Table data statistical method and device, equipment and storage medium

Abstract

The invention relates to a table data statistical method and device, equipment and a storage medium. The method comprises the steps that a data statistical area is determined in a table; determining a to-be-applied target counting mode of the data statistical region under the condition of determining that the data statistical region contains the merged cells; and carrying out statistics on the data in the data statistics area according to the target counting mode, and displaying a statistics result. In this way, the most suitable target counting mode for the data content in the merged cells can be selected according to actual requirements, higher flexibility is provided for data statistics, the method can adapt to different statistical scenes, and meanwhile the accuracy of statistical results is greatly improved.

Description

Technical Field

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

[0002] The filtering and statistical function of the table is a very critical part of data processing and analysis. It allows users to filter data according to specific conditions and perform statistics and analysis on the filtered data.

[0003] Currently, when table processing software performs screening and statistics on a specified area in a table (such as a column area), each cell is considered as an independent data point for statistics. The cell mentioned here refers to the smallest unit that constitutes the table.

[0004] However, this statistical method may not be applicable when facing merged cells, because merged cells are formed by merging two or more cells (the smallest unit of a table), and these cells share a data content (that is, the data content in the merged cell). This results in the possibility that the data content in the merged cells is counted repeatedly by mistake when the data content in the merged cells is counted according to the above statistical method. For example, see Figure 1 For example, we filter and count column C using the above statistical method. The statistical result of the data content "90" is "2", which means there are two people who scored 90 points, but in fact there is only one person who scored 90 points. Summary of the invention

[0005] The present application provides a table data statistics method, device, equipment and storage medium to solve the technical problem that the existing technology treats each cell in the table as an independent data point for statistics, which may result in inaccurate statistical results that do not conform to the actual situation when facing merged cells.

[0006] In a first aspect, the present application provides a method for statistically analyzing table data, the method comprising:

[0007] Determine the statistical area of ​​the data in the table;

[0008] In the case where it is determined that the data statistics area includes merged cells, determining a target counting method to be applied to the data statistics area;

[0009] The data in the data statistics area are counted according to the target counting method, and the statistical results are displayed.

[0010] In a possible implementation manner, performing statistics on the data in the data statistics area according to the target counting method includes:

[0011] Determine the data content appearing in the data statistics area;

[0012] When the target counting mode is the first counting mode, determining the number of occurrences of the data content in the data statistics area, and determining the number of occurrences as the statistical result of the data content;

[0013] When the target counting method is the second counting method, the number of cells occupied by the data content in the data statistics area is determined, and the number of occupied cells is determined as the statistical result of the data content, wherein the number of occupied cells of the data content in the merged cell is determined according to the number of sub-cells included in the merged cell.

[0014] In a possible implementation, the method further includes:

[0015] In response to the counting mode switching operation on the data statistics area, statistics are re-performed on the data in the data statistics area according to the switched counting mode, and the statistical results are displayed.

[0016] In a possible implementation manner, determining the target counting method to be applied to the data statistical area includes:

[0017] Acquire historical statistical records of the table; and determine a target counting method to be applied in the data statistics area according to the historical statistical records.

[0018] In a possible implementation manner, performing statistics on the data in the data statistics area according to the target counting method includes:

[0019] Determine target screening criteria;

[0020] Determine a target cell in the data statistics area where data satisfies the target screening condition;

[0021] The data in the target cell is counted according to the target counting method.

[0022] In a possible implementation, determining the target screening conditions includes:

[0023] Determine the data type of the data in the data statistics area;

[0024] Loading a filter that matches the data type, wherein the filter provides a filter condition setting interface, and different filters provide different filter condition parameters;

[0025] The filtering condition customized by the user in the filtering condition setting interface is obtained, and the obtained customized filtering condition is determined as the target filtering condition.

[0026] In a possible implementation, determining the target screening conditions includes:

[0027] Determine the data type of the data in the data statistics area;

[0028] A preset filter panel is displayed, and target filter conditions matching the data type are loaded into the filter panel.

[0029] In a possible implementation, determining the target screening conditions includes:

[0030] Obtaining historical screening behavior data of the form;

[0031] The target screening condition is determined according to the historical screening behavior data.

[0032] In a possible implementation, determining the target screening conditions includes:

[0033] Display the preset filter panel;

[0034] Obtaining the filtering conditions customized by the user in the filtering interface provided by the filtering panel;

[0035] The customized filtering condition is determined as the target filtering condition.

[0036] In a possible implementation, the method further includes:

[0037] In response to a save operation on the customized filtering condition, the customized filtering condition is saved.

[0038] In a second aspect, the present application provides a table data statistics device, the device comprising:

[0039] A statistical area determination module is used to determine the data statistical area in the table;

[0040] A counting method determination module, used for determining a target counting method to be applied to the data statistics area when it is determined that the data statistics area contains merged cells;

[0041] The statistics module is used to count the data in the data statistics area according to the target counting method and display the statistical results.

[0042] In a possible implementation, the statistics module includes:

[0043] A content identification unit, used to determine the data content appearing in the data statistics area;

[0044] A first statistical unit, configured to determine the number of occurrences of the data content in the data statistical area when the target counting method is the first counting method, and determine the number of occurrences as a statistical result of the data content;

[0045] The second statistical unit is used to determine the number of cells occupied by the data content in the data statistical area when the target counting method is the second counting method, and determine the number of occupied cells as the statistical result of the data content, wherein the number of occupied cells of the data content in the merged cell is determined according to the number of sub-cells included in the merged cell.

[0046] In a possible implementation, the device further includes:

[0047] The switching module is used for, in response to the counting mode switching operation on the data statistics area, re-stating the data in the data statistics area according to the switched counting mode and displaying the statistical results.

[0048] In a possible implementation manner, the counting mode determination module includes:

[0049] The automatic recommendation unit is used to obtain the historical statistical records of the table; and determine the target counting method to be applied in the data statistical area according to the historical statistical records.

[0050] In a possible implementation, the statistical module includes:

[0051] A screening condition determination unit, used to determine a target screening condition;

[0052] A screening unit, used for determining a target cell whose data satisfies the target screening condition from the data statistical area;

[0053] A statistical unit is used to count the data in the target cell according to the target counting method.

[0054] In a possible implementation, the screening condition determination unit includes:

[0055] A data type determination subunit, used to determine the data type of the data in the data statistics area;

[0056] A filter loading subunit, used to load a filter matching the data type, wherein the filter provides a filter condition setting interface, and different filters provide different filter condition parameters;

[0057] The custom subunit is used to obtain the filtering condition customized by the user in the filtering condition setting interface, and determine the obtained customized filtering condition as the target filtering condition.

[0058] In a possible implementation, the screening condition determination unit includes:

[0059] A data type determination subunit, used to determine the data type of the data in the data statistics area;

[0060] The condition loading subunit is used to display a preset filter panel and load a target filter condition matching the data type in the filter panel.

[0061] In a possible implementation manner, the screening condition determination unit is specifically used to:

[0062] Obtaining historical screening behavior data of the form;

[0063] The target screening condition is determined according to the historical screening behavior data.

[0064] In a possible implementation, the screening condition determination unit includes:

[0065] A display subunit, used to display a preset filter panel;

[0066] A custom subunit, used to obtain the user's custom filtering conditions in the filtering interface provided by the filtering panel;

[0067] The customized filtering condition is determined as the target filtering condition.

[0068] In a possible implementation, the device further includes:

[0069] The saving module is used to save the customized filtering condition in response to a saving operation on the customized filtering condition.

[0070] In a third aspect, the present application provides a device comprising: a processor and a memory, wherein the processor is used to execute a tabular data statistics program stored in the memory to implement the tabular data statistics method described in any one of the first aspects.

[0071] In a fourth aspect, the present application provides a storage medium storing one or more programs, and the one or more programs can be executed by one or more processors to implement the tabular data statistics method described in any one of the first aspects.

[0072] The above-mentioned technical scheme provided by the embodiment of the present application has the following advantages over the prior art: the method provided by the embodiment of the present application, by determining a data statistical area in a table, and when determining that the data statistical area contains merged cells, determines the target counting method to be applied to the data statistical area, performs statistics on the data in the data statistical area according to the target counting method, and displays the statistical results, thereby allowing the most appropriate target counting method for the data content in the merged cells to be selected according to actual needs, which provides higher flexibility for data statistics, can adapt to different statistical scenarios, and greatly improves the accuracy of the statistical results. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0074] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0075] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0076] Figure 1 A schematic diagram of an application scenario of a table statistics method provided in an embodiment of the present application;

[0077] Figure 2 A flowchart of an embodiment of a table data statistics method provided in an embodiment of the present application;

[0078] Figure 3 A schematic diagram of another application scenario of the table statistics method provided in an embodiment of the present application;

[0079] Figure 4 Example of selecting pages for counting method;

[0080] Figure 5 A flowchart of another method for statistically analyzing tabular data provided in an embodiment of the present application;

[0081] Figure 6 A schematic diagram of the interface display of the filter provided in the embodiment of the present application;

[0082] Figure 7 Example of a selection page for filter criteria;

[0083] Figure 8 A block diagram of an embodiment of a table data statistics device provided in an embodiment of the present application;

[0084] Fig. 9 A schematic diagram of the structure of a device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0086] The disclosure below provides many different embodiments or examples to realize the different structures of the present application. In order to simplify the disclosure of the present application, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present application. In addition, the present application can repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0087] In order to solve the technical problem that the method of treating each cell in a table as an independent data point for statistics in the prior art may result in inaccurate statistical results that do not conform to the actual situation when facing merged cells, the present application provides a table data statistics method, device, equipment and storage medium. The method allows the selection of the most appropriate target counting method for the data content in the merged cells according to actual needs, provides higher flexibility for data statistics, can adapt to different statistical scenarios, and greatly improves the accuracy of statistical results.

[0088] Figure 2 The following is a flow chart of an embodiment of a table data statistics method provided in the present application. Figure 2 As shown, the process includes the following steps:

[0089] Step 201: Determine the data statistics area in the table.

[0090] The above-mentioned table can be any table, which can be a table in a traditional format, such as a table in .xls, .xlsx and other formats, or a new generation of smart table, online table, and the embodiment of the present application does not limit this.

[0091] From the perspective of access methods, the above-mentioned table can be a collaborative document, which allows multiple users to collaboratively edit it online, or allows multiple users to spread it to each other through mobile storage media (such as USB flash drives) to collaboratively edit it. Taking online collaborative editing as an example, there are currently two different document collaboration methods, namely online collaborative documents and collaborative documents within a local area network. Among them, online collaborative documents are a way to share and edit documents through the Internet, store documents on a cloud server, allow multiple users to access, edit and comment on documents at the same time, and provide real-time collaboration and communication functions. Collaborative documents within a local area network refer to a method in which multiple users can edit and modify the same document in a local area network by sharing folders or using professional collaborative editing software. The table in the embodiment of the present application can be any of the above-mentioned collaborative documents, and the embodiment of the present application does not limit this.

[0092] The data statistics area is usually a row or column in a table, for example Figure 1 Of course, in practical applications, the data statistics area can be flexibly set according to actual needs, such as being set to an area consisting of multiple rows and columns of cells, which is not limited in the present embodiment of the application.

[0093] As an optional implementation method, the user can select an area containing data content in the table using the mouse or keyboard, and then click the filter icon in the tab. At this time, in response to the triggering operation of the filter icon, the filter statistics function is triggered, and the area selected by the user in the table is determined as the data statistics area.

[0094] Step 202: When it is determined that the data statistics area contains merged cells, determine a target counting method to be applied to the data statistics area.

[0095] Step 203: Count the data in the data statistics area according to the target counting method, and display the statistical results.

[0096] It is understood by those skilled in the art that a merged cell is a special cell. It is not the smallest unit of a table, but a cell formed by merging multiple independent cells (i.e., the smallest unit of a table). Merged cells can make a table more neat visually and help organize table data in some scenarios. However, merged cells bring complexity to data statistics and filtering. For example, see the above Figure 1 It can be seen from the description that performing statistics on the data content in the merged cells according to the screening statistics method in the prior art may result in erroneous statistical results.

[0097] In this regard, the embodiment of the present application proposes that, when it is determined that the data statistics area contains merged cells, the data content in the data statistics area is no longer counted indiscriminately according to the established screening statistics method in the prior art, but different counting methods are predefined, and the target counting method to be applied to the data statistics area this time is determined from these predefined counting methods, and then the data in the data statistics area is counted according to the target counting method, and the statistical results are displayed. This processing can achieve the selection of an appropriate counting method according to the actual situation when the data statistics area contains merged cells, so as to ensure the accuracy of the statistical results.

[0098] The counting method refers to the method for counting the data content in the merged cells. In one embodiment, two different counting methods are predefined: a first counting method and a second counting method.

[0099] The first counting method refers to: counting according to the actual number of occurrences of the data content in the data statistics area. Based on this, when the target counting method is the first counting method, the specific implementation of counting the data in the data statistics area according to the target counting method in the above step 203 includes: determining the number of occurrences of the data content in the data statistics area, and determining the number of occurrences as the statistical result of the data content.

[0100] For example, in Figure 1 In the table shown, the data statistics area (column C) includes a merged cell, which contains two sub-cells, and the two sub-cells share the data content "90". According to the first counting method, the data content "90" is counted, and the number of occurrences of the data content in the data statistics area is determined to be 1, so that the statistical result of the data content is determined to be 1. It can be seen that the statistical result of the data content "90" is 1, which is accurate and consistent with the actual situation.

[0101] It should be noted that when performing data statistics according to the first counting method, the data content in the cell is usually counted as a whole, which means that if a cell contains other strings in addition to the specific string, then when counting the number of occurrences of the specific string, the data content in the cell will not be counted as a match. Figure 1 In the table shown, if the data content in a cell is "9090", then when counting the number of occurrences of the statistical content "90", the cell will not be included in the count.

[0102] In addition, in practical applications, there may be some special cases or special requirements. In these cases or special requirements, the number of occurrences of the data content in the cell is not counted as a whole, but partial matches are allowed to be counted. For example, suppose Figure 1 In the table shown, the data content in a cell is "9090", so when counting the number of occurrences of the data content "90", for this cell, record that the data content "90" appears twice. It should be noted that this is only an example made to enable those skilled in the art to better understand the counting method under the special cases or special needs described above. In actual applications, users can customize such special cases or special needs according to actual business needs or scenarios. For example, options are stored in a cell, such as options A, B, and C. When counting the number of occurrences of any selected option in the cell, a partial match method can be used for counting, rather than counting the three options as a whole.

[0103] In one embodiment, determining the number of occurrences of data content in a data statistics area and determining the number of occurrences as a statistical result of the data content may specifically include: obtaining the data content of cells in the data statistics area, wherein the cells include merged cells and ordinary cells, and the merged cells are obtained by merging at least two adjacent ordinary cells, and the ordinary cells are non-merged cells, which are the smallest constituent units of a table; according to the obtained data content, classifying cells with the same data content, and determining the target number of cells in each category, wherein the target number is counted in the following manner: the merged cells are counted as a whole, and the ordinary cells that make up the merged cells do not participate in the counting, that is, one merged cell is counted as one cell, and one ordinary cell is counted as one cell; determining the target number of cells in the classification of the same data content as the statistical result of the data content. For example, Figure 1 In the data of Example C, ordinary cells C5 and C6 are merged into a merged cell with the content "90". When counting is performed in the first counting method, the count of the merged cell is 1, that is, the content "90" appears once.

[0104] In another embodiment, the number of occurrences of data content in a data statistics area is determined, and the number of occurrences is determined as a statistical result of the data content, which may specifically include: traversing the cells in the data statistics area; determining whether the currently traversed cell is a child cell of a merged cell; if the currently traversed cell is not a child cell of a merged cell, obtaining the data content of the cell, and recording the data content as appearing once, and using the next cell as the currently traversed cell.

[0105] When the currently traversed cell is a child cell of a merged cell, classification discussion is required:

[0106] The first case: the cell used to store content in the merged cell is a preset sub-cell in the merged cell, such as the first sub-cell of the merged cell, and other sub-cells do not store content.

[0107] For the first case: when the cell currently traversed belongs to a child cell of a merged cell, determine whether the cell currently traversed is a preset child cell of the merged cell for storing content; when the cell currently traversed is a preset child cell of the merged cell for storing content, obtain the data content of the cell, and record the data content as appearing once, record the merged cell where the cell is located as a cell that has been counted, and use the next cell as the cell currently traversed; when the cell currently traversed is not a preset child cell of the merged cell for storing content, if the merged cell where the cell is located has been recorded as a cell that has been counted, skip the cell, and use the next cell as the cell currently traversed; if the merged cell where the cell is located has not been recorded as a cell that has been counted, obtain the data content of the preset child unit of the merged cell where the cell is located for storing content, and record the data content as appearing once, record the merged cell where the cell is located as a cell that has been counted, and use the next cell as the cell currently traversed. The above operation steps can skip the sub-cells that do not store content, thereby avoiding the statistical results being erroneous due to the sub-cells being counted when counting the results of "content is empty"; because when traversing to the preset sub-cells of non-merged cells, it is still determined that the merged cells have not been recorded, and the additional acquisition and statistics of the merged cell data are added, thereby avoiding the situation in the screening process that the filtered cells include merged cells but do not include the preset sub-cells that store content, resulting in the data content of the merged cell being ignored in the statistics.

[0108] The second case: The cells used by the merged cell to store content are all the subcells in the merged cell.

[0109] For the second case: if the currently traversed cell is a child cell of a merged cell, determine whether the merged cell where the cell is located has been recorded as a counted cell; if it is determined that the merged cell where the cell is located has been recorded as a counted cell, skip the cell and use the next cell as the currently traversed cell; if it is determined that the merged cell where the cell is located has not been recorded as a counted cell, obtain the data content of the cell, record the data content as appearing once, record the merged cell where the cell is located as a counted cell, and use the next cell as the currently traversed cell. This operation can avoid repeated counting of the content in the merged cell, resulting in the same content being counted multiple times and incorrect statistical results.

[0110] The second counting method refers to: counting the number of cells actually occupied by the data content in the data statistics area, where the cell refers to an independent cell, that is, the smallest component unit of the table. Based on this, when the target counting method is the second counting method, the specific implementation of counting the data in the data statistics area according to the target counting method in the above step 203 includes: determining the number of cells occupied by the data content in the data statistics area, and determining the number of occupied cells as the statistical result of the data content, wherein the number of cells occupied by the data content in the merged cell is determined according to the number of sub-cells contained in the merged cell.

[0111] For example, in Figure 3 In the table shown, the data statistics area (column C) includes a merged cell, which contains two sub-cells, and the two sub-cells share the data content "90". According to the second counting method, the data content "90" is counted to determine that the number of cells occupied by the data content in the data statistics area is 2, so that the statistical result of the data content is determined to be 2. It can be seen that the statistical result of the data content "90" is 2, which is accurate and consistent with the actual situation.

[0112] In one embodiment, the number of cells occupied by data content in a data statistics area is determined, and the number of occupied cells is determined as a statistical result of the data content, specifically including: traversing the cells in the data statistics area; determining whether the currently traversed cell is a child cell of a merged cell; if the currently traversed cell is not a child cell of a merged cell, obtaining the data content of the cell, and recording the data content as appearing once, and taking the next cell as the currently traversed cell.

[0113] When the currently traversed cell is a child cell of a merged cell, classification discussion is required:

[0114] The first case: the cell used to store content in the merged cell is a preset sub-cell in the merged cell, such as the first sub-cell of the merged cell, and other sub-cells do not store content.

[0115] For the first case: when the cell currently traversed belongs to a sub-cell of a merged cell, determine whether the cell currently traversed belongs to a preset sub-cell of the merged cell for storing content; when the cell currently traversed belongs to a preset sub-cell of the merged cell for storing content, obtain the data content of the cell, record the data content as appearing once, and use the next cell as the cell currently traversed; when the cell currently traversed does not belong to a preset sub-cell of the merged cell for storing content, obtain the data content of the preset sub-cell of the merged cell where the cell is located for storing content, record the data content as appearing once, and use the next cell as the cell currently traversed. The above operation steps also add the acquisition and statistics of the merged cell data when traversing to the preset sub-cell of the non-merged cell, thus avoiding the situation that the filtered cells include the merged cell but do not include the preset sub-cell for storing content during the screening process, resulting in the data content of the merged cell being ignored and counted, and also avoiding the statistical result error caused by counting the sub-cell when counting the result of "content is empty".

[0116] The second case: The cells used by the merged cell to store content are all the subcells in the merged cell.

[0117] For the second case: when the currently traversed cell is a child cell of a merged cell, the data content of the cell is obtained, and the data content is recorded as appearing once, and the next cell is used as the currently traversed cell.

[0118] The technical solution provided by the embodiment of the present application determines a data statistical area in a table, and when it is determined that the data statistical area contains merged cells, determines the target counting method to be applied in the data statistical area, performs statistics on the data in the data statistical area according to the target counting method, and displays the statistical results. This allows the most appropriate target counting method for the data content in the merged cells to be selected according to actual needs, which provides higher flexibility for data statistics, can adapt to different statistical scenarios, and greatly improves the accuracy of the statistical results.

[0119] Above through Figure 2 The illustrated process explains the technical solution provided by the embodiment of the present application as a whole. The following describes how to determine the target counting method to be applied in the data statistical area through different embodiments.

[0120] In one embodiment, the specific implementation of determining the target counting method to be applied in the data statistics area includes: obtaining historical statistical records of a table; and determining the target counting method to be applied in the data statistics area according to the historical statistical records.

[0121] In one example, a counting method selection page can be displayed, in which at least the counting method corresponding to each historical statistical record is displayed; a selection operation acting on the selection page is received; and in response to the selection operation, the counting method corresponding to the historical statistical record acted on by the selection operation is used as the target counting method.

[0122] Among them, the historical statistical record of the user on the table refers to: the record data of the user's screening and statistical behavior on the data statistical area in the table at a historical time, including at least the counting method used by the user, such as one of the first counting method and the second counting method, in addition, it can also include the corresponding statistical results, user feedback data on the statistical results, etc. The above data can be collected through log records, event tracking or user behavior analysis tools. Of course, when collecting these behavioral data, it is necessary to comply with relevant privacy policies and data protection regulations to ensure that the collection and use of user data is legal and permitted by the user.

[0123] The historical statistical records usually include multiple records, and one historical statistical record corresponds to a user's screening statistical behavior. The multiple historical statistical records can be historical statistical records of at least one user for a table. When only one user's historical statistical record for a table is included, the one user is usually the current logged-in user of the table, and of course it can also be other users, such as the user specified by the current logged-in user of the table.

[0124] See also Figure 4 , an example of selecting a page for counting method. Figure 4 As shown, the selection page may include the user's historical statistical records of the table, wherein historical statistical record 1 is a record of the historical statistical behavior of user 1, historical statistical record 2 is a record of the historical statistical behavior of user 2, and historical statistical record 3 is a record of the historical statistical behavior of user 3. For each historical statistical record, the selection page may display the corresponding counting method and statistical results ( Figure 4 The advantage of this process is that the user can see the statistical results under the counting mode without actually performing the statistical operation, so that the user can choose the most appropriate counting mode accordingly, thereby improving the accuracy of the statistical results.

[0125] It should be noted that Figure 4 This is only an exemplary design of the counting method selection page. In actual applications, other designs may also be adopted, and the embodiments of the present application do not limit this.

[0126] In another example, when determining the target counting method to be applied to the current data statistical area based on the user's historical statistical records, the historical statistical records can be analyzed by machine learning and / or statistical methods to identify the counting method that the user tends to use when performing data screening statistics, or the commonly used counting method, so as to predict the counting method that the user may currently need. Finally, the predicted counting method is determined as the target counting method to be applied to the current data statistical area.

[0127] This method can recommend counting methods that may be of interest to users or that meet their habits / needs based on their historical behaviors, so that the executing entity of the embodiments of the present application can more intelligently determine the user's counting needs based on the user's historical behaviors, thereby providing a more personalized and efficient data processing experience, and helping to better meet the needs of users and improve their work efficiency and accuracy.

[0128] In another embodiment, the specific implementation of determining the target counting method to be applied in the data statistics area includes: determining all predefined different counting methods as the target counting methods.

[0129] Among them, the different counting methods mentioned above may include each predefined counting method, such as the first counting method and the second counting method described above. Then in the above embodiment, the execution subject of the embodiment of the present application will count the data in the data statistics area according to each predefined counting method, and generate corresponding statistical results. These preview statistical results will be displayed to the user in a visual form so that the user can clearly see the statistical effect under each counting method.

[0130] This method allows users to intuitively see the impact of different counting methods on statistical results, thereby facilitating users to choose the most appropriate counting method and improve the accuracy of statistical results.

[0131] The above two different embodiments describe how to determine the target counting method to be applied in the data statistics area. It should be noted that the above embodiments are only exemplary descriptions of determining the target counting method to be applied in the data statistics area. In practical applications, there may be other methods. For example, in another embodiment, the specific implementation of determining the target counting method to be applied in the data statistics area includes: determining the belonging cell area of ​​the merged cell, and when the belonging cell area of ​​the merged cell is also a merged cell, determining the first counting method as the target counting method to be applied in the data statistics area; when the belonging cell area of ​​the merged cell is not a merged cell, determining the second counting method as the target counting method to be applied in the data statistics area. Among them, the belonging cell area of ​​the merged cell refers to the cell area that has an attribution relationship with the merged cell. The above-mentioned attribution relationship is reflected in the table layout as follows: the merged cell and its corresponding belonging cell area are adjacent in the table layout, which can be horizontally adjacent or vertically adjacent, and the number of rows (or columns) merged by the merged cell is the same as the number of rows (or columns) contained in the corresponding belonging cell area. It is reflected in the content as follows: there is a certain association relationship between the content in the merged cell and the content in the corresponding belonging cell area. The association relationship can be a hierarchical, superior-subordinate, or inclusion relationship. For example, the merged cell contains a summary title or overview, and the target cell area contains detailed information related to the title or overview. For another example, the merged cell contains a field name, and the target cell area contains the various field values ​​of the field.

[0132] For example, see Figure 1 In the example, the cell area to which the merged cells in column C belong is the cell area composed of the cells in 5 rows of column B and 6 rows of column B, and this cell area is a merged cell. According to the above description, the first counting method is determined as the target counting method to be applied to column C. At this time, counting the data content "90" in the merged cells in column C according to the first counting method will determine that the number of occurrences of the data content in the data statistical area is 1, thereby determining that the statistical result of the data content is 1. It can be seen that the statistical result of the data content "90" is 1, which is accurate and consistent with the actual situation.

[0133] See also Figure 3In the example, the cell area to which the merged cells in column C belong is the cell area composed of the cells in the 5th row of column B and the 6th row of column B, and this cell area is not a merged cell. According to the above description, the second counting method is determined as the target counting method to be applied to column C. At this time, counting the data content "90" in the merged cells in column C according to the second counting method will determine that the number of cells occupied by the data content in the data statistical area is 2, thereby determining that the statistical result of the data content is 2. It can be seen that the statistical result of the data content "90" is 2, which is accurate and consistent with the actual situation.

[0134] In one embodiment, the technical solution provided by the embodiment of the present application also allows for flexible switching of the counting method applied to the data statistics area. For example, after counting the data in the data statistics area according to the first counting method and displaying the statistical results, the data in the data statistics area can be counted again according to the second counting method and the latest statistical results can be displayed. In this regard, the embodiment of the present application proposes: in response to the counting method switching operation of the data statistics area, the data in the data statistics area is counted again according to the switched counting method, and the statistical results are displayed. In the above example, the switched counting method is the second counting method.

[0135] As an optional implementation method, the user can trigger operations (such as clicking, checking, etc.) through interactive elements (such as buttons, selection boxes, etc.) on the statistical result display interface to implement the counting mode switching operation. Figure 1 or Figure 3 In the example of , the user can select between "Count by grid" and "Count by content" as needed to implement the counting method switching operation. Among them, "Count by grid" corresponds to the second counting method described above, and "Count by content" corresponds to the first counting method described above.

[0136] As another optional implementation, the user may implement the counting mode switching operation by triggering a shortcut key on the keyboard.

[0137] The above embodiment helps to better meet the needs of users by allowing flexible switching of the counting method applied in the data statistics area, facilitates users to select the most appropriate counting method, and improves the accuracy of statistical results.

[0138] Figure 5 A flow chart of another embodiment of a method for statistically analyzing tabular data provided in an embodiment of the present application. Figure 5 The process shown in the figure includes the following steps based on the above embodiment:

[0139] Step 501: Determine the data statistics area in the table.

[0140] Step 502: When it is determined that the data statistics area contains merged cells, determine a target counting method to be applied to the data statistics area.

[0141] For detailed descriptions of step 501 to step 502, please refer to the relevant descriptions in the above embodiments, which will not be repeated here.

[0142] Step 503: Determine target screening conditions.

[0143] Step 504: Determine the target cells whose data meet the target screening conditions from the data statistics area.

[0144] Step 505: Count the data in the target cell according to the target counting method.

[0145] For ease of understanding, steps 503 to 505 are described in a unified manner as follows:

[0146] The filtering and statistics function of the table consists of two parts, namely filtering and statistics. Filtering refers to filtering data from the data statistics area according to specific filtering conditions, and statistics refers to statistics and analysis of the filtered data. Of course, in actual applications, if no filtering conditions are set, all data contents in the data statistics area will be counted. For example, Figure 1 or Figure 3 .

[0147] Based on this, in step 503 to step 505, first determine a screening condition (for the convenience of description, referred to as a target screening condition), and then determine the target cell whose data meets the target screening condition from the data statistics area. Here, the target cell meets the target screening condition means that the data content in the target cell meets the screening condition. Finally, the data in the target cell is counted according to the target counting method. This process can effectively filter out the target cells that meet specific conditions from the data statistics area, and count and analyze these data according to the predefined target counting method. This method can effectively ensure the accuracy and reliability of the statistical results when processing complex table data containing merged cells.

[0148] The target filtering condition can be a simple condition, such as greater than a certain value, or a complex condition. Here, a complex condition refers to a logical operation involving multiple fields, such as the filtering condition is "field A is greater than a certain value and field B is less than a certain value", or a logical operation involving different conditions on the same field, such as the filtering condition is "field C starts with ** and ends with **", or the filtering condition is "field C starts with ** or ends with **".

[0149] In one embodiment, determining a target cell in a data statistics region where data meets a target screening condition specifically includes: traversing cells in the data statistics region; when the currently traversed cell is a normal cell, i.e., a child cell of a non-merged cell, obtaining the data content of the cell, and determining the cell as a target cell when it is determined that the data content meets the target screening condition. When the currently traversed cell is a child cell of a merged cell, obtaining the data content of the merged cell where the cell is located, and when it is determined that the data content meets the target screening condition, the merged cell where the cell is located is used as the target cell. For the description of the statistical method of the target cell in step 505, refer to the description of the statistical method of the data in the data statistics region in step 203.

[0150] In one embodiment, the specific implementation of determining the target filtering condition includes: determining the data type of data in the data statistics area; loading a filter that matches the data type, wherein the filter provides a filtering condition setting interface, and different filters provide different filtering condition parameters; obtaining the filtering condition customized by the user in the filtering condition setting interface, and determining the obtained customized filtering condition as the target filtering condition.

[0151] The data type can be a numeric type, a date type, a text type, etc.

[0152] It is understood by those skilled in the art that data of different data types have their own characteristics. Therefore, for data content of different data types, the available filtering conditions are not exactly the same. For example, for data content of numerical type, the filtering conditions such as equal to, greater than, less than, between, first N items (N is a natural number, such as 10), higher than the average, and lower than the average can be used. For data content of date type, the filtering conditions such as equal to, before, after, and between can be used. For data content of text type, the filtering conditions such as starts with, ends with, contains, does not contain, equal to, and are not equal to can be used.

[0153] Accordingly, in this embodiment, different filters are predefined for different data types. The common point of these filters is that they provide a filter condition setting interface. The difference is that different filters provide different filter condition parameters. The filter condition parameters refer to the parameters representing the filter conditions such as equal to, greater than, before, after, containing, beginning with, etc. described above. For example, see Figure 6 (a) An example of the filter condition setting interface provided for filters corresponding to numeric types. Figure 6 (b) An example of a filter condition setting interface provided for a filter corresponding to a text type. It can be seen that the filter condition parameters that can be defined in the filters corresponding to the numeric type and the text type are not exactly the same.

[0154] Furthermore, as an optional implementation method, the correspondence between data types and filters is preset, and these correspondences can be one-to-one or many-to-one, that is, one filter can correspond to at least one data type. Then, when determining the target filtering condition, the data type of the data in the data statistics area is determined, and then the above correspondence is searched according to the data type, and the filter corresponding to the data type is determined as the filter matching the data type, and then the filter is loaded and displayed on the interface.

[0155] like Figure 6 As shown, the user can customize the filtering condition in the filtering condition setting interface provided by the filter, such as the customized filtering condition is "greater than 90", and the customized filtering condition is "begins with A". At this time, the obtained customized filtering condition is determined as the target filtering condition.

[0156] The above embodiment implements a method of automatically determining and loading filters according to the data type of the data content to be counted. This method can provide users with definable filtering conditions applicable to the data content to be counted for users to customize the filtering conditions. Compared with providing all filtering conditions to users indiscriminately, this can narrow the user's selection range, thereby improving the efficiency of users setting filtering conditions and improving user experience.

[0157] In another embodiment, the specific implementation of determining the target screening condition includes: acquiring historical screening behavior data of the table; and determining the target screening condition according to the historical screening behavior data.

[0158] In one example, a filter condition selection page can be displayed, in which at least the filter conditions corresponding to each historical filter record are displayed; a selection operation acting on the selection page is received; and in response to the selection operation, the filter conditions corresponding to the historical filter records acting on the selection operation are used as target filter conditions.

[0159] Among them, the user's historical filtering behavior data on the table refers to: the record data of the user's filtering and statistical behavior on the data statistical area in the table at a historical time, including at least the filtering conditions and counting methods used by the user. In addition, it may also include other data, such as behavior time, filtering results, user feedback data on the filtering results, etc. Among them, the filtering conditions used by the user can be preset filtering conditions or user-defined filtering conditions, which are not restricted here. The above data can be collected through log records, event tracking or user behavior analysis tools. Of course, when collecting these behavioral data, it is necessary to comply with relevant privacy policies and data protection regulations to ensure that the collection and use of user data is legal and permitted by the user.

[0160] The historical screening behavior data usually includes multiple items, and one item of historical screening behavior data corresponds to a user's screening statistical behavior. The multiple items of historical screening behavior data can be at least one user's historical screening behavior record for a table. When only one user's historical screening behavior record for a table is included, the one user is usually the current logged-in user of the table, and of course it can also be other users, such as a user specified by the current logged-in user of the table.

[0161] See also Figure 7 , which is an example of a filter selection page. Figure 7 As shown, the selection page may include multiple users' historical filtering behavior data records for the table, and the historical filtering behavior data includes corresponding filtering results, namely "above 90 points (10)", "above 95 points (4)", and "full marks (0)". In actual applications, when a user clicks on a filtering result corresponding to one of the historical filtering behavior data records, such as clicking on "above 90 points (10)", filtering statistics according to the corresponding filtering conditions (such as above 90 points) and counting methods can be automatically triggered. The advantage of this processing is that the historical filtering statistics results can be seen without the user setting the filtering conditions, and there is no need to actually perform filtering statistics.

[0162] It should be noted that Figure 7 This is only an exemplary design of the filter condition selection page. In actual applications, other designs may also be used, and the embodiments of the present application do not limit this.

[0163] In another example, when determining the target filtering condition for this time based on the user's historical filtering behavior data, the historical filtering behavior data can be analyzed by machine learning and / or statistical methods to identify the filtering conditions that the user tends to use when performing data filtering statistics, or the commonly used filtering conditions, and then predict the filtering conditions that the user may currently need. Finally, the predicted filtering conditions are determined as the target filtering conditions for this time. In addition, the filtering conditions that the user used most recently can also be determined as the target filtering conditions.

[0164] This approach can recommend filtering conditions that may be of interest to users or that meet their habits / needs based on their historical behaviors, so that the executing entity of the embodiments of the present application can more intelligently determine the user's filtering needs based on the user's historical behaviors, thereby providing a more personalized and efficient data processing experience, and helping to better meet the needs of users and improve their work efficiency and accuracy.

[0165] In yet another embodiment, the specific implementation of determining the target filtering condition includes: determining the data type of the data in the data statistics area; displaying a preset filtering panel, and loading the target filtering condition matching the data type in the filtering panel.

[0166] In this embodiment, some screening conditions can be pre-set for different data types. For example, for numerical types, the pre-set screening conditions include: top N items, higher than average, and lower than average. Here, the screening conditions corresponding to each data type can be statically set, that is, once set, they will not change unless manually operated. It can also be dynamically set, that is, after one setting, it can also change even without human operation, such as regular changes. Among them, as an optional implementation method, the screening conditions can be dynamically adjusted according to the historical screening behavior data of the user described above. For example, the screening conditions with higher user usage frequency are given priority. This method increases the flexibility of the technical solution of the embodiment of the present application.

[0167] In this embodiment, a preset filter panel is displayed, and the filter conditions set for the data type of the data content in the data statistics area are loaded in the filter panel, that is, the filter conditions that match the data content in the data statistics area are used as target filter conditions, and each target filter condition is used to filter the data content in the data statistics area to obtain and display the corresponding filter results.

[0168] This method allows users to obtain filtering results under multiple filtering conditions at one time, enabling users to intuitively and conveniently see the filtering results under different filtering conditions, thereby improving user experience.

[0169] In yet another embodiment, the specific implementation of determining the target filtering condition includes: displaying a preset filtering panel; obtaining the filtering condition customized by the user in the filtering interface provided by the filtering panel; and determining the customized filtering condition as the target filtering condition.

[0170] In this embodiment, based on the above embodiment, a filtering interface may be provided in the filtering panel, which may be a series of form elements (such as text boxes, drop-down selection boxes, check boxes, sliders, etc.), each of which corresponds to one or more conditional parameters of filtering conditions. By adjusting the values ​​or options of these elements, users can customize the required filtering conditions. The filtering interface may also be an AIGC (Artificial Intelligence Generated Content) interface, which provides an input box in which users can enter prompt instructions in natural language to dynamically generate filtering conditions or issue filtering instructions. For example, the user enters a natural language instruction "Filter out the sales in the last 7 days". This method provides users with a flexible and efficient filtering panel, which not only improves the user's filtering experience, but also makes full use of the advantages of artificial intelligence technology to optimize the screening process.

[0171] In a specific embodiment, for an example using AIGC, a natural language instruction preset by a user may be determined, and the natural language instruction and the data in the data statistics area are sorted and input into a model corresponding to AIGC for processing to output an operation instruction of an operation table, which may be a filtering instruction with filtering conditions; and the instruction is executed to obtain a filtering result for the data.

[0172] Alternatively, the data in the natural language instructions and data statistics area are sorted and input into the corresponding model of AIGC for processing to output the analysis results of the data.

[0173] Similar, such as Figure 7 The "90 points or more (10)", "95 points or more (4)", "full score (0)" and so on shown in the figure can also correspond to the filtering interface, through which the user can customize the required filtering conditions or AIGC (Artificial Intelligence Generated Content) interface. For example, for the "90 points or more (10)" interface, its natural language instruction can be "Please confirm the number of records with a score of more than 90 in the data."

[0174] Further, in response to the save operation on the customized screening condition, the customized screening condition is saved. Optionally, the saved screening condition can be used by the user to trace back historical operation records, re-apply, export, etc.

[0175] The above embodiment provides an intuitive user interface through a filter panel, allowing the user to easily set filter conditions and allowing the user to freely define filter conditions according to their own needs, thereby achieving a highly flexible data filtering process.

[0176] Figure 8 A block diagram of an embodiment of a table data statistics device provided in an embodiment of the present application, such as Figure 8 As shown, the device comprises:

[0177] A statistical area determination module 81 is used to determine a data statistical area in a table;

[0178] A counting method determination module 82, for determining a target counting method to be applied to the data statistics area when it is determined that the data statistics area contains merged cells;

[0179] The statistics module 83 is used to count the data in the data statistics area according to the target counting method and display the statistical results.

[0180] In a possible implementation, the statistical module 83 includes:

[0181] A content identification unit, used to determine the data content appearing in the data statistics area;

[0182] A first statistical unit, configured to determine the number of occurrences of the data content in the data statistical area when the target counting method is the first counting method, and determine the number of occurrences as a statistical result of the data content;

[0183] The second statistical unit is used to determine the number of cells occupied by the data content in the data statistical area when the target counting method is the second counting method, and determine the number of occupied cells as the statistical result of the data content, wherein the number of occupied cells of the data content in the merged cell is determined according to the number of sub-cells included in the merged cell.

[0184] In a possible implementation, the device further includes:

[0185] The switching module is used for, in response to the counting mode switching operation on the data statistics area, re-stating the data in the data statistics area according to the switched counting mode and displaying the statistical results.

[0186] In a possible implementation, the counting mode determination module 82 includes:

[0187] The automatic recommendation unit is used to obtain the historical statistical records of the table; and determine the target counting method to be applied in the data statistical area according to the historical statistical records.

[0188] In a possible implementation, the statistical module 83 includes:

[0189] A screening condition determination unit, used to determine a target screening condition;

[0190] A screening unit, used for determining a target cell whose data satisfies the target screening condition from the data statistical area;

[0191] A statistical unit is used to count the data in the target cell according to the target counting method.

[0192] In a possible implementation, the screening condition determination unit includes:

[0193] A data type determination subunit, used to determine the data type of the data in the data statistics area;

[0194] A filter loading subunit, used to load a filter matching the data type, wherein the filter provides a filter condition setting interface, and different filters provide different filter condition parameters;

[0195] The custom subunit is used to obtain the filtering condition customized by the user in the filtering condition setting interface, and determine the obtained customized filtering condition as the target filtering condition.

[0196] In a possible implementation, the screening condition determination unit includes:

[0197] A data type determination subunit, used to determine the data type of the data in the data statistics area;

[0198] The condition loading subunit is used to display a preset filter panel and load a target filter condition matching the data type in the filter panel.

[0199] In a possible implementation manner, the screening condition determination unit is specifically used to:

[0200] Obtaining historical screening behavior data of the form;

[0201] The target screening condition is determined according to the historical screening behavior data.

[0202] In a possible implementation, the screening condition determination unit includes:

[0203] A display subunit, used to display a preset filter panel;

[0204] A custom subunit, used to obtain the user's custom filtering conditions in the filtering interface provided by the filtering panel;

[0205] The customized filtering condition is determined as the target filtering condition.

[0206] In a possible implementation, the device further includes:

[0207] The saving module is used to save the customized filtering condition in response to a saving operation on the customized filtering condition.

[0208] like Fig. 9 As shown, an embodiment of the present application provides a device, including a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114.

[0209] A memory 113, for storing computer programs;

[0210] In one embodiment of the present application, the processor 111 is used to execute the program stored in the memory 113 to implement the statistical method of the table data provided by any of the above method embodiments, including:

[0211] Determine the statistical area of ​​the data in the table;

[0212] In the case where it is determined that the data statistics area includes merged cells, determining a target counting method to be applied to the data statistics area;

[0213] The data in the data statistics area are counted according to the target counting method, and the statistical results are displayed.

[0214] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the statistical method for tabular data provided in any one of the aforementioned method embodiments are implemented.

[0215] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0216] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0217] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0218] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A method for statistically analyzing tabular data, characterized in that: The method comprises: Determine the statistical area in the table; In the case where it is determined that the data statistics area includes merged cells, determining a target counting method to be applied to the data statistics area; The data in the data statistics area are counted according to the target counting method, and the statistical results are displayed.

2. The method according to claim 1, characterized in that The counting of the data in the data statistics area according to the target counting method includes: Determine the data content appearing in the data statistics area; When the target counting mode is the first counting mode, determining the number of occurrences of the data content in the data statistics area, and determining the number of occurrences as the statistical result of the data content; When the target counting method is the second counting method, the number of cells occupied by the data content in the data statistics area is determined, and the number of occupied cells is determined as the statistical result of the data content, wherein the number of occupied cells of the data content in the merged cell is determined according to the number of sub-cells included in the merged cell.

3. The method according to claim 1, characterized in that The method further comprises: In response to the counting mode switching operation on the data statistics area, statistics are re-performed on the data in the data statistics area according to the switched counting mode, and the statistical results are displayed.

4. The method according to claim 1, characterized in that The determining of the target counting method to be applied in the data statistics area includes: Acquire historical statistical records of the table; and determine a target counting method to be applied in the data statistics area according to the historical statistical records.

5. The method according to claim 1, characterized in that The counting of the data in the data statistics area according to the target counting method includes: Determine target screening criteria; Determine a target cell in the data statistics area where data satisfies the target screening condition; The data in the target cell is counted according to the target counting method.

6. The method according to claim 5, characterized in that The determining of target screening conditions includes: Determine the data type of the data in the data statistics area; Loading a filter that matches the data type, wherein the filter provides a filter condition setting interface, and different filters provide different filter condition parameters; The filtering condition customized by the user in the filtering condition setting interface is obtained, and the obtained customized filtering condition is determined as the target filtering condition.

7. The method according to claim 5, characterized in that The determining of target screening conditions includes: Determine the data type of the data in the data statistics area; A preset filter panel is displayed, and target filter conditions matching the data type are loaded into the filter panel.

8. The method according to claim 5, characterized in that The determining of target screening conditions includes: Obtaining historical screening behavior data of the form; The target screening condition is determined according to the historical screening behavior data.

9. The method according to claim 5, characterized in that The determining of target screening conditions includes: Display the preset filter panel; Obtaining the filtering conditions customized by the user in the filtering interface provided by the filtering panel; The customized filtering condition is determined as the target filtering condition.

10. A tabular data statistics device, characterized in that: The device comprises: A statistical area determination module is used to determine the data statistical area in the table; A counting method determination module, used for determining a target counting method to be applied to the data statistics area when it is determined that the data statistics area contains merged cells; The statistics module is used to count the data in the data statistics area according to the target counting method and display the statistical results.

11. A device, characterized in that: include: A processor and a memory, wherein the processor is used to execute a tabular data statistics program stored in the memory to implement the tabular data statistics method described in any one of claims 1 to 9.

12. A storage medium, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the tabular data statistics method described in any one of claims 1-9.

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