CN113139102A - Data processing method, data processing device, nonvolatile storage medium and processor - Google Patents
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Abstract
The invention discloses a data processing method, a data processing device, a nonvolatile storage medium and a processor. Wherein, the method comprises the following steps: acquiring target data; determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data; and determining the chart type matched with the target data according to the plurality of similarities and the chart database. The method solves the technical problem that the chart type matched with the target data is difficult to determine.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a data processing method, an apparatus, a non-volatile storage medium, and a processor.
Background
The data visualization technology can help people to find out needed information more quickly and help people to master changes of different data sets more quickly. However, while the visualization technology is vigorously developed, in many scenarios, due to low professionalism and insufficient experience of data users in the aspect of data visualization, the data users often do not know what data visualization form to use to obtain a better data visualization effect, and a more intuitive data analysis result is obtained.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
Embodiments of the present invention provide a data processing method, an apparatus, a non-volatile storage medium, and a processor, so as to at least solve the technical problem that it is difficult to determine a graph type matching target data.
According to an aspect of an embodiment of the present invention, there is provided a data processing method including: acquiring target data; determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data; and determining the chart type matched with the target data according to the plurality of similarities and the chart database.
Optionally, determining a chart type matching the target data according to the plurality of similarities and the chart database includes: determining the number k of second data corresponding to a second chart type in the plurality of first data, wherein the first chart type comprises the second chart type, the number k of the second data is smaller than the number of data corresponding to any other chart type, and the other chart type is a chart type except for the second chart type in the first chart type; determining k third data with the maximum similarity to the target data in the plurality of first data; and determining the chart type matched with the target data according to the third data and the chart type of the third data.
Optionally, determining a chart type matching the target data according to the third data and the chart type of the third data includes: determining a third chart type included in the chart type corresponding to the third data as the chart type matched with the target data, wherein the number of data corresponding to the third chart type in the third data is the largest.
Optionally, when the number of data corresponding to at least two third graph types is the largest, determining that a third graph type included in the graph types corresponding to the third data is a graph type matched with the target data includes: determining fourth data having a similarity with the target data ranked at a (k + 1) th bit among the plurality of first data; determining a fourth chart type included in the chart type corresponding to the third data and the chart type corresponding to the fourth data as a chart type matched with the target data, wherein the number of data corresponding to the fourth chart type in the third data and the fourth data is the largest.
Optionally, determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively includes: acquiring fifth data, wherein the fifth data is any one of the first data; acquiring a title of the target data and a title of the fifth data, a data source of the target data and a data source of the fifth data, and a data type of the target data and a data type of the fifth data; and determining the similarity of the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
According to another aspect of the embodiments of the present invention, there is also provided a data processing apparatus, including: the acquisition module is used for acquiring target data; the first determining module is used for determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data; and the second determining module is used for determining the chart type matched with the target data according to the similarity and the chart database.
Optionally, the second determining module includes: the first determining unit is configured to determine a number k of second data corresponding to a second graph type in the plurality of first data, where the first graph type includes the second graph type, the number k of the second data is smaller than a number of data corresponding to any one other graph type, and the other graph type is a graph type other than the second graph type in the first graph type; the second determining unit is configured to determine k third data with the largest similarity to the target data from among the plurality of first data; and the third determining unit is used for determining the chart type matched with the target data according to the third data and the chart type of the third data.
Optionally, the first determining module includes: the device comprises a first acquisition unit, a second acquisition unit and a third determination unit, wherein the first acquisition unit is used for acquiring fifth data, and the fifth data is any one of the first data; the second obtaining unit is configured to obtain a title of the target data and a title of the fifth data, a data source of the target data and a data source of the fifth data, and a data type of the target data and a data type of the fifth data; the third determining unit is configured to determine a similarity between the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
According to still another aspect of the embodiments of the present invention, there is also provided a nonvolatile storage medium, where the nonvolatile storage medium includes a stored program, and when the program runs, the apparatus where the nonvolatile storage medium is located is controlled to execute any one of the above data processing methods.
According to still another aspect of the embodiments of the present invention, there is further provided a processor, where the processor is configured to execute a program, where the program executes the data processing method described in any one of the above.
In the embodiment of the invention, a mode of processing the target data according to the chart database is adopted, a plurality of similarities obtained by comparing the target data with a plurality of first data stored in the chart database are determined, and the chart type matched with the target data is determined according to the similarities and the chart database, so that the aim of determining the chart type matched with the target data is fulfilled, the technical effect of simply and accurately determining the chart type matched with the target data is realized, and the technical problem that the chart type matched with the target data is difficult to determine is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a data processing method provided according to an embodiment of the invention;
fig. 2 is a block diagram of a data processing apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided a data processing method, it should be noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, target data is acquired. The target data may be a set of data to be processed, and acquiring the set of data to be processed may include acquiring a data type, a data source, a title of the data, a specific data value, and the like of the set of data.
Step S104, determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data.
As an optional implementation manner, a chart database may be preset, and a plurality of first data and a first chart type corresponding to each first data are stored in the chart database, where each first data may be a set of data, and the set of first data may include a data type, a data source, a title of the data, a specific data value, and the like of the set of data. And determining the similarity of the target data compared with the plurality of first data respectively by traversing each first data in the chart database and comparing the target data with each first data. Each group of first data in the chart database and the first chart type corresponding to the first data can be a pair of combinations with good corresponding effects of verified data and charts, and the first chart type can well reflect the characteristics and effective information of the first data after being checked or marked in advance.
And step S106, determining the chart type matched with the target data according to the plurality of similarities and the chart database. In this step, data with high similarity to the target data can be obtained by judging the similarity between the plurality of first data in the graph database and the target data, and the graph type matched with the target data is determined according to the data with high similarity and the graph type of the data as a reference basis. The chart type matched with the target data may be a chart type suitable for displaying characteristics or information of the target data, and the chart type may be a line chart, a graph, a bar chart, a pie chart, or the like, which is not limited herein.
Through the steps, the target data are processed according to the chart database, the similarity obtained by comparing the target data with the first data stored in the chart database is determined, and the chart type matched with the target data is determined according to the similarity and the chart database, so that the aim of determining the chart type matched with the target data is fulfilled, the technical effect of simply and accurately determining the chart type matched with the target data is achieved, and the technical problem that the chart type matched with the target data is difficult to determine is solved.
As an alternative embodiment, the plurality of similarities obtained by comparing the target data with the plurality of first data respectively may be obtained as follows: acquiring fifth data, wherein the fifth data is any one of the first data; acquiring a title of target data, a title of fifth data, a data source of the target data, a data source of the fifth data, a data type of the target data and a data type of the fifth data; and determining the similarity of the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data. In this embodiment, the target data may be sequentially compared with each first data in the graph database to obtain a plurality of similarities, and the fifth data is any one of the first data.
As an alternative implementation, the similarity between two data may be determined according to a plurality of dimensional parameters, such as a title of the data, a data source of the data, and a type of the data, where the data source of the data may include a source from which the data is collected, for example, the data is collected time data, space data, user identification data, and the like; the type of data may include a structure of the data, for example, the type of data may include a discrete type, a continuous type, a date type, and the like.
Table 1 is an information table of data provided according to the present alternative embodiment, and as shown in table 1, respective parameters of 4 different pieces of data are shown, where the title of data 1 is "click rate change curve", an X-axis data source thereof is time, a data type of the X-axis is date type, a Y-axis data source thereof is click rate, a Y-axis data type thereof is numerical type, and an icon type corresponding to data 1 is a line graph. Data parameters of data 2, data 3, and data 4 are shown in table 1, and are not described in detail.
TABLE 1
As an alternative implementation, after the respective titles, data sources and data types of the target data and the fifth data are obtained, the similarity between the two data may be determined through the following steps:
and step S11, comparing the titles of the two data, counting 1 point under the condition that at least 3 same Chinese characters exist in the two titles, and additionally recording 1 point for each additional Chinese character.
And step S12, comparing the data sources of the independent variables of the two data, and if the data sources are the same, recording 1 point.
Step S13, the data types of the arguments of the two data are compared, and if the data types are the same, 1 point is marked.
And step S14, comparing the data sources of the dependent variables of the two data, and if the data sources are the same, recording the score of 1.
Step S15, the data types of the dependent variables of the two data are compared, and if the data types are the same, 1 point is marked.
And step S16, summing the total scores of the steps S11 to S15 to obtain the similarity between the target data and the fifth data, wherein the larger the sum is, the larger the similarity is.
As an alternative embodiment, determining the chart type matching the target data according to the plurality of similarities and the chart database may be performed as follows: determining the number k of second data corresponding to a second chart type in the plurality of first data, wherein the first chart type comprises the second chart type, the number k of the second data is smaller than the number of data corresponding to any other chart type, and the other chart types are chart types except the second chart type in the first chart type; determining k third data with the maximum similarity to the target data in the plurality of first data; and determining the chart type matched with the target data according to the third data and the chart type of the third data.
In this optional embodiment, the second chart type is a specific chart type in the first chart type, and the determining of the second chart type may be performed in the following manner: in the first data, the number of the first data corresponding to each of all chart types is counted, for example, in 10 first data, there are 2 data corresponding to a line graph, 3 data corresponding to a bar graph, and 5 data corresponding to a pie graph; determining a second chart type, where data corresponding to the second chart type is second data, and the quantity of the second data is the smallest one of the quantities of data corresponding to all kinds of chart types, and is denoted as k, in the above example, the smallest quantity of data corresponding to the chart types is 2, so that the second chart type is a line graph, and k is equal to 2; then, the first data are sorted according to the size of the similarity, and k first data with the maximum similarity are determined as third data, in the above example, 2 data with the maximum similarity are determined as the third data; and then, determining the chart type matched with the target data according to the k pieces of third data and the chart types corresponding to the k pieces of third data, for example, if the 2 pieces of third data with the largest similarity are all line graphs, determining the chart type matched with the target data as a line graph.
As an alternative embodiment, determining the chart type matching the target data according to the third data and the chart type of the third data may be performed by: and determining a third chart type included in the chart type corresponding to the third data as the chart type matched with the target data, wherein the number of data corresponding to the third chart type in the third data is the largest.
As an optional implementation manner, the third data may correspond to a plurality of chart types, and by determining the number of data corresponding to each of the plurality of chart types, the chart type with the largest number of data corresponding to the third data may be determined, that is, the third chart type, where the third chart type is the chart type matched with the target data.
As an alternative embodiment, when the number of data corresponding to at least two third graph types is the largest, it may be determined that the third graph type included in the graph types corresponding to the third data is the graph type matching the target data by: determining fourth data having a similarity with the target data ranked at a (k + 1) th bit among the plurality of first data; and determining that the chart type corresponding to the third data and a fourth chart type included in the chart type corresponding to the fourth data are the chart type matched with the target data, wherein the number of data corresponding to the fourth chart type in the third data and the fourth data is the largest.
The present alternative embodiment may unambiguously determine the fourth graph type that matches the target data by introducing fourth data having a similarity rank at the (k + 1) th bit when the third data includes a plurality of third graph types, where the fourth graph type may be the graph type in which the number of corresponding data is the largest and unique among the third data and the fourth data.
As an alternative embodiment, the chart type of the target data match may be determined as follows.
Step S21, the data title of the target data a, the data source and the data type of the data independent variable, the data source and the data type of the dependent variable, and the value range are obtained.
Step S22, obtaining a plurality of data B in the database, where the data B may be placed in the same logical location (for example, on the same cloud host) as the apparatus for operating the embodiment, or may be placed in different logical locations (for example, one is placed in a public cloud, and the other is placed in a private cloud).
Step S23, calculating the number of the data B, and recording as n;
step S24, calculating the proportion of different chart types in the data B, for example, the proportion of a line chart is 20%, the proportion of a curve chart is 30%, and the proportion of a bar chart is 50%;
step S25, comparing the target data A with the plurality of data B respectively to obtain the similarity S between the target data A and the plurality of data B respectivelyABTo obtain SAB1，SAB2，SAB3，SAB4And so on to SABn；
Step S26, determining the number k of data corresponding to the graph type with the smallest proportion in step S24, for example, in this embodiment, k is 20% n;
step S27, dividing the n SABSorting according to size, and taking k S with maximum similarityABRecording the data B corresponding to the data B;
step S28, determining the chart type corresponding to each of the k data B in the step S27, and determining the chart type with the maximum number of corresponding data as the chart type matched with the target data A according to the number of the data corresponding to each of the k data B;
in step S29, if there is more than one chart type with the largest number of corresponding data B in step S28, the data arranged at the (k + 1) th bit in the similarity ranking is obtained and added to the k data B, and step S28 is repeated until the only chart type with the largest number of corresponding data is determined and determined as the chart type matching the target data a.
Example 2
According to an embodiment of the present invention, there is also provided a data processing apparatus for implementing the data processing method, and fig. 2 is a block diagram of a structure of the data processing apparatus according to the embodiment of the present invention, as shown in fig. 2, the data processing apparatus includes: an acquisition module 22, a first determination module 24 and a second determination module 26, which are explained below.
An obtaining module 22, configured to obtain target data;
a first determining module 24, connected to the obtaining module 22, configured to determine a plurality of similarities obtained by comparing the target data with a plurality of first data, respectively, where the plurality of first data are stored in a graph database, and the graph database further includes at least one first graph type corresponding to the plurality of first data;
and a second determining module 26, connected to the first determining module 24, for determining the chart type matching the target data according to the plurality of similarities and the chart database.
It should be noted here that the obtaining module 22, the first determining module 24 and the second determining module 26 correspond to steps S102 to S106 in embodiment 1, and the three modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 1.
As an alternative embodiment, the second determining module 26 may include a first determining unit 261, a second determining unit 262, and a third determining unit 263, where the first determining unit 261 is configured to determine a number k of second data corresponding to a second chart type in a plurality of first data, where the first chart type includes the second chart type, the number k of the second data is smaller than a number of data corresponding to any other chart type, and the other chart type is a chart type other than the second chart type in the first chart type; a second determining unit 262, configured to determine k third data with the largest similarity to the target data from among the plurality of first data; a third determining unit 263, configured to determine a chart type matching the target data according to the third data and the chart type of the third data.
As an alternative embodiment, the first determining module 24 may include a first obtaining unit 241, a second obtaining unit 242, and a third determining unit 243, where the first obtaining unit 241 is configured to obtain fifth data, where the fifth data is any one of the first data; a second obtaining unit 242, configured to obtain a title of the target data and a title of the fifth data, a data source of the target data and a data source of the fifth data, and a data type of the target data and a data type of the fifth data; the third determining unit 243 is configured to determine similarity between the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
Example 3
An embodiment of the present invention may provide a computer device, and optionally, in this embodiment, the computer device may be located in at least one network device of a plurality of network devices of a computer network. The computer device includes a memory and a processor.
The memory may be configured to store software programs and modules, such as program instructions/modules corresponding to the data processing method and apparatus in the embodiments of the present invention, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, so as to implement the data processing method. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory located remotely from the processor, and these remote memories may be connected to the computer terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring target data; determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data; and determining the chart type matched with the target data according to the plurality of similarities and the chart database.
Optionally, the processor may further execute the program code of the following steps: determining the chart type matched with the target data according to the plurality of similarities and the chart database, wherein the step of determining the chart type matched with the target data comprises the following steps: determining the number k of second data corresponding to a second chart type in the plurality of first data, wherein the first chart type comprises the second chart type, the number k of the second data is smaller than the number of data corresponding to any other chart type, and the other chart types are chart types except the second chart type in the first chart type; determining k third data with the maximum similarity to the target data in the plurality of first data; and determining the chart type matched with the target data according to the third data and the chart type of the third data.
Optionally, the processor may further execute the program code of the following steps: determining the chart type matched with the target data according to the third data and the chart type of the third data, wherein the determining comprises the following steps: and determining a third chart type included in the chart type corresponding to the third data as the chart type matched with the target data, wherein the number of data corresponding to the third chart type in the third data is the largest.
Optionally, the processor may further execute the program code of the following steps: determining that the third chart type included in the chart types corresponding to the third data is the chart type matched with the target data under the condition that the number of the data corresponding to the at least two third chart types is the largest, wherein the determining comprises the following steps: determining fourth data having a similarity with the target data ranked at a (k + 1) th bit among the plurality of first data; and determining that the chart type corresponding to the third data and a fourth chart type included in the chart type corresponding to the fourth data are the chart type matched with the target data, wherein the number of data corresponding to the fourth chart type in the third data and the fourth data is the largest.
Optionally, the processor may further execute the program code of the following steps: determining a plurality of similarities obtained by comparing the target data with the plurality of first data respectively, including: acquiring fifth data, wherein the fifth data is any one of the first data; acquiring a title of target data, a title of fifth data, a data source of the target data, a data source of the fifth data, a data type of the target data and a data type of the fifth data; and determining the similarity of the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
Example 4
Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the nonvolatile storage medium may be configured to store the program code executed by the data processing method provided in embodiment 1.
Optionally, in this embodiment, the nonvolatile storage medium may be located in any one of computer terminals in a computer terminal group in a computer network, or in any one of mobile terminals in a mobile terminal group.
Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring target data; determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data; and determining the chart type matched with the target data according to the plurality of similarities and the chart database.
Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the chart type matched with the target data according to the plurality of similarities and the chart database, wherein the step of determining the chart type matched with the target data comprises the following steps: determining the number k of second data corresponding to a second chart type in the plurality of first data, wherein the first chart type comprises the second chart type, the number k of the second data is smaller than the number of data corresponding to any other chart type, and the other chart types are chart types except the second chart type in the first chart type; determining k third data with the maximum similarity to the target data in the plurality of first data; and determining the chart type matched with the target data according to the third data and the chart type of the third data.
Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the chart type matched with the target data according to the third data and the chart type of the third data, wherein the determining comprises the following steps: and determining a third chart type included in the chart type corresponding to the third data as the chart type matched with the target data, wherein the number of data corresponding to the third chart type in the third data is the largest.
Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining that the third chart type included in the chart types corresponding to the third data is the chart type matched with the target data under the condition that the number of the data corresponding to the at least two third chart types is the largest, wherein the determining comprises the following steps: determining fourth data having a similarity with the target data ranked at a (k + 1) th bit among the plurality of first data; and determining that the chart type corresponding to the third data and a fourth chart type included in the chart type corresponding to the fourth data are the chart type matched with the target data, wherein the number of data corresponding to the fourth chart type in the third data and the fourth data is the largest.
Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining a plurality of similarities obtained by comparing the target data with the plurality of first data respectively, including: acquiring fifth data, wherein the fifth data is any one of the first data; acquiring a title of target data, a title of fifth data, a data source of the target data, a data source of the fifth data, a data type of the target data and a data type of the fifth data; and determining the similarity of the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A data processing method, comprising:
acquiring target data;
determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data;
and determining the chart type matched with the target data according to the plurality of similarities and the chart database.
2. The method of claim 1, wherein determining the chart type that matches the target data based on the plurality of similarities and the chart database comprises:
determining the number k of second data corresponding to a second chart type in the plurality of first data, wherein the first chart type comprises the second chart type, the number k of the second data is smaller than the number of data corresponding to any other chart type, and the other chart type is a chart type except for the second chart type in the first chart type;
determining k third data with the maximum similarity to the target data in the plurality of first data;
and determining the chart type matched with the target data according to the third data and the chart type of the third data.
3. The method of claim 2, wherein determining a chart type matching the target data based on the third data and the chart type of the third data comprises:
determining a third chart type included in the chart type corresponding to the third data as the chart type matched with the target data, wherein the number of data corresponding to the third chart type in the third data is the largest.
4. The method according to claim 3, wherein in a case that the number of data corresponding to at least two third graph types is the largest, determining that a third graph type included in the graph types corresponding to the third data is a graph type matching the target data comprises:
determining fourth data having a similarity with the target data ranked at a (k + 1) th bit among the plurality of first data;
determining a fourth chart type included in the chart type corresponding to the third data and the chart type corresponding to the fourth data as a chart type matched with the target data, wherein the number of data corresponding to the fourth chart type in the third data and the fourth data is the largest.
5. The method of claim 1, wherein determining a plurality of similarities between the target data and the first data comprises:
acquiring fifth data, wherein the fifth data is any one of the first data;
acquiring a title of the target data and a title of the fifth data, a data source of the target data and a data source of the fifth data, and a data type of the target data and a data type of the fifth data;
and determining the similarity of the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
6. A data processing apparatus, comprising:
the acquisition module is used for acquiring target data;
the first determining module is used for determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data;
and the second determining module is used for determining the chart type matched with the target data according to the similarity and the chart database.
7. The method of claim 6, wherein the second determining module comprises: a first determination unit, a second determination unit, and a third determination unit, wherein,
the first determining unit is configured to determine a number k of second data corresponding to a second graph type in the plurality of first data, where the first graph type includes the second graph type, the number k of the second data is smaller than a number of data corresponding to any one other graph type, and the other graph type is a graph type other than the second graph type in the first graph type;
the second determining unit is configured to determine k third data with the largest similarity to the target data from among the plurality of first data;
and the third determining unit is used for determining the chart type matched with the target data according to the third data and the chart type of the third data.
8. The method of claim 6, wherein the first determining module comprises: a first obtaining unit, a second obtaining unit, and a third determining unit, wherein,
the first obtaining unit is configured to obtain fifth data, where the fifth data is any one of the first data;
the second obtaining unit is configured to obtain a title of the target data and a title of the fifth data, a data source of the target data and a data source of the fifth data, and a data type of the target data and a data type of the fifth data;
the third determining unit is configured to determine a similarity between the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
9. A non-volatile storage medium, comprising a stored program, wherein when the program is executed, a device in which the non-volatile storage medium is located is controlled to execute the data processing method according to any one of claims 1 to 5.
10. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to execute the data processing method according to any one of claims 1 to 5 when running.
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