Methods, devices, electronic equipment, and media for displaying tabular data

Abstract

This invention provides a method, apparatus, electronic device, and medium for displaying tabular data. The method includes: determining the types of multiple target fields and their logical relationships based on the data content of multiple target fields in a target table; determining a recommended total importance score for each of the multiple target fields based on their types and logical relationships; sorting the multiple target fields according to their recommended total importance scores; and displaying the processed data content of the multiple target fields based on the sorting results. The tabular data display method provided by this invention enables accurate and clear data viewing, visualizes the data, improves the accuracy of data display, and enhances the user experience.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and medium for displaying tabular data. Background Technology

[0002] As data statistics continue to develop, people's requirements for data processing are also increasing. They hope that while meeting normal office requirements, they can also meet different application scenarios and different data display requirements.

[0003] Currently, in practical applications, users need to manually summarize and statistically analyze the data in the data table. There are two ways to achieve this: one is based on the filtering analysis under the "Filter" function, which counts the category counts within each field; the other is to use the "PivotTable" function, which selects fields and then counts the category counts. However, neither of these methods can intuitively display the data to the user or accurately analyze it, resulting in a poor user experience. Summary of the Invention

[0004] Based on the problems existing in the prior art, the present invention proposes a method, device, electronic device and medium for displaying tabular data, which can meet the needs of accurately and clearly viewing data, making data visually displayed, improving the accuracy of data display and enhancing user experience.

[0005] In a first aspect, the present invention provides a method for displaying tabular data, comprising:

[0006] Based on the data content of multiple target fields in the target table, determine the types of the multiple target fields and the logical relationships between them;

[0007] Based on the types of the multiple target fields and the logical relationships between them, the total importance score for each of the multiple target fields is determined.

[0008] Based on the total score recommended according to the importance of each of the multiple target fields, the multiple target fields are sorted, and the processed data content of the multiple target fields is displayed according to the sorting results.

[0009] Furthermore, according to the tabular data display method provided by the present invention, determining the types of the multiple target fields based on the data content of multiple target fields in the target table includes:

[0010] Perform the following processing for each target field in the target table:

[0011] Obtain the data content of the target field; wherein, the data content of the target field includes the content of the field title of the target field and the data content of the cell corresponding to the target field;

[0012] The type of the target field is determined based on the content of the field title and the data content of the corresponding cell.

[0013] The types mentioned include dimension types and metric types.

[0014] Furthermore, according to the tabular data display method provided by the present invention, determining the type of the target field based on the content of the field title of the target field and the data content of the cell corresponding to the target field includes:

[0015] If the data content of the cell corresponding to the target field satisfies a date-type regular expression or a time-type regular expression, the type of the target field is determined to be the date-time type in the dimension type.

[0016] If the word segmentation result of the field title of the target field or the word segmentation result of the data content of the corresponding cell matches a pre-defined proper noun of the name class, the type of the target field is determined to be the name class in the dimension type;

[0017] If the word segmentation result of the field title of the target field matches a pre-defined coded number class proper noun, and the data content of the cell corresponding to the target field is a number, then the type of the target field is determined to be the coded number class in the dimension type;

[0018] If the word segmentation result of the field title of the target field matches a pre-defined price-related proper noun, or if the data content of the cell corresponding to the target field matches a currency format, then the type of the target field is determined to be the amount type in the measurement type.

[0019] If the word segmentation result of the field title of the target field matches a pre-defined percentage-type proper noun, or if the data content of the cell corresponding to the target field matches a percentage format, the type of the target field is determined to be a percentage-type measurement.

[0020] Furthermore, according to the tabular data display method provided by the present invention, determining the logical relationship between the multiple target fields based on the data content of multiple target fields in the target table includes:

[0021] For multiple target fields that belong to the dimension type among the multiple target fields, determine the correspondence between the different target fields;

[0022] For multiple target fields that belong to the measurement type among the multiple target fields, determine the operational relationships between the different target fields;

[0023] For any one of the multiple target fields that is of dimension type and any one of the target fields that is of measure type, if the data content in the corresponding cells of the two fields satisfies the statistical formula, it is determined that there is a statistical dependency between them.

[0024] Furthermore, according to the tabular data display method provided by the present invention, determining the correspondence between different target fields for multiple target fields belonging to the dimension type among the multiple target fields includes:

[0025] If there is a one-to-one correspondence between the data content in the cell corresponding to the first target field and the data content in the cell corresponding to the second target field, the first target field and the second target field are regarded as target fields with an equality relationship; wherein, the first target field is any one of multiple target fields belonging to the dimension type, and the second target field is any one of multiple target fields belonging to the dimension type that is different from the first target field;

[0026] If each data content in the cell corresponding to the first target field corresponds to at least one data content in the cell corresponding to the second target field, and any data content in the cell corresponding to the second target field corresponds to only one data content in the cell corresponding to the first target field, then the first target field is determined to contain the second target field.

[0027] If each data content in the cell corresponding to the first target field corresponds to at least two data contents in the cell corresponding to the second target field, and each data content in the cell corresponding to the second target field corresponds to at least two data contents in the cell corresponding to the first target field, then the first target field and the second target field are determined to be independent target fields.

[0028] Furthermore, according to the tabular data display method provided by the present invention, determining the operational relationship between different target fields for multiple target fields belonging to the measurement type among the multiple target fields includes:

[0029] If there is a one-to-one correspondence between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field, and the difference between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field is an arbitrary constant, then the third target field and the fourth target field are determined to be target fields with an equality relationship; wherein, the third target field is any one of multiple target fields belonging to the measurement type, and the fourth target field is any one of multiple target fields belonging to the measurement type that is different from the third target field;

[0030] If the data content in the cell corresponding to the third target field has the same number format, the same unit, or the same order of magnitude as the data content in the cell corresponding to the fourth target field, then the third target field and the fourth target field are determined to be similar target fields.

[0031] If the difference between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field is equal to the data content in the cell corresponding to the fifth target field, then it is determined that the third target field contains the fourth target field, and the third target field contains the fifth target field; wherein, the fifth target field is any one of the multiple target fields belonging to the measurement type that is different from the third target field and the fourth target field;

[0032] If the data content in the cell corresponding to the third target field is the function value of the first function, and the data content in the cell corresponding to the fourth target field is the variable of the first function, then the third target field depends on the fourth target field.

[0033] If the data content in the cell corresponding to the third target field is the function value of the first function, the data content in the cell corresponding to the fourth target field is the variable of the first function, and the data content in the cell corresponding to the fourth target field is the function value of the second function, and the data content in the cell corresponding to the fifth target field is the variable of the second function, then the third target field is determined to be dependent on the fifth target field.

[0034] Furthermore, according to the tabular data display method provided by the present invention, determining the recommended total score for the importance of each of the multiple target fields based on their types and logical relationships includes:

[0035] Based on the types of the multiple target fields, determine the first-category importance recommendation score for each of the multiple target fields;

[0036] Based on the logical relationships between the multiple target fields, determine the second-category importance recommendation score for each of the multiple target fields;

[0037] Based on the first-type importance recommendation score and the second-type importance recommendation score of each of the multiple target fields, the total importance recommendation score of each of the multiple target fields is determined.

[0038] Furthermore, according to the tabular data display method provided by the present invention, determining the first-category importance recommendation score for each of the plurality of target fields based on their types includes:

[0039] Based on the data content of the target table, determine the scenario corresponding to the target table;

[0040] When the target table is in the first scenario, the recommended first-class importance scores are set for the target fields of the following dimension types in descending order of scores: target fields of name type, target fields of time and date type, and target fields of other dimension types besides name type and time and date type; the recommended first-class importance scores are set for the target fields of the following metric types in descending order of scores: target fields of amount type, target fields of percentage type, and target fields of other dimension types besides amount type and percentage type.

[0041] When the target table is in the second scenario, the recommended first-class importance scores are set for the target fields of the following dimension types in descending order of scores: target fields of date and time, target fields of name, and target fields of other dimension types besides name and date and time; the recommended first-class importance scores are set for the target fields of the following metric types in descending order of scores: target fields of amount, target fields of percentage, and target fields of other dimension types besides amount and percentage.

[0042] Furthermore, according to the tabular data display method provided by the present invention, the step of sorting the multiple target fields according to the total score recommended based on the importance of each of the multiple target fields, and visually displaying the processed data content of the multiple target fields according to the sorting result, includes:

[0043] The multiple target fields are divided into multiple target fields belonging to the dimension type and multiple target fields belonging to the metric type;

[0044] The first ranking result is obtained by sorting the total recommended scores of multiple target fields belonging to the dimension type according to their respective importance.

[0045] The second ranking result is obtained by sorting the total recommended scores of multiple target fields belonging to the metric type according to their respective importance.

[0046] Based on the first sorting result, select a first number of target fields belonging to the dimension type; based on the second sorting result, select a second number of target fields belonging to the measure type.

[0047] The data content of the first number of target fields belonging to the dimension type and the data content of the second number of target fields belonging to the metric type are processed, and the processing results are displayed.

[0048] Furthermore, according to the tabular data display method provided by the present invention, before determining the types of the multiple target fields and the logical relationships between the multiple target fields based on the data content of the multiple target fields in the target table, the method further includes:

[0049] For each target field in the target table, the following processing is performed: the data content of the target field is standardized and formatted to obtain standardized data content;

[0050] The step of determining the types of the multiple target fields and the logical relationships between them based on the data content of multiple target fields in the target table includes:

[0051] Based on the standardized data content of multiple target fields in the target table, determine the types of the multiple target fields and the logical relationships between them.

[0052] Secondly, the present invention also provides a device for displaying tabular data, comprising:

[0053] The first determining module is used to determine the types of the multiple target fields and the logical relationships between the multiple target fields based on the data content of the multiple target fields in the target table;

[0054] The second determining module is used to determine the total importance score of each of the multiple target fields based on the types of the multiple target fields and the logical relationships between the multiple target fields;

[0055] The sorting and display module is used to sort the multiple target fields according to their respective importance and the total score, and to display the processed data content of the multiple target fields according to the sorting results.

[0056] Thirdly, the present invention also provides an electronic device, comprising: a processor, a memory, and a bus, wherein,

[0057] The processor and the memory communicate with each other via the bus;

[0058] The memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the steps of the tabular data display method described above.

[0059] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the steps of the table data display method as described in any of the above.

[0060] This invention provides a method, apparatus, electronic device, and medium for displaying tabular data. The method includes: determining the types of multiple target fields and their logical relationships based on the data content of multiple target fields in a target table; determining a recommended total importance score for each of the multiple target fields based on their types and logical relationships; sorting the multiple target fields according to their recommended total importance scores; and displaying the processed data content of the multiple target fields based on the sorting results. The tabular data display method provided by this invention enables accurate and clear data viewing, visualizes the data, improves the accuracy of data display, and enhances the user experience. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0062] Figure 1 This is a flowchart illustrating the method for displaying tabular data provided by the present invention;

[0063] Figure 2 This is a flowchart illustrating a data type determination method provided by the present invention;

[0064] Figure 3 This is an example diagram illustrating the data type determination provided by the present invention;

[0065] Figure 4 These are example diagrams illustrating the data determination methods for different application scenarios provided by this invention;

[0066] Figure 5 This is one of the example diagrams of data visualization provided by the present invention;

[0067] Figure 6 This is the second example diagram of data visualization provided by the present invention;

[0068] Figure 7 This is the third example diagram of the data visualization provided by this invention;

[0069] Figure 8 This is the fourth example of the data visualization provided by this invention;

[0070] Figure 9 This is a schematic diagram of the structure of the tabular data display device provided by the present invention;

[0071] Figure 10 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0073] Figure 1 This is a flowchart illustrating the method for displaying tabular data provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the method for displaying tabular data provided by the present invention includes:

[0074] Step 101: Based on the data content of multiple target fields in the target table, determine the types of multiple target fields and the logical relationships between them.

[0075] In this embodiment, it is necessary to determine the types of multiple target fields and their logical relationships based on the data content of multiple target fields in the target table. Here, a field refers to each column corresponding to each cell, including the row header and the corresponding data content within the row header. The data from the row header to the last record in that column is called a field, and the target field is the column the user needs to select. Alternatively, a field can refer to each row corresponding to each cell, including the column header and the corresponding data content within the column header. The data from the column header to the last record in that row is called a field, and the target field is the row the user needs to select. The following examples all use the example where the target field is the column the user needs to select. It should be noted that when the target field is the row the user needs to select, the following examples follow the same logic and will not be elaborated further.

[0076] In this embodiment, logical relationships refer to the relationships between multiple target fields, such as dependency relationships, equality relationships, inclusion relationships, and operational relationships between multiple target fields of a metric type. For example, target field 1 and target field 2 have an inclusion relationship. It should be noted that the types of target fields and the logical relationships between multiple target fields are determined based on the data content of each target field.

[0077] It should be noted that the data content includes not only the data content of the field headers corresponding to each target field, but also the data content of the cells corresponding to each target field. In this embodiment, the field header refers to the row header. For example, if the row header is "Name," then the data content below the column containing that row header is the data content of the cell corresponding to the target field. The specific data content can be set according to the user's actual needs and is not specifically limited here.

[0078] Step 102: Determine the recommended total score for the importance of each of the multiple target fields based on their types and logical relationships.

[0079] In this embodiment, it is necessary to determine the recommended total importance score for each of the multiple target fields based on their types and logical relationships. The recommended total importance score can be used to rank the multiple target fields, and the higher the recommended total importance score, the higher the ranking position during the ranking process.

[0080] Step 103: Sort the multiple target fields according to their respective importance and the total score, and display the processed data content of the multiple target fields according to the sorting results.

[0081] In this embodiment, multiple target fields need to be sorted according to their respective importance and recommended total scores. Then, the processed data content of the target fields is visualized based on the sorting results. Data processing refers to statistical calculations performed on the data content of the target fields, such as summation, average calculation, sorting, and aggregation analysis. Each type of field has different statistical attributes, which indicate which fields in the field list can be manipulated and displayed by the user. Specific statistical information is shown in Table 1 below.

[0082] Table 1

[0083]

[0084] As shown in Table 1 above, the classification of each field determines which fields belong to dimension fields and which belong to measure fields. Dimension fields describe what the problem is and are essentially "categorical fields," while measure fields describe quantity and are quantitative fields. Date fields belong to dimension types; however, in this embodiment, for clarity, date fields are extracted from dimension fields and treated as a separate category. It should be noted that sorting refers to sorting multiple count items within a single field, which is different from sorting different fields; aggregation refers to filtering field data according to year, month, and day formats; null values ​​are special indicators that do not have any specific meaning. Other operations are the same as in existing technologies and will not be described in detail here.

[0085] It should be noted that this embodiment also requires sorting based on the total importance recommendation score. For example, if the total importance recommendation score for target field 1 determined in step 102 is 98, the total importance recommendation score for target field 2 is 88, and the total importance recommendation score for target field 3 is 92, then the three target fields are sorted according to their total importance recommendation scores, with higher total importance recommendation scores ranking higher. The sorted result is target field 1, target field 3, and target field 2. Then, based on the determined sorted result, the processed data content of each target field is visualized.

[0086] According to the tabular data display method provided by this invention, based on the data content of multiple target fields in the target table, the types of the multiple target fields and the logical relationships between them are determined. Based on the types of the multiple target fields and the logical relationships between them, a recommended total score for the importance of each of the multiple target fields is determined. The multiple target fields are then sorted according to their respective recommended total scores. Finally, the processed data content of the multiple target fields is visualized based on the sorting results. This tabular data display method provides accurate and clear data viewing, enabling data visualization, improving the accuracy of data display, and enhancing the user experience.

[0087] Based on any of the above embodiments, in this embodiment, the types of multiple target fields are determined according to the data content of multiple target fields in the target table, including:

[0088] Perform the following processing on each target field in the target table:

[0089] Retrieve the data content of the target field; the data content of the target field includes the content of the field title and the data content of the cell corresponding to the target field;

[0090] The type of the target field is determined based on the content of the field title and the data content of the corresponding cell.

[0091] The types include dimension types and metric types.

[0092] In this embodiment, the following processing operations need to be performed on each target field in the obtained target table: obtain the data content of the target field, which includes the content of the field title of the target field and the data content of the cell corresponding to the target field; then determine the type of the target field based on the content of the field title of the target field and the data content of the cell corresponding to the target field. The type of the target field includes dimension type and measure type. For example, when the data content of the cell corresponding to the target field is text, the type of the target field is determined to be dimension type; when the data content of the cell corresponding to the target field is numeric, the type of the target field is determined to be measure type.

[0093] According to the tabular data display method provided by the present invention, by obtaining the data content of the target field and then determining the type of the target field based on the content of the field title and the data content of the corresponding cell, a data foundation can be provided for subsequent tabular data display, thereby improving the accuracy of data visualization.

[0094] Based on any of the above embodiments, in this embodiment, the type of the target field is determined according to the content of the field title of the target field and the data content of the cell corresponding to the target field, including:

[0095] If the data content of the cell corresponding to the target field satisfies a date-type regular expression or a time-type regular expression, the type of the target field is determined to be the date / time type in the dimension type; where date / time type includes time type and date type.

[0096] If the word segmentation result of the field title of the target field or the word segmentation result of the data content of the corresponding cell matches a pre-defined proper noun of the name class, the type of the target field is determined to be the name class in the dimension type;

[0097] If the word segmentation result of the target field title matches a pre-defined coded number class proper noun, and the data content of the corresponding cell of the target field is a number, then the type of the target field is determined to be the coded number class in the dimension type;

[0098] When the word segmentation result of the field title of the target field matches a pre-set price-related proper noun, or when the data content of the cell corresponding to the target field matches the currency format, determine that the type of the target field is the amount type in the metric type;

[0099] When the word segmentation result of the field title of the target field matches a pre-set percentage-related proper noun, or when the data content of the cell corresponding to the target field matches the percentage format, determine that the type of the target field is the percentage type in the metric type.

[0100] In this embodiment, as Figure 2 shown, it is necessary to obtain the data source of the target table. According to the data content of the target field in the target table, when the data content of the cell corresponding to the target field meets the date-type regular expression or meets the time-type regular expression, determine that the type of the target field is the time / date type in the dimension type. Among them, the secondary classification of the dimension type includes the time / date type, the number / encoding type, the name type, and the note text type, and each dimension type has its own corresponding importance recommended score.

[0101] In this embodiment, when the word segmentation result of the field title of the target field or the word segmentation result of the data content of the corresponding cell matches a pre-set name-related proper noun, determine that the type of the target field is the name type in the dimension type; when the word segmentation result of the field title of the target field matches a pre-set coding / number-related proper noun and the data content of the cell corresponding to the target field is a number, determine that the type of the target field is the coding / number type in the dimension type.

[0102] In this embodiment, fields that meet the formats such as "yyyy-mm-dd", "yyyy年mm月dd日", etc. are determined as the date type, and fields that meet the formats such as "hh时mm分", "hh:mm:ss", etc. are determined as the time type; word segmentation can also be performed on the field title and cell content to identify those containing proper names such as person names and place names, such as "Student Name - Zhang XX", which are classified into the name type dimension; for fields where the data content corresponding to the cell is pure numbers, but the field title contains keywords such as "coding", "serial number", "order number", "number", "mobile phone number", "account number", or "card number", they are classified into the coding / number type dimension.

[0103] It should be noted that the secondary categories of measures include monetary values ​​(fields with cell formats such as "￥xx", "$xx", or "xx yuan", or those with cell formats set to "Currency" or "Accounting" in WPS Spreadsheets), integer values, percentage values ​​(fields with cell formats such as "xx%", or those with cell formats set to "Percentage" in WPS Spreadsheets), decimal values ​​(fields that are not monetary, percentage, or scientific notation, but contain decimal points), and scientific notation values ​​(fields with cell formats set to "Scientific Notation" in WPS Spreadsheets). Different recommended importance scores are assigned to each measure type.

[0104] In this embodiment, when the word segmentation result of the target field's field title matches a pre-defined price-related proper noun, or when the data content of the cell corresponding to the target field matches a currency format, the type of the target field is determined to be the amount category in the measurement type; when the word segmentation result of the target field's field title matches a pre-defined percentage-related proper noun, or when the data content of the cell corresponding to the target field matches a percentage format, the type of the target field is determined to be the percentage category in the measurement type.

[0105] In this embodiment, as Figure 3 As shown, fields whose titles contain keywords such as "price," "amount," or "unit price," or whose data format is in currency formats such as "x yuan," "￥x," or "$x" (WPS spreadsheet cell formatting is set to "#,##0.00," "_*#,##0_; _*-#,##0_; _*"-"_; _@_," "#,##0," etc.), are identified as monetary measures. Fields whose titles contain keywords such as "percentage," "ratio," "percentage," or "rate," or whose data format is in percentage format (WPS spreadsheet cell formatting is set to "0%" etc.), are identified as percentage measures.

[0106] According to the tabular data display method provided by the present invention, the type of each target field is determined based on the data content of the acquired target field, which can improve the accuracy of data visualization and enhance user experience.

[0107] Based on any of the above embodiments, in this embodiment, determining the logical relationships between multiple target fields according to the data content of multiple target fields in the target table includes:

[0108] For multiple target fields that belong to the dimension type, determine the correspondence between the different target fields;

[0109] For multiple target fields that belong to the measure type, determine the operational relationships between the different target fields;

[0110] For any one of the multiple target fields that is of dimension type and any one of the target fields that is of measure type, if the data content in the corresponding cells of the two fields satisfies the statistical formula, it is determined that there is a statistical dependency between them.

[0111] It should be noted that statistical dependency refers to a "logical relationship" in which a change in one piece of data will affect its relationship with another piece of data.

[0112] In this embodiment, it is necessary to determine other dependencies between the target field of the measure type and the target field of the dimension type. There are statistical formulas between the measure type and the dimension type, such as u = vlookup(column where v is located), where v represents the number of the column to be searched, vlookup is an important function in Excel used to search for data, and u represents the value obtained after querying and statistics. The specific calculation methods in the statistical formula can include basic calculation methods such as addition, subtraction, multiplication, and division.

[0113] It should be noted that the u=vlookup function will be explained through the following specific examples, and the specific data content is shown in Table 2 below.

[0114] Table 2

[0115]

[0116] If you enter the function u = VLOOKUP(A5,A1:C5,3,FALSE) in column D, that is, in Table 2 with a range of A1 to C5, you will horizontally look up the value in the third column (column C) corresponding to cell A5 (B European-style smoke machine), and the result will be 3522. At this time, it is determined that there is a dependency relationship between column C (measure) and column A (dimension).

[0117] According to the tabular data display method provided by the present invention, by determining the correspondence, operation relationship or dependency relationship between different target fields, the accuracy of data visualization display can be improved by determining the relationship between multiple target fields.

[0118] Based on any of the above embodiments, in this embodiment, for multiple target fields belonging to the dimension type among multiple target fields, determining the correspondence between different target fields includes:

[0119] When there is a one-to-one correspondence between the data content in the cell corresponding to the first target field and the data content in the cell corresponding to the second target field, the first target field and the second target field are taken as target fields with an equal relationship; where the first target field is any one of multiple target fields belonging to the dimension type, and the second target field is any one of multiple target fields belonging to the dimension type that is different from the first target field.

[0120] When each data content in the cell corresponding to the first target field corresponds to at least one data content in the cell corresponding to the second target field, and any one data content in the cell corresponding to the second target field corresponds to only one data content in the cell corresponding to the first target field, it is determined that the first target field contains the second target field.

[0121] When each data content in the cell corresponding to the first target field corresponds to at least two data contents in the cell corresponding to the second target field, and each data content in the cell corresponding to the second target field corresponds to at least two data contents in the cell corresponding to the first target field, it is determined that the first target field and the second target field are independent target fields.

[0122] In this embodiment, it is necessary to analyze multiple target fields belonging to the dimension type among multiple target fields, analyze the corresponding relationships between different target fields, and then group and process each target field according to the corresponding relationships. Moreover, the higher the level, the higher the recommended score for importance. When there is a one-to-one correspondence between the data content in the cell corresponding to the first target field obtained and the data content in the cell corresponding to the second target field, the first target field and the second target field need to be determined as equal target fields. For example, if the data content of the cells corresponding to target field A and target field B has a one-to-one correspondence, that is, the data content of the first cell in target field A is "Fang M", the data content of the second cell is "Mei A", the data content of the first cell in target field B is also "Fang M", and the data content of the second cell is also "Mei A", then target field A and target field B are called equal fields.

[0123] It should be noted that when each data content in the cell corresponding to the first target field corresponds to at least one data content in the cell corresponding to the second target field, and one data content in the cell corresponding to the second target field corresponds to only one data content in the cell corresponding to the first target field, the first target field is determined to contain the second target field, that is, the two have an inclusion relationship. For example, if we find target field B and target field C with a one-to-many relationship, where each data content in target field B corresponds to one or more data contents in target field C; but one data content in target field C corresponds to only one data content in target field B, that is, the data contents of multiple cells in target field B are: Fang M, Mei A; and the data contents of multiple cells in target field C are: Fang M, Mei A, Xiao C, Mei A, then target field B is said to contain target field C.

[0124] It should be noted that when each data content in the cell corresponding to the first target field corresponds to at least one data content in the cell corresponding to the second target field, and each data content in the cell corresponding to the second target field corresponds to at least one data content in the cell corresponding to the first target field, the first target field and the second target field are determined to be independent target fields. For example, when target fields A and E are found to have a many-to-many relationship, that is, when each data content in the cell corresponding to target field A corresponds to at least one data content in the cell corresponding to target field E, and each data content in the cell corresponding to target field E corresponds to at least one data content in the cell corresponding to target field A, target fields A and target fields E are determined to be independent target fields.

[0125] It should be noted that multiple target fields with inclusion relationships and multiple target fields with equal dimension relationships are chained together and merged into one group.

[0126] For example, the examples are shown in Table 3 below.

[0127] Table 3

[0128] Electrical Appliance Classification Appliance Name Count item: Appliance name Fang A Electric Appliances Fang A near the smoking machine 4 Fang A Oven 18 Fang A European-style range hood 8 Square sink dishwasher 1 Fang A microwave oven 3 Fang A dishwasher 6 Fang A disinfection cabinet 18 Fang A smoke machine 120 Fang A stove 132 Fang A Steam Microwave Integrated Machine 2 Square steam oven 7 Westgate A Siemens A Oven 1 Siemens A Microwave Oven 1 Siemens A dishwasher 6

[0129] The target fields are "Appliance Category" and "Appliance Name". Each data item in the "Appliance Category" field corresponds to multiple data items in the "Appliance Name" field, but one data item in the "Appliance Name" field corresponds to only one data item in the "Appliance Category" field. Therefore, "Appliance Category" contains "Appliance Name". That is, "Appliance Category" and "Appliance Name" are multiple target fields in the grouped dimension, and the "Appliance Category" field is a field of the large set dimension, while "Appliance Name" is a field of the included dimension. The large set dimension field refers to the target field that belongs to the group and has a large range within the group.

[0130] It should be noted that when the category count items of two target fields have a one-to-many relationship, such as Siemens A in the appliance category having multiple category count items in the appliance name, such as Siemens A range hood and stove set, Siemens A stove, etc., the appliance category is said to contain appliance name, where "one" is the larger set dimension and "many" is the included dimension.

[0131] According to the tabular data display method provided by the present invention, by determining and analyzing the field type of the target field of the dimension type, the accuracy of the visualization display of the target field can be improved and the efficiency of data visualization processing can be increased.

[0132] Based on any of the above embodiments, in this embodiment, for multiple target fields belonging to the measurement type among multiple target fields, determining the operational relationship between different target fields includes:

[0133] If there is a one-to-one correspondence between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field, and the difference between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field is an arbitrary constant, then the third target field and the fourth target field are determined to be target fields with an equality relationship; wherein, the third target field is any one of multiple target fields belonging to the measure type, and the fourth target field is any one of multiple target fields belonging to the measure type that is different from the third target field;

[0134] If the data content in the cell corresponding to the third target field has the same number format, the same unit, or the same order of magnitude as the data content in the cell corresponding to the fourth target field, then the third target field and the fourth target field are determined to be similar target fields.

[0135] If the difference between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field is equal to the data content in the cell corresponding to the fifth target field, then the third target field is determined to contain the fourth target field, and the third target field contains the fifth target field; wherein, the fifth target field is any one of the multiple target fields belonging to the measurement type that is different from the third target field and different from the fourth target field;

[0136] If the data content in the cell corresponding to the third target field is the function value of the first function, and the data content in the cell corresponding to the fourth target field is the variable of the first function, then the third target field depends on the fourth target field.

[0137] If the data content in the cell corresponding to the third target field is the function value of the first function, the data content in the cell corresponding to the fourth target field is the variable of the first function, and the data content in the cell corresponding to the fourth target field is the function value of the second function, and the data content in the cell corresponding to the fifth target field is the variable of the second function, then the third target field is determined to be a composite dependency of the fifth target field.

[0138] In this embodiment, it is necessary to analyze the operational relationships between measurement types and process them in groups. The higher the level, the higher the recommended importance score. When there is a one-to-one correspondence between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field, and the difference between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field is an arbitrary constant, the third target field and the fourth target field are determined to be equal target fields. If a pair of equal measurement fields p and q are found, satisfying p = q + any constant, the target fields p and q are called equal target fields.

[0139] In this embodiment, when the data content in the cell corresponding to the third target field has the same number format, the same unit, or the same order of magnitude as the data content in the cell corresponding to the fourth target field, the third target field and the fourth target field are determined to be similar target fields. If target fields x and y with the same number format / unit / order of magnitude are found, they are called similarity measures, and all target fields with similarity measures are called similarity measure groups.

[0140] In this embodiment, when the difference between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field is equal to the data content in the cell corresponding to the fifth target field, it is determined that the third target field contains the fourth target field and the third target field contains the fifth target field. For example, if a metric target field q and a target field r with an addition-subtraction relationship are found, r = qs (s is any metric), then target field q contains target field r and target field q contains target field s.

[0141] In this embodiment, when the data content in the cell corresponding to the third target field is the function value of the first function and the data content in the cell corresponding to the fourth target field is the variable of the first function, the third target field is determined to depend on the fourth target field. When the data content in the cell corresponding to the third target field is the function value of the first function, the data content in the cell corresponding to the fourth target field is the variable of the first function, and the data content in the cell corresponding to the fourth target field is the function value of the second function and the data content in the cell corresponding to the fifth target field is the variable of the second function, the third target field is determined to be compositely dependent on the fifth target field. If a metric target field h, target field l, target field m, and target field o with formula dependencies are found, o = f1(l) + f2(m), m = f3(h), then the metric target field o depends on the metric target field l and the metric target field m, the metric target field m depends on the metric target field h, and the metric target field o is compositely dependent on the metric target field h.

[0142] According to the tabular data display method provided by the present invention, by determining and analyzing the field types of target fields of dimension type and target fields of measure type, the accuracy of the visualization display of target fields can be improved and the efficiency of data visualization processing can be increased.

[0143] Based on any of the above embodiments, in this embodiment, the recommended total score for the importance of each of the multiple target fields is determined according to the types of the multiple target fields and the logical relationships between the multiple target fields, including:

[0144] Based on the types of multiple target fields, determine the recommended first-category importance score for each of the multiple target fields;

[0145] Based on the logical relationships between multiple target fields, determine the recommended score for the second type of importance of each target field;

[0146] Based on the first-type importance recommendation scores and the second-type importance recommendation scores of each of the multiple target fields, the total importance recommendation score for each of the multiple target fields is determined.

[0147] In this embodiment, it is necessary to determine the first-class importance recommendation score for each of the multiple target fields according to their types. The first-class importance recommendation score is determined based on the secondary classification results of the dimensions or the secondary classification results of the measures. For example, in this embodiment, the importance scores of each dimension type are ordered as follows: Name > Date and Time > Other, and the importance scores of the measures type are ordered as follows: Amount > Percentage > Other. The specific sorting method is described in the following embodiment and will not be detailed here.

[0148] In this embodiment, it is necessary to determine the second type of importance recommendation score for each of the multiple target fields based on the logical relationship between them. The second type of importance recommendation score is determined based on the logical relationship between the multiple target fields, such as inclusion relationship, equality relationship, independence relationship, etc.

[0149] In this embodiment, the total recommended importance score for each of the multiple target fields needs to be determined based on their respective first-category importance recommendation scores and second-category importance recommendation scores. The weight of each category of importance recommendation score needs to be set, and the total recommended importance score for each target field is calculated based on these weights. For example, assuming the weight of the first-category importance recommendation score is 0.6 and the weight of the second-category importance recommendation score is 0.4, and the first-category importance recommendation score for target field 1 is determined to be 90 and the second-category importance recommendation score is 80, then the calculated total recommended importance score for target field 1 is 90*0.6 + 80*0.4 = 86. The total recommended importance scores for multiple target fields are calculated sequentially, and then they are sorted according to their total scores.

[0150] According to the tabular data display method provided by the present invention, a first-type importance recommendation score is determined for each of the multiple target fields based on their types; a second-type importance recommendation score is determined for each of the multiple target fields based on their logical relationships; and a total importance recommendation score is determined for each of the multiple target fields based on their first-type importance recommendation scores and their second-type importance recommendation scores, which are used in subsequent data sorting processing to improve the accuracy of data visualization.

[0151] Based on any of the above embodiments, in this embodiment, determining the first-category importance recommendation score for each of the multiple target fields according to their types includes:

[0152] Based on the data content of the target table, determine the scenario corresponding to the target table;

[0153] When the target table is in the first scenario, the recommended first-class importance scores should be set for the target fields of the following dimension types in descending order of scores: target fields of name type, target fields of date and time type, and target fields of other dimension types excluding name and date and time type; the recommended first-class importance scores should also be set for the target fields of the following measure types in descending order of scores: target fields of amount type, target fields of percentage type, and target fields of other dimension types excluding amount and percentage type.

[0154] When the target table is in the second scenario, the recommended first-class importance scores should be set for the target fields of the following dimension types in descending order of score: target fields of date and time, target fields of name, and target fields of other dimension types besides name and date and time; the recommended first-class importance scores should also be set for the target fields of the following measure types in descending order of score: target fields of amount, target fields of percentage, and target fields of other dimension types besides amount and percentage.

[0155] In this embodiment, as Figure 4 As shown, the application scenarios include checklist / logo system, data collection and distribution system, summary and transmission / certificate system, instruction system, statistical analysis system, and schedule system. Among them, the checklist / logo system mainly consists of checklists and tables, with keywords such as checklist and summary; the data collection and distribution system mainly consists of registration forms and pricing forms, with keywords such as sign-in and application; the summary and transmission / certificate system mainly consists of quotation sheets and settlement sheets, with keywords such as quotation and acceptance; the instruction system has keywords such as rules and regulations and standards; the statistical analysis system mainly consists of analysis tables, with keywords such as statistics and analysis; and the schedule system mainly consists of schedules, with keywords such as plan and planning. This embodiment requires identifying keywords in the title content of the target table, confirming the application scenario of the target table, and then determining the order of different importance recommendation scores based on different application scenarios. The field list will display the corresponding field information on one screen, and then display dimension type fields and measure type fields according to a certain display ratio. In this embodiment, the field list is set to display a maximum of 8 fields, with dimension type fields and measure type fields arranged in a 5:3 ratio until all target fields are displayed. The arrangement order is based on the target field scoring results. It should be noted that the maximum number of fields displayed on one screen and the sorting ratio can be set according to the user's actual needs and are not specifically limited here.

[0156] It should be noted that the first type of scenario is the conventional scenario, specifically including data collection and distribution, summary transmission / certificate / voucher transmission, and guidance / instruction. When the target table is in the first type of scenario, the recommended first-type importance score should be set for the target fields of the following dimension types in descending order of score: target fields of name type, target fields of date and time type, and target fields of other dimension types excluding name and date and time type. Then, the recommended first-type importance score should be set for the target fields of the following measurement types in descending order of score: target fields of amount type, target fields of percentage type, and target fields of other dimension types excluding amount and percentage type.

[0157] It should be noted that the second type of scenario includes list-based scenarios, statistical analysis scenarios, and schedule-based scenarios. When the target table is in the second type of scenario, the recommended first-level importance score should be set for the target fields of the following dimension types in descending order of score: target fields of time and date type, target fields of name type, and target fields of other dimension types besides name and time and date type; the recommended first-level importance score should be set for the target fields of the following measure types in descending order of score: target fields of amount type, target fields of percentage type, and target fields of other dimension types besides amount and percentage type.

[0158] According to the tabular data display method provided by the present invention, the application scenario of the target table is identified by recognizing the data content in the target table, and different rankings with different importance recommendation scores are determined according to different application scenarios. By determining different ranking results with different importance recommendation scores for different application scenarios, different data displays are performed according to different ranking results, making the data visualization display more targeted and improving the accuracy of the data visualization display.

[0159] Based on any of the above embodiments, in this embodiment, the second-category importance recommendation score for each of the multiple target fields is determined according to the logical relationship between them, including:

[0160] For multiple target fields that belong to the dimension type, merge fields with an inclusion relationship into one group, or merge fields that are equal into one group;

[0161] The following target fields of different dimension types are assigned a second-category importance score in descending order of their scores: target fields that are merged into groups and belong to a large range within those groups, independent target fields, and target fields that are merged into groups but do not belong to any of those groups and belong to a large range.

[0162] For multiple target fields that belong to the metric type, the recommended scores for the second type of importance are set in descending order of scores: target fields with composite dependencies, similar target fields, included target fields, included target fields, dependent target fields, equal target fields, and remaining target fields.

[0163] For cases where any target field belonging to the dimension type has a statistical dependency relationship with any target field belonging to the measure type, a second-type importance recommendation score higher than a preset threshold is set for each of the target fields belonging to the dimension type and the target field belonging to the measure type.

[0164] In this embodiment, it is necessary to determine the recommended second-type importance score of each of the multiple target fields based on the logical relationship between them, and to determine the order of the recommended second-type importance scores of each target field. It should be noted that determining the recommended second-type importance score of each of the multiple target fields based on the logical relationship between them provides data support for the subsequent visualization of the sorting results determined by the recommended second-type importance scores, thereby improving the processing speed of data visualization.

[0165] In this embodiment, for multiple target fields of a dimension type, fields with inclusion relationships are merged, or fields that are equal are merged. Then, the merged target fields are assigned a second type of importance recommendation score in descending order of their scores. Here, "larger range" refers to a range of values ​​in which the number of target fields of the same type within a group exceeds a preset value. For example, if the preset value is 5, then if the number of target fields of a certain type in the group is determined to be 8, then this type of target field is determined to be merged into the group and belongs to the target fields with a larger range within the group. It should be noted that in other embodiments, "larger range" can have other representations, which can be set according to the user's actual needs and are not specifically limited here.

[0166] According to the tabular data display method provided by the present invention, the order of the second-class importance recommendation scores of multiple target fields is determined based on the logical relationship between them, which can improve the accuracy of data visualization and display and improve the efficiency of data processing.

[0167] Based on any of the above embodiments, in this embodiment, multiple target fields are ranked according to their respective importance and recommended total scores. The processed data content of the multiple target fields is then displayed based on the ranking results, including:

[0168] The target fields are divided into multiple target fields belonging to the dimension type and multiple target fields belonging to the measure type;

[0169] The first ranking result is obtained by sorting the total recommended scores of multiple target fields belonging to the dimension type according to their respective importance.

[0170] The second ranking result is obtained by sorting the total recommended scores of multiple target fields belonging to the metric type according to their respective importance.

[0171] Based on the first sorting result, select the first number of target fields belonging to the dimension type; based on the second sorting result, select the second number of target fields belonging to the measure type.

[0172] Data processing is performed on the data content of the first number of target fields belonging to the dimension type and the data content of the second number of target fields belonging to the metric type, and the processing results are visualized.

[0173] In this embodiment, multiple target fields of the dimension type are first sorted according to their respective recommended total scores based on their importance, resulting in a first sorting result. Then, multiple target fields of the metric type are sorted according to their respective recommended total scores based on their importance, resulting in a second sorting result. Next, based on a pre-set display ratio, a first number of dimension type target fields and a second number of metric type target fields are selected. The data content of the selected target fields is processed, and the processing results are then visualized. It should be noted that the pre-set display ratio can be any ratio value, such as 5:3, 9:1, or 5:5, and can be set according to the user's actual needs; no specific limitation is made here.

[0174] According to the tabular data display method provided by the present invention, multiple target fields are divided into target fields belonging to dimension type and target fields belonging to metric type. Then, the target fields are processed in a certain way, and the processing results are visualized, thereby improving the accuracy and processing speed of data visualization.

[0175] Based on any of the above embodiments, in this embodiment, the obtained processing results are visualized, including:

[0176] The processing results are displayed in a view format;

[0177] Receive a first input; wherein the first input includes at least one displayed data item;

[0178] Based on the data items contained in the first input, search for the data items in the data content of the cells corresponding to all the displayed target fields;

[0179] Display the search results.

[0180] In this embodiment, the processing results need to be displayed in the form of a bar view. Based on the data items contained in the received first input information, the data item is searched for in the data content of the cells corresponding to all displayed target fields, and the search results are displayed. For example... Figure 5 As shown, Figure 5 The bottom left corner displays the data processing linkage function. By specifying the storage date, it can determine all order numbers, customer names, contact numbers, and store names for that day, enabling linked data processing. The first input refers to an operation that contains at least one data item to be searched.

[0181] In this embodiment, as Figure 5 shown, the field list expands from the bottom of the original data, and the expansion entry is located in the data interpretation - field management area, positioned to assist users in selecting analysis fields and viewing detailed field information. The auxiliary analysis - type field list only supports operations such as sorting (as Figure 6 shown, date fields are in ascending order of date, and other fields are in descending order of the count result of the counting item), linkage (as Figure 7 shown, when selecting the counting item of "Fang X灶具", the linkage results of other counting items are shown in blue bars. If there is no blue bar display, it means that there is no data result for this counting item when "appliance name = Fang X灶具"), and card recommendation sorting and other functions to view field details. At the same time, to avoid the misunderstanding that the original data will be changed, the auxiliary analysis form does not support users to directly select fields to add to the analysis.

[0182] In this embodiment, as Figure 8 shown, the field list information is located in the Figure 8 lower left corner, and the start entry is located in the tab. It is a more complete form of the field list, positioned to provide an overall analysis container with fields as the granularity. The detailed analysis form supports the linkage operation between the field list and the cleaned data source. For example, the two modules can synchronously filter classification counting items and synchronously modify data, support selecting any field to add to the analysis, and the data interpretation pane will respond to operations such as filtering and data modification of the field, and synchronously refresh the data interpretation result.

[0183] According to the method for displaying tabular data provided by the present invention, the obtained processing result is displayed in the form of a view; then, according to the data items included in the first input, the data items are searched in the data content of the cells corresponding to all target fields shown, and the search results are displayed, enabling users to clearly view the data items to be searched and improving the accuracy of data visualization display.

[0184] Based on any of the above embodiments, in this embodiment, before determining the types of multiple target fields and the logical relationships between multiple target fields according to the data content of multiple target fields in the target table, the method further includes:

[0185] Performing the following processing for each target field in the target table: uniformly standardizing the data format of the data content of the target field to obtain standardized data content;

[0186] Determining the types of multiple target fields and the logical relationships between multiple target fields according to the data content of multiple target fields in the target table includes:

[0187] Determining the types of multiple target fields and the logical relationships between multiple target fields according to the standardized data content of multiple target fields in the target table.

[0188] In this embodiment, the data content of multiple target fields in the acquired target table needs to be standardized and formatted, which is called data cleaning. The data cleaning process includes three parts: algorithm cleaning, data suggestions, and manual adjustments. Users can select any worksheet and request the data cleaning algorithm to automatically clean the user's data source: including extracting and standardizing the table area, selecting the field range, and standardizing and formatting the data of the count items to support the display of field information at the granular level of categorized count items.

[0189] To preserve the original user data to the greatest extent possible, the data cleaning algorithm provides suggestions for the following issues, which can be addressed by the user. Specific issues include: suggesting modification of invalid data such as "December 35, 2021"; unifying the amounts such as "3 yuan / jin" and "17 yuan"; removing redundant content such as blank cells in "Simon A" and "Simon A"; and correcting typos such as "bright" and "quality". This ensures that classification and counting items can be correctly clustered and statistically analyzed. It should be noted that, in addition to the above suggested corrections, the system can also automatically correct these issues.

[0190] It should be noted that the field list provides data cleaning functions, including renaming category count items, merging category count items, and replacing null values, without changing the original row and column structure (it does not support adding or deleting category count items, and does not change the number of items), to meet users' needs for partial data modification based on category count items.

[0191] It should be noted that after completing the data format standardization process, the types of multiple target fields and the logical relationships between them are determined based on the obtained standardized data content. The types include dimension types and measure types, and the logical relationships can be inclusion relationships, equality relationships, etc. Specific determination methods can be found in the above embodiments and will not be described in detail here.

[0192] According to the tabular data display method provided by the present invention, by performing data format standardization processing on multiple target fields in the target table, and then determining the type of multiple target fields and the logical relationship between multiple target fields based on the obtained standardized data content, the accuracy of the data can be guaranteed and the accuracy of data visualization display can be improved.

[0193] Based on any of the above embodiments, it is also necessary to perform statistical analysis on the data, and to analyze the different attributes of each type of field card. The attribute information represents which user operations and content display are supported in the field list.

[0194] It should be noted that by introducing feature labels that relate to field classification and structure, importance recommendation scores are assigned to target fields of dimension type (descriptive data) and target fields of measure type (statistical data) to support the ranking position of the target field in the field list.

[0195] The secondary categories of dimensions include: date and time, number and code, name, and notes / text. The recommended order of importance scores for the first category is: name > date and time > others. The secondary categories of measures include: amount, quantity (integer), percentage, and decimal and scientific notation. The recommended order of importance scores for the first category is: amount > percentage > others.

[0196] The second type of importance recommendation score for each target field is determined based on the logical relationships between multiple target fields. The mapping relationships between dimensions are analyzed and grouped; the higher the level, the higher the second type of importance recommendation score: Target fields A and B with a one-to-one relationship are considered equal; target fields B and C with a one-to-many relationship are identified, where each data point in target field B corresponds to one or more data points in target field C, but one data point in target field C corresponds to only one data point in target field B, thus determining that target field B contains target field C; target fields A and E with a many-to-many relationship are identified, then target fields A and E are considered unrelated. The second type of importance recommendation score, from highest to lowest, is as follows: target fields merged into a group and belonging to a larger range within that group; independent target fields; and target fields merged into a group but not belonging to a larger range within that group.

[0197] It should be noted that, based on the operational relationships between the target fields of the metric type, and grouped processing is performed, for example, if a pair of metric fields p and q are found to be equal, satisfying p = q + any constant, then target fields p and q are called equal target fields; if target fields x and y are found to have the same numeric format / unit / order of magnitude, then target fields x and y are called similar metrics, and all target fields of similar metrics are called similar metric groups; if a metric target field q and target field r are found to have an addition / subtraction relationship, r = qs (where s is any metric), then target field q contains target field r, and target field q contains target field s. It should be noted that the inclusion relationship between metric types usually indicates a field relationship suitable for drawing a stacked graph, not a clustered graph.

[0198] Specifically, for multiple target fields of a measure type, the recommended scores for the second type of importance are set from high to low as follows: target fields with composite dependencies, similar target fields, included target fields, included target fields, dependent target fields, equal target fields, and remaining target fields. Target fields of equal measure types within the same group do not participate in the analysis simultaneously. The target fields with higher importance scores are recommended to participate in the analysis. All target fields of similar measure types can participate in the analysis simultaneously.

[0199] It is also necessary to determine other dependencies between the target fields of the measure type and the target fields of the dimension type. There are statistical formulas between the measure type and the dimension type, such as u = vlookup(column containing v). The specific calculation methods in the statistical formula can include basic calculation methods such as addition, subtraction, multiplication, and division. The target fields that belong to the calculation methods of the measure type / dimension type have a higher level, and the corresponding importance recommendation score is also higher.

[0200] It should be noted that the order of the recommended scores for the first type of importance is determined according to different application scenarios. In a typical scenario, the recommended scores for the first type of importance of multiple target fields of a dimension type are ordered as follows: Name > Date / Time > Other; the recommended scores for the first type of importance of multiple target fields of a metric type are ordered as follows: Amount > Percentage > Other.

[0201] For scenarios involving lists, statistical analysis, or daily planning: the recommended order of the first importance score for multiple target fields of dimension type is time / date > name > others; for multiple target fields of measure type, the recommended order of the measure of the first importance score is amount > percentage > others.

[0202] It should be noted that in the intelligent analysis function, the field list can display a maximum of 8 fields per screen, with dimensions and measures arranged in a 5:3 ratio until all fields are listed. The order is based on the field scoring results. The field list is primarily presented in two formats: auxiliary analysis and detailed analysis.

[0203] Figure 9 The present invention provides a data visualization display device, such as... Figure 9 As shown, the data visualization display device provided by the present invention includes:

[0204] The first determining module 901 is used to determine the type of multiple target fields and the logical relationship between multiple target fields based on the data content of multiple target fields in the target table;

[0205] The second determining module 902 is used to determine the recommended total score for the importance of each of the multiple target fields based on the types of the multiple target fields and the logical relationships between the multiple target fields;

[0206] The sorting and display module 903 is used to sort multiple target fields according to their respective importance and recommend total scores, and then visualize the processed data content of the multiple target fields based on the sorting results.

[0207] According to the data visualization device provided by this invention, based on the data content of multiple target fields in a target table, the types of the multiple target fields and the logical relationships between them are determined. Based on the types of the multiple target fields and the logical relationships between them, a recommended total score for the importance of each of the multiple target fields is determined. The multiple target fields are then sorted according to their respective recommended total scores. Based on the sorting results, the processed data content of the multiple target fields is visualized. The data visualization device provided by this invention enables accurate and clear data viewing, improves the accuracy of data display, and enhances the user experience.

[0208] Furthermore, the first determining module 901 is also used for:

[0209] Perform the following processing on each target field in the target table:

[0210] Obtain the data content of the target field; wherein, the data content of the target field includes the content of the field title of the target field and the data content of the cell corresponding to the target field;

[0211] The type of the target field is determined based on the content of the field title and the data content of the corresponding cell.

[0212] The types include dimension types and metric types.

[0213] The tabular data display device provided by the present invention obtains the data content of the target field, and then determines the type of the target field based on the content of the field title of the target field and the data content of the cell corresponding to the target field. This provides a data basis for subsequent tabular data display and improves the accuracy of data visualization.

[0214] Furthermore, the first determining module 901 is also used for:

[0215] If the data content of the cell corresponding to the target field satisfies a date-type regular expression or a time-type regular expression, the type of the target field is determined to be the date / time type in the dimension type.

[0216] If the word segmentation result of the field title of the target field or the word segmentation result of the data content of the corresponding cell matches a pre-defined proper noun of the name class, the type of the target field is determined to be the name class in the dimension type;

[0217] If the word segmentation result of the target field title matches a pre-defined coded number class proper noun, and the data content of the corresponding cell of the target field is a number, then the type of the target field is determined to be the coded number class in the dimension type;

[0218] If the word segmentation result of the target field's field title matches a pre-defined price-related proper noun, or if the data content of the corresponding cell of the target field matches a currency format, then the type of the target field is determined to be the amount type in the measurement type.

[0219] If the word segmentation result of the target field's field title matches a pre-defined percentage-type proper noun, or if the data content of the corresponding cell of the target field matches a percentage format, the type of the target field is determined to be a percentage-type measurement.

[0220] The tabular data display device provided by the present invention determines the type of each target field based on the data content of the acquired target field, thereby improving the accuracy of data visualization and enhancing the user experience.

[0221] Furthermore, the first determining module 901 is also used for:

[0222] For multiple target fields that belong to the dimension type, determine the correspondence between the different target fields;

[0223] For multiple target fields that belong to the measure type, determine the operational relationships between the different target fields;

[0224] For any one of the multiple target fields that is of dimension type and any one of the target fields that is of measure type, if the data content in the corresponding cells of the two fields satisfies the statistical formula, it is determined that there is a statistical dependency between them.

[0225] The tabular data display device provided by the present invention can improve the accuracy of data visualization by determining the correspondence, operation relationship or dependency relationship between different target fields and the relationship between multiple target fields.

[0226] Furthermore, the first determining module 901 is also used for:

[0227] If there is a one-to-one correspondence between the data content in the cell corresponding to the first target field and the data content in the cell corresponding to the second target field, the first target field and the second target field are regarded as target fields with an equality relationship; wherein, the first target field is any one of multiple target fields belonging to the dimension type, and the second target field is any one of multiple target fields belonging to the dimension type that is different from the first target field;

[0228] If each data content in the cell corresponding to the first target field corresponds to at least one data content in the cell corresponding to the second target field, and any data content in the cell corresponding to the second target field corresponds to only one data content in the cell corresponding to the first target field, then the first target field is determined to contain the second target field.

[0229] If each data content in the cell corresponding to the first target field corresponds to at least two data contents in the cell corresponding to the second target field, and each data content in the cell corresponding to the second target field corresponds to at least two data contents in the cell corresponding to the first target field, then the first target field and the second target field are determined to be independent target fields.

[0230] The tabular data display device provided by the present invention can improve the accuracy of the visualization display of target fields and improve the efficiency of data visualization processing by determining and analyzing the field type of the target field of the dimension type.

[0231] Furthermore, the first determining module 901 is also used for:

[0232] If there is a one-to-one correspondence between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field, and the difference between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field is an arbitrary constant, then the third target field and the fourth target field are determined to be target fields with an equality relationship; wherein, the third target field is any one of multiple target fields belonging to the measurement type, and the fourth target field is any one of multiple target fields belonging to the measurement type that is different from the third target field;

[0233] If the data content in the cell corresponding to the third target field has the same number format, the same unit, or the same order of magnitude as the data content in the cell corresponding to the fourth target field, then the third target field and the fourth target field are determined to be similar target fields.

[0234] If the difference between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field is equal to the data content in the cell corresponding to the fifth target field, then it is determined that the third target field contains the fourth target field, and the third target field contains the fifth target field; wherein, the fifth target field is any one of the multiple target fields belonging to the measurement type that is different from the third target field and the fourth target field;

[0235] If the data content in the cell corresponding to the third target field is the function value of the first function, and the data content in the cell corresponding to the fourth target field is the variable of the first function, then the third target field depends on the fourth target field.

[0236] If the data content in the cell corresponding to the third target field is the function value of the first function, the data content in the cell corresponding to the fourth target field is the variable of the first function, and the data content in the cell corresponding to the fourth target field is the function value of the second function, and the data content in the cell corresponding to the fifth target field is the variable of the second function, then the third target field is determined to be dependent on the fifth target field.

[0237] The tabular data display device provided by the present invention can improve the accuracy of the visualization display of target fields and improve the efficiency of data visualization processing by determining and analyzing the field types of target fields of dimension type and target fields of measurement type.

[0238] Furthermore, the second determining module 902 is also used for:

[0239] Based on the types of multiple target fields, determine the recommended first-category importance score for each of the multiple target fields;

[0240] Based on the logical relationships between multiple target fields, determine the recommended score for the second type of importance of each target field;

[0241] Based on the first-type importance recommendation scores and the second-type importance recommendation scores of each of the multiple target fields, the total importance recommendation score for each of the multiple target fields is determined.

[0242] According to the tabular data display device provided by the present invention, a first-type importance recommendation score is determined for each of the multiple target fields based on their types; a second-type importance recommendation score is determined for each of the multiple target fields based on the logical relationship between them; and a total importance recommendation score is determined for each of the multiple target fields based on both the first-type importance recommendation score and the second-type importance recommendation score, which is used in subsequent data sorting processing to improve the accuracy of data visualization.

[0243] Furthermore, determining the first-category importance recommendation score for each of the multiple target fields based on their types includes:

[0244] Based on the data content of the target table, determine the scenario corresponding to the target table;

[0245] When the target table is in the first scenario, the recommended first-class importance scores should be set for the target fields of the following dimension types in descending order of scores: target fields of name type, target fields of date and time type, and target fields of other dimension types excluding name and date and time type; the recommended first-class importance scores should also be set for the target fields of the following measure types in descending order of scores: target fields of amount type, target fields of percentage type, and target fields of other dimension types excluding amount and percentage type.

[0246] When the target table is in the second scenario, the recommended first-class importance scores should be set for the target fields of the following dimension types in descending order of score: target fields of date and time, target fields of name, and target fields of other dimension types besides name and date and time; the recommended first-class importance scores should also be set for the target fields of the following measure types in descending order of score: target fields of amount, target fields of percentage, and target fields of other dimension types besides amount and percentage.

[0247] The tabular data display device provided by the present invention identifies the application scenario of the target table by recognizing the data content in the target table, determines different rankings with different importance recommendation scores according to different application scenarios, and displays different data based on different ranking results, making the data visualization display more targeted and improving the accuracy of the data visualization display.

[0248] Furthermore, the sorting and display module 903 is also used for:

[0249] The target fields are divided into multiple target fields belonging to the dimension type and multiple target fields belonging to the measure type;

[0250] The first ranking result is obtained by sorting the total recommended scores of multiple target fields belonging to the dimension type according to their respective importance.

[0251] The second ranking result is obtained by sorting the total recommended scores of multiple target fields belonging to the metric type according to their respective importance.

[0252] Based on the first sorting result, select the first number of target fields belonging to the dimension type; based on the second sorting result, select the second number of target fields belonging to the measure type.

[0253] The data content of the first number of target fields belonging to the dimension type and the data content of the second number of target fields belonging to the metric type are processed, and the processing results are displayed.

[0254] The tabular data display device provided by the present invention improves the accuracy and processing speed of data visualization by dividing multiple target fields into target fields belonging to dimension type and target fields belonging to metric type, then processing the target fields and visualizing the processing results.

[0255] Furthermore, the tabular data display device is also used for:

[0256] For each target field in the target table, perform the following processing: standardize the data format of the target field to obtain standardized data content;

[0257] Based on the data content of multiple target fields in the target table, determine the types of the multiple target fields and the logical relationships between them, including:

[0258] Based on the standardized data content of multiple target fields in the target table, determine the types of the multiple target fields and the logical relationships between them.

[0259] The tabular data display device provided by the present invention can ensure data accuracy and improve the accuracy of data visualization by performing data format standardization processing on multiple target fields in the target table, and then determining the type of multiple target fields and the logical relationship between multiple target fields based on the obtained standardized data content.

[0260] Since the device described in this embodiment of the invention is based on the same principle as the method described in the above embodiments, more detailed explanations will not be repeated here.

[0261] Figure 10 This is a schematic diagram of the physical structure of the electronic device provided in the embodiments of the present invention, such as... Figure 10 As shown, the present invention provides an electronic device, including: a processor 1001, a memory 1002 and a bus 1003;

[0262] The processor 1001 and the memory 1002 communicate with each other via the bus 1003.

[0263] The processor 1001 is used to call program instructions in the memory 1002 to execute the methods provided in the above method embodiments, such as: determining the types of the multiple target fields and the logical relationships between the multiple target fields according to the data content of the multiple target fields in the target table; determining the recommended total importance score of each of the multiple target fields according to the types of the multiple target fields and the logical relationships between the multiple target fields; sorting the multiple target fields according to the recommended total importance score of each of the multiple target fields; and displaying the processed data content of the multiple target fields according to the sorting result.

[0264] This invention provides a non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the methods provided in the above-described method embodiments. These instructions include, for example,: determining the types of multiple target fields and their logical relationships based on the data content of multiple target fields in a target table; determining a recommended total importance score for each of the multiple target fields based on their types and logical relationships; sorting the multiple target fields according to their recommended total importance scores; and displaying the processed data content of the multiple target fields based on the sorting results.

[0265] This invention also provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, enable the computer to perform the methods provided by the above-described methods. The method includes: determining the types of the multiple target fields and the logical relationships between them based on the data content of multiple target fields in a target table; determining the recommended total importance score for each of the multiple target fields based on their types and logical relationships; sorting the multiple target fields according to their respective recommended total importance scores; and displaying the processed data content of the multiple target fields based on the sorting results.

[0266] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0267] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for displaying tabular data, characterized in that, include: Based on the data content of multiple target fields in the target table, determine the types of the multiple target fields and the logical relationships between them; Based on the types of the multiple target fields and the logical relationships between them, the total importance score for each of the multiple target fields is determined. Based on the total score recommended according to the importance of each of the multiple target fields, the multiple target fields are sorted, and the processed data content of the multiple target fields is displayed according to the sorting result; The step of determining the recommended total score for the importance of each of the multiple target fields based on their types and logical relationships includes: Based on the types of the multiple target fields, determine the first-category importance recommendation score for each of the multiple target fields; Based on the logical relationships between the multiple target fields, determine the second-category importance recommendation score for each of the multiple target fields; Based on the first type of importance recommendation score and the second type of importance recommendation score of each of the multiple target fields, the total importance recommendation score of each of the multiple target fields is determined; The step of determining the second-category importance recommendation score for each of the multiple target fields based on their logical relationships includes: For multiple target fields that belong to the dimension type, merge fields with an inclusion relationship into one group, or merge fields that are equal into one group; The following target fields of different dimension types are assigned a second-category importance score in descending order of their scores: target fields that are merged into groups and belong to a large range within those groups, independent target fields, and target fields that are merged into groups but do not belong to any of those groups and belong to a large range. For multiple target fields that belong to the metric type, the recommended scores for the second type of importance are set in descending order of scores: target fields with composite dependencies, similar target fields, included target fields, included target fields, dependent target fields, equal target fields, and remaining target fields. For cases where any target field belonging to the dimension type has a statistical dependency relationship with any target field belonging to the measure type, a second-type importance recommendation score higher than a preset threshold is set for each of the target fields belonging to the dimension type and the target field belonging to the measure type.

2. The method for displaying tabular data according to claim 1, characterized in that, The step of determining the types of the multiple target fields based on their data content in the target table includes: Perform the following processing for each target field in the target table: Obtain the data content of the target field; wherein, the data content of the target field includes the content of the field title of the target field and the data content of the cell corresponding to the target field; The type of the target field is determined based on the content of the field title and the data content of the corresponding cell. The types mentioned include dimension types and metric types.

3. The method for displaying tabular data according to claim 2, characterized in that, Determining the type of the target field based on the content of the field title and the data content of the corresponding cell includes: If the data content of the cell corresponding to the target field satisfies a date-type regular expression or a time-type regular expression, the type of the target field is determined to be the date-time type in the dimension type. If the word segmentation result of the field title of the target field or the word segmentation result of the data content of the corresponding cell matches a pre-defined proper noun of the name class, the type of the target field is determined to be the name class in the dimension type; If the word segmentation result of the field title of the target field matches a pre-defined coded number class proper noun, and the data content of the cell corresponding to the target field is a number, then the type of the target field is determined to be the coded number class in the dimension type; If the word segmentation result of the field title of the target field matches a pre-defined price-related proper noun, or if the data content of the cell corresponding to the target field matches a currency format, then the type of the target field is determined to be the amount type in the measurement type. If the word segmentation result of the field title of the target field matches a pre-defined percentage-type proper noun, or if the data content of the cell corresponding to the target field matches a percentage format, the type of the target field is determined to be a percentage-type measurement.

4. The method for displaying tabular data according to claim 1, characterized in that, The step of determining the logical relationships between multiple target fields based on their data content in the target table includes: For multiple target fields that belong to the dimension type among the multiple target fields, determine the correspondence between the different target fields; For multiple target fields that belong to the measurement type among the multiple target fields, determine the operational relationships between the different target fields; For any one of the multiple target fields that is of dimension type and any one of the target fields that is of measure type, if the data content in the corresponding cells of the two fields satisfies the statistical formula, it is determined that there is a statistical dependency between them.

5. The method for displaying tabular data according to claim 4, characterized in that, For the multiple target fields that belong to the dimension type, determining the correspondence between different target fields includes: If there is a one-to-one correspondence between the data content in the cell corresponding to the first target field and the data content in the cell corresponding to the second target field, the first target field and the second target field are regarded as target fields with an equality relationship; wherein, the first target field is any one of multiple target fields belonging to the dimension type, and the second target field is any one of multiple target fields belonging to the dimension type that is different from the first target field; If each data content in the cell corresponding to the first target field corresponds to at least one data content in the cell corresponding to the second target field, and any data content in the cell corresponding to the second target field corresponds to only one data content in the cell corresponding to the first target field, then the first target field is determined to contain the second target field. If each data content in the cell corresponding to the first target field corresponds to at least two data contents in the cell corresponding to the second target field, and each data content in the cell corresponding to the second target field corresponds to at least two data contents in the cell corresponding to the first target field, then the first target field and the second target field are determined to be independent target fields.

6. The method for displaying tabular data according to claim 4, characterized in that, For the multiple target fields belonging to the measurement type, determining the operational relationships between different target fields includes: If there is a one-to-one correspondence between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field, and the difference between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field is an arbitrary constant, then the third target field and the fourth target field are determined to be target fields with an equality relationship; wherein, the third target field is any one of multiple target fields belonging to the measurement type, and the fourth target field is any one of multiple target fields belonging to the measurement type that is different from the third target field; If the data content in the cell corresponding to the third target field has the same number format, the same unit, or the same order of magnitude as the data content in the cell corresponding to the fourth target field, then the third target field and the fourth target field are determined to be similar target fields. If the difference between the data content in the cell corresponding to the third target field and the data content in the cell corresponding to the fourth target field is equal to the data content in the cell corresponding to the fifth target field, then it is determined that the third target field contains the fourth target field, and the third target field contains the fifth target field; wherein, the fifth target field is any one of the multiple target fields belonging to the measurement type that is different from the third target field and the fourth target field; If the data content in the cell corresponding to the third target field is the function value of the first function, and the data content in the cell corresponding to the fourth target field is the variable of the first function, then the third target field depends on the fourth target field. If the data content in the cell corresponding to the third target field is the function value of the first function, the data content in the cell corresponding to the fourth target field is the variable of the first function, and the data content in the cell corresponding to the fourth target field is the function value of the second function, and the data content in the cell corresponding to the fifth target field is the variable of the second function, then the third target field is determined to be dependent on the fifth target field.

7. The method for displaying tabular data according to claim 1, characterized in that, The step of determining the first-class importance recommendation score for each of the multiple target fields based on their types includes: Based on the data content of the target table, determine the scenario corresponding to the target table; When the target table is in the first scenario, the recommended first-class importance scores are set for the target fields of the following dimension types in descending order of scores: target fields of name type, target fields of time and date type, and target fields of other dimension types besides name type and time and date type; the recommended first-class importance scores are set for the target fields of the following metric types in descending order of scores: target fields of amount type, target fields of percentage type, and target fields of other dimension types besides amount type and percentage type. When the target table is in the second scenario, the recommended first-class importance scores are set for the target fields of the following dimension types in descending order of scores: target fields of date and time, target fields of name, and target fields of other dimension types besides name and date and time; the recommended first-class importance scores are set for the target fields of the following metric types in descending order of scores: target fields of amount, target fields of percentage, and target fields of other dimension types besides amount and percentage.

8. The method for displaying tabular data according to claim 1, characterized in that, The process of recommending total scores based on the importance of each target field, ranking the target fields accordingly, and then visualizing the processed data content of the target fields based on the ranking results includes: The multiple target fields are divided into multiple target fields belonging to the dimension type and multiple target fields belonging to the metric type; The first ranking result is obtained by sorting the total recommended scores of multiple target fields belonging to the dimension type according to their respective importance. The second ranking result is obtained by sorting the total recommended scores of multiple target fields belonging to the metric type according to their respective importance. Based on the first sorting result, select a first number of target fields belonging to the dimension type; based on the second sorting result, select a second number of target fields belonging to the measure type. The data content of the first number of target fields belonging to the dimension type and the data content of the second number of target fields belonging to the metric type are processed, and the processing results are displayed.

9. The method for displaying tabular data according to any one of claims 1 to 8, characterized in that, Before determining the types of the multiple target fields and their logical relationships based on their data content in the target table, the method further includes: For each target field in the target table, the following processing is performed: the data content of the target field is standardized and formatted to obtain standardized data content; The step of determining the types of the multiple target fields and the logical relationships between them based on the data content of multiple target fields in the target table includes: Based on the standardized data content of multiple target fields in the target table, determine the types of the multiple target fields and the logical relationships between them.

10. A device for displaying tabular data, characterized in that, include: The first determining module is used to determine the types of the multiple target fields and the logical relationships between the multiple target fields based on the data content of the multiple target fields in the target table; The second determining module is used to determine the total importance score of each of the multiple target fields based on the types of the multiple target fields and the logical relationships between the multiple target fields; The sorting and display module is used to sort the multiple target fields according to their respective importance and the total recommended score, and to display the processed data content of the multiple target fields according to the sorting results. The second determining module is further configured to determine the first type importance recommendation score for each of the plurality of target fields based on the type of the plurality of target fields; Based on the logical relationships between the multiple target fields, determine the second-category importance recommendation score for each of the multiple target fields; Based on the first type of importance recommendation score and the second type of importance recommendation score of each of the multiple target fields, the total importance recommendation score of each of the multiple target fields is determined; The second determining module is further configured to, for multiple target fields belonging to the dimension type among multiple target fields, merge fields with inclusion relationships into a group, or merge equal fields into a group; set second-type importance recommended scores for the target fields of the following dimension types in descending order of scores: target fields merged into a group and belonging to a large range within the group, independent target fields, and target fields merged into a group but not belonging to a large range within the group; for multiple target fields belonging to the measure type among multiple target fields, set second-type importance recommended scores in descending order of scores: target fields with composite dependencies, similar target fields, included target fields, included target fields, dependent target fields, equal target fields, and remaining target fields; and for cases where any target field belonging to the dimension type among multiple target fields has a statistical dependency relationship with any target field belonging to the measure type, set second-type importance recommended scores higher than a preset threshold for each of the target fields belonging to the dimension type and the target field belonging to the measure type.

11. An electronic device, characterized in that, include: Processor, memory, and bus, among which, The processor and the memory communicate with each other via the bus; The memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the steps of the tabular data display method as described in any one of claims 1 to 9.

12. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to perform the steps of the method for displaying tabular data as described in any one of claims 1 to 9.

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