CN114077628B - Method and device for processing chart, electronic device and storage medium - Google Patents
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Abstract
本公开涉及图表的处理方法和装置、电子设备和存储介质，涉及计算机技术领域。所述图表的处理方法包括：获取所述图表中的第一目标数据序列，所述第一目标数据序列以时间为序；获得第一相关性，所述第一相关性是所述第一目标数据序列的数据项与所述第一目标数据序列的数据项所对应的类别值之间的相关性；根据所述第一相关性，输出与所述第一相关性相关的图表处理结果。
The present disclosure relates to a method and device for processing a chart, an electronic device and a storage medium, and to the field of computer technology. The method for processing a chart includes: obtaining a first target data sequence in the chart, the first target data sequence being in time order; obtaining a first correlation, the first correlation being the correlation between a data item of the first target data sequence and a category value corresponding to the data item of the first target data sequence; and outputting a chart processing result related to the first correlation according to the first correlation.
Description
Technical Field
The present disclosure relates to the field of computer technology, and more particularly, to a method and apparatus for processing a chart, an electronic device, and a storage medium.
Background
The chart is a form presentation form or a graphic presentation form of the sequence data, and plays an important role in offices. Currently, chart software can automatically generate a chart from sequence data, but still requires manual processing of the chart to generate a data conclusion.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide a method and apparatus for processing a chart, an electronic device, and a storage medium, which can automatically mine key information of the chart to generate a relevant data conclusion.
According to a first aspect of the present disclosure, a method of processing a graph is provided. The method comprises the following steps:
Acquiring a first target data sequence in the chart, wherein the first target data sequence is in time sequence;
Obtaining a first correlation, wherein the first correlation is the correlation between a data item of the first target data sequence and a class value corresponding to the data item of the first target data sequence;
And outputting a graph processing result related to the first correlation according to the first correlation.
Optionally, before acquiring the first target data sequence in the chart, the method further comprises:
Acquiring a data sequence in the chart;
And under the condition that the class value corresponding to the data item of the data sequence represents time and the class value corresponding to the data item of the data sequence has a sequence relation, determining the data sequence as the first target data sequence.
Optionally, in the case where there are N first target time data sequences and there is a time-sequential relationship between the N first target data sequences, the N is an integer and N is not less than 2, the method further includes:
Processing a data item of an nth first target data sequence to obtain target data corresponding to the nth first target data sequence, wherein the nth first target data sequence is any one of the N first target data sequences;
According to the time sequence relation among the N first target data sequences, giving class values to target data corresponding to the N first target data sequences as data items so as to construct a second target data sequence with time sequence;
obtaining a second correlation, wherein the second correlation is the correlation between the data item of the second target data sequence and the class value corresponding to the data item of the second target data sequence;
And outputting a graph processing result corresponding to the second correlation according to the second correlation.
Optionally, the outputting, according to the first correlation, a graph processing result corresponding to the first correlation includes:
determining a relation between two adjacent data items in the sequence direction in the first target data sequence under the condition that the first correlation represents positive correlation between the data items of the first target data sequence and the class values corresponding to the data items;
And generating a graph processing result corresponding to the first correlation according to the relation between two adjacent data items in the sequence direction in the first target data sequence.
Optionally, the determining the relation between two adjacent data items in the sequence direction in the first target data sequence comprises calculating the speed increase of the latter data item relative to the former data item for the two adjacent data items in the first target data sequence;
the method comprises the steps of determining whether a data item is negatively increased according to the speed increase of the data item relative to a data item, and generating a graph processing result corresponding to the first correlation according to the number of the negatively increased data items in the first target data sequence and the positions of the negatively increased data items in the first target data sequence.
Optionally, the outputting, according to the first correlation, a graph processing result corresponding to the first correlation includes:
Determining a relation between two adjacent data items in the sequence direction in the first target data sequence under the condition that the first correlation represents negative correlation of the data items of the first target data sequence and the class values corresponding to the data items;
And generating a graph processing result corresponding to the first correlation according to the relation between two adjacent data items in the sequence direction in the first target data sequence.
Optionally, the determining the relation between two adjacent data items in the sequence direction in the first target data sequence comprises calculating the speed increase of the latter data item relative to the former data item for the two adjacent data items in the first target data sequence;
The method comprises the steps of generating a graph processing result corresponding to the first correlation according to the relation between two adjacent data items in the sequence direction in the first target data sequence, determining whether the data item is growing or not according to the speed increasing of the data item relative to the data item, and generating the graph processing result corresponding to the first correlation according to the number of the data items growing in the first target data sequence and the positions of the data items growing in the first target data sequence.
Optionally, the outputting, according to the first correlation, a graph processing result corresponding to the first correlation includes:
determining an average value of the data items of the first target data sequence and a maximum value and a minimum value in the data items of the first target data sequence under the condition that the first correlation characterizes the data items of the first target data sequence and the class values corresponding to the data items are irrelevant;
determining a first difference value and a second difference value, wherein the first difference value is the difference value between the maximum value and the average value, and the second difference value is the difference value between the average value and the minimum value;
Outputting a class value corresponding to the maximum value under the condition that the first difference value is larger than the second difference value;
and outputting a class value corresponding to the minimum value under the condition that the first difference value is smaller than the second difference value.
Optionally, the method further comprises:
for two adjacent data items in the first target data sequence, calculating the speed increase of the latter data item relative to the former data item;
Judging whether the latter data item is positive growth or negative growth according to the speed increase of the latter data item relative to the former data item;
and outputting a corresponding chart processing result according to the category value of the positively-increased data item and the category value of the negatively-increased data item.
According to a second aspect of the present disclosure, a graph processing apparatus is provided. The device comprises:
The first acquisition module is used for acquiring a first target data sequence in the chart, wherein the first target data sequence is in time sequence;
The second acquisition module is used for acquiring a first correlation between the data item of the first target data sequence and a class value corresponding to the data item of the first target data sequence;
and the output module is used for outputting a graph processing result related to the first correlation according to the first correlation.
According to a third aspect of the present disclosure, there is also provided an electronic device comprising a memory for storing a computer program and a processor for executing the computer program to implement the method of the first aspect of the present disclosure.
According to a fourth aspect of the present disclosure there is also provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of the first aspect of the present disclosure.
According to the graph processing method and device, the electronic equipment and the storage medium, for the data sequence taking time as an axis in the graph, the key information can be mined to generate the related data conclusion, excessive human intervention is not needed, automation of graph processing is realized, graph processing time is saved, and convenience is brought to users.
The foregoing objects, features and advantages of the disclosure will be more readily apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are required to be used in the embodiments will be briefly described below. It is appreciated that the following drawings depict only certain embodiments of the invention and are therefore not to be considered limiting of its scope. Other relevant drawings may be made by those of ordinary skill in the art without undue burden from these drawings.
FIG. 1 is a schematic diagram of an electronic device that may be used to implement an embodiment of the present disclosure, provided by one embodiment of the present disclosure;
FIGS. 2-8 illustrate flowcharts of methods of processing charts provided by embodiments of the present disclosure;
9-10 illustrate schematic diagrams of charts provided by embodiments of the present disclosure;
fig. 11 shows a schematic diagram of a processing apparatus of a graph provided by an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
< Hardware configuration >
Fig. 1 is a schematic structural diagram of an electronic device that may be used to implement embodiments of the present disclosure. The electronic device may be used to implement the chart processing method of the embodiments of the present disclosure.
The electronic device 1000 may be a smart phone, a portable computer, a desktop computer, a tablet computer, a server, etc., and is not limited herein.
The electronic device 1000 may include, but is not limited to, a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, and the like. The processor 1100 may be a central processing unit CPU, a graphics processor GPU, a microprocessor MCU, etc. for executing computer programs/instructions, which may be written in an instruction set of an architecture such as x86, arm, RISC, MIPS, SSE, etc. The memory 1200 includes, for example, ROM (read only memory), RAM (random access memory), nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, a USB interface, a serial interface, a parallel interface, and the like. The communication device 1400 can perform wired communication using an optical fiber or a cable, or perform wireless communication, for example, and specifically can include WiFi communication, bluetooth communication, 2G/3G/4G/5G communication, and the like. The display device 1500 is, for example, a liquid crystal display, a touch display, or the like. The input device 1600 may include, for example, a touch screen, keyboard, somatosensory input, and the like. The speaker 1700 is for outputting audio signals. Microphone 1800 is used to collect audio signals.
The memory 1200 of the electronic device 1000 is used for storing a computer program/instructions for controlling the processor 1100 to operate to implement a graph processing method according to an embodiment of the present disclosure. The skilled person can design the computer program/instructions according to the disclosed aspects of the present disclosure. How the computer program/instructions control the processor to operate is well known in the art and will not be described in detail here. The electronic device 1000 may be installed with an intelligent operating system (e.g., windows, linux, android, IOS, etc. systems) and application software.
It will be appreciated by those skilled in the art that although a plurality of devices of the electronic device 1000 are shown in fig. 1, the electronic device 1000 of the embodiments of the present disclosure may involve only some of the devices thereof, for example, only the processor 1100 and the memory 1200, etc.
Fig. 9 and 10 are graphs corresponding to the same source data, in which fig. 9 illustrates a graphical form of the graph and fig. 10 illustrates a tabular form of the graph. The chart includes a plurality of data sequences, wherein fig. 9 and fig. 10 only show one data sequence, the data sequence is {9700,109267,6800,20864,17602,68028,27400,5000,92855,27400,15650,15520,32450}, the total is 13 data items, the class value corresponding to the data items represents the time, for example, the class value of "1" represents "2 months and 8 days", the class value of "2" represents "2 months and 11 days", and the like.
Referring now to fig. 3 in conjunction with fig. 9 and 10, a method for processing a chart provided by an embodiment of the present disclosure is described, the method including steps S102-S106.
Step S102, a first target data sequence in the chart is acquired, and the first target data sequence is in time sequence.
In one example, prior to step S102, a process of determining a first target sequence in the graph is also included. First, each data sequence in the chart is acquired. And determining the data sequence as a first target data sequence under the condition that the class value corresponding to the data item of the data sequence represents time and the class value corresponding to the data item of the data sequence has a sequence relation.
Referring to the data sequence shown in fig. 9 and 10, 13 category values corresponding to 13 data items are "1", "2", "13", respectively, there is a sequential relationship between the category values, and the category values characterize time, it may be determined that the data sequence is in time series, and it may be determined that the data sequence is the first target sequence.
In one example, determining whether the data sequence is time-sequential includes obtaining a graph type corresponding to the data sequence. In the case where the graph type corresponding to the data sequence is a line graph, the data sequence is determined to be a sequence in time.
Step S104, obtaining a first correlation, wherein the first correlation is the correlation between the data item of the first target data sequence and the class value corresponding to the data item of the first target data sequence.
In one example, a correlation coefficient between a data item of the first target data sequence and a class value corresponding to the data item may be calculated, and whether the data item of the first target data sequence is positively correlated, negatively correlated, or uncorrelated with the class value may be determined based on the correlation coefficient. The correlation refers to the degree of correlation of two variables, wherein the two variables are data items of the first target data sequence and class values corresponding to the data items of the first target data sequence.
In one example, the correlation coefficient takes a value between-1 and 1, 0 indicating no correlation, and a larger absolute value of the correlation coefficient indicates a larger first correlation, positive values tend to be positive correlations, and negative values tend to be negative correlations. In one example, the correlation coefficient may be a covariance between a data item of the first target data sequence and a class value corresponding to the data item. Covariance can reflect the synergistic relationship of two variables X and Y, i.e., whether the trends of the two variables are consistent. If the variable X becomes larger and the variable Y becomes larger, this means that the two variables are changing in the same direction, and the covariance is positive. If the variable X becomes large and the variable Y becomes small, meaning that both variables are inversely varying, the covariance is negative. In this example, the covariance between the two is calculated using the data item of the first target data sequence as the variable X and the corresponding class value as the variable Y.
In one example, if a first correlation coefficient between a data item of the data sequence and a class value corresponding to the data item is greater than a positive threshold, the class value corresponding to the data item of the data sequence is determined to be positive. The positive threshold is, for example, 0.7.
In one example, if a first correlation coefficient between a data item of the data sequence and a class value corresponding to the data item is less than a negative threshold, it is determined that the class value corresponding to the data item of the data sequence is negatively correlated. The negative threshold is, for example, -0.7.
In one example, if a first correlation coefficient between a data item of the data sequence and a class value corresponding to the data item is less than or equal to a positive threshold and greater than or equal to a negative threshold, the class value corresponding to the data item of the data sequence is determined to be uncorrelated. The positive threshold is, for example, 0.7 and the negative threshold is, for example, -0.7, i.e. if the first correlation coefficient is less than or equal to 0.7 and greater than or equal to-0.7, it is determined that the data item of the data sequence is uncorrelated with the class value to which the data item corresponds.
Step S106, outputting a graph processing result related to the first correlation according to the first correlation.
Step S106 is illustrated below:
In one example, referring to FIG. 4, step S106 includes steps S402-S404.
Step S402, determining a relation between two adjacent data items in the sequence direction in the first target data sequence under the condition that the first correlation represents positive correlation of the data items of the first target data sequence and the class values corresponding to the data items.
Step S404, generating a graph processing result corresponding to the first correlation according to the relation between two adjacent data items in the sequence direction in the first target data sequence.
In step S402, determining a relationship between two data items that are sequentially adjacent in the first target data sequence may include calculating an acceleration of a subsequent data item relative to a previous data item for two data items that are sequentially adjacent in the first target data sequence.
For the ith data item in the first target data sequence, K i＝(Si-Si-1)/Si-1, wherein S i is the ith data item in the first target data sequence, S i-1 is the i-1 th data item in the first target data sequence, i is an integer and i is not less than 2.S i-1 and S i are adjacent in a sequential direction, and K i is the acceleration of the ith data item relative to the i-1 th data item in the first target data sequence.
In step S404, generating a graph processing result corresponding to the first correlation according to a relationship between two data items adjacent in a sequence direction in the first target data sequence may include determining whether the subsequent data item is a negative growth according to a speed increase of the subsequent data item relative to the previous data item, and generating a graph processing result corresponding to the first correlation according to the number of the negative growth data items in the first target data sequence and positions of the negative growth data items in the first target data sequence.
If K i is positive, it is determined that the ith data item S i is growing. If K i is negative, it is determined that the ith data item S i is a negative increase.
Generating a graph processing result corresponding to the first correlation according to the number of negatively growing data items in the first target data sequence and the positions of the negatively growing data items in the first target data sequence, for example, may be:
If the number of negatively growing data items in the first target data sequence is zero, the generated graph processing conclusion is a steady growth.
If the number of negatively growing data items in the first target data sequence is 1 and the unique negatively growing data item is the last data item of the first target data sequence, then the generated graph processing conclusion includes an overall growth but a slight slowing of recent increases.
If the number of negatively growing data items in the first target data sequence is 1, assuming that the time characterized by the class value corresponding to the unique data item is T11, the generated graph processing conclusion includes that the other times continuously grow except for the occurrence of the decline at the time T11.
If the number of the negatively increased data items in the first target data sequence is greater than or equal to 1, selecting the data item with the smallest speed increasing, and assuming that the time represented by the class value corresponding to the data item with the smallest speed increasing is T12, generating a graph processing conclusion including the fastest decrease at the time T12.
For the data sequence taking time as the axis in the chart, the chart processing method of the example can be utilized to mine out the key information to generate the related data conclusion, excessive human intervention is not needed, automation of chart processing is realized, chart processing time is saved, and convenience is brought to users.
In one example, referring to FIG. 5, step S106 includes steps S502-S504.
Step S502, determining a relation between two adjacent data items in the sequence direction in the first target data sequence under the condition that the first correlation represents that the data items of the first target data sequence are in negative correlation with the class values corresponding to the data items.
Step S504, generating a graph processing result corresponding to the first correlation according to the relation between two adjacent data items in the sequence direction in the first target data sequence.
In step S502, determining a relationship between two data items that are sequentially adjacent in the first target data sequence may include calculating an acceleration of a subsequent data item relative to a previous data item for two data items that are sequentially adjacent in the first target data sequence.
For the ith data item in the first target data sequence, K i＝(Si-Si-1)/Si-1, wherein S i is the ith data item in the first target data sequence, S i-1 is the i-1 th data item in the first target data sequence, i is an integer and i is not less than 2.S i-1 and S i are adjacent in a sequential direction, and K i is the acceleration of the ith data item relative to the i-1 th data item in the first target data sequence.
In step S504, generating a graph processing result corresponding to the first correlation according to the relation between two data items adjacent in the sequence direction in the first target data sequence may include determining whether the latter data item is growing according to the speed increase of the latter data item relative to the former data item, and generating a graph processing result corresponding to the first correlation according to the number of the growing data items in the first target data sequence and the positions of the growing data items in the first target data sequence.
If K i is positive, it is determined that the ith data item S i is growing. If K i is negative, it is determined that the ith data item S i is a negative increase.
Generating a graph processing result corresponding to the first correlation based on the number of growing data items in the first target data sequence and the positions of the growing data items in the first target data sequence may be, for example:
if the number of growing data items in the first target data sequence is zero, the generated graph processing conclusion includes a steady decrease.
If the number of growing data items in the first target data sequence is 1 and the unique growing data item is the last data item of the first target data sequence, then the generated graph processing conclusion includes an overall decrease but a slightly slower recent decrease rate.
If the number of growing data items in the first target data sequence is 1, assuming that the time characterized by the class value corresponding to the unique data item is T21, the generated graph processing conclusion includes that the time is continuously reduced except for the time T21.
If the number of the data items growing in the first target data sequence is greater than or equal to 1, selecting the data item with the largest speed increasing, and assuming that the time represented by the class value corresponding to the data item with the largest speed increasing is T22, generating a graph processing conclusion comprises that the data item grows most rapidly at the time T22.
For the data sequence taking time as the axis in the chart, the chart processing method of the example can be utilized to mine out the key information to generate the related data conclusion, excessive human intervention is not needed, automation of chart processing is realized, chart processing time is saved, and convenience is brought to users.
In one example, referring to FIG. 6, step S106 includes steps S602-S606.
Step S602, determining an average value of the data items of the first target data sequence and a maximum value and a minimum value of the data items of the first target data sequence when the first correlation characterizes the data items of the first target data sequence and the class values corresponding to the data items are uncorrelated.
Step S604, determining a first difference value and a second difference value, where the first difference value is the difference value between the maximum value and the average value, and the second difference value is the difference value between the average value and the minimum value.
Step S606, outputting a class value corresponding to the maximum value when the first difference value is larger than the second difference value. And outputting a class value corresponding to the minimum value under the condition that the first difference value is smaller than the second difference value.
For example, when the first difference is greater than the second difference, a class value corresponding to the maximum value is output, and the class value corresponding to the maximum value is assumed to be time T31, and the generated graph processing result includes the maximum value at time T31.
For example, in the case where the first difference is smaller than the second difference, the class value corresponding to the minimum value is time T32, and the generated graph processing result includes the minimum value at time T32.
For the data sequence taking time as the axis in the chart, the chart processing method of the example can be utilized to mine out the key information to generate the related data conclusion, excessive human intervention is not needed, automation of chart processing is realized, chart processing time is saved, and convenience is brought to users.
Referring to fig. 7, the processing method of the graph may further include steps S702 to S706.
Step S702, for two adjacent data items in the first target data sequence, calculating the speed increase of the latter data item relative to the former data item.
In step S702, for the ith data item in the first target data sequence, K i＝(Si-Si-1)/Si-1, wherein S i is the ith data item in the first target data sequence, S i-1 is the i-1 th data item in the first target data sequence, i is an integer and i is not less than 2.S i-1 and S i are adjacent in a sequential direction, and K i is the acceleration of the ith data item relative to the i-1 th data item in the first target data sequence.
Step S704, according to the speed increase of the latter data item relative to the former data item, judging whether the latter data item is positive or negative.
In step S704, generating a graph processing result corresponding to the first correlation based on the relationship between two data items adjacent in the sequential direction in the first target data sequence may include determining whether the subsequent data item is a negative growth based on the increase in speed of the subsequent data item relative to the previous data item, and generating a graph processing result corresponding to the first correlation based on the number of negative growth data items in the first target data sequence and the positions of the negative growth data items in the first target data sequence.
If K i is positive, it is determined that the ith data item S i is growing. If K i is negative, it is determined that the ith data item S i is a negative increase.
Step S706, outputting a corresponding chart processing result according to the category value of the data item with positive growth of the collection and the category value of the data item with negative growth of the collection.
In step S706, the collection is classified and collected in the same type, according to the classification value of the positive growing data item of the collection, the classification value of all the calculated positive growing data items can be arranged according to the time sequence or other sequences, at least one classification value of the positive growing data item with the sequence of continuous uninterrupted is used as collection data, after all the collection data are arranged, the processing result corresponding to the classification value of the positive growing data item is output, the processing result can be described as how many times and time periods of positive growth are shared in the graph processing, the classification value of the negative growing data item of the collection can be the classification value of all the calculated negative growing data items, the classification value of at least one negative growing data item with the sequence of continuous uninterrupted is used as collection data, after all the collection data are arranged, the processing result corresponding to the classification value of the negative growing data item is output, the processing result can be described as how many times and time periods of negative growth are shared in the graph processing, and the total length of the negative growing data item can be compared with the total time period, and the total length of the negative growing data item can be compared with the total length of the graph.
Referring to FIG. 8, where there are N first target time data sequences and there is a time-sequential relationship between N first target data sequences (the N first target data sequences may belong to the same graph or multiple graphs), N is an integer and N+.2, the method may include steps S802-S808.
Step S802, processing the data item of the nth first target data sequence to obtain target data corresponding to the nth first target data sequence, wherein the nth first target data sequence is any one of the N first target data sequences.
The target data corresponding to the first target data sequence is data determined according to the data items in the first target data sequence. For example, the target data corresponding to the first target data sequence may be an average of all data items in the first target data sequence. For example, the target data corresponding to the first target data sequence may be the maximum of all data items in the first target data sequence. For example, the target data corresponding to the first target data sequence may be a minimum value of all data items in the first target data sequence. For example, the target data corresponding to the first target data sequence may be the difference between the maximum value and the minimum value.
Step S804, according to the time sequence relation among the N first target data sequences, the target data corresponding to the N first target data sequences are used as data items to be endowed with category values so as to construct a second target data sequence with time sequence.
The time sequence relation among the N first target data sequences can be determined according to the time range corresponding to the first target data sequences. For example, the time range corresponding to the first target data sequence is the first half of the year, and the time range corresponding to the second first target data sequence is the second half of the year, so that the time sequence relationship between the first target data sequence and the second first target data sequence is that the first target data sequence is before and the second first target data sequence is after.
And taking target data corresponding to the N first target data sequences as N new data items, and arranging the new data items into new sequences according to the time sequence relation among the N first target data sequences. And assigning class values to the data items in the new sequence, so that the class values corresponding to the data items in the new sequence have a sequence relation, and a second target data sequence in sequence of time is obtained.
Step S806, obtaining a second correlation, where the second correlation is a correlation between the data item of the second target data sequence and the class value corresponding to the data item of the second target data sequence.
Step S808, outputting a graph processing result corresponding to the second correlation according to the second correlation.
In this example, if there are a plurality of first target time data sequences and there is a time-sequential relationship between the first target data sequences, target data corresponding to the first target data sequences is assigned as a data item to a class value to construct a second target data sequence in time. Then, through steps S806 to S808, the graph processing result for the second target data sequence is output based on the similar manner to the foregoing steps 104 to S106.
In the example, a plurality of data sequences taking time as an axis in the chart can be comprehensively analyzed, a plurality of relations between the data sequences taking time as an axis are mined to generate related data conclusions, excessive human intervention is not needed, automation of chart processing is realized, chart processing time is saved, and convenience is brought to users.
Referring to fig. 11, in the present embodiment, a graph processing apparatus is also provided. The processing device 20 of the chart comprises the following modules:
a first obtaining module 21, configured to obtain a first target data sequence in the chart, where the first target data sequence is in time order.
The second obtaining module 22 is configured to obtain a first correlation, where the first correlation is a correlation between a data item of the first target data sequence and a class value corresponding to the data item of the first target data sequence.
A first output module 23, configured to output a graph processing result related to the first correlation according to the first correlation.
In one example, the processing device of the chart further includes a determination module.
And the determining module is used for acquiring the data sequence in the chart, and determining the data sequence as the first target data sequence under the condition that the class value corresponding to the data item of the data sequence represents the time and the class value corresponding to the data item of the data sequence has a sequence relation.
In one example, the processing device of the chart further includes a first processing module, a building module, a third obtaining module, and a second output module.
The first processing module is used for processing the data item of the nth first target data sequence to obtain target data corresponding to the nth first target data sequence when N first target time data sequences exist and a time sequence relation exists among the N first target data sequences, wherein N is an integer and is more than or equal to 2, and the nth first target data sequence is any one of the N first target data sequences.
And the construction module is used for giving the class value to the target data corresponding to the N first target data sequences as data items according to the time sequence relation among the N first target data sequences so as to construct a second target data sequence in time sequence.
And the third acquisition module is used for acquiring a second correlation, wherein the second correlation is the correlation between the data item of the second target data sequence and the class value corresponding to the data item of the second target data sequence.
And the second output module is used for outputting a graph processing result corresponding to the second correlation according to the second correlation.
In one example, outputting a graph processing result corresponding to the first correlation according to the first correlation includes determining a relationship between two data items adjacent in a sequence direction in the first target data sequence and generating a graph processing result corresponding to the first correlation according to the relationship between two data items adjacent in the sequence direction in the first target data sequence when the first correlation characterizes the first target data sequence and the class value corresponding to the data items is positive correlation.
In this example, determining the relationship between two data items that are sequentially adjacent in the first target data sequence includes calculating a speed increase of a subsequent data item relative to a previous data item for two data items that are sequentially adjacent in the first target data sequence. Generating a graph processing result corresponding to the first correlation according to the relation between two adjacent data items in the sequence direction in the first target data sequence, wherein the graph processing result corresponding to the first correlation comprises the steps of determining whether the data item is negatively increased according to the speed increase of the data item relative to the data item, and generating the graph processing result corresponding to the first correlation according to the number of the negatively increased data items in the first target data sequence and the positions of the negatively increased data items in the first target data sequence.
In one example, outputting a graph processing result corresponding to the first correlation according to the first correlation includes determining a relationship between two data items adjacent in a sequence direction in the first target data sequence and generating a graph processing result corresponding to the first correlation according to the relationship between two data items adjacent in the sequence direction in the first target data sequence when the first correlation characterizes the data items of the first target data sequence and the class values corresponding to the data items are negative correlations.
In this example, determining the relationship between two data items that are sequentially adjacent in the first target data sequence includes calculating a speed increase of a subsequent data item relative to a previous data item for two data items that are sequentially adjacent in the first target data sequence. Generating a graph processing result corresponding to the first correlation according to the relation between two adjacent data items in the sequence direction in the first target data sequence, wherein the graph processing result corresponding to the first correlation is generated according to the increasing speed of the next data item relative to the previous data item, whether the next data item is increasing or not is determined, and the graph processing result corresponding to the first correlation is generated according to the number of the increasing data items in the first target data sequence and the positions of the increasing data items in the first target data sequence.
In one example, according to the first correlation, a graph processing result corresponding to the first correlation is output, wherein the graph processing result comprises the steps of determining an average value of data items of a first target data sequence and a maximum value and a minimum value in the data items of the first target data sequence when the data items of the first target data sequence are uncorrelated with the class values corresponding to the data items, determining a first difference value and a second difference value, wherein the first difference value is the difference value of the maximum value and the average value, the second difference value is the difference value of the average value and the minimum value, outputting the class value corresponding to the maximum value when the first difference value is larger than the second difference value, and outputting the class value corresponding to the minimum value when the first difference value is smaller than the second difference value.
In one example, the processing device of the graph further includes a second processing module and a third output module.
And the second processing module is used for calculating the speed increase of the next data item relative to the previous data item for two adjacent data items in the first target data sequence, and judging whether the next data item is positive or negative according to the speed increase of the next data item relative to the previous data item.
And the third output module is used for outputting a corresponding chart processing result according to the category value of the positively-increased data item and the category value of the negatively-increased data item.
The embodiment also provides electronic equipment, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor is used for executing the computer program to realize the processing method of the chart of the first aspect of the disclosure.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of processing a graph of the first aspect of the present disclosure.
In this embodiment, an electronic device is also provided. The electronic device includes a memory and a processor. The processor is used for executing the computer program to realize the processing method of the chart provided by any embodiment.
In the present embodiment, a computer-readable storage medium is also provided. The computer readable storage medium stores thereon a computer program which, when executed by a processor, implements the method for processing a graph provided in any of the above embodiments.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the server embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference is made to the description of the method embodiment for relevant points.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The present invention may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, punch cards or intra-groove protrusion structures such as those having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of the present invention may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as SMALLTALK, C ++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which can execute the computer readable program instructions.
Various aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvements in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
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