CN113807066A - Chart generation method and device and electronic equipment - Google Patents
Chart generation method and device and electronic equipment Download PDFInfo
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Abstract
The invention discloses a chart generation method, a chart generation device and electronic equipment, wherein the method comprises the following steps: presetting a plurality of different models, wherein each preset model represents an analysis dimension, and matching a first field of a data source with a preset model by reading the first field in the data source; and in the case that a plurality of first target fields matched with the target model are detected and the plurality of first target fields have incidence relations, generating a chart based on the configuration information of the model and the data of the first target fields. Therefore, fields in the data source can be automatically matched with the model to generate the chart, and valuable reference is provided for a user to select a proper chart analysis scheme, so that the user can quickly select the proper chart, and the chart generation period is shortened.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method and an apparatus for generating a graph, and an electronic device.
Background
In the existing data analysis system, abundant charts are used for displaying analysis results, and qualitative analysis and quantitative analysis results of data can be accurately displayed. Through the display of the chart, the past data condition, the current data condition and the future development trend can be visually displayed, the decision layer is facilitated to better master the current situation, and the prediction and decision can be made for the future.
However, in the prior art, the chart is usually created according to the business requirements, the user selects the chart form according to the business requirements, and creates the chart according to the data provided by the user, but in most cases, the user is uncertain about what kind of analysis is performed on the data and what kind of form of chart is not used for presentation, which brings great difficulty to the analysis of the data and the creation of the chart, and the user's requirements are difficult to achieve.
Disclosure of Invention
In view of this, the embodiment of the invention discloses a chart generation method, a chart generation device and an electronic device, which can automatically provide a chart with multiple analysis dimensions for a user to select, so that the user can quickly select a proper analysis scheme, the chart generation period is shortened, and the efficiency is improved.
The embodiment of the invention discloses a chart generation method, which comprises the following steps:
reading a first field of a data source;
detecting whether a plurality of first target fields matched with the target model exist; each preset model represents an analysis dimension, the target model is any one or more than one of the preset models, and the first target field is any one of the first fields;
and if a plurality of first target fields matched with the target model exist in the first fields and the incidence relation exists among the first target fields, generating a chart based on the data of the first target fields and the configuration information of the target model.
Optionally, the detecting whether there are a plurality of first target fields matching the target model includes:
acquiring a second field group of each model; the second field set comprises a plurality of second fields;
traversing the first fields of the data source, and detecting whether a plurality of first fields matched with the second field group of any model exist;
and if the plurality of first target fields are detected to be matched with the second field group of the target model, the plurality of first target fields are matched with the target model.
Optionally, the generating a graph based on the data of the plurality of first target fields and the configuration information of the target model includes:
establishing a corresponding relation between the data of the first target field and a second target field of the target model based on the matching relation between the first target field and the second target field;
calling data of a first target field;
and generating a chart based on the data of the first target field, the corresponding relation between the data of the first target field and the second target field and the configuration information of the target model.
Optionally, the method further includes:
responding to a chart adjusting instruction, and analyzing the adjusting instruction to obtain adjusting information;
and adjusting the chart according to the adjustment information.
Optionally, the method further includes:
obtaining label information of a model corresponding to a plurality of target charts; the target graph is any one of the graphs;
charts having the same label information are combined into one album.
The embodiment of the invention discloses a chart generating device, which comprises:
a reading unit, configured to read a first field of a data source;
the matching unit is used for detecting whether a plurality of first target fields matched with the target model exist or not; each preset model represents an analysis dimension, the target model is any one or more than one of the preset models, and the first target field is any one of the first fields;
and the chart generation unit is used for generating a chart based on the data of the first target fields and the configuration information of the target model if the first fields have a plurality of first target fields matched with the target model and the first target fields have an incidence relation.
Optionally, the matching unit includes:
an obtaining subunit, configured to obtain a second field group of each model; the second field set comprises a plurality of second fields;
the detection subunit is used for traversing the first fields of the data source and detecting whether a plurality of first fields matched with the second field group of any model exist;
and the matching subunit is used for indicating that the plurality of first target fields are matched with the target model if the plurality of first target fields are detected to be matched with the second field group of the target model.
Optionally, the chart generating unit includes:
the relation establishing subunit is used for establishing a corresponding relation between the data of the first target field and the second target field based on the matching relation between the first target field and the second target field of the target model;
the calling subunit is used for calling the data of the first target field;
and the chart generation subunit is used for generating a chart based on the data of the first target field, the corresponding relation between the data of the first target field and the second target field and the configuration information of the target model.
Optionally, the method further includes:
a chart adjustment subunit to:
responding to a chart adjusting instruction, and analyzing the adjusting instruction to obtain adjusting information;
adjusting the chart according to the adjustment information
The embodiment of the invention discloses an electronic device, which comprises:
a memory and a processor;
the memory is used for storing programs, and the processor is used for executing the chart generation method when the programs in the memory are executed.
The embodiment of the invention discloses a chart generation method, a chart generation device and electronic equipment, wherein the method comprises the following steps: presetting a plurality of different models, wherein each preset model represents an analysis dimension, and matching a first field of a data source with a preset model by reading the first field in the data source; and in the case that a plurality of first target fields matched with the target model are detected and the plurality of first target fields have incidence relations, generating a chart based on the configuration information of the model and the data of the first target fields. Therefore, fields in the data source can be automatically matched with the model to generate the chart, and valuable reference is provided for a user to select a proper chart analysis scheme, so that the user can quickly select the proper chart, and the chart generation period is shortened.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flow chart diagram illustrating a chart generation method according to embodiment 1 of the present invention;
fig. 2 is a schematic flow chart diagram illustrating a chart generation method according to embodiment 2 of the present invention;
fig. 3 is a schematic flow chart of a chart generation method according to embodiment 3 of the present invention;
FIG. 4 is a schematic structural diagram of a chart generation apparatus according to an embodiment of the present invention;
fig. 5 shows a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, a user needs to select a chart form according to business requirements and make a chart through data provided by the user, but in most cases, the user cannot determine what kind of analysis is performed on the data and also does not determine which kind of form of chart is adopted for presentation, so that great difficulty is brought to making the chart, the requirements of the user are difficult to achieve, and the chart can be continuously adjusted to meet the requirements of the user as far as possible, so that the labor is consumed, and the efficiency is extremely low.
In order to solve the above problem, in this embodiment, a plurality of models are pre-constructed, each preset model represents an analysis dimension, when a graph needs to be generated, a first field of a data source is read, the first field of the data source is matched with the preset model, and if a plurality of first target fields matched with a target model exist in the first field and an association relationship exists between the plurality of first target fields, the graph is generated based on data of the plurality of first target fields and configuration information of the target model; each preset model represents an analysis dimension, the target model is any one or more of the preset models, and the first target field is any one or more of the first fields. Therefore, different fields in the data source can be combined to generate charts with different analysis dimensions based on the preset model, so that various chart showing forms are provided for a user, a client can quickly select a proper chart, and the chart generation period is shortened.
The chart generation method will be described in detail below:
example 1
Referring to fig. 1, a schematic flow chart of a chart generation method provided in embodiment 1 of the present invention is shown, and in this embodiment, the method includes:
s101: reading a first field of a data source;
in this embodiment, the data source includes data to be analyzed, and the data source includes: fields and data, a field indicating the type of data, a field for example comprising: income amount, time, department, etc. For the convenience of distinction, in this embodiment, the fields in the data source are represented by the first field, and the first field is all the fields in the data source. S102: detecting whether a plurality of first target fields matched with the target model exist; each preset model represents an analysis dimension, the target model is any one or more than one of the preset models, and the first target field is any one of the first fields;
in this embodiment, different types of models are preset, and each type of model represents a different analysis dimension, and includes, for example: revenue analysis dimensions, people flow analysis, and the like.
And, the different kinds of models contain fields that are not exactly the same, such as a model of revenue analysis dimension, for example, revenue, time; the staff flow analysis dimension contains fields such as number of staff, time.
Optionally, the generating process of the model includes:
acquiring data of charts with different analysis dimensions, and performing semantic analysis on the data of the charts with different analysis dimensions to obtain description information of the charts with different analysis dimensions;
and performing word segmentation processing on the description information of the graphs with different analysis dimensions to obtain a second field of each model, and setting configuration information of the models based on the description information of the graphs with different analysis dimensions.
The data of the graphs with different analysis dimensions may be crawled from a data classification website, and the description information of the graphs is obtained by performing semantic analysis on the data of the graphs with different analysis dimensions, for example, the description information includes: annual company income proportion analysis, department profit proportion, profit ranking of each part of the company and the like.
In this embodiment, the second field obtained by the word segmentation process is information related to data, and the configuration information is information related to generating a chart.
Wherein the configuration information may include: analysis methods and chart types.
For example, the following steps are carried out: the description information of the chart is annual company income commensuration analysis, and the fields obtained after splitting comprise: yearly, company income, comparability, analysis, wherein the second field corresponding to each year may be time, the dimension is year, the second field corresponding to company income is income, the model is used for comparably analyzing annual income, then the configuration information may be advanced aggregation calculation for comparably analyzing, and besides, the configuration information may include chart type: a bar graph or a line graph.
In this embodiment, as can be known from the above description, the data source includes a plurality of fields, and a field in the data source is represented as a first field, where when detecting whether there are a plurality of first target fields matching a target model, all the fields in the data source are traversed, so as to detect whether there is a first target field matching a certain preset model, optionally, the following two ways may be included to detect whether there are a plurality of first target fields matching a model:
the first implementation mode comprises the following steps:
acquiring a second field group of each model; the second field set comprises a plurality of second fields;
traversing the first fields of the data source, and detecting whether a plurality of first fields matched with the second field group of any model exist;
and if the plurality of first target fields are detected to be matched with the second field group of the target model, the plurality of first target fields are matched with the target model.
For example, assume that the second set of fields in the annual corporate revenue comparability model for revenue includes: a revenue field and a time field, a first field in the data source comprising a plurality of, when the presence of the revenue field and the time field in the data source is detected, indicating the presence of the first field matching the revenue analysis model.
In this embodiment, the matching of the first field in the data source and the second field of the model may be understood as a match if the meaning of the first field representation and the meaning of the second field representation are the same.
In this embodiment, when detecting whether there is a first field matching a second field group of the model, a preset semantic analysis model may be used, and whether the first field matches the second field in the second field group of the model is detected through the semantic analysis model.
The semantic analysis model can be obtained by training in advance, some fields possibly used for constructing the chart are used as training samples, and the semantic analysis model to be trained is trained by taking the output result close to the standard classification result as the purpose.
For example, the following steps are carried out: if the first field is a year and the second field of a certain preset model is time, wherein the attribute of the year is time, the task year and the task time belong to the same class through the semantic analysis model, and the first field is matched with the second field.
Furthermore, in order to obtain a more accurate matching result, when detecting whether the first field is matched with the field in the second field group through the semantic analysis model, the judgment can be performed by combining the comment of the first field.
For example, the following steps are carried out: for example, the first field is: revenue (as profit) is obtained, and therefore, when detecting whether the first field and the second field are matched, the revenue can be adopted to be matched and verified with the second field of each preset model, and the matching and verification can be assisted by the profit obtained by the comment of the first field.
The second embodiment:
as can be seen from the above description, each model represents different analysis dimensions, and then the tag description information may be represented as a description of different analysis dimensions, and the semantic analysis model analyzes the field information of the data source and the tag description information of the model to determine whether the first field matches the tag description information.
S103: and if a plurality of first target fields matched with the target model exist in the first fields and the incidence relation exists among the first target fields, generating a chart based on the data of the first target fields and the configuration information of the target model.
In this embodiment, the conditions for generating the graph based on the preset model include:
condition 1: a plurality of first target fields matched with the model exist in the data source;
condition 2: there is an associative relationship between a plurality of first target fields that match the model.
In this embodiment, the method for detecting whether there is an association relationship between a plurality of first target fields matching the model includes multiple methods, for example, the method may be determined based on a relationship between fields in a data source or a relationship between data tables to which the fields belong. When fields in the same data table in the data source are represented as fields with association relationship, or each field contained in the data table with association relationship is a field with association relationship.
When the above two conditions are satisfied, a graph may be generated based on the data of the first target field and the configuration information of the target model.
In this embodiment, the configuration information of the target model represents relevant information for generating a chart, wherein the analysis method of the chart, the type of the chart, and the like.
Wherein, under one implementation, the generating of the graph comprises:
establishing a corresponding relation between the data of the first target field and the second target field based on the matching relation between the first target field and the second target field of the target model;
calling data of a first target field;
and generating a chart based on the data of the first target field, the corresponding relation between the data of the first target field and the second target field and the configuration information of the target model.
In this embodiment, the corresponding relationship between the data of the first target field and the second target field is established, which means that the data of the first target field and the second target field are bound, for example, the address of the first target field and the second target field may be bound.
In this embodiment, each model may be presented in any form of a chart, and a default chart type may be preset without human intervention, where the default chart type may be any one or more chart types, and may also be an optimal chart type. That is, the configuration information of the target model includes information of the preset chart or charts.
In this embodiment, as can be known from the above description, when the target model is one or more of the preset models, the generated chart may be generated based on at least one model, when the chart is generated based on each model, each model includes at least one chart form, and when the configuration information includes information of at least one chart, when the chart is generated based on one model, at least one chart may be generated.
For example, the following steps are carried out: when the chart needs to be generated, reading a first field in the data source, and if the first field in the data source comprises: and matching fields in the data source with a second field group of a preset model, wherein if the fields contained in the first model (represented as a revenue analysis dimension) are revenue and time, the 'company revenue' and 'revenue' of the first field are considered to be matched, and the 'year' of the first field and the 'time' of the second field are matched, the first field matched with the model exists in the data source. A chart is generated from the configuration information of the model representing the revenue analysis dimension and the data of the "company revenue" field and the "revenue" field. If the second model includes second fields of "spending" and "time," then the "year" of the first field in the data source matches the "time" of the second field, and the "spending" of the first field in the data source matches the "spending" of the second field, then a chart may be generated based on the data of the "year" and "spending" fields of the first field and the configuration information of the second field.
In the embodiment, a plurality of different models are preset, each preset model represents one analysis dimension, and the first field of the data source is matched with the preset model by reading the first field in the data source; and in the case that a plurality of first target fields matched with the target model are detected and the plurality of first target fields have incidence relations, generating a chart based on the configuration information of the model and the data of the first target fields. Therefore, fields in the data source can be automatically matched with the model to generate the chart, and valuable reference is provided for a user to select a proper chart analysis scheme, so that the user can quickly select the proper chart, and the chart generation period is shortened.
Example 2
Specifically, referring to fig. 2, a flow diagram of a chart generation method provided in embodiment 2 of the present invention is shown, where the method includes:
s201: reading a first field of a data source;
in this embodiment, S201 is the same as S101, and is not described again in this embodiment.
S202: detecting whether a plurality of first target fields matched with the target model exist; each preset model represents an analysis dimension, the target model is any one or more than one of the preset models, and the first target field is any one of the first fields;
in this embodiment, S202 is the same as S102, and is not described again in this embodiment.
S203: and if a plurality of first target fields matched with the target model exist in the first fields and the incidence relation exists among the first target fields, generating a chart based on the data of the first target fields and the configuration information of the target model.
In this embodiment, S203 is the same as S103, and is not described again in this embodiment.
S204: responding to a chart adjusting instruction, and analyzing the adjusting instruction to obtain adjusting information;
in this embodiment, the chart adjustment instruction is triggered by a user and is used to instruct execution of a chart adjustment operation.
In this embodiment, the chart adjustment instructions include multiple types, for example, the second field name is modified, the analysis dimension is set, and the chart type is replaced, and each different chart adjustment instruction includes different adjustment information.
For example, the following steps are carried out: when the adjustment instruction is to modify the second field name, the adjustment information includes: the name of the second field before adjustment and the name of the second field after adjustment; when the adjustment instruction is to modify the analysis dimension, the adjustment information includes: an analysis method, a second field name; when the adjustment instruction is a type of a replacement chart, the adjustment information includes: the type of chart after replacement.
Furthermore, in order to facilitate user operation, a visual adjustment interface is provided, and a user can select an adjusted item and set adjustment information of the adjusted item.
S205: and adjusting the chart according to the adjustment information.
In this embodiment, the chart adjustment operation is performed through the adjustment information to adjust the generated chart.
For example, the following steps are carried out: when the first field of the data source is matched with the model, the first field name in the data source and the second field name of the model are different, so that the mismatching condition may occur during matching, and a user can replace the field and the data in the chart in a field correcting mode. Or when the second field provided in the model does not meet the requirement, the second field provided by the model can be modified; or the user determines the analysis mode of the data through the generated chart, but the analysis dimension of the chart can be modified under the condition that the analysis mode does not exist in the model, so that the generated chart can be modified.
For example, the following steps are carried out: when the chart needs to be generated, reading a first field in the data source, and if the first field in the data source comprises: the method comprises the steps of matching fields in a data source with a second field group of a preset model, wherein if a certain model (represented as a revenue analysis dimension) comprises fields of income and time, the 'company income' and the 'income' of a first field are considered to be matched, and the 'year' of the first field and the 'time' of a second field are matched, the fact that the first field matched with the model exists in the data source is indicated. A chart is generated from the configuration information of the model representing the revenue analysis dimension and the data of the "company revenue" field and the "revenue" field. However, the generated chart may not meet the user's requirement, and the user may adjust the generated chart, for example, the adjustment instruction is to modify the chart type, and the currently generated chart type may be replaced with the chart type selected by the user. Or the user considers that the result of semantic analysis is not accurate and needs to replace the data of a certain field, the data of each field in the model can be reset.
In this embodiment, when the automatically generated chart cannot meet the user requirement, the user may adjust the generated chart according to the requirement, so as to obtain the chart matching with the user requirement.
Example 3
Further, after the chart is manufactured, in order to more clearly embody the analysis result of the data source in the system, the generated chart needs to be summarized, and specifically, referring to fig. 3, a further schematic flow chart of the chart generating method provided in the embodiment of the present invention is shown, where in the embodiment of the present invention, the method includes:
s301: reading a first field of a data source;
in this embodiment, S301 is the same as S101, and is not described again in this embodiment.
S302: detecting whether a plurality of first target fields matched with the target model exist; each preset model represents an analysis dimension, the target model is any one or more than one of the preset models, and the first target field is any one of the first fields;
in this embodiment, S302 is the same as S102, and is not described again in this embodiment.
S303: and if a plurality of first target fields matched with the target model exist in the first fields and the incidence relation exists among the first target fields, generating a chart based on the data of the first target fields and the configuration information of the target model.
In this embodiment, S303 is the same as S303 described above, and details are not described again in this embodiment.
S304: obtaining label information of a model corresponding to a plurality of target charts; the target graph is any one of the graphs;
s305: charts having the same label information are combined into one album.
In this embodiment, each model is preset with label information, which is used to indicate the type of model analysis, such as company excess, company income, company staff flow, and the like.
Wherein the label of each model is preset.
In this embodiment, the plurality of target charts mentioned in S304 may be all the generated charts, or may be a plurality of charts selected by the user from the generated charts.
In addition, when the charts with the same label information are combined into an album, the layout and placement of the album may be preset, may be random, or may be a placement form selected by a user in the preset layout and placement. Also, before executing S304, the generated chart may be adjusted based on the user' S requirement
In this embodiment, the generated charts are combined into an album, so that the analysis condition of the data can be more clearly displayed.
Example 4
Referring to fig. 4, a schematic structural diagram of a chart generating apparatus according to an embodiment of the present invention is shown, in this embodiment, the apparatus includes:
a reading unit 401, configured to read a first field of a data source;
a matching unit 402, configured to detect whether there are multiple first target fields matching the target model; each preset model represents an analysis dimension, the target model is any one or more than one of the preset models, and the first target field is any one of the first fields;
the graph generating unit 403 is configured to, if a plurality of first target fields matching the target model exist in the first field and an association relationship exists between the plurality of first target fields, generate a graph based on data of the plurality of first target fields and configuration information of the target model.
Optionally, the matching unit includes:
an obtaining subunit, configured to obtain a second field group of each model; the second field set comprises a plurality of second fields;
the detection subunit is used for traversing the first fields of the data source and detecting whether a plurality of first fields matched with the second field group of any model exist;
and the matching subunit is used for indicating that the plurality of first target fields are matched with the target model if the plurality of first target fields are detected to be matched with the second field group of the target model.
Optionally, the chart generating unit includes:
the relation establishing subunit is used for establishing a corresponding relation between the data of the first target field and the second target field based on the matching relation between the first target field and the second target field of the target model;
the calling subunit is used for calling the data of the first target field;
and the chart generation subunit is used for generating a chart based on the data of the first target field, the corresponding relation between the data of the first target field and the second target field and the configuration information of the target model.
Optionally, the method further includes:
a chart adjustment subunit to:
responding to a chart adjusting instruction, and analyzing the adjusting instruction to obtain adjusting information;
and adjusting the chart according to the adjustment information.
Matching the first field of the data source with a preset model by reading the first field in the data source; each model represents an analysis dimension, and when a plurality of first target fields matched with the model are detected and association relations exist among the plurality of first target fields, the model can be adapted to the data in the data source, and a chart is generated based on the configuration information of the model and the data of the first target fields. Therefore, the method can automatically generate the chart based on the field matching model in the data source, and provides valuable reference for a user to select a proper analysis scheme, so that the client can quickly select the proper analysis scheme, and the generation period of the chart is shortened.
Example 5
Referring to fig. 5, a schematic structural diagram of an electronic device according to an embodiment of the present invention is shown, where in this embodiment, the electronic device includes:
a memory 501 and a processor 502;
the memory 501 is used for storing programs, and the processor 502 is used for executing the chart generation method described below when the programs in the memory are executed:
reading a first field of a data source;
detecting whether a plurality of first target fields matched with the target model exist; each preset model represents an analysis dimension, the target model is any one or more than one of the preset models, and the first target field is any one of the first fields;
and if a plurality of first target fields matched with the target model exist in the first fields and the incidence relation exists among the first target fields, generating a chart based on the data of the first target fields and the configuration information of the target model.
Optionally, the detecting whether there are a plurality of first target fields matching the target model includes:
acquiring a second field group of each model; the second field set comprises a plurality of second fields;
it is detected whether there are a plurality of first fields that match the second field group of any of the models.
Optionally, the generating a graph based on the data of the plurality of first target fields and the configuration information of the target model includes:
establishing a corresponding relation between the data of the first target field and a second target field of the target model based on the matching relation between the first target field and the second target field;
calling data of a first target field;
and generating a chart based on the data of the first target field, the corresponding relation between the data of the first target field and the second target field and the configuration information of the target model.
Optionally, the method further includes:
responding to a chart adjusting instruction, and analyzing the adjusting instruction to obtain adjusting information;
and adjusting the chart according to the adjustment information.
Optionally, the method further includes:
obtaining label information of a model corresponding to a plurality of target charts; the target graph is any one of the graphs;
charts having the same label information are combined into one album.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A chart generation method, comprising:
reading a first field of a data source;
detecting whether a plurality of first target fields matched with the target model exist; each preset model represents an analysis dimension, the target model is any one or more than one of the preset models, and the first target field is any one of the first fields;
and if a plurality of first target fields matched with the target model exist in the first fields and the incidence relation exists among the first target fields, generating a chart based on the data of the first target fields and the configuration information of the target model.
2. The method of claim 1, wherein detecting whether there are a plurality of first target fields that match a target model comprises:
acquiring a second field group of each model; the second field set comprises a plurality of second fields;
traversing the first fields of the data source, and detecting whether a plurality of first fields matched with the second field group of any model exist;
and if the plurality of first target fields are detected to be matched with the second field group of the target model, the plurality of first target fields are matched with the target model.
3. The method of claim 2, wherein generating a graph based on the data of the plurality of first goal fields and the configuration information of the goal model comprises:
establishing a corresponding relation between the data of the first target field and a second target field of the target model based on the matching relation between the first target field and the second target field;
calling data of a first target field;
and generating a chart based on the data of the first target field, the corresponding relation between the data of the first target field and the second target field and the configuration information of the target model.
4. The method of claim 1, further comprising:
responding to a chart adjusting instruction, and analyzing the adjusting instruction to obtain adjusting information;
and adjusting the chart according to the adjustment information.
5. The method of claim 1, further comprising:
obtaining label information of a model corresponding to a plurality of target charts; the target graph is any one of the graphs;
charts having the same label information are combined into one album.
6. A chart generating apparatus, comprising:
a reading unit, configured to read a first field of a data source;
the matching unit is used for detecting whether a plurality of first target fields matched with the target model exist or not; each preset model represents an analysis dimension, the target model is any one or more than one of the preset models, and the first target field is any one of the first fields;
and the chart generation unit is used for generating a chart based on the data of the first target fields and the configuration information of the target model if the first fields have a plurality of first target fields matched with the target model and the first target fields have an incidence relation.
7. The apparatus of claim 6, wherein the matching unit comprises:
an obtaining subunit, configured to obtain a second field group of each model; the second field set comprises a plurality of second fields;
the detection subunit is used for traversing the first fields of the data source and detecting whether a plurality of first fields matched with the second field group of any model exist;
and the matching subunit is used for indicating that the plurality of first target fields are matched with the target model if the plurality of first target fields are detected to be matched with the second field group of the target model.
8. The apparatus of claim 6, wherein the graph generation unit comprises:
the relation establishing subunit is used for establishing a corresponding relation between the data of the first target field and the second target field based on the matching relation between the first target field and the second target field of the target model;
the calling subunit is used for calling the data of the first target field;
and the chart generation subunit is used for generating a chart based on the data of the first target field, the corresponding relation between the data of the first target field and the second target field and the configuration information of the target model.
9. The apparatus of claim 6, further comprising:
a chart adjustment subunit to:
responding to a chart adjusting instruction, and analyzing the adjusting instruction to obtain adjusting information;
and adjusting the chart according to the adjustment information.
10. An electronic device, comprising:
a memory and a processor;
the memory is used for storing programs, and the processor is used for executing the data processing method of any one of the claims 1-5 when the programs in the memory are executed.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202111087111.7A CN113807066A (en) | 2021-09-16 | 2021-09-16 | Chart generation method and device and electronic equipment |
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| CN202111087111.7A CN113807066A (en) | 2021-09-16 | 2021-09-16 | Chart generation method and device and electronic equipment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115080553A (en) * | 2022-07-21 | 2022-09-20 | 广东广物优车科技有限公司 | Intelligent monitoring method for export goods |
| CN115858893A (en) * | 2023-03-02 | 2023-03-28 | 极限数据(北京)科技有限公司 | Data visualization analysis method and device, electronic equipment and storage medium |
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2021
- 2021-09-16 CN CN202111087111.7A patent/CN113807066A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115080553A (en) * | 2022-07-21 | 2022-09-20 | 广东广物优车科技有限公司 | Intelligent monitoring method for export goods |
| CN115080553B (en) * | 2022-07-21 | 2022-11-08 | 广东广物优车科技有限公司 | Intelligent monitoring method for export goods |
| CN115858893A (en) * | 2023-03-02 | 2023-03-28 | 极限数据(北京)科技有限公司 | Data visualization analysis method and device, electronic equipment and storage medium |
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