Data display method and device and storage medium

Abstract

The invention discloses a data display method and device and a storage medium, and belongs to the field of computers. The method comprises the steps that semantic information of a plurality of first fields to be displayed and a data set of the first fields are obtained, the first fields are part of or all fields in a data storage file, and the data set of the first fields is a data set stored in the data storage file; obtaining a to-be-displayed chart type and chart configuration information corresponding to the chart type based on the semantic information of the plurality of first fields and the data set of the plurality of first fields; and displaying a first chart based on the chart configuration information and the data set of the plurality of first fields, the first chart being a chart corresponding to the chart type, and the first chart being used for reflecting data included in the data set of the plurality of first fields. The method can improve the precision of the displayed chart.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular to a method, device and storage medium for displaying data. Background Art

[0002] Using visual charts to display data is one of the most important functions of business intelligence products, and it is also one of the main ways to analyze data and transmit data information. Due to the intuitiveness of charts, visual charts can make abstract data intuitive and easy to understand.

[0003] In the related art, a user can select multiple fields to be displayed in a data table, thereby obtaining data belonging to the multiple fields from the data table, determining a chart type to be displayed based on the multiple fields, and using a chart corresponding to the chart type to display the data belonging to the multiple fields based on default chart configuration information corresponding to the chart type.

[0004] The chart displayed based on the default chart configuration information corresponding to the chart type may not match the data of the multiple fields, so the accuracy of the chart displayed by the related technology is low. Summary of the invention

[0005] The present application provides a method, device and storage medium for displaying data to improve the accuracy of displaying charts. The technical solution is as follows:

[0006] In a first aspect, the present application provides a method for displaying data, the method being applied to a cloud service platform, the cloud service platform including a data storage file, the data storage file being used to store a data set of multiple fields, the data set of the field including multiple data belonging to the field. Obtain semantic information of multiple first fields to be displayed and the data set of the multiple first fields, the multiple first fields being part or all of the fields in the data storage file, the data set of the multiple first fields being the data set stored in the data storage file. Based on the semantic information of the multiple first fields and the data set of the multiple first fields, obtain a chart type to be displayed and chart configuration information corresponding to the chart type. Display a first chart based on the chart configuration information and the data set of the multiple first fields, the first chart being a chart corresponding to the chart type, the first chart being used to reflect the data included in the data set of the multiple first fields.

[0007] Based on the semantic information of the multiple first fields and the data set of the multiple first fields, the chart type to be displayed and the chart configuration information corresponding to the chart type are obtained, and the first chart is displayed based on the chart configuration information and the data set of the multiple first fields. In this way, the first chart can be consistent with the data of the multiple first fields, thereby improving the accuracy of the displayed chart.

[0008] In a possible implementation, the cloud service platform also includes a first correspondence relationship, the first correspondence relationship is used to save the correspondence between the feature vector, the chart type of the second chart, and the chart configuration information of the second chart, the second chart is a chart that has been displayed to reflect the data included in the data set of multiple second fields, and the feature vector is used to reflect the semantic information of the multiple second fields and the data set of the multiple second fields. Obtain a first feature vector, the first feature vector is used to reflect the semantic information of the multiple first fields and the data set of the multiple first fields. Obtain the similarity between the first feature vector and each feature vector in the first correspondence relationship. Obtain a feature vector whose similarity with the first feature vector exceeds a first threshold from the first correspondence relationship as the second feature vector. Obtain the chart type corresponding to the second feature vector from the first correspondence as the chart type to be displayed and obtain the chart configuration information corresponding to the second feature vector as the chart configuration information corresponding to the chart type to be displayed.

[0009] Since the first correspondence is used to store the correspondence between the feature vector, the chart type of the second chart and the chart configuration information of the second chart, obtaining the chart type and chart configuration information to be displayed from the first correspondence can improve the efficiency of obtaining the chart type and chart configuration information.

[0010] In another possible implementation, the chart configuration information includes a slot for each first field, and the slot is a display element of the first chart. At least one field type and the number of fields belonging to each field type are obtained based on the semantic information of the multiple first fields, and the at least one field type is the field type to which the multiple first fields belong. Based on the at least one field type, the number of fields of each field type, and the data set of the multiple first fields, the chart type to be displayed and the slot corresponding to each first field are obtained. In this way, the accuracy of the obtained chart type and slot can be improved.

[0011] In another possible implementation, the multiple first fields include a first target field whose field type is a measurement type, and the chart configuration information also includes one or more of the following: a coordinate system scale of the first chart, or a numerical scale value range of the first chart. Based on the data and the number of data included in the first data set, the information entropy of the linear coordinate system scale and the information entropy of the logarithmic coordinate system scale are obtained, and the first data set is the data set of the first target field. Based on the information entropy of the linear coordinate system scale and the information entropy of the logarithmic coordinate system scale, the coordinate system scale is obtained. Based on the data included in the first data set and the type of chart to be displayed, the numerical scale value range is set. In this way, the coordinate system scale of the first chart and the numerical scale value range of the first chart can be accurately obtained.

[0012] In another possible implementation, based on the maximum data and the minimum data included in the first data set, N data intervals and the data included in each data interval are determined, the data included in each data interval is the data in the first data set, and N is the number of data included in the first data set. Based on N and the data included in each data interval, the data distribution probability of each data interval is counted. Based on N and the data distribution probability of each data interval, the information entropy of the linear coordinate system scale is obtained.

[0013] In another possible implementation, a logarithmic operation with a specified value as the base is performed on each data included in the first data set to obtain the logarithm of each data. Based on the maximum logarithm and the minimum logarithm included in the logarithmic set, N logarithmic intervals and the logarithms included in each logarithmic difference are determined, the logarithmic set includes the logarithm of each data, the logarithmic interval includes the logarithmic number in the logarithmic set, and N is the number of data included in the first data set. Based on N and the logarithmic number included in each logarithmic interval, the logarithmic distribution probability of each logarithmic interval is counted. Based on N and the logarithmic distribution probability of each logarithmic interval, the information entropy of the logarithmic coordinate system scale is obtained.

[0014] In another possible implementation, the value scale range includes the value range and baseline position of the first chart. When the chart type is a chart type other than a line chart and an area chart, the value range and baseline position of the first chart are set based on the maximum data and the minimum data included in the first data set. When the chart type is a line chart or an area chart, outliers in the first data set are removed to obtain a second data set, and the value range and baseline position of the first chart are set based on the maximum data and the minimum data included in the second data set.

[0015] In another possible implementation, multiple first fields include a second target field whose corresponding slot is a color slot, and the chart configuration information also includes the first color of each data included in the third data set, and the third data set is a data set of the second target field. If the data included in the third data set is ordinal data, the second color of each data included in the third data set is obtained based on the semantic information of the second target field. If the data included in the third data set is non-ordinal data, the second color of each data included in the third data set is obtained based on the second corresponding relationship and each data included in the third data set, and the second corresponding relationship is used to save the corresponding relationship between the feature vector of the data and the color of the data. The second color of each data included in the third data set is adjusted to obtain the first color of each data included in the third data set, and the similarity between the first colors of any two data included in the third data set exceeds the second threshold. In this way, the color corresponding to each data in the third data set can be accurately obtained.

[0016] In another possible implementation manner, a reference color is acquired based on the semantic information of the second target field; and the brightness of the reference color is adjusted to obtain the second color of each data included in the third data set.

[0017] In another possible implementation, the third data set includes the first data, and a third feature vector is obtained based on the first data, and the third feature vector is used to reflect the first data. Based on the third feature vector and the feature vector of each data included in the second correspondence, the similarity between the first data and each data included in the second correspondence is obtained. If there is target data in the data included in the second correspondence whose similarity with the first data exceeds a third threshold, the color of the target data is determined as the second color of the first data. If there is no target data in the data included in the second correspondence whose similarity with the first data exceeds the third threshold, the second color of the first data is obtained based on the colors of m target data and the similarity between the first data and the m target data, where m is an integer greater than 1, and the m target data are the m data with the greatest similarity with the first data in the second correspondence.

[0018] In a second aspect, the present application provides a device for displaying data, which is used to execute the method in the first aspect or any possible implementation of the first aspect. Specifically, the device includes a unit for executing the method in the first aspect or any possible implementation of the first aspect.

[0019] In a third aspect, the present application provides a computing device cluster, the cluster comprising at least one computing device, each device in the at least one computing device comprising at least one processor and at least one memory, the at least one memory storing computer-readable instructions; the at least one processor executes the computer-readable instructions so that the cluster implements the method in the first aspect or any possible implementation manner of the first aspect.

[0020] In a fourth aspect, the present application provides a computer program product, which includes a computer program stored in a computer-readable storage medium, and the computer program is loaded by a processor to implement the method in the first aspect or any possible implementation manner of the first aspect.

[0021] In a fifth aspect, the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program is loaded by a processor to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0022] In a sixth aspect, the present application provides a chip comprising a memory and a processor, wherein the memory is used to store computer instructions, and the processor is used to call and run the computer instructions from the memory to execute the method in the first aspect or any possible implementation of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of a network architecture provided by an embodiment of the present application;

[0024] Figure 2 is a flow chart of a method for displaying data provided by an embodiment of the present application;

[0025] Figure 3 is a flow chart of another method for displaying data provided by an embodiment of the present application;

[0026] Figure 4 is a schematic diagram of a recommendation model provided in an embodiment of the present application;

[0027] Figure 5 is a schematic diagram of a device structure for displaying data provided by an embodiment of the present application;

[0028] Figure 6 is a schematic diagram of the structure of a computing device provided in an embodiment of the present application;

[0029] Figure 7 It is a schematic diagram of the structure of a cluster provided in an embodiment of the present application;

[0030] Figure 8 It is a schematic diagram of the structure of another cluster provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0032] See also Figure 1 An embodiment of the present application provides a network architecture 100, which includes a cloud service platform 101 and a terminal device 102, and the cloud service platform 101 can communicate with the terminal device 102.

[0033] The cloud service platform 101 includes at least one data storage file. For each data storage file, the data storage file is used to store data sets of multiple fields. For each field, the data set of the field includes multiple data belonging to the field.

[0034] The data storage file may be a structured data storage file or a semi-structured data storage file.

[0035] If the data storage file is a structured data storage file, the data storage file can be a data table. The multiple fields included in the data storage file are the multiple columns of the data table. For each field, the data set of the field is a column of data in the data table.

[0036] For example, referring to the data table shown in Table 1, the data table includes three fields, namely the country field, the time field, and the output value field. The data table is used to save the data set of the country field (the first column of data in Table 1), the data set of the time field (the second column of data in Table 1), and the data set of the output value field (the third column of data in Table 1).

[0037] Table 1

[0038] nation time output value Picture House 1 2019 5000 Picture House 2 2019 4500 Picture House 3 2019 3000 Picture House 1 2020 6000 Picture House 2 2020 5500 Picture House 3 2020 5600 Picture House 1 2021 4700 Picture House 2 2021 2700 Picture House 3 2021 2300

[0039] If the data storage file is a semi-structured data storage file, the semi-structured data storage file is used to store data in the form of tag blocks. The multiple tag blocks included in the semi-structured data storage file are multiple fields. The semi-structured data storage file is a non-list form storage file. In the semi-structured data storage file, data belonging to the same field is centrally stored in a tag block, that is, each data set included in the data set of a field is stored in a tag block in the semi-structured data storage file. Optionally, the structured data storage file is an extensible markup language (xml) file, etc.

[0040] For example, in the semi-structured data storage file shown below, the data set belonging to the country field is centrally stored in a tag block in the xml file, where <country> is the start tag of the tag block and < / country> is the end tag of the tag block; the data set belonging to the time field is centrally stored in a tag block in the xml file, where <time> is the start tag of the tag block and < / time> is the end tag of the tag block; the data set belonging to the output value field is centrally stored in a tag block in the xml file, where <output value> is the start tag of the tag block and < / output value> is the end tag of the tag block.

[0041] Semi-structured data storage file:

[0042] <title> nation< / title>

[0043] <country>

[0044] Country 1;

[0045] Country 2;

[0046] Country 3;

[0047] Country 1;

[0048] Country 2;

[0049] Country 3;

[0050] Country 1;

[0051] Country 2;

[0052] Country 3;

[0053] < / Country>

[0054] <Time> 2019; 2019; 2019; 2020; 2020; 2020; 2021; 2021; 2021;

[0064] < / Time>

[0065] <Output Value> 5000; 4500; 3000; 6000; 5500; 5600; 4700; 2700; 2300;

[0075] < / Output Value>

[0076]

[0077] In some embodiments, the data storage file may also store semantic information of the multiple fields and / or content such as metadata of the multiple fields.

[0078] For each field, the semantic information of the field includes one or more of the following: the field type of the field, the field name of the field, or the description information of the field, etc. Optionally, the field type of the field may be a dimension type or a metric type, and the dimension type may be a time dimension or a category dimension, etc.

[0079] The metadata of the field includes statistical information of the data set of the field. For example, the metadata of the field includes the maximum data, the minimum data, the number of data, the number of distinct data, the average value, the standard deviation, and / or the variance in the data set of the field.

[0080] In some embodiments, for each data storage file, the cloud service platform 101 can display multiple fields included in the data storage file to the terminal device 102, and the user can select multiple first fields to be displayed from the data storage file, and the multiple first fields are part of or all of the fields included in the data storage file.

[0081] The terminal device 102 sends a display request including the multiple first fields to the cloud service platform 101. The cloud service platform 101 receives the display request and displays a first chart on the terminal device 102 based on the display request, the first chart being used to reflect the data included in the data set of the multiple first fields, the data set of the multiple first fields being the data set stored in the data storage file.

[0082] See also Figure 2 The present application embodiment provides a method 200 for displaying data, wherein the method 200 is applied to Figure 1 The network architecture 100 shown includes a cloud service platform, the cloud service platform includes a first correspondence relationship, the first correspondence relationship is used to save the correspondence between the feature vector, the chart type of the second chart and the chart configuration information of the second chart, the second chart is a chart that has been displayed to reflect the data included in the data set of multiple second fields, and the feature vector is used to reflect the semantic information of the multiple second fields and the data set of the multiple second fields. The method 200 includes the following process of steps 201 to 206.

[0083] Step 201: Acquire semantic information of a plurality of first fields to be displayed and a data set of the plurality of first fields, wherein the plurality of first fields are part or all of the fields in a data storage file.

[0084] In step 201, the cloud service platform may display multiple field names included in the data storage file to the user, and the user may select multiple first fields to be displayed from multiple fields corresponding to the multiple field names included in the data storage file. The cloud service platform obtains semantic information of the multiple first fields and data sets of the multiple first fields from the data storage file.

[0085] In some embodiments, the cloud service platform can display identification information of at least one data storage file on the user's terminal device, and the user can select the identification information of a data storage file. The terminal device sends the identification information of the data storage file to the cloud service platform, obtains multiple field names included in the data storage file based on the identification information of the data storage file, and displays the multiple field names on the terminal device. The user can select the names of multiple first fields that need to be displayed from the multiple field names included in the data storage file. The terminal device sends the names of the multiple first fields to the cloud service platform. Based on the names of the multiple first fields, the cloud service platform obtains the semantic information of the multiple first fields and the data sets of the multiple first fields from the data storage file.

[0086] Step 202: Obtain a first feature vector, where the first feature vector is used to reflect the semantic information of the plurality of first fields and the data set of the plurality of first fields.

[0087] In step 202, the semantic information of the plurality of first fields and the data set of the plurality of first fields may be converted into a first feature vector by the vector generation algorithm. The first feature vector is a mathematical representation of the semantic information of the plurality of first fields and the data set of the plurality of first fields.

[0088] Step 203: Obtain the similarity between the first feature vector and each feature vector in the first corresponding relationship.

[0089] Optionally, the similarity between the first feature vector and each feature vector in the first corresponding relationship may be cosine similarity or the like.

[0090] In some embodiments, for each feature vector in the first corresponding relationship, the similarity between the first feature vector and the feature vector may be obtained according to the following formula.

[0091] ρ=Max(distance(P sample -P input )), P input =<C,T,V,S> ;

[0092] In the above formula, ρ is the similarity between the first eigenvector and the eigenvector in the first corresponding relationship, P input is the first eigenvector, P sample is the characteristic vector in the first corresponding relationship, C is used to reflect the field name and description information of the multiple first fields, T is used to reflect the field type of the multiple first fields, V is used to reflect the data set of the multiple first fields, and S is used to reflect the statistical information of the data set of the multiple first fields.

[0093] Step 204: obtaining, from the first corresponding relationship, a feature vector whose similarity with the first feature vector exceeds a first threshold as a second feature vector.

[0094] In step 204, if there is a feature vector whose similarity with the first feature vector exceeds the first threshold in the first correspondence, then a feature vector whose similarity with the first feature vector exceeds the first threshold, or a feature vector with the greatest similarity with the first feature vector is selected as the second feature vector. If there is no feature vector whose similarity with the first feature vector exceeds the first threshold in the first correspondence, then the following is performed: Figure 3 The method 300 shown is used to display data, and a detailed description is not given here.

[0095] Step 205: acquiring the chart type corresponding to the second feature vector from the first corresponding relationship as the chart type to be displayed and acquiring the chart configuration information corresponding to the second feature vector as the chart configuration information corresponding to the chart type to be displayed.

[0096] In some embodiments, the chart configuration information includes a slot for each first field, the slot being a display element of a first chart; the first chart is a chart corresponding to the chart type to be displayed. The first chart may be a table, a line chart, or a bar chart.

[0097] If the plurality of first fields include a first target field whose field type is a measurement type, the chart configuration information further includes one or more of the following: a coordinate system scale of the first chart, or a numerical scale range of the first chart. Optionally, the coordinate system scale of the first chart may be a linear coordinate system scale, or may be a logarithmic coordinate system scale. The data value scale range of the first chart includes a value range and a baseline value of the first chart.

[0098] If the plurality of first fields include a second target field whose corresponding slot is a color slot, the chart configuration information further includes a first color of each data included in the data set of the second target field.

[0099] Step 206: Display a first chart based on the chart configuration information and the data sets of the plurality of first fields, where the first chart is used to reflect the data included in the data sets of the plurality of first fields.

[0100] In some embodiments, the cloud service platform can display a first chart on a terminal device, and set the first chart based on the chart configuration information and the data included in the data set of the multiple first fields. For example, the coordinate system scale for displaying the first chart is set based on the coordinate system scale of the first chart, the data value scale value range of the first chart includes the value range and baseline position of the first chart, the baseline position is set to the baseline position of the first chart, and the maximum value and minimum value displayed by the first chart are respectively set to the maximum value and minimum value in the value range. Based on the first color of each data included in the data set of the second target field, the color of the graphic corresponding to each data is set in the first chart.

[0101] In an embodiment of the present application, since the first correspondence is used to store the correspondence between the feature vector, the chart type of the second chart, and the chart configuration information of the second chart, the second chart is a chart that has been displayed to reflect the data included in the data set of multiple second fields. In this way, the semantic information of the multiple first fields to be displayed and the data set of the multiple first fields are obtained, and based on the semantic information of the multiple first fields and the data set of the multiple first fields, the chart type to be displayed and the chart configuration information that are compatible with the data included in the multiple first fields and the data set of the multiple first fields are obtained from the first correspondence. The first chart is displayed based on the chart configuration information, so that the first chart is compatible with the data included in the multiple first fields and the data set of the multiple first fields, thereby improving the display accuracy. In addition, since the chart type and chart configuration information to be displayed are obtained from the first correspondence, the chart type and chart configuration information to be displayed can be quickly obtained, thereby improving the efficiency of displaying charts.

[0102] See also Figure 3 The present application embodiment provides a method 300 for displaying data, wherein the method 300 is applied to Figure 1 In the network architecture 100 shown, the method 300 includes the following process from step 301 to step 307.

[0103] Step 301: Acquire semantic information of a plurality of first fields to be displayed and a data set of the plurality of first fields, wherein the plurality of first fields are part or all of the fields in a data storage file.

[0104] In step 301, the cloud service platform may display multiple field names included in the data storage file to the user, and the user may select multiple first fields to be displayed from multiple fields corresponding to the multiple field names included in the data storage file. The cloud service platform obtains semantic information of the multiple first fields and data sets of the multiple first fields from the data storage file.

[0105] In some embodiments, the cloud service platform can display identification information of at least one data storage file on the user's terminal device, and the user can select the identification information of a data storage file. The terminal device sends the identification information of the data storage file to the cloud service platform, obtains multiple field names included in the data storage file based on the identification information of the data storage file, and displays the multiple field names on the terminal device. The user can select the names of multiple first fields that need to be displayed from the multiple field names included in the data storage file. The terminal device sends the names of the multiple first fields to the cloud service platform. Based on the names of the multiple first fields, the cloud service platform obtains the semantic information of the multiple first fields and the data sets of the multiple first fields from the data storage file.

[0106] Each data set of the first field can be a column of data, so the data sets of the multiple first fields can form a data table with multiple columns and multiple rows, and the number of rows of the data table can be counted. Optionally, the data table can also be pre-processed, for example, missing values ​​in the data table can be filled, for example, missing values ​​can be filled by interpolation, filling the average value, filling zero values ​​or null values, etc.

[0107] In some embodiments, the cloud service platform may also obtain metadata of the multiple first fields from the data storage file.

[0108] Step 302: Based on the semantic information of the multiple first fields and the data sets of the multiple first fields, obtain the chart type to be displayed and the slot of each first field, and the chart configuration information corresponding to the chart type includes the slot of each first field.

[0109] In step 302, the following operations 3021-3022 may be used to obtain the chart type to be displayed and the slot of each first field.

[0110] 3021: Based on the semantic information of the multiple first fields, obtain at least one field type and the number of fields belonging to each field type, where the at least one field type is the field type to which the multiple first fields belong.

[0111] For each first field, the semantic information of the first field includes the field type of the first field, and the field type of the first field may be a dimension type or a metric type.

[0112] If the field type of the first field is a dimension type, it means that the first field is a string type field, that is, the data set of the first field includes a string. If the field type of the first field is a measurement type, it means that the first field is a numerical type field, that is, the data set of the first field includes a numerical value.

[0113] In some embodiments, the dimension type may be a time dimension or a category dimension, etc.

[0114] In 3021, for each first field, the field type of the first field is obtained from the semantic information of the first field. After the field type of each first field is obtained, for each field type, the number of fields of the field type in the multiple first fields is counted.

[0115] 3022: Based on the at least one field type, the number of fields of each field type, and the data set of the multiple first fields, obtain a chart type to be displayed and a slot corresponding to each first field.

[0116] The cloud service platform includes a recommendation model for recommending a chart type to be displayed and a slot corresponding to each first field based on the at least one field type, the number of fields of each field type, and the data set of the multiple first fields.

[0117] In some embodiments, the recommendation model defines multiple recommendation conditions and a chart type corresponding to each recommendation condition. In 3022, the recommendation condition satisfied by the at least one field type, the number of fields of each field type, and the data set of the multiple first fields is determined, and the chart type corresponding to the recommendation condition is recommended as the chart type to be displayed.

[0118] In some embodiments, for at least one slot corresponding to the chart to be recommended, the recommendation model also defines matching conditions for each slot. For each slot, a first field whose field type and / or data set meets the matching conditions of the slot is selected from the multiple first fields, and the slot is used as the slot corresponding to the first field.

[0119] For example, Figure 4 An example of a recommendation model is shown, in which each path in the recommendation model represents a recommendation condition and the chart type corresponding to the recommendation condition, and some paths also represent at least one slot and the condition corresponding to each slot. The letters D, T, C, and M in the recommendation model represent different field types. The letter D represents the dimension type, the letter T represents the time dimension, the letter C represents the category dimension, and the letter M represents the metric type. For any field type represented by a letter, the combination of the letter and the number represents the field type and the number of fields belonging to the field type. Figure 4 In the recommended model shown, M0 indicates 0 measurement type fields, D0M1+ indicates 0 dimension type fields and one or more measurement type fields, D1 indicates a dimension type field, D1M1 indicates a dimension type field and a measurement type field, T1M1 indicates a time dimension field and a measurement type field, and C1M1 indicates a category dimension field and a measurement type field. Figure 4 The meanings of other letter and number combinations are not explained one by one.

[0120] Next, Figure 4 The recommended model shown is used as an example to illustrate how to obtain the chart type to be displayed, including the following points.

[0121] 1. If there is no first field of a metric type in the plurality of first fields (satisfying M0), it is recommended that the chart type to be displayed is a table.

[0122] 2. If the multiple first fields do not have a first field of dimension type but have one or more first fields of measurement type (satisfying D0M1+), it is recommended that the chart type to be displayed is a table.

[0123] 3. If the multiple first fields include a first field of a dimension type (satisfying D1), determine the number of fields of the measurement type included in the multiple first fields. If the number of fields of the measurement type is 1 (satisfying D1M1), execute 31 as follows; if the number of fields of the measurement type is greater than or equal to 2 (satisfying D1M2+), execute 32.

[0124] 31. Determine whether the first field of the dimension type is the first field of the time dimension or the first field of the category dimension. If the first field of the dimension type is the first field of the time dimension (satisfying T1M1), execute 311 as follows; if the first field of the dimension type is the first field of the category dimension (satisfying C1M1), execute 312 as follows.

[0125] 311. Obtain the maximum data and the minimum data from the data set of the first field of the time dimension, calculate the ratio of the difference between the maximum data and the minimum data to the maximum data, and if the minimum data is greater than 0 and the ratio exceeds a ratio threshold (such as 0.5), recommend that the chart type to be displayed is an area chart, and determine that the slot corresponding to the first field of the time dimension is a dimension slot (such as using Figure 4 The first field of the metric type corresponds to the metric slot (such as using Figure 4 If the minimum data is not greater than 0 or the ratio does not exceed the ratio threshold (such as 0.5), it is recommended that the chart type to be displayed is a line chart, and the slot corresponding to the first field of the time dimension is determined to be a dimension slot (such as using Figure 4 The first field of the metric type corresponds to the metric slot (such as using Figure 4 metric slot [M] in .

[0126] 312. For the number of rows of the data table composed of the data sets of the plurality of first fields, if the number of rows does not exceed the first row number threshold (such as 40), it is recommended that the chart type to be displayed is a bar chart, and the slot corresponding to the first field of the category dimension is determined to be a dimension slot (such as using Figure 4 The first field of the metric type corresponds to the metric slot (such as using Figure 4 If the number of rows exceeds the first row number threshold (such as 40), the recommended chart type to be displayed is a table.

[0127] 32. Determine whether the first field of the dimension type is the first field of the time dimension or the first field of the category dimension. If the first field of the dimension type is the first field of the time dimension (satisfying T1M2+), execute 321 as follows; if the first field of the dimension type is the first field of the category dimension (satisfying C1M2+), execute 322 as follows.

[0128] 321. It is recommended that the chart type to be displayed is a line chart, and the slot corresponding to the first field of the time dimension is determined to be the dimension slot (such as using Figure 4 The dimension slot [T] in the metric is represented by the dimension slot [T] in the metric type, and the slot corresponding to each first field of the metric type is the metric slot.

[0129] 322. If the number of rows does not exceed the second row number threshold (such as 80), it is recommended that the chart type to be displayed is a bar chart, and the slot corresponding to the first field of the category dimension is determined to be a dimension slot and the slot corresponding to each first field of the metric type is determined to be a metric slot. If the number of rows exceeds the second row number threshold (such as 80), it is recommended that the chart type to be displayed is a table.

[0130] 4. If the multiple first fields include first fields of two dimension types (satisfying D2), determine the number of first fields of the measurement type included in the multiple first fields. If the number of first fields of the measurement type is 1 (satisfying D2M1), execute the following 41. If the number of first fields of the measurement type is greater than or equal to 2 (satisfying D2M2+), it is recommended that the chart type to be displayed is a table.

[0131] 41. Determine the first fields of the two dimension types. If the first fields of the two dimension types are the first fields of two time dimensions (satisfying T2M1), it is recommended that the chart type to be displayed is a table; if the first fields of the two dimension types include the first field of a time dimension and the first field of a category dimension (satisfying C1T1M1), execute 411 as follows; if the first fields of the two dimension types are the first fields of two category dimensions (satisfying C2M1), execute 412 as follows.

[0132] 411. Obtain the data set of the first field of the category dimension including the number of first data after deduplication. Optionally, obtain the data set of the first field of the category dimension including the number of first data after deduplication from metadata of the field of the category dimension; or count the data set of the first field of the category dimension including the number of first data after deduplication.

[0133] If the number of first data exceeds the first threshold (such as 14), the data set of the first field of the time dimension is obtained, including the number of second data after deduplication. If the number of second data exceeds the second threshold (such as 14), the recommended chart type to be displayed is a table. If the number of second data does not exceed the second threshold (such as 14), the recommended chart type to be displayed is a line chart. The slot corresponding to the first field of the category dimension is determined to be a dimension slot (such as using Figure 4 The first field of the metric type corresponds to the metric slot (such as using Figure 4 The first field of the time dimension corresponds to the color slot (such as using Figure 4 )

[0134] If the number of the first data does not exceed the first number threshold (such as 14), it is recommended that the chart type to be displayed is a line chart, and the slot corresponding to the first field of the time dimension is determined to be a dimension slot (such as using Figure 4 The first field of the metric type corresponds to the metric slot (such as using Figure 4 The first field of the category dimension corresponds to the color slot (such as using Figure 4 )

[0135] 412. The first fields of the two category dimensions are called field 1 and field 2. If the number of rows does not exceed the second row number threshold (e.g., 80), it is recommended that the chart type to be displayed is a bar chart. The number of third data after deduplication included in the data set of field 1 is obtained, and the number of fourth data after deduplication included in the data set of field 2 is obtained. If the number of third data is less than the number of fourth data, it is determined that the slot corresponding to field 1 is a dimension slot (e.g., using Figure 4 ), the slot corresponding to field 2 is the color slot (such as using Figure 4 The first field of this metric type corresponds to the metric slot (for example, using Figure 4 If the number of rows exceeds the second row number threshold (e.g., 80), the recommended chart type to be displayed is a table.

[0136] 5. If the multiple first fields include three or more first fields of dimension type (satisfying D3+), it is recommended that the chart type to be displayed is a table.

[0137] When the type of the chart to be displayed is a table, it is determined that the slots corresponding to the multiple first fields are all column slots.

[0138] In some embodiments, the multiple first fields include a first target field whose field type is a measurement type, and the chart configuration information also includes one or more of the following: a coordinate system scale of the first chart, or a numerical scale range of the first chart, and the first chart is a chart corresponding to the chart type to be displayed.

[0139] Step 303: Based on the data and the number of data included in the first data set, obtain the information entropy of the linear coordinate system scale and the information entropy of the logarithmic coordinate system scale, the first data set being the data set of the first target field.

[0140] The information entropy of the linear coordinate system scale can be obtained according to the following process 3031-3033, which is:

[0141] 3031: Based on the maximum data and the minimum data included in the first data set, determine N data intervals and the data included in each data interval, the data included in each data interval is the data in the first data set, and N is the number of data included in the first data set.

[0142] Assuming that the first data set includes 5 data, the maximum data included in the first data set is 100, and the minimum data is 0, then 5 data intervals are determined, the first data interval is greater than or equal to 0 and less than or equal to 20, the second data interval is greater than 20 and less than or equal to 40, the third data interval is greater than 40 and less than or equal to 60, the fourth data interval is greater than 60 and less than or equal to 80, and the fifth data interval is greater than 80 and less than or equal to 100. Then, the data included in the 5 data intervals are respectively obtained from the 5 data included in the first data set.

[0143] 3032: Based on N and the data included in each data interval, count the data distribution probability of each data interval.

[0144] For the data included in each data interval, the number of data included in the data interval is counted, and the ratio of the number of data included in the data interval to N is equal to the data distribution probability of the data interval.

[0145] 3033: Based on N and the data distribution probability of each data interval, obtain the information entropy of the linear coordinate system scale.

[0146] Based on N and the data distribution probability of each data interval, the information entropy of the linear coordinate system scale is obtained by the following first formula.

[0147] The first formula is:

[0148] In the first formula, H Linear is the information entropy of the linear coordinate system, f i is the data distribution probability of the i-th data interval.

[0149] The information entropy of the linear coordinate system scale can be obtained according to the following process 2131-2134, which is:

[0150] 2131: Perform a logarithmic operation with a specified value as a base on each data included in the first data set to obtain the logarithm of each data.

[0151] The specified value may be 2 or 10. Assume that the specified value is 10, the first data set includes data x, and a logarithm operation with a base of 10 is performed on the data x, and the logarithm of the data x is log 10 x Perform the above logarithmic operation on each other data in the first data set to obtain the logarithm of each other data. Then, the logarithm of each data included in the first data set is combined into a logarithmic set.

[0152] 2132: Based on the maximum logarithm and the minimum logarithm included in the logarithm set, determine N logarithm intervals and the logarithms included in each logarithm difference, and the logarithms included in each logarithm interval are the logarithms in the logarithm set.

[0153] Assuming that the first data set includes 5 data, that is, N=5, the maximum logarithm included in the logarithm set is 10, and the minimum logarithm is 0, then 5 logarithm intervals are determined, the first logarithm interval is greater than or equal to 0 and less than or equal to 2, the second logarithm interval is greater than 2 and less than or equal to 4, the third logarithm interval is greater than 4 and less than or equal to 6, the fourth logarithm interval is greater than 6 and less than or equal to 8, and the fifth logarithm interval is greater than 8 and less than or equal to 10. Then, the logarithms included in the 5 logarithm intervals are obtained from the 5 logarithms included in the logarithm set.

[0154] 2133: Based on N and the number of logarithms included in each logarithmic interval, count the logarithmic distribution probability of each logarithmic interval.

[0155] For the logarithms included in each logarithmic interval, the number of logarithms included in the logarithmic interval is counted, and the ratio of the number of logarithms included in the logarithmic interval to N is equal to the logarithmic distribution probability of the data interval.

[0156] 2134: Based on N and the logarithmic distribution probability of each logarithmic interval, obtain the information entropy of the logarithmic coordinate system scale.

[0157] Based on N and the logarithmic distribution probability of each logarithmic interval, the information entropy of the logarithmic coordinate system scale is obtained by the following second formula.

[0158] The second formula is:

[0159] In the second formula, H Log is the information entropy of the logarithmic coordinate system, F i is the logarithmic distribution probability of the ith logarithmic interval.

[0160] Step 304: Obtain a coordinate system scale based on the information entropy of the linear coordinate system scale and the information entropy of the logarithmic coordinate system scale.

[0161] If the information entropy of the linear coordinate system scale is greater than the information entropy of the logarithmic coordinate system scale, the obtained coordinate system scale is a linear coordinate system scale; if the information entropy of the linear coordinate system scale is less than the information entropy of the logarithmic coordinate system scale, the obtained coordinate system scale is a logarithmic coordinate system scale.

[0162] Step 305: Based on the data included in the first data set and the type of chart to be displayed, set the value range of the numerical scale.

[0163] The numerical scale range includes the range of the first chart and the baseline position.

[0164] In some embodiments, when the chart type to be displayed is a chart type other than a line chart and an area chart, the value range of the first chart is set to include the maximum value and the minimum value of the first data set, and the baseline position is set to the minimum value of the first data set or 0, etc. When the chart type to be displayed is a line chart or an area chart, outliers in the first data set are obtained, and the outliers are removed from the first data set to obtain the second data set. The value range of the first chart is set to include the maximum value and the minimum value of the second data set, and the baseline position is set to the minimum value of the second data set or 0, etc.

[0165] In some embodiments, the standard deviation and mean of the data included in the first data set are obtained, and for each data y in the first data set, if the data y satisfies the conditions of the following third formula, it is determined that the data y is an outlier; if the data y does not satisfy the conditions of the following third formula, it is determined that the data y is not an outlier.

[0166] The third formula is: |y-μ|>3σ;

[0167] In the third formula, σ is the standard deviation and μ is the mean.

[0168] In some embodiments, based on each data included in the first data set, the standard deviation and the average value of the data included in the first data set are calculated. Alternatively, the standard deviation and the average value of the data included in the first data set are obtained from the metadata of the first target field.

[0169] In some embodiments, the plurality of first fields include a second target field whose corresponding slot is a color slot, the chart configuration information further includes a first color of each data included in a third data set, and the third data set is a data set of the second target field.

[0170] Step 306: Determine a first color of each data included in the third data set based on the data included in the third data set.

[0171] In step 306 , the first color of each data included in the third data set may be determined through the following process 3061 - 3064 .

[0172] 3061: Determine whether the data included in the third data set is ordinal data or non-ordinal data.

[0173] In 3061, the semantic information of the second target field and the data included in the third data set are input into an ordinal recognition model, so that the ordinal recognition model determines whether the data included in the third data set is ordinal type data or non-ordinal type data based on the semantic information of the second target field and the data included in the third data set.

[0174] In some embodiments, an ordinal recognition model can be obtained by training a binary classification neural network model using at least one first training sample. For any first training sample among the at least one first training sample, the first training sample includes multiple data of ordinal type and semantic information of fields to which the multiple data belong, and the label of the first training sample is an ordinal type. Alternatively, the first training sample includes multiple data of non-ordinal type and semantic information of fields to which the multiple data belong, and the label of the first training sample is a non-ordinal type.

[0175] 3062 : If the data included in the third data set is ordinal type data, based on the semantic information of the second target field, obtain the second color of each data included in the third data set, and execute 3064 .

[0176] In 3062, the semantic information of the second target field is input into the color recognition model, so that the color recognition model outputs a reference color based on the semantic information of the second target field, and the second color of each data included in the third data set is obtained by adjusting the brightness of the reference color multiple times.

[0177] For example, assuming that the third data set includes 3 data, the base color output by the color recognition model based on the semantic information of the second target field is blue, and light blue and dark blue are obtained by adjusting the brightness of the base color. The second color corresponding to the first data in the third data set is set to light blue, the second color corresponding to the second data is set to blue, and the second color corresponding to the third data is set to dark blue.

[0178] In some embodiments, a color recognition model can be obtained by training a neural network model through at least one second training sample. For any second training sample of the at least one second training sample, the second training sample includes semantic information and a reference color of a field.

[0179] 3063: If the data included in the third data set is non-ordinal data, based on the second correspondence relationship and each data included in the third data set, obtain the second color of each data included in the third data set, and the second correspondence relationship is used to save the correspondence between the feature vector of the data and the color of the data.

[0180] The feature vector of each data and the color of each data included in the second corresponding relationship may be manually configured, or obtained by crawling through an image search engine.

[0181] For example, crawling a server of an image website through an image search engine, if the crawled server of the image website includes an image with data, extracting the data and the color of the area where the data is located in the image from the image, obtaining a feature vector of the data, and storing the feature vector of the data and the extracted color in a second corresponding relationship. Optionally, the number here can be text or a number, etc.

[0182] In 3063, for each data in the third data set, for ease of explanation, the data is referred to as first data, a third feature vector is obtained based on the first data, and the third feature vector is used to reflect the first data. Based on the third feature vector and the feature vector of each data included in the second corresponding relationship, the similarity between the first data and each data included in the second corresponding relationship is obtained.

[0183] If there is target data in the data included in the second correspondence relationship whose similarity with the first data exceeds the third threshold, the color of the target data is determined as the second color of the first data. Optionally, a data is randomly selected as the target data from the data included in the second correspondence relationship whose similarity with the first data exceeds the third threshold, or the data with the greatest similarity with the first data is selected as the target data.

[0184] If there is no target data in the data included in the second correspondence relationship whose similarity with the first data exceeds a third threshold, m target data with the greatest similarity with the first data are selected from the second correspondence relationship, and the second color of the first data is obtained based on the colors of the m target data and the similarity between the first data and the m target data, where m is an integer greater than 1.

[0185] Optionally, the similarity between the first data and the m target data may be used as the weight of the m target data, and based on the weight of the m target data, the colors of the m target data may be weighted averaged to obtain the second color of the first data.

[0186] 3064: Adjust the second color of each data included in the third data set to obtain the first color of each data included in the third data set, and the similarity between the first colors of any two data included in the third data set exceeds a second threshold.

[0187] In some embodiments, for any two adjacent data in the third data set, when the similarity between the second colors of the two data does not exceed the second threshold, the second color of each data included in the third data set is adjusted to obtain the first color of each data included in the third data set. Otherwise, if the similarity between the second colors of each two adjacent data in the third data set exceeds the second threshold, the second color of each data in the third data set can be directly used as the first color of each data.

[0188] In 3064, the second color of each data included in the third data set can be adjusted through the following operations (1)-(5) to obtain the first color of each data included in the third data set.

[0189] (1): For the second color of the i-th data in the third data set, the second color of the i-th data is mapped to the color space (CIELAB Color Space, CIELAB) to obtain the first color component L of the i-th data i , the second color component A i and the third color component B i .

[0190] (2): Initialize the velocity V of the first color component Li , the velocity V of the second color component Ai and the velocity V of the third color component Bi The initial value of and the initial value of the attenuation coefficient α.

[0191] In some embodiments, the velocity V of the first color component is initialized Li , the velocity V of the second color componentAi and the velocity V of the third color component Bi The initial values ​​of are all 0, and the initial value of the attenuation coefficient α is initialized to 1.

[0192] (3) Settings

[0193] (4): Calculate the first color component L of the i-th data according to the following fourth formula: i , the second color component A i and the third color component B i .

[0194] The fourth formula is:

[0195]

[0196]

[0197]

[0198] Among them, L j is the first color component of the jth data, A j is the second color component of the jth data, B j is the third color component of the jth data, S repulsive is the strength coefficient of the repulsion between colors, S position is the intensity factor at which the color remains in its original position.

[0199] Then, set Among them, in order to prevent the position from crossing the boundary, if L i >L max , then reset L i =L max as well as If L i <L min , reset L i =L min , If L min <L i <L max ,, reset V decay is an attenuation coefficient less than 1.

[0200] set up Among them, in order to prevent the position from crossing the boundary, if A i >A max , then reset A i =A max as well as If Ai <A min , reset A i = A min , If A min < A i < A max ,, reset And,

[0201] Set Wherein, to prevent out-of-bounds position, if B i > B max , then reset B i = B max And If B i < B min , reset B i = B min , If B min < B i < B,, reset

[0202] Wherein, L max is the maximum value of the preset first color component, L min is the minimum value of the preset first color component, A max is the maximum value of the preset second color component, A min is the minimum value of the preset second color component, B max is the maximum value of the preset third color component, B min is the minimum value of the preset third color component.

[0203] (5): Set the attenuation coefficient α = α + (α target - α) * α decay , where α target is the target value (is a preset value), α decay is the attenuation coefficient (is a preset value), if α > α target , then return to execute (4), if α < α target , then end the execution.

[0204] If α < α target , then obtain the first color component L of the i-th data obtained at this time i , the second color component A i and the third color component B i , based on the first color component L of the i-th data i , the second color component A iand the third color component B i , get the first color of the i-th data.

[0205] In some embodiments, the technician may set the first color of each data in the third data set, obtain the first color of each data in the third data set, and obtain the feature vector of each data in the third data set, and store the feature vector of each data in the third data set and the first color in the second corresponding relationship.

[0206] Step 307: Display a first chart based on the chart configuration information and the data set of the plurality of first fields, where the first chart is used to reflect the data included in the data set of the plurality of first fields.

[0207] In some embodiments, a first feature vector is obtained, the first feature vector is used to reflect the semantic information of the plurality of first fields and the data set of the plurality of first fields, and the first feature vector, the chart type to be displayed and the chart configuration information are correspondingly stored in a first correspondence relationship.

[0208] In an embodiment of the present application, semantic information of multiple first fields to be displayed and data sets of multiple first fields are obtained, and based on the semantic information of the multiple first fields and the data sets of the multiple first fields, the chart type to be displayed and the slots of the multiple first fields that are compatible with the multiple first fields and the data sets of the multiple first fields are obtained through a recommendation model. For the first target field of the metric type, the coordinate system scale of the first chart is obtained based on the first data set of the first target field, and the numerical scale value range is set based on the data included in the first data set and the chart type to be displayed. And for the second target field whose corresponding slot is the color slot, the first color of each data included in the third data set is determined based on the data included in the third data set of the second target field. Thus, based on the slots of the multiple first fields, the coordinate system scale and numerical scale value range of the first chart, and the first color of each data included in the third data set, the first chart is configured so that the first chart is compatible with the data included in the multiple first fields and the data sets of the multiple first fields, thereby improving the display accuracy.

[0209] See also Figure 5 The present application embodiment provides a device 500 for displaying data, and the device 500 is applied to a cloud service platform, and the cloud service platform can be Figure 1 The cloud service platform in the network architecture shown is Figure 2 The cloud service platform in the method 200 may be Figure 3The cloud service platform in the method 300 is shown. The cloud service platform includes a data storage file, and the data storage file is used to store a data set of multiple fields, and the data set of the field includes multiple data belonging to the field. The device 500 includes:

[0210] An acquiring unit 501 is used to acquire semantic information of a plurality of first fields to be displayed and a data set of the plurality of first fields, wherein the plurality of first fields are part or all of the fields in the data storage file, and the data set of the plurality of first fields is a data set stored in the data storage file;

[0211] The acquisition unit 501 is further configured to acquire a chart type to be displayed and chart configuration information corresponding to the chart type based on the semantic information of the plurality of first fields and the data set of the plurality of first fields;

[0212] The display unit 502 is used to display a first chart based on the chart configuration information and the data set of the multiple first fields. The first chart is a chart corresponding to the chart type, and the first chart is used to reflect the data included in the data set of the multiple first fields.

[0213] Optionally, the detailed implementation process of the acquisition unit 501 acquiring the semantic information of the plurality of first fields and the data set of the plurality of first fields can be found in Figure 2 Steps 201 and 202 of the method 200 are shown. Figure 3 The relevant contents of step 301 of the method 300 are not described in detail here.

[0214] Optionally, the detailed implementation process of the acquisition unit 501 acquiring the chart type and the chart configuration information corresponding to the chart type can be found in Figure 2 Steps 202-205 of method 200 and Figure 3 The relevant contents of steps 302 - 306 of the method 300 are not described in detail here.

[0215] Optionally, the display unit 502 displays the detailed implementation process of the first chart. Figure 2 Step 206 of method 200 and Figure 3 The relevant contents of step 307 of the method 300 are not described in detail here.

[0216] Optionally, the cloud service platform further includes a first correspondence relationship, the first correspondence relationship being used to store a correspondence relationship between the feature vector, the chart type of the second chart, and the chart configuration information of the second chart, the second chart being a displayed chart for reflecting data included in a data set of a plurality of second fields, and the feature vector being used to reflect semantic information of the plurality of second fields and the data set of the plurality of second fields;

[0217] The acquisition unit 501 is used to:

[0218] Acquire a first feature vector, where the first feature vector is used to reflect semantic information of the plurality of first fields and a data set of the plurality of first fields;

[0219] Obtaining the similarity between the first feature vector and each feature vector in the first corresponding relationship;

[0220] Acquire, from the first corresponding relationship, a feature vector whose similarity with the first feature vector exceeds a first threshold as a second feature vector;

[0221] The chart type corresponding to the second feature vector is obtained from the first corresponding relationship as the chart type to be displayed, and the chart configuration information corresponding to the second feature vector is obtained as the chart configuration information corresponding to the chart type to be displayed.

[0222] Optionally, the detailed implementation process of the acquisition unit 501 acquiring the first feature vector is shown in Figure 2 The relevant contents of step 202 of the method 200 are not described in detail here.

[0223] Optionally, the detailed implementation process of the acquisition unit 501 acquiring the similarity between the first feature vector and each feature vector in the first corresponding relationship is shown in Figure 2 The relevant contents of step 203 of the method 200 are not described in detail here.

[0224] Optionally, the detailed implementation process of the acquisition unit 501 acquiring the second feature vector is shown in Figure 2 The relevant contents of step 204 of the method 200 are not described in detail here.

[0225] Optionally, the detailed implementation process of the acquisition unit 501 acquiring the chart type to be displayed and acquiring the chart configuration information corresponding to the chart type to be displayed can be found in Figure 2 The relevant contents of step 205 of the method 200 are not described in detail here.

[0226] Optionally, the chart configuration information includes a slot for each first field, the slot being a display element of the first chart;

[0227] The acquisition unit 501 is used to:

[0228] Acquire at least one field type and the number of fields belonging to each field type based on the semantic information of the multiple first fields, the at least one field type being the field type to which the multiple first fields belong;

[0229] Based on at least one field type, the number of fields of each field type and a data set of multiple first fields, a chart type to be displayed and a slot corresponding to each first field are obtained.

[0230] Optionally, the detailed implementation process of the acquisition unit 501 acquiring at least one field type and the number of fields belonging to each field type can be found in Figure 3 The relevant contents of step 3021 of the method 300 are not described in detail here.

[0231] Optionally, the detailed implementation process of the acquisition unit 501 acquiring the chart type to be displayed and the slot corresponding to each first field can be found in Figure 3 The relevant contents of step 3022 of the method 300 are not described in detail here.

[0232] Optionally, the plurality of first fields include a first target field whose field type is a measurement type, and the chart configuration information further includes one or more of the following: a coordinate system scale of the first chart, or a numerical scale value range of the first chart;

[0233] The acquisition unit 501 is further used for:

[0234] Based on the data and the number of data included in the first data set, obtaining information entropy of a linear coordinate system scale and information entropy of a logarithmic coordinate system scale, the first data set being a data set of a first target field;

[0235] Obtaining the scale of the coordinate system based on the information entropy of the linear coordinate system scale and the information entropy of the logarithmic coordinate system scale;

[0236] A numerical scale value range is set based on the data included in the first data set and the type of chart to be displayed.

[0237] Optionally, the detailed implementation process of the acquisition unit 501 acquiring the information entropy of the linear coordinate system scale and the information entropy of the logarithmic coordinate system scale can be found in Figure 3 The relevant contents of step 303 of the method 300 are not described in detail here.

[0238] Optionally, the detailed implementation process of the acquisition unit 501 acquiring the coordinate system scale is shown in Figure 3 The relevant contents of step 304 of the method 300 are not described in detail here.

[0239] Optionally, the detailed implementation process of the acquisition unit 501 setting the value range of the numerical scale can be found in Figure 3 The relevant contents of step 305 of the method 300 are not described in detail here.

[0240] Optionally, the acquiring unit 501 is configured to:

[0241] Based on the maximum data and the minimum data included in the first data set, determine N data intervals and the data included in each data interval, the data included in each data interval is the data in the first data set, and N is the number of data included in the first data set;

[0242] Based on N and the data included in each data interval, the data distribution probability of each data interval is counted;

[0243] Based on N and the data distribution probability of each data interval, the information entropy of the linear coordinate system scale is obtained.

[0244] Optionally, the acquiring unit 501 is configured to:

[0245] Performing a logarithm operation with a specified value as a base on each data included in the first data set to obtain the logarithm of each data;

[0246] Based on the maximum logarithm and the minimum logarithm included in the logarithm set, determine N logarithm intervals and the logarithm included in each logarithm difference, the logarithm set includes the logarithm of each data, the logarithm included in each logarithm interval is the logarithm in the logarithm set, and N is the number of data included in the first data set;

[0247] Based on N and the number of logarithms included in each logarithmic interval, the logarithmic distribution probability of each logarithmic interval is counted;

[0248] Based on N and the logarithmic distribution probability of each logarithmic interval, the information entropy of the logarithmic coordinate system scale is obtained.

[0249] Optionally, the numerical scale value range includes the value range and the baseline position of the first chart;

[0250] The acquisition unit 501 is used to:

[0251] When the chart type is a chart type other than a line chart and an area chart, setting a value range and a baseline position of the first chart based on maximum data and minimum data included in the first data set;

[0252] When the chart type is a line chart or an area chart, outliers in the first data set are removed to obtain a second data set, and the value range and baseline position of the first chart are set based on the maximum data and the minimum data included in the second data set.

[0253] Optionally, the plurality of first fields include a second target field whose corresponding slot is a color slot, the chart configuration information further includes a first color of each data included in a third data set, and the third data set is a data set of the second target field;

[0254] The acquisition unit 501 is further used for:

[0255] If the data included in the third data set is ordinal data, obtaining a second color of each data included in the third data set based on the semantic information of the second target field;

[0256] If the data included in the third data set is non-ordinal data, based on the second correspondence relationship and each data included in the third data set, obtaining the second color of each data included in the third data set, wherein the second correspondence relationship is used to store the correspondence between the feature vector of the data and the color of the data;

[0257] The second color of each data included in the third data set is adjusted to obtain the first color of each data included in the third data set, and the similarity between the first colors of any two data included in the third data set exceeds a second threshold.

[0258] Optionally, the acquisition unit 501 acquires the second color of each data included in the third data set based on the semantic information of the second target field. Figure 3 The relevant contents of step 3062 of the method 300 are not described in detail here.

[0259] Optionally, the acquisition unit 501 acquires the second color of each data included in the third data set based on the second corresponding relationship and each data included in the third data set. For a detailed implementation process, see Figure 3 The relevant contents of step 3063 of the method 300 are not described in detail here.

[0260] Optionally, the acquisition unit 501 adjusts the second color of each data included in the third data set to obtain the first color of each data included in the third data set. For a detailed implementation process, see Figure 3 The relevant contents of step 3064 of the method 300 are not described in detail here.

[0261] Optionally, the acquiring unit 501 is configured to:

[0262] Based on the semantic information of the second target field, obtaining a reference color;

[0263] The brightness of the reference color is adjusted to obtain the second color of each data included in the third data set.

[0264] Optionally, the third data set includes the first data, and the acquiring unit 501 is configured to:

[0265] Acquire a third feature vector based on the first data, where the third feature vector is used to reflect the first data;

[0266] Based on the third feature vector and the feature vector of each data included in the second correspondence, obtaining the similarity between the first data and each data included in the second correspondence;

[0267] If there is target data in the data included in the second corresponding relationship whose similarity with the first data exceeds a third threshold, the color of the target data is determined as the second color of the first data;

[0268] If there is no target data in the data included in the second correspondence relationship whose similarity with the first data exceeds a third threshold, the second color of the first data is obtained based on the colors of m target data and the similarity between the first data and the m target data, where m is an integer greater than 1, and the m target data are the m data in the second correspondence relationship with the greatest similarity with the first data.

[0269] In an embodiment of the present application, since the acquisition unit obtains the chart type to be displayed and the chart configuration information corresponding to the chart type based on the semantic information of the multiple first fields and the data set of the multiple first fields, the display unit displays the first chart based on the chart configuration information and the data set of the multiple first fields, so that the first chart can be consistent with the data of the multiple first fields, thereby improving the accuracy of the displayed chart.

[0270] See also Figure 6 , the present application embodiment provides a computing device 600. For example, the computing device 600 may be Figure 1 The cloud service platform in the network architecture shown includes devices, or Figure 2 The device in the cloud service platform of the method 200 shown, or Figure 3 Devices in a cloud service platform of the method 300 are shown.

[0271] like Figure 6 As shown, computing device 600 includes: bus 602, processor 604, memory 606 and communication interface 608. Processor 604, memory 606 and communication interface 608 communicate through bus 602. Computing device 600 can be a server or a terminal device. It should be understood that the present application does not limit the number of processors and memories in computing device 600.

[0272] The bus 602 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6The bus 602 may include a path for transmitting information between various components of the computing device 600 (eg, the memory 606, the processor 604, and the communication interface 608).

[0273] The processor 604 may include any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0274] The memory 606 may include a volatile memory, such as a random access memory (RAM). The processor 604 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0275] See also Figure 6 , the memory 606 stores executable program codes, and the processor 604 executes the executable program codes to respectively implement Figure 5 The functions of the acquisition unit 501 and the display unit 502 in the device 500 shown in the figure are implemented to realize the method provided by any of the above embodiments. That is, the memory 606 stores instructions for executing the method provided by any of the above embodiments. Or,

[0276] The communication interface 608 uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the computing device 600 and other devices or communication networks.

[0277] The embodiment of the present application also provides a cluster. The cluster includes at least one computing device. The computing device can be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a terminal device such as a desktop computer, a laptop computer, or a smart phone.

[0278] like Figure 7As shown, the cluster includes at least one computing device 600. The memory 606 in one or more computing devices 600 in the cluster may store the same instructions for executing the method provided in any of the above embodiments.

[0279] In some possible implementations, the memory 606 of one or more computing devices 600 in the cluster may also store partial instructions for executing the above method for displaying data. In other words, the combination of one or more computing devices 600 may jointly execute instructions for executing the method for providing display data in any of the above embodiments.

[0280] In some possible implementations, one or more computing devices in the cluster may be connected via a network, which may be a wide area network or a local area network. Figure 8 A possible implementation is shown. Figure 8 As shown, two computing devices 600A and 600B are connected via a network. Specifically, they are connected to the network via a communication interface in each computing device.

[0281] In this type of possible implementation, the memory 606 in the computing device 600A stores the execution Figure 5 Instructions for obtaining the functions of the unit 501 in the embodiment shown. Meanwhile, the memory 606 in the computing device 600B stores instructions for executing the following Figure 5 Instructions for the functionality of display unit 602 in the illustrated embodiment.

[0282] It should be understood that Figure 8 The functions of the computing device 600A shown in FIG. 6A may also be completed by multiple computing devices 600. Similarly, the functions of the computing device 600B may also be completed by multiple computing devices 600.

[0283] The present application embodiment also provides another cluster. The connection relationship between the computing devices in the cluster can be similar to that of Figure 8 The connection mode of the cluster of the acquisition function is different in that the memory 606 in one or more computing devices 600 in the cluster may store the same instructions for executing the method provided by any of the above embodiments.

[0284] In some possible implementations, the memory 606 of one or more computing devices 600 in the cluster may also store partial instructions for executing the method provided in any of the above embodiments. In other words, the combination of one or more computing devices 600 may jointly execute instructions for executing the method provided in any of the above embodiments.

[0285] The present application also provides a computer program product including instructions. The computer program product may be software or a program product including instructions that can be run on a computing device or stored in any available medium. When the computer program product is run on at least one computing device, the at least one computing device executes the method provided in any of the above embodiments.

[0286] The embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that can be stored by a computing device or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk). The computer-readable storage medium includes instructions that instruct the computing device to execute the method provided in any of the above embodiments.

[0287] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0288] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for displaying data, characterized in that: The method is applied to a cloud service platform, the cloud service platform includes a data storage file, the data storage file is used to store a data set of multiple fields, the data set of the field includes multiple data belonging to the field, including: Acquire semantic information of a plurality of first fields to be displayed and a data set of the plurality of first fields, wherein the plurality of first fields are part or all of the fields in the data storage file, and the data set of the plurality of first fields is a data set stored in the data storage file; Based on the semantic information of the plurality of first fields and the data set of the plurality of first fields, acquiring a chart type to be displayed and chart configuration information corresponding to the chart type; A first chart is displayed based on the chart configuration information and the data set of the plurality of first fields, wherein the first chart is a chart corresponding to the chart type, and the first chart is used to reflect the data included in the data set of the plurality of first fields.

2. The method according to claim 1, characterized in that The cloud service platform further includes a first correspondence relationship, the first correspondence relationship being used to store a correspondence relationship between a feature vector, a chart type of a second chart, and chart configuration information of the second chart, the second chart being a displayed chart for reflecting data included in a data set of a plurality of second fields, the feature vector being used to reflect semantic information of the plurality of second fields and the data set of the plurality of second fields; The acquiring, based on the semantic information of the plurality of first fields and the data set of the plurality of first fields, the chart type to be displayed and the chart configuration information corresponding to the chart type comprises: Acquire a first feature vector, where the first feature vector is used to reflect semantic information of the plurality of first fields and a data set of the plurality of first fields; Obtaining a similarity between the first feature vector and each feature vector in the first corresponding relationship; Acquire, from the first corresponding relationship, a feature vector whose similarity with the first feature vector exceeds a first threshold as a second feature vector; The chart type corresponding to the second feature vector is obtained from the first corresponding relationship as the chart type to be displayed, and the chart configuration information corresponding to the second feature vector is obtained as the chart configuration information corresponding to the chart type to be displayed.

3. The method according to claim 1, characterized in that The chart configuration information includes a slot for each first field, the slot being a display element of the first chart; The acquiring, based on the semantic information of the plurality of first fields and the data set of the plurality of first fields, the chart type to be displayed and the chart configuration information corresponding to the chart type comprises: Acquire at least one field type and the number of fields belonging to each field type based on the semantic information of the multiple first fields, wherein the at least one field type is the field type to which the multiple first fields belong; Based on the at least one field type, the number of fields of each field type and the data set of the plurality of first fields, the chart type to be displayed and the slot corresponding to each first field are acquired.

4. The method according to claim 3, characterized in that The plurality of first fields include a first target field whose field type is a measurement type, and the chart configuration information further includes one or more of the following: a coordinate system scale of the first chart, or a numerical scale value range of the first chart; The obtaining of the chart configuration information corresponding to the chart type further includes: Based on the data and the number of data included in the first data set, obtaining information entropy of a linear coordinate system scale and information entropy of a logarithmic coordinate system scale, wherein the first data set is a data set of the first target field; Acquire the coordinate system scale based on the information entropy of the linear coordinate system scale and the information entropy of the logarithmic coordinate system scale; The numerical scale range is set based on the data included in the first data set and the type of chart to be displayed.

5. The method according to claim 4, characterized in that The obtaining the information entropy of the linear coordinate system scale based on the data and the number of data included in the first data set includes: Based on the maximum data and the minimum data included in the first data set, determine N data intervals and the data included in each data interval, the data included in each data interval is the data in the first data set, and N is the number of data included in the first data set; Based on N and the data included in each data interval, counting the data distribution probability of each data interval; Based on N and the data distribution probability of each data interval, the information entropy of the linear coordinate system scale is obtained.

6. The method according to claim 4 or 5, characterized in that The step of obtaining information entropy in a logarithmic coordinate system scale based on the data and the number of data included in the first data set includes: Performing a logarithm operation with a specified value as a base on each data included in the first data set to obtain the logarithm of each data; Based on the maximum logarithm and the minimum logarithm included in the logarithm set, determining N logarithm intervals and the logarithms included in each logarithm difference, the logarithm set includes the logarithm of each data, the logarithm included in each logarithm interval is the logarithm in the logarithm set, and N is the number of data included in the first data set; Based on N and the number of logarithms included in each logarithmic interval, counting the logarithmic distribution probability of each logarithmic interval; Based on N and the logarithmic distribution probability of each logarithmic interval, the information entropy of the logarithmic coordinate system scale is obtained.

7. The method according to any one of claims 4 to 6, characterized in that: The numerical scale value range includes the value range and the baseline position of the first chart; The step of setting the numerical scale range based on the data included in the first data set and the type of chart to be displayed includes: When the chart type is a chart type other than a line chart and an area chart, setting a value range and a baseline position of the first chart based on maximum data and minimum data included in the first data set; When the chart type is a line chart or an area chart, outliers in the first data set are removed to obtain a second data set, and the value range and baseline position of the first chart are set based on maximum data and minimum data included in the second data set.

8. The method according to any one of claims 3 to 7, characterized in that: The plurality of first fields include a second target field whose corresponding slot is a color slot, the chart configuration information further includes a first color of each data included in a third data set, and the third data set is a data set of the second target field; The obtaining of the chart configuration information corresponding to the chart type further includes: If the data included in the third data set is ordinal data, acquiring a second color of each data included in the third data set based on the semantic information of the second target field; If the data included in the third data set is non-ordinal data, based on a second correspondence relationship and each data included in the third data set, obtaining a second color of each data included in the third data set, wherein the second correspondence relationship is used to store a correspondence relationship between a feature vector of the data and the color of the data; The second color of each data included in the third data set is adjusted to obtain the first color of each data included in the third data set, and the similarity between the first colors of any two data included in the third data set exceeds a second threshold.

9. The method according to claim 8, characterized in that The acquiring, based on the semantic information of the second target field, the second color of each data included in the third data set comprises: Based on the semantic information of the second target field, obtaining a reference color; The brightness of the reference color is adjusted to obtain the second color of each data included in the third data set.

10. The method according to claim 8 or 9, characterized in that The third data set includes the first data, and acquiring the second color of each data included in the third data set based on the second corresponding relationship and each data included in the third data set includes: Acquire a third feature vector based on the first data, where the third feature vector is used to reflect the first data; Based on the third feature vector and the feature vector of each data included in the second corresponding relationship, obtaining the similarity between the first data and each data included in the second corresponding relationship; If there is target data in the data included in the second corresponding relationship whose similarity with the first data exceeds a third threshold, the second color of the first data is determined by the color of the target data; If there is no target data in the data included in the second correspondence relationship whose similarity with the first data exceeds a third threshold, the second color of the first data is obtained based on the colors of m target data and the similarity between the first data and the m target data, where m is an integer greater than 1, and the m target data are the m data in the second correspondence relationship with the greatest similarity with the first data.

11. A device for displaying data, characterized in that: The device is applied to a cloud service platform, the cloud service platform includes a data storage file, the data storage file is used to store a data set of multiple fields, the data set of the field includes multiple data belonging to the field, including: an acquisition unit, configured to acquire semantic information of a plurality of first fields to be displayed and a data set of the plurality of first fields, wherein the plurality of first fields are part or all of the fields in the data storage file, and the data set of the plurality of first fields is a data set stored in the data storage file; The acquisition unit is further configured to acquire a chart type to be displayed and chart configuration information corresponding to the chart type based on the semantic information of the plurality of first fields and the data set of the plurality of first fields; The display unit is used to display a first chart based on the chart configuration information and the data set of the multiple first fields, wherein the first chart is a chart corresponding to the chart type and is used to reflect the data included in the data set of the multiple first fields.

12. The device according to claim 11, characterized in that The cloud service platform further includes a first correspondence relationship, the first correspondence relationship being used to store a correspondence relationship between a feature vector, a chart type of a second chart, and chart configuration information of the second chart, the second chart being a displayed chart for reflecting data included in a data set of a plurality of second fields, the feature vector being used to reflect semantic information of the plurality of second fields and the data set of the plurality of second fields; The acquisition unit is used to: Acquire a first feature vector, where the first feature vector is used to reflect semantic information of the plurality of first fields and a data set of the plurality of first fields; Obtaining a similarity between the first feature vector and each feature vector in the first corresponding relationship; Acquire, from the first corresponding relationship, a feature vector whose similarity with the first feature vector exceeds a first threshold as a second feature vector; The chart type corresponding to the second feature vector is obtained from the first corresponding relationship as the chart type to be displayed, and the chart configuration information corresponding to the second feature vector is obtained as the chart configuration information corresponding to the chart type to be displayed.

13. The device according to claim 11, characterized in that The chart configuration information includes a slot for each first field, the slot being a display element of the first chart; The acquisition unit is used to: Acquire at least one field type and the number of fields belonging to each field type based on the semantic information of the multiple first fields, wherein the at least one field type is the field type to which the multiple first fields belong; Based on the at least one field type, the number of fields of each field type and the data set of the plurality of first fields, the chart type to be displayed and the slot corresponding to each first field are acquired.

14. The device according to claim 13, characterized in that The plurality of first fields include a first target field whose field type is a measurement type, and the chart configuration information further includes one or more of the following: a coordinate system scale of the first chart, or a numerical scale value range of the first chart; The acquisition unit is further used for: Based on the data and the number of data included in the first data set, obtaining information entropy of a linear coordinate system scale and information entropy of a logarithmic coordinate system scale, wherein the first data set is a data set of the first target field; Acquire the coordinate system scale based on the information entropy of the linear coordinate system scale and the information entropy of the logarithmic coordinate system scale; The numerical scale range is set based on the data included in the first data set and the type of chart to be displayed.

15. The device according to claim 14, characterized in that The acquisition unit is used to: Based on the maximum data and the minimum data included in the first data set, determine N data intervals and the data included in each data interval, the data included in each data interval is the data in the first data set, and N is the number of data included in the first data set; Based on N and the data included in each data interval, counting the data distribution probability of each data interval; Based on N and the data distribution probability of each data interval, the information entropy of the linear coordinate system scale is obtained.

16. The device according to claim 14 or 15, characterized in that The acquisition unit is used to: Performing a logarithm operation with a specified value as a base on each data included in the first data set to obtain the logarithm of each data; Based on the maximum logarithm and the minimum logarithm included in the logarithm set, determining N logarithm intervals and the logarithms included in each logarithm difference, the logarithm set includes the logarithm of each data, the logarithm included in each logarithm interval is the logarithm in the logarithm set, and N is the number of data included in the first data set; Based on N and the number of logarithms included in each logarithmic interval, counting the logarithmic distribution probability of each logarithmic interval; Based on N and the logarithmic distribution probability of each logarithmic interval, the information entropy of the logarithmic coordinate system scale is obtained.

17. The device according to any one of claims 14 to 16, characterized in that The numerical scale value range includes the value range and the baseline position of the first chart; The acquisition unit is used to: When the chart type is a chart type other than a line chart and an area chart, setting a value range and a baseline position of the first chart based on maximum data and minimum data included in the first data set; When the chart type is a line chart or an area chart, outliers in the first data set are removed to obtain a second data set, and the value range and baseline position of the first chart are set based on maximum data and minimum data included in the second data set.

18. The device according to any one of claims 13 to 17, characterized in that: The plurality of first fields include a second target field whose corresponding slot is a color slot, the chart configuration information further includes a first color of each data included in a third data set, and the third data set is a data set of the second target field; The acquisition unit is further used for: If the data included in the third data set is ordinal data, acquiring a second color of each data included in the third data set based on the semantic information of the second target field; If the data included in the third data set is non-ordinal data, based on a second correspondence relationship and each data included in the third data set, obtaining a second color of each data included in the third data set, wherein the second correspondence relationship is used to store a correspondence relationship between a feature vector of the data and the color of the data; The second color of each data included in the third data set is adjusted to obtain the first color of each data included in the third data set, and the similarity between the first colors of any two data included in the third data set exceeds a second threshold.

19. The device according to claim 18, characterized in that The acquisition unit is used to: Based on the semantic information of the second target field, obtaining a reference color; The brightness of the reference color is adjusted to obtain the second color of each data included in the third data set.

20. The device according to claim 18 or 19, characterized in that The third data set includes the first data, and the acquiring unit is used to: Acquire a third feature vector based on the first data, where the third feature vector is used to reflect the first data; Based on the third feature vector and the feature vector of each data included in the second corresponding relationship, obtaining the similarity between the first data and each data included in the second corresponding relationship; If there is target data in the data included in the second corresponding relationship whose similarity with the first data exceeds a third threshold, the second color of the first data is determined by the color of the target data; If there is no target data in the data included in the second correspondence relationship whose similarity with the first data exceeds a third threshold, the second color of the first data is obtained based on the colors of m target data and the similarity between the first data and the m target data, where m is an integer greater than 1, and the m target data are the m data in the second correspondence relationship with the greatest similarity with the first data.

21. A computing device cluster, characterized in that: The cluster includes at least one computing device, each of the at least one computing device includes at least one processor and at least one memory, and the at least one memory stores computer-readable instructions; the at least one processor executes the computer-readable instructions so that the cluster executes the method according to any one of claims 1 to 10.

22. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

23. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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