled in according to the data in Figure 4, and the first data comments are obtained as "Competitor D ranks first", "Competitor C ranks last", "Competitor D is greater than Competitor B", etc.

[0087] Optionally, generating a first output chart based on the first data review includes: when there is a preset first keyword in the first data review, obtaining a first adjustment operation corresponding to the first keyword; adjusting the first alternative output chart according to the first adjustment operation to obtain the first output chart.

[0088] Optionally, the first keyword includes: words used to represent trend semantics, words used to represent extreme value semantics, words used to represent comparative semantics, etc.

[0089] In some embodiments, the words used to represent trend semantics include: gradually increase, gradually decrease, gradually rise, gradually decrease, etc.; the words used to represent extreme value semantics include: maximum, minimum, highest, lowest, longest, shortest, etc.; the words used to represent comparative semantics include: compare, compare, contrast, etc.

[0090] Optionally, the first adjustment operation includes: adding a trend line, highlighting extreme values, presenting graphics with comparative relationships in different display modes, etc.

[0091] Optionally, the first adjustment operation corresponding to the vocabulary used to represent trend semantics includes: adding a trend line; the first adjustment operation corresponding to the vocabulary used to represent extreme value semantics includes: highlighting the extreme value, and / or highlighting the specific numerical value; the first adjustment operation corresponding to the vocabulary used to represent comparative semantics includes: presenting graphics with comparative relationships in different display methods.

[0092] Combine Figure 3 and Figure 4 As shown, Figure 3 and Figure 4 All of them are schematic diagrams of obtaining a first output chart provided by the embodiments of the present disclosure; in some embodiments, when there is a preset first keyword in the first data review: "gradually increasing", a trend line is added on the basis of the first alternative output chart to obtain the first output chart; when there is a preset first keyword in the first data review: "maximum", the extreme value part is highlighted with color on the basis of the first alternative output chart, and the extreme value is marked on the first alternative output chart to obtain the first output chart; in this way, the first alternative output chart is adjusted according to the first data review, and the generated first output chart can better match the first data review, better express and explain the user analysis data, and facilitate user understanding.

[0093] Optionally, after recommending the determined first recommended chart type, the method further includes: performing data mining on the user analysis data using a preset data mining method; upon obtaining a data mining result, obtaining a second recommended chart type corresponding to the data mining method; generating a second data commentary based on the second recommended chart type and the data mining result; and generating a second output chart based on the second data commentary. In this way, the data mining method can uncover hidden information contained in the user analysis data, which is difficult to obtain by browsing the user analysis data. Therefore, the data mining method can effectively uncover deeper information hidden in the user analysis data, more effectively assisting users in data analysis and facilitating better interpretation of the user analysis data.

[0094] Optionally, the preset data mining methods include: central tendency analysis, dispersion trend analysis and correlation analysis, etc.; wherein, central tendency analysis includes mean analysis calculation, median analysis calculation or mode analysis calculation; dispersion trend analysis includes range analysis calculation, four-point difference analysis calculation, mean difference analysis calculation, variance analysis calculation or standard deviation analysis calculation.

[0095] Optionally, obtaining the second recommended chart type corresponding to the data mining method includes: matching the second recommended chart type corresponding to the data mining method from a preset chart type database; the chart type database stores the correspondence between the data mining method and the second recommended chart type.

[0096] Optionally, a second data review is generated based on the second recommended chart type and the data mining results, including: matching a second alternative comment sentence corresponding to the second recommended chart type and the data mining results from a preset corpus; the corpus stores the correspondence between the second recommended chart type and the second alternative comment sentence, and the correspondence between the data mining results and the second alternative comment sentence; and filling the second alternative comment sentence with data in the second alternative output chart to obtain a second data review.

[0097] In some embodiments, the second candidate output chart is an example table of sales figures for East China and West China. Data mining is performed on the user analysis data corresponding to the second candidate output chart using standard deviation analysis in the deviation trend analysis. The resulting second candidate data comment includes, "The sales gap between XXX is larger than the sales gap between XX." The second candidate comment is populated with data from the second candidate output chart, resulting in the second data comment, "The sales gap between East China is larger than the sales gap between West China." This data mining method can be used to obtain a standard for the degree of dispersion of data distribution, thereby measuring the degree to which data values ​​deviate from the arithmetic mean, facilitating a better interpretation of the user analysis data.

[0098] Optionally, generating a second output chart based on the second data review includes: generating a second alternative output chart corresponding to the user analysis data based on the second recommended chart type; when there is a preset second keyword in the second data review, obtaining a second adjustment operation corresponding to the second keyword; adjusting the second alternative output chart according to the second adjustment operation to obtain a second output chart.

[0099] Optionally, the second keyword includes: mean, median, mode, range, interquartile range, mean difference, variance or standard deviation, etc.

[0100] Optionally, the second adjustment operation includes: highlighting the mean, median, mode, range, interquartile range, mean difference, variance or standard deviation, and / or highlighting a specific value.

[0101] In some embodiments, the second adjustment operation corresponding to the average includes: highlighting the average; the second adjustment operation corresponding to the average difference includes: highlighting the average difference.

[0102] In some embodiments, when there is a preset second keyword "standard deviation" in the second data review, the standard deviation is marked on the second alternative output chart based on the second alternative output chart to obtain the second output chart; this achieves the adjustment of the second alternative output chart according to the second data review, and at the same time enables the generated second output chart to better match the second data review, better express and explain the user analysis data, and facilitate user understanding.

[0103] Combine Figure 5 As shown, an embodiment of the present disclosure provides a method for obtaining a first recommended chart type, including:

[0104] Step S501, identifying the data structure of user analysis data; the data structure includes the number and type of metrics, and the number and type of dimensions;

[0105] Step S502: Match at least one candidate chart type corresponding to the number and type of metrics and the number and type of dimensions from a preset chart type database; the chart type database stores a correspondence between the number and type of metrics and the candidate chart types, and a correspondence between the number and type of dimensions and the candidate chart types;

[0106] Step S503, determining the matched candidate chart types as a chart type range;

[0107] Step S504: identifying the semantics of fields in the user analysis data, and if it is identified that the user analysis data contains preset semantic content, determining an alternative chart type in the chart type range that corresponds to the semantic content as a first recommended chart type;

[0108] Step S505: recommending the determined first recommended chart type.

[0109] The method for obtaining recommended chart types provided by the embodiments of the present disclosure identifies the data structure of user analysis data; determines a chart type range based on the data structure; the chart type range includes at least one candidate chart type; identifies the semantics of the fields in the user analysis data, and determines a first recommended chart type from the chart type range; and recommends the determined first recommended chart type. By performing semantic recognition on the user analysis data to obtain a data structure, a rough range of chart types can be first determined based on the data structure of the user analysis data, thereby narrowing the range of the first recommended chart type. A chart type that meets the user's needs can then be determined from the chart type range based on the identified semantic content, thereby enabling the chart types recommended to the user to more accurately meet the user's needs.

[0110] Combine Figure 6 As shown, an embodiment of the present disclosure provides a method for obtaining a recommended chart type, including:

[0111] Step S601, identifying the data structure of user analysis data;

[0112] Step S602: determining a chart type range according to the data structure; the chart type range includes at least one candidate chart type;

[0113] Step S603, identifying the semantics of the fields in the user analysis data, and determining a first recommended chart type from a range of chart types;

[0114] Step S604, recommending the determined first recommended chart type;

[0115] Step S605, obtaining data characteristics corresponding to the user analysis data;

[0116] Step S606 , generating a first data review based on the first recommended chart type and data characteristics;

[0117] Step S607, generating a first output chart according to the first data review;

[0118] Step S608, performing data mining on the user analysis data using a preset data mining method;

[0119] Step S609: when the data mining result is obtained, obtaining a second recommended chart type corresponding to the data mining method;

[0120] Step S610, generating a second data review according to the second recommended chart type and the data mining result;

[0121] Step S611: Generate a second output chart based on the second data review.

[0122] The method for obtaining recommended chart types provided by the embodiment of the present disclosure is adopted, by identifying the data structure of the user analysis data; determining the chart type range based on the data structure; including at least one alternative chart type in the chart type range; identifying the field semantics in the user analysis data, and determining a first recommended chart type from the chart type range; and recommending the determined first recommended chart type. By performing semantic recognition on the user analysis data, it is possible to determine a chart type that meets the user's needs from the chart type range, so that the chart type recommended to the user can more accurately meet the user's needs; at the same time, by generating a first data comment and a second data comment, and obtaining a first output chart and a second output chart based on the first data comment and the second data comment, respectively, the user analysis data can be better interpreted, so that the generated first output chart and second output chart are more consistent with the first data comment and the second data comment, thereby improving the usability of the overall data interpretation.

[0123] Combine Figure 7As shown, an embodiment of the present disclosure provides a device for obtaining recommended chart types, including: a first identification module 701, a determination module 702, a second identification module 703 and a recommendation module 704; the first identification module 701 is configured to identify the data structure of user analysis data and send the data structure to the determination module; the determination module 702 is configured to receive the data structure sent by the first identification module, determine the chart type range according to the data structure, and send the chart type range to the second identification module; the chart type range includes at least one alternative chart type; the second identification module 703 is configured to receive the chart type range sent by the determination module, identify the field semantics in the user analysis data, determine the first recommended chart type from the chart type range, and send the first recommended chart type to the recommendation module; the recommendation module 704 is configured to receive the first recommended chart type sent by the second identification module, and recommend the determined first recommended chart type.

[0124] The apparatus for obtaining recommended chart types provided by the embodiments of the present disclosure employs a first identification module to identify the data structure of user analysis data; a determination module to determine a range of chart types based on the data structure; the range of chart types includes at least one candidate chart type; a second identification module to identify the semantics of fields in the user analysis data and determine a first recommended chart type from the range of chart types; and a recommendation module to recommend the determined first recommended chart type. By performing semantic identification on the user analysis data, a chart type that meets the user's needs can be determined from the range of chart types, thereby enabling the chart types recommended to the user to more accurately meet the user's needs.

[0125] Optionally, the data structure includes the number and type of metrics, and the number and type of dimensions, and the determination module determines the chart type range based on the data structure in the following manner: matching at least one alternative chart type corresponding to the number and type of metrics and the number and type of dimensions from a preset chart type database; the chart type database stores the correspondence between the number and type of metrics and the alternative chart types, and the correspondence between the number and type of dimensions and the alternative chart types; and determining each matched alternative chart type as the chart type range.

[0126] Optionally, the second identification module determines the first recommended chart type from the chart type range in the following manner: when identifying that preset semantic content exists in the user analysis data, determining the candidate chart type corresponding to the semantic content in the chart type range as the first recommended chart type.

[0127] Optionally, the device for obtaining a recommended chart type further includes: a generation module, the generation module being configured to obtain data characteristics corresponding to the user analysis data after recommending the determined first recommended chart type; generate a first data review based on the first recommended chart type and the data characteristics; and generate a first output chart based on the first data review.

[0128] Optionally, the generation module obtains data characteristics corresponding to the user analysis data in the following manner, including: generating a first alternative output chart corresponding to the user analysis data based on a first recommended chart type; performing chart analysis processing on the first alternative output chart to obtain data characteristics corresponding to the first alternative output chart; or matching data characteristics corresponding to the first alternative output chart from a preset feature database, wherein the feature database stores the correspondence between the first alternative output chart and the data characteristics.

[0129] Optionally, the generation module generates a first output chart based on the first data review in the following manner, including: when there is a preset first keyword in the first data review, obtaining a first adjustment operation corresponding to the first keyword; adjusting the first alternative output chart according to the first adjustment operation to obtain the first output chart.

[0130] Optionally, the generation module generates a first data comment based on the first recommended chart type and data characteristics in the following manner, including: matching a first alternative comment sentence corresponding to the first recommended chart type and the data characteristics from a preset corpus; the corpus stores the correspondence between the first recommended chart type and the first alternative comment sentence, and the correspondence between the data characteristics and the first alternative comment sentence; and filling the first alternative comment sentence with the data in the first alternative output chart to obtain the first data comment.

[0131] Optionally, after generating the first output chart based on the first data review, the generation module further includes: performing data mining on the user analysis data using a preset data mining method; when obtaining the data mining results, obtaining a second recommended chart type corresponding to the data mining method; generating a second data review based on the second recommended chart type and the data mining results; and generating a second output chart based on the second data review.

[0132] Optionally, the generation module generates a second output chart based on the second data review in the following manner, including: generating a second alternative output chart corresponding to the user analysis data based on the second recommended chart type; when there is a preset second keyword in the second data review, obtaining a second adjustment operation corresponding to the second keyword; adjusting the second alternative output chart according to the second adjustment operation to obtain a second output chart.

[0133] Optionally, the generation module generates a second data review based on the second recommended chart type and the data mining results in the following manner, including: matching a second alternative comment sentence corresponding to the second recommended chart type and the data mining results from a preset corpus; the corpus stores the correspondence between the second recommended chart type and the second alternative comment sentence, and the correspondence between the data mining results and the second alternative comment sentence; and filling the second alternative comment sentence with the data in the second alternative output chart to obtain the second data review.

[0134] Combine Figure 8 As shown, 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 can communicate with each other through the bus 803. The communication interface 802 can be used for information transmission. The processor 800 can call the logic instructions in the memory 801 to execute the method for obtaining the recommended chart type of the above embodiment.

[0135] In addition, the program instructions in the memory 801 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0136] Memory 801, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of the present disclosure. Processor 800 executes the program instructions / modules stored in memory 801 to perform functional applications and data processing, thereby implementing the method for obtaining recommended chart types in the above-mentioned embodiments.

[0137] The memory 801 may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the terminal device. Furthermore, the memory 801 may include high-speed random access memory and non-volatile memory.

[0138] The electronic device provided by the embodiments of the present disclosure identifies the data structure of user analysis data; determines a range of chart types based on the data structure; the range of chart types includes at least one candidate chart type; identifies the semantic meaning of fields in the user analysis data, determines a first recommended chart type from the range of chart types; and recommends the determined first recommended chart type. By performing semantic recognition on the user analysis data, a chart type that meets the user's needs can be determined from the range of chart types, thereby enabling the chart types recommended to the user to more accurately meet the user's needs.

[0139] Optionally, the electronic device includes: a computer, a server, etc.

[0140] An embodiment of the present disclosure provides a storage medium, wherein program instructions, when executed, execute the above-mentioned method for obtaining recommended chart types.

[0141] An embodiment of the present disclosure provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the above-mentioned method for obtaining recommended chart types.

[0142] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0143] The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present disclosure. The aforementioned storage medium may be a non-transitory storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program code, or a transient storage medium.

[0144] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to also include plural forms. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variations "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups of these. In the absence of further restrictions, an element defined by the sentence "comprising a..." does not exclude the presence of other identical elements in the process, method or device that includes the element. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments can be referenced to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can be found in the description of the method part.

[0145] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. The technicians will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0146] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units can be merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, and can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of the present disclosure may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0147] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the systems, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for obtaining a recommended chart type, characterized in that: include: Identify the data structure of user analysis data; determining a chart type range according to the data structure; wherein the chart type range includes at least one candidate chart type; identifying field semantics in the user analysis data, and determining a first recommended chart type from the range of chart types; Recommending a first recommended chart type determined; generating a first candidate output chart corresponding to the user analysis data according to the first recommended chart type; Obtaining data characteristics corresponding to the user analysis data; generating a first data review according to the first recommended chart type and the data characteristics; In a case where a preset first keyword exists in the first data review, a first adjustment operation corresponding to the first keyword is obtained; and a first candidate output chart is adjusted according to the first adjustment operation to obtain a first output chart.

2. The method according to claim 1, characterized in that The data structure includes the number and type of metrics and the number and type of dimensions. Determining a chart type range based on the data structure includes: matching at least one candidate chart type corresponding to the number and type of the metric and the number and type of the dimension from a preset chart type database; the chart type database storing a correspondence between the number and type of the metric and the candidate chart type, and a correspondence between the number and type of the dimension and the candidate chart type; The matched candidate chart types are determined as the chart type range.

3. The method according to claim 1, characterized in that Determining a first recommended chart type from the range of chart types includes: In a case where it is identified that the user analysis data contains preset semantic content, a candidate chart type corresponding to the semantic content in the chart type range is determined as a first recommended chart type.

4. The method according to claim 1, wherein Obtaining data characteristics corresponding to the user analysis data includes: Performing chart analysis on the first candidate output chart to obtain data features corresponding to the first candidate output chart; or matching data features corresponding to the first candidate output chart from a preset feature database, wherein the feature database stores a correspondence between the first candidate output chart and the data features.

5. The method according to claim 1, wherein Generating a first data review according to the first recommended chart type and the data characteristics includes: Matching a first candidate comment sentence corresponding to the first recommended chart type and the data characteristics from a preset corpus; the corpus stores the correspondence between the first recommended chart type and the first candidate comment sentence, and the correspondence between the data characteristics and the first candidate comment sentence; The first candidate comment statement is filled with the data in the first candidate output chart to obtain the first data comment.

6. The method according to claim 1, wherein After the first recommended chart type is determined, it also includes: Performing data mining on the user analysis data using a preset data mining method; When the data mining result is obtained, obtaining a second recommended chart type corresponding to the data mining method; generating a second data review according to the second recommended chart type and the data mining result; A second output graph is generated based on the second data review.

7. The method according to claim 6, characterized in that Generating a second output chart according to the second data review includes: generating a second candidate output chart corresponding to the user analysis data according to the second recommended chart type; When a preset second keyword exists in the second data review, obtaining a second adjustment operation corresponding to the second keyword; The second candidate output chart is adjusted according to the second adjustment operation to obtain a second output chart.

8. The method according to claim 7, characterized in that Generating a second data review according to the second recommended chart type and the data mining result includes: Matching a second candidate review sentence corresponding to the second recommended chart type and the data mining result from a preset corpus; the corpus stores a correspondence between the second recommended chart type and the second candidate review sentence, and a correspondence between the data mining result and the second candidate review sentence; The second candidate comment statement is filled with the data in the second candidate output chart to obtain a second data comment.

9. A device for obtaining recommended chart types, characterized in that: include: a first identification module configured to identify a data structure of user analysis data; a determination module configured to determine a range of chart types according to the data structure; the range of chart types includes at least one candidate chart type; a second identification module 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 recommended chart type; a generating module configured to generate a first candidate output chart corresponding to the user analysis data based on the first recommended chart type; obtain data characteristics corresponding to the user analysis data; and generate a first data commentary based on the first recommended chart type and the data characteristics; In a case where a preset first keyword exists in the first data review, a first adjustment operation corresponding to the first keyword is obtained; and a first candidate output chart is adjusted according to the first adjustment operation to obtain a first output chart.

10. An electronic device comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for obtaining a recommended chart type according to any one of claims 1 to 8 when running the program instructions.

11. A storage medium storing program instructions, characterized in that: When the program instructions are executed, the method for obtaining recommended chart types according to any one of claims 1 to 8 is executed.

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