Interaction method and device, electronic device, computer-readable medium, and computer program

Abstract

The present disclosure discloses an interaction method and apparatus, an electronic device, and a computer-readable medium, which includes, when a table content editing page corresponding to a target table is displayed and a formula editing operation is triggered on the table content editing page, determining overall complexity expression data of a target formula corresponding to the formula editing operation and displaying the overall complexity expression data on the table content editing page, thereby allowing a user to query the overall complexity expression data of the target formula as quickly as possible after editing the target formula, thereby satisfying the user's need to understand the complexity of the target formula and thereby improving the user experience. In addition, the overall complexity expression data is obtained by theoretically deriving the complexity expression data of the smallest calculation unit in the target formula, thereby improving the efficiency of determining the overall complexity expression data and thereby improving the user experience.

Description

[Technical Field]

[0001] [CROSS-REFERENCE TO RELATED APPLICATIONS] This application claims the benefit of Chinese patent application number 202211484415.1, filed on November 24, 2022, the entire contents of which are incorporated herein by reference.

[0002] [Technical field] The present disclosure relates to the field of Internet technology, and in particular to an interaction method and apparatus, an electronic device, and a computer-readable medium. [Background technology]

[0003] With the widespread use of computers, the application scenarios of spreadsheets are becoming more and more numerous. Here, with respect to a spreadsheet, a user can perform some interaction operations on the spreadsheet (e.g., operations such as inputting data into the spreadsheet and inputting formulas into the spreadsheet) to realize the user's interaction needs with the spreadsheet (e.g., needs for inputting data, needs for inputting formulas, etc.).

[0004] However, some interaction processes with spreadsheets are flawed, resulting in a poor user experience. Summary of the Invention

[0005] To solve the above technical problems, the present disclosure provides an interaction method and apparatus, an electronic device, and a computer-readable medium.

[0006] To achieve the above objectives, the technical solution according to the present disclosure is as follows:

[0007] The present disclosure provides an interaction method, the method comprising: Displaying a table content edit page corresponding to the target table; in response to a formula editing operation triggered on the table content editing page, identifying overall complexity representation data of a target formula corresponding to the formula editing operation, and displaying the overall complexity representation data of the target formula on the table content editing page; The overall complexity representation data is determined based on the complexity representation data of the smallest calculation unit in the target mathematical formula.

[0008] In one possible embodiment, the process of identifying the overall complexity representation data of the target mathematical formula comprises: performing a minimum calculation unit analysis process on the target formula to obtain at least one calculation unit to be used; Identifying the complexity expression data of the target formula itself based on the complexity expression data of the at least one calculation unit to be used; and determining overall complexity expression data of the target formula based on the self complexity expression data of the target formula.

[0009] In one possible embodiment, determining the overall complexity representation data of the target formula based on the self complexity representation data of the target formula includes: If there is a predetermined dependency between the target formula and at least one reference formula in the target table, add the complexity expression data of the target formula itself and the complexity expression data of the at least one reference formula itself to obtain the overall complexity expression data of the target formula; If there is no predetermined dependency between the target formula and each formula in the target table, the target formula's own complexity expression data is identified as the target formula's overall complexity expression data.

[0010] In one possible embodiment, determining the complexity representation data of the target formula based on the complexity representation data of the at least one computing unit to be used comprises: The method includes determining the complexity representation data of the target formula itself based on the complexity representation data of the at least one calculation unit to be used and the unit type of the at least one calculation unit to be used.

[0011] In one possible embodiment, determining the complexity representation data of the target formula based on the complexity representation data of the at least one computing unit to be used and the unit type of the at least one computing unit to be used comprises: Selecting at least one function calculation unit belonging to a predetermined unit type from the at least one calculation unit to be used based on a unit type of the at least one calculation unit to be used; and specifying the sum of the complexity expression data of all the function calculation units as the complexity expression data of the target formula itself.

[0012] In one possible embodiment, the process of identifying the complexity expression data of the function calculation unit includes: Obtaining magnitude representation data of the input parameters of the function calculation unit; Identifying a complexity prediction unit corresponding to the function calculation unit based on a unit identifier of the function calculation unit; inputting the magnitude representation data into the complexity prediction unit to obtain complexity representation data of the function calculation unit output from the complexity prediction unit.

[0013] In one possible embodiment, the complexity prediction unit is obtained by performing a fitting process based on a fitting reference data set corresponding to the function calculation unit and a curve to be fitted corresponding to the function calculation unit, wherein the fitting reference data set includes at least one first input parameter magnitude representation data corresponding to the function calculation unit and actual complexity representation data corresponding to the at least one first input parameter magnitude representation data; Or, The complexity prediction unit is obtained by performing a training process based on a training data set corresponding to the function calculation unit and a model to be trained corresponding to the function calculation unit, and the training data set includes at least one second input parameter magnitude representation data corresponding to the function calculation unit and actual complexity representation data corresponding to the at least one second input parameter magnitude representation data.

[0014] In one possible embodiment, if there is an upstream calculation unit corresponding to the function calculation unit in the target formula, the magnitude representation data of the input parameters of the function calculation unit is determined based on the magnitude representation data of the output result of the upstream calculation unit.

[0015] In one possible embodiment, the magnitude representation data of the output result of the upstream computing unit is specified based on the unit type of the upstream computing unit.

[0016] In one possible embodiment, the method comprises: displaying adjustment advice prompt information corresponding to the target formula in response to a formula editing operation triggered on the table content editing page; The method further includes displaying a formula adjustment guide interface corresponding to the target formula in response to a trigger operation on the adjustment advice prompt information.

[0017] The present disclosure further provides an interaction device, the device comprising: a first display module for displaying a table content editing page corresponding to the target table; a second display module for, in response to a formula editing operation triggered on the table content editing page, identifying overall complexity representation data of a target formula corresponding to the formula editing operation and displaying the overall complexity representation data of the target formula on the table content editing page; The overall complexity representation data is determined based on the complexity representation data of the smallest calculation unit in the target mathematical formula.

[0018] The present disclosure further provides an electronic device, the electronic device including a processor and a memory; the memory is used to store instructions or computer programs; The processor is used to execute the instructions or computer programs in the memory to cause the electronic device to perform the interaction method according to the present disclosure.

[0019] The present disclosure further provides a computer-readable medium having stored thereon instructions or a computer program that, when executed on a device, causes the device to perform an interaction method according to the present disclosure.

[0020] The present disclosure provides a computer program product, said computer program product comprising a computer program embodied on a non-transitory computer readable medium, said computer program comprising program code for performing an interaction method according to the present disclosure.

[0021] Compared with the prior art, the present disclosure has at least the following advantages:

[0022] In the technical solution disclosed herein, for an electronic device, when the electronic device is displaying a table content editing page corresponding to a target table (a certain spreadsheet), when the electronic device receives a formula editing operation triggered on the table content editing page, it will determine the overall complexity expression data of the target formula corresponding to the formula editing operation, and display the overall complexity expression data of the target formula on the table content editing page, allowing the user to query the overall complexity expression data of the target formula as quickly as possible after editing the target formula, thereby meeting the user's need to understand the complexity of the target formula and thereby improving the user experience.

[0023] Furthermore, since the overall complexity expression data of the target formula is obtained by theoretically deriving the complexity expression data of the smallest calculation unit related to the target formula, there is no need to complete the execution process for the target formula when obtaining the overall complexity expression data of the target formula. Thus, it is possible to effectively avoid adverse effects (e.g., time consumption) caused by the execution of the target formula. This is advantageous in improving the efficiency of identifying the overall complexity expression data of the target formula, and further advantageous in improving the real-time performance of displaying the overall complexity expression data, thereby better improving the user experience. [Brief explanation of the drawings]

[0024] In order to more clearly explain the embodiments of the present disclosure or the technical solutions in the prior art, the following will briefly introduce the drawings necessary for describing the embodiments or the prior art. The drawings in the following description are only some of the embodiments described in the present disclosure, and it is obvious to those skilled in the art that other drawings can be obtained based on these drawings without any creative efforts.

[0025] [Figure 1] 1 is a flowchart of an interaction method according to an embodiment of the present disclosure. [Figure 2] FIG. 2 is a schematic diagram of a formula analysis tree according to an embodiment of the present disclosure. [Figure 3] FIG. 2 is a schematic diagram of dependencies between formulas according to an embodiment of the present disclosure. [Figure 4] 1 is a schematic diagram of the structure of an interaction device according to an embodiment of the present disclosure; [Figure 5] 1 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0026] In order to allow those skilled in the art to better understand the solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be described below clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only some embodiments of the present disclosure and do not represent all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present disclosure without any creative efforts fall within the scope of protection of the present disclosure.

[0027] To better understand the technical solution of the present disclosure, the interaction method of the present disclosure will be described below with reference to several drawings. As shown in Figure 1, the interaction method of the present disclosure includes the following steps S1 to S2. Here, Figure 1 is a flowchart of the interaction method of the present disclosure.

[0028] S1: Display the table content editing page corresponding to the target table.

[0029] Here, the target table refers to a spreadsheet that needs to undergo formula complexity identification processing, and the present disclosure does not limit the target table.

[0030] The table content editing page is used to provide a user with a page for performing related editing functions (e.g., editing functions such as data entry, formula entry, etc.) on a target table, and the present disclosure does not limit the embodiment of the table content editing page, and may be implemented, for example, by adopting any one page, existing or future, that can perform editing operations on formulas.

[0031] S2: In response to a formula editing operation triggered on a table content editing page, identify overall complexity expression data of a target formula corresponding to the formula editing operation, and display the overall complexity expression data of the target formula on the table content editing page, where the overall complexity expression data is identified based on the complexity expression data of the smallest calculation unit in the target formula.

[0032] Here, the formula editing operation is used to input a formula into the target table, and the present disclosure does not limit the formula editing operation, and may be implemented by adopting, for example, any operation that can realize writing a formula into a spreadsheet, whether conventional or future.

[0033] The target formula refers to a formula that is input into a spreadsheet by a user performing the formula editing operation. For example, the target formula may be the formula shown in area 201 in Figure 2. The main meaning of the formula shown in area 201 is to add up the sales figures of all members belonging to group A in a spreadsheet named Table.

[0034] The above "overall complexity representation data of a target formula" is used to describe the complexity situation (e.g., time complexity, etc.) that appears in the execution of the target formula, and the present application does not limit the overall complexity representation data, and may, for example, include at least time complexity.

[0035] In practice, for one mathematical formula (e.g., the mathematical formula shown in area 201 in FIG. 2 ), the formula is generally composed of several calculation units (e.g., the smallest calculation units shown in areas 202 to 225 in FIG. 2 ), and the complexity of the mathematical formula is affected by the complexity of these calculation units themselves. In light of this, the present application further provides one process for identifying the above-mentioned "overall complexity expression data of a target mathematical formula," and specifically, the overall complexity expression data of the target mathematical formula may be identified based on the complexity expression data of the smallest calculation unit in the target mathematical formula. For ease of understanding, the following description will be given with reference to an example.

[0036] For example, the process of identifying the "overall complexity expression data of the target mathematical formula" may specifically include the following steps 11 to 13.

[0037] Step 11: Perform a minimum calculation unit analysis process on the target formula to obtain at least one calculation unit to be used.

[0038] Here, the minimum calculation unit refers to a calculation unit that cannot be further decomposed into analytical parts (e.g., the calculation unit shown in area 202 in Figure 2) that can be called when executing on a formula in the target table, and each formula is composed of one or more minimum calculation units.

[0039] The minimum calculation unit analysis process is used to perform analysis on the constituent units of one mathematical formula, and the present disclosure does not limit the embodiment of the minimum calculation unit analysis process.

[0040] The "at least one calculation unit to be used" is used to describe each minimum calculation unit related to the target formula. For example, if the target formula is the formula shown in area 201 in Fig. 2, the "at least one calculation unit to be used" may include the calculation units shown in areas 202, 204, 205, 207, 208, 209, 211, 213, 216, 217, 218, 220, 221, 222, 223, and 225 in Fig. 2.

[0041] Furthermore, the present disclosure does not limit the above-mentioned "at least one calculation unit to be used," and may include, for example, at least one function calculation unit (e.g., the calculation units shown in areas 202, 204, 205, 208, 211, 213, 216, 217, 221, and 222 in FIG. 2), at least one data reference unit (e.g., the calculation units shown in areas 207, 209, 218, 220, and 225 in FIG. 2), and at least one constant use unit (e.g., the calculation unit shown in area 223 in FIG. 2). Here, each function calculation unit belongs to a function class node, which generally needs to perform logical operations; each data reference unit belongs to a reference class node, which generally is used to retrieve some data from a data source; and each constant use unit belongs to a constant class node, which generally simply refers to using a certain type of data value (e.g., string, number, etc.).

[0042] Note that the present disclosure does not limit the representation method of the "at least one computing unit to be used," and for example, it may be represented by adopting a tree structure similar to that shown in FIG.

[0043] Step 12: Identify the complexity expression data of the target formula itself based on the complexity expression data of the at least one calculation unit to be used.

[0044] In the embodiment of the present disclosure, after analyzing at least one computing unit to be used from the target formula, the complexity expression data of all computing units to be used may be added to obtain the self-complexity expression data of the target formula, so that the self-complexity expression data can represent the complexity situation of the target formula itself.

[0045] In practice, the degree of complexity impact on the target formula varies depending on the type of calculation unit. For example, because the process of obtaining a constant class node consumes very little time, the constant class node has almost no impact on the target formula. Therefore, in order to improve the efficiency of complexity identification, the complexity caused by the constant class node can be ignored in the process of identifying self-complexity expression data for the target formula. Similarly, the process of obtaining a reference class node consumes very little time, and the reference class node has almost no impact on the target formula. Therefore, in order to improve the efficiency of complexity identification, the complexity caused by the reference class node can be ignored in the process of identifying self-complexity expression data for the target formula. However, the process of obtaining a function class node requires a lot of resources (e.g., time resources), and therefore the function class node has a significant impact on the target formula. Therefore, it is necessary to consider the complexity caused by the function class node in the process of identifying self-complexity expression data for the target formula.

[0046] As can be seen from the above, in order to better improve the efficiency of determining the complexity of a formula in some application scenarios, the present disclosure further provides one possible embodiment of step 12 above, which may be specifically as follows: Determine the self-complexity expression data of the target formula based on the complexity expression data of the at least one computing unit to be used and the unit type of the at least one computing unit to be used, where the unit type is used to describe the type to which a computing unit belongs (for example, the unit type of the function computing unit is a function class node, the unit type of the data reference unit is a reference class node, and the unit type of the constant using unit is a constant class node). For better understanding, the following description will be given with reference to an example.

[0047] For example, in one possible embodiment, the above step 12 may specifically include the following steps 121 to 122.

[0048] Step 121: Select at least one function calculation unit from the at least one calculation unit to be used based on the unit type of the at least one calculation unit to be used, where the function calculation unit belongs to a predetermined unit type.

[0049] Here, the predetermined unit type may be preset based on the application scenario, for example, in one possible embodiment, the predetermined unit type may specifically be the above-mentioned function class node.

[0050] The "function calculation unit" refers to the smallest calculation unit that exists in the target formula and belongs to a function class node.

[0051] Step 122: Identify the sum of the complexity expression data of all the function calculation units as the complexity expression data of the target formula itself.

[0052] Here, the complexity representation data of the i-th function calculation unit is used to represent the complexity status (e.g., time complexity) exhibited by the i-th function calculation unit, where i is a positive integer, i≦I, and I is a positive integer, and I represents the number of calculation units in the "at least one function calculation unit."

[0053] Furthermore, the present disclosure does not limit the process of identifying the "complexity expression data of the i-th function calculation unit," and may be implemented by, for example, adopting any conventional or future method for acquiring the complexity of a calculation unit, or by adopting a manual annotation method.

[0054] In practice, the complexity of one function calculation unit not only depends on the realization complexity of the function related to the function calculation unit, but also on the data scale of the input parameters of the function calculation unit (for example, the larger the data scale of the input parameters, the larger the time complexity of the function calculation unit). Based on this, the present disclosure further provides one specifying process of the above-mentioned "complexity expression data of the i-th function calculation unit", which may specifically include the following steps 21 to 23.

[0055] Step 21: Obtain the magnitude representation data of the input parameters of the i-th function calculation unit.

[0056] The above-mentioned "scale representation data of input parameters of the i-th function calculation unit" is used to represent the data scale (e.g., the size of the data amount) reached by the input parameters of the i-th function calculation unit, and the present disclosure does not limit the "scale representation data of input parameters of the i-th function calculation unit", and may be, for example, the amount of data carried by the input parameters of the i-th function calculation unit.

[0057] Furthermore, the present disclosure does not limit the specific method of the "scale expression data of the input parameter of the i-th function calculation unit."

[0058] In practice, for two computing units having an adjacent relationship in data transmission in one mathematical formula, the input parameters of the downstream computing unit (e.g., the computing unit shown in area 213 in FIG. 2) are generally determined based on the output results of the upstream computing units (e.g., the computing units shown in areas 214 and 215 in FIG. 2), and the magnitude representation data of the input parameters of the downstream computing unit may be determined based on the magnitude representation data of the output results of the upstream computing unit. Based on this, the present disclosure further provides a specific process for the above-mentioned "magnitude representation data of the input parameters of the i-th function computing unit", which may be specifically as follows: When there is an upstream computing unit corresponding to the i-th function computing unit in the target mathematical formula, the magnitude representation data of the input parameters of the i-th function computing unit can be determined based on the magnitude representation data of the output results of the upstream computing unit. Here, the upstream computing unit is used to provide the input parameters to the i-th function computing unit, and the present application does not limit the upstream computing unit. For example, the upstream computing unit may be a single smallest computing unit or a composite computing unit composed of multiple smallest computing units.

[0059] The above-mentioned "scale representation data of the output result of the upstream computing unit" is used to represent the data scale reached by the output result of the upstream computing unit, and the present disclosure does not limit the identification process of the "scale representation data of the output result of the upstream computing unit". For example, specifically, the scale representation data of the output result of the upstream computing unit may be identified based on the unit type of the upstream computing unit.

[0060] Furthermore, the present disclosure is not limited to the above step of "determining the scale representation data of the output result of the upstream computing unit based on the unit type of the upstream computing unit," and may be specifically as follows, for example: When the unit type of the upstream computing unit is a constant type node, if the output result of the upstream computing unit belongs to a basic data type (e.g., character, number, etc.), the scale representation data of the output result of the upstream computing unit can be determined to be 1; when the output result of the upstream computing unit belongs to a set type (e.g., vector, matrix, etc.), the scale representation data of the output result of the upstream computing unit can be determined to be the magnitude of the data amount of the set; when the unit type of the upstream computing unit is a reference type node, the scale representation data of the output result of the upstream computing unit can be determined to be the magnitude of the data amount of the referenced data source; when the unit type of the upstream computing unit is a function type node, the scale representation data of the output result of the upstream computing unit can be determined to be the magnitude of the data amount of the output result.

[0061] The present disclosure does not limit the embodiment of the above step of "determining the scale expression data of the input parameters of the i-th function calculation unit based on the scale expression data of the output result of the upstream calculation unit." For example, when there are N upstream calculation units corresponding to the i-th function calculation unit in the target formula, the specific example may be as follows: perform aggregation processing on the scale expression data of the output results of the first upstream calculation unit to the scale expression data of the output results of the N-th upstream calculation unit to obtain the scale expression data of the input parameters of the i-th function calculation unit, thereby including the scale expression data of the output results of the N upstream calculation units in the "scale expression data of the input parameters of the i-th function calculation unit," where N is a positive integer.

[0062] Step 22: Identify the complexity prediction unit corresponding to the i-th function calculation unit based on the unit identifier of the i-th function calculation unit.

[0063] Here, the unit indicator of the i-th function calculation unit is used to uniquely represent the i-th function calculation unit, and the present disclosure does not limit the embodiment of the "unit indicator of the i-th function calculation unit".

[0064] The above "complexity prediction unit corresponding to the i-th function calculation unit" refers to a calculation unit that is pre-constructed and suitable for performing complexity prediction processing on the i-th function calculation unit. The present disclosure does not limit the embodiment of the "complexity prediction unit corresponding to the i-th function calculation unit." For example, the "complexity prediction unit corresponding to the i-th function calculation unit" may be implemented by adopting a pre-fitted curve function. It may also be implemented by adopting a pre-trained machine learning model. For ease of understanding, the following description will be given with reference to two examples.

[0065] In Example 1, when the above-mentioned "complexity prediction unit corresponding to the i-th function calculation unit" is a pre-fitted curve function, the process of identifying the "complexity prediction unit corresponding to the i-th function calculation unit" may specifically be to perform a fitting process based on the fitting reference dataset corresponding to the i-th function calculation unit and the curve to be fitted corresponding to the i-th function calculation unit, to obtain the complexity prediction unit corresponding to the i-th function calculation unit.

[0066] The "fitting reference dataset corresponding to the i-th function calculation unit" refers to a dataset required for fitting the complexity prediction curve function corresponding to the i-th function calculation unit. The present disclosure does not limit the "fitting reference dataset corresponding to the i-th function calculation unit." For example, the "fitting reference dataset corresponding to the i-th function calculation unit" may include at least one first input parameter scale representation data corresponding to the i-th function calculation unit and actual complexity representation data corresponding to the at least one first input parameter scale representation data. Here, the first input parameter scale representation data is used to describe the data scale reached by the input parameters of the i-th function calculation unit in a calculation process, and the actual complexity representation data corresponding to the first input parameter scale representation data is used to represent the complexity situation actually exhibited by the i-th function calculation unit having the first input parameter scale representation data. The present disclosure does not limit the manner of obtaining the "actual complexity representation data corresponding to the first input parameter scale representation data." For example, the present disclosure may employ a method such as manual annotation.

[0067] The "curve to be fitted corresponding to the i-th function calculation unit" refers to a curve function to be fitted when fitting to the complexity prediction curve function corresponding to the i-th function calculation unit, and the "curve to be fitted corresponding to the i-th function calculation unit" has some parameters that need to be determined by the fitting process. Also, the "curve to be fitted corresponding to the i-th function calculation unit" may be set in advance based on the i-th function calculation unit.

[0068] The present disclosure is not limited to the embodiment of the above-described "fitting process," and may be implemented by adopting, for example, any one of conventional or future curve function fitting methods.

[0069] As can be seen from the relevant content of Example 1 above, in some cases, a complexity prediction unit corresponding to a function calculation unit may be identified by a curve fitting method, so that the complexity prediction unit can represent the relationship between the input parameter scale of the function calculation unit and the complexity status of the function calculation unit (for example, the tendency of the complexity status of the function calculation unit to change with the change in the input parameter scale of the function calculation unit), and then the complexity status of the function calculation unit when the input parameter scale is different can be identified based on the complexity prediction unit.

[0070] In Example 2, when the "complexity prediction unit corresponding to the i-th function calculation unit" is a pre-trained machine learning model, the process of identifying the "complexity prediction unit corresponding to the i-th function calculation unit" may specifically be to perform a training process based on the training dataset corresponding to the i-th function calculation unit and the model to be trained corresponding to the i-th function calculation unit to obtain the complexity prediction unit corresponding to the i-th function calculation unit.

[0071] The "training dataset corresponding to the i-th function computing unit" refers to a dataset required for training a complexity prediction model corresponding to the i-th function computing unit. The present disclosure does not limit the "training dataset corresponding to the i-th function computing unit." For example, the "training dataset corresponding to the i-th function computing unit" may include at least one second input parameter scale representation data corresponding to the i-th function computing unit and actual complexity representation data corresponding to the at least one second input parameter scale representation data. Here, the second input parameter scale representation data is used to describe the data scale reached by the input parameters of the i-th function computing unit in a calculation process, and the actual complexity representation data corresponding to the second input parameter scale representation data is used to represent the complexity situation actually exhibited by the i-th function computing unit having the second input parameter scale representation data. The present disclosure does not limit the method for obtaining the "actual complexity representation data corresponding to the second input parameter scale representation data." For example, the present disclosure may employ a method such as manual annotation.

[0072] The "model to be trained corresponding to the i-th function calculation unit" refers to the machine learning model that needs to be trained when training the complexity prediction model corresponding to the i-th function calculation unit, and the "model to be trained corresponding to the i-th function calculation unit" has some network parameters that need to be identified by the training process. In addition, the "model to be trained corresponding to the i-th function calculation unit" may be set in advance based on the i-th function calculation unit.

[0073] Note that the present disclosure is not limited to the embodiment of the above-described "training process," and may be implemented by adopting, for example, any one of conventional or future model training methods.

[0074] As can be seen from the relevant content of Example 2 above, in some cases, a complexity prediction unit corresponding to a function calculation unit may be identified through a machine learning model training method, so that the complexity prediction unit can represent the relationship between the input parameter scale of the function calculation unit and the complexity status of the function calculation unit, and then the complexity status of the function calculation unit when the input parameter scale is different can be identified based on the complexity prediction unit.

[0075] In practice, in some application scenarios, after obtaining the complexity prediction units corresponding to each function calculation unit, the correspondence between the unit indicators of each function calculation unit and the complexity prediction units corresponding to each function calculation unit can be first associated, and then a mapping relationship can be established using these correspondence relationships, so that the mapping relationship can represent the correspondence between the unit indicators of each function calculation unit and the complexity prediction units corresponding to each function calculation unit, and then the mapping relationship can be used to obtain the complexity prediction units corresponding to each function calculation unit.

[0076] It can be seen from this that in one possible embodiment, the above step 22 may specifically be as follows: after obtaining the unit indicator of the i-th function calculation unit, search for the complexity prediction unit corresponding to the unit indicator of the i-th function calculation unit from the pre-established mapping relationship, and identify it as the complexity prediction unit corresponding to the i-th function calculation unit.

[0077] As can be seen from the relevant content of step 22 above, after determining that the target formula has an i-th function calculation unit, a complexity prediction unit corresponding to the i-th function calculation unit can be determined based on the unit identifier of the i-th function calculation unit, so that the complexity prediction unit can determine the complexity representation data of the i-th function calculation unit based on the magnitude representation data of the input parameters of the i-th function calculation unit, and then the complexity situation of the target formula can be predicted based on the complexity representation data of the i-th function calculation unit.

[0078] Step 23: Input the magnitude representation data of the input parameters of the i-th function calculation unit into the complexity prediction unit corresponding to the i-th function calculation unit, and obtain the complexity representation data of the i-th function calculation unit output from the complexity prediction unit.

[0079] As can be seen from the relevant content of the above steps 21 to 23, for any one function calculation unit, the complexity expression data of the i-th function calculation unit can be predicted based on the scale expression data of the input parameters of the function calculation unit and the complexity prediction unit corresponding to the function calculation unit, so that the complexity expression data can represent the complexity situation exhibited by the function calculation unit when it is the scale expression data.

[0080] As can be seen from the relevant content of steps 121 and 122, for a target formula, after analyzing at least one calculation unit to be used from the target formula, first, select a function calculation unit belonging to a function class node from these calculation units to be used, and then identify the sum of the complexity expression data of all the function calculation units as the self-complexity expression data of the target formula, so that the self-complexity expression data can represent the complexity of the target formula itself, and thus, it is possible to improve the complexity identification efficiency on the premise of ensuring the complexity identification effect.

[0081] Step 13: Identify the overall complexity expression data of the target formula based on the complexity expression data of the target formula itself.

[0082] It should be noted that the present disclosure does not limit the embodiment of step 13. For example, in some application scenarios (for example, scenarios where there is no reference relationship between different formulas in a spreadsheet), step 13 may be specifically as follows: Identify the self-complexity expression data of the target formula as the overall complexity expression data of the target formula.

[0083] In practice, in some other application scenarios (for example, scenarios where there may be a reference relationship between different formulas in a spreadsheet), when a formula does not refer to the output results of other formulas in the spreadsheet, the actual complexity of the formula is its own complexity; however, when the formula needs to refer to the output results of other formulas in the spreadsheet, the actual complexity of the formula is not only affected by its own complexity but also by the complexity of the other formulas themselves. Based on this, the present disclosure further provides one possible embodiment of the above step 13, which may specifically include the following steps 131 to 132.

[0084] Step 131: If there is a predetermined dependency between the target formula and at least one reference formula in the target table, add the complexity expression data of the target formula itself and the complexity expression data of the at least one reference formula itself to obtain the overall complexity expression data of the target formula.

[0085] Here, the j-th formula to be referenced refers to a formula that exists in the target table and has a predetermined dependency relationship with the target formula, where j is a positive integer, j≦J, and J is a positive integer and represents the number of formulas in the "at least one formula to be referenced."

[0086] The "predetermined dependency" may be set in advance, and may be, for example, specifically as follows: the target formula directly references the output result of the jth reference formula, or the target formula indirectly references the output result of the jth reference formula. For ease of understanding, the following description will be given with reference to an example.

[0087] For example, when the target table includes Formulas 1 to 7 in FIG. 3, if the target formula is Formula 1 in FIG. 3, Formula 1 needs to be executed based on the output results of Formulas 2, 3, and 5, and therefore it can be determined that Formula 1 directly references the output results of Formulas 2, 3, and 5, and thus there is a direct dependency between Formula 1 and any of Formulas 2, 3, and 5. Furthermore, Formula 2 needs to be executed based on the output results of Formulas 4 and 6, and therefore when Formula 1 is executed, it indirectly calls the output results of Formulas 4 and 6 just as it calls the output result of Formula 2, and therefore Formula 1 indirectly references the output results of Formulas 4 and 6, and therefore there is an indirect dependency between Formula 1 and any of Formulas 4 and 6.

[0088] As can be seen from this, as shown in FIG. 3, when the target table includes Formula 1 to Formula 7 in FIG. 3, Formula 1 needs to directly refer to the output results of Formula 2, Formula 3, and Formula 5, Formula 2 needs to directly refer to Formula 4 and Formula 6, Formula 3 needs to directly refer to Formula 4, Formula 4 needs to directly refer to Formula 6, and Formula 5 needs to directly refer to Formula 7. Therefore, when the target formula is Formula 1 in FIG. 3, the target formula directly refers to the output results of Formula 2, Formula 3, and Formula 5, and indirectly refers to the output results of Formula 4, Formula 6, and Formula 7. Therefore, Formulas 2 to 7 and the target formula all satisfy a predetermined dependency, and at least one formula to be referenced corresponding to the target formula includes Formula 2 to Formula 7.

[0089] The self-complexity expression data of the jth reference formula is used to describe the complexity situation of the jth reference formula itself, and the process of identifying the "self-complexity expression data of the jth reference formula" is similar to the process of identifying the "self-complexity expression data of the target formula" mentioned above, and for the sake of brevity, the description will be omitted here.

[0090] As can be seen from the relevant content of step 131, for a target formula in the target table, if there is a predetermined dependency relationship between the target formula and J reference formulas in the target table, the complexity expression data of the target formula (e.g., 20 corresponding to Formula 1 in FIG. 3), the complexity expression data of the first reference formula (e.g., 20 corresponding to Formula 2 in FIG. 3), the complexity expression data of the second reference formula (e.g., 30 corresponding to Formula 3 in FIG. 3), and the complexity expression data of the Jth reference formula (e.g., 70 corresponding to Formula 7 in FIG. 3) may be added to obtain overall complexity expression data of the target formula (e.g., 290 corresponding to Formula 1 in FIG. 3), so that the overall complexity expression data can describe the complexity situation (e.g., time consumption situation) that appears when the target formula is actually executed.

[0091] 3, the value "20" in the (20,290) two-element tuple corresponding to Formula 1 in FIG. 3 indicates the complexity expression data of Formula 1 itself, and the value "290" indicates the overall complexity expression data of Formula 1. The value "20" in the (20,120) two-element tuple corresponding to Formula 2 in FIG. 3 indicates the complexity expression data of Formula 2 itself, and the value "120" indicates the overall complexity expression data of Formula 2. The value "30" in the (30,130) two-element tuple corresponding to Formula 3 in FIG. 3 indicates the complexity expression data of Formula 3 itself, and the value "130" indicates the overall complexity expression data of Formula 3. In the two-element tuple (40,100) corresponding to Formula 4 in FIG. 3, the value “40” indicates the complexity expression data of Formula 4 itself, and the value “100” indicates the overall complexity expression data of Formula 4. (This is the analogy.)

[0092] Step 132: If there is no predetermined dependency between the target formula and each formula in the target table, the complexity expression data of the target formula itself is determined as the overall complexity expression data of the target formula.

[0093] In the present disclosure, for a target formula in the target table, if there is no predetermined dependency between the target formula and any formula in the target table, it can be determined that the target formula does not refer to the output result of any formula in the target table, and thus it can be determined that the actual complexity situation of the target formula is only affected by its own complexity situation, so the self-complexity representation data of the target formula can be identified as the overall complexity representation data of the target formula.

[0094] As can be seen from the relevant content of steps 131 and 132 above, for a formula, if the formula directly or indirectly references the output results of other formulas in the spreadsheet, the overall complexity representation data of the formula may be determined based on the complexity of the formula itself and the complexity of all the referenced formulas.

[0095] As can be seen from the relevant content of S1 to S2 above, in the interaction method according to the embodiment of the present disclosure, when the electronic device is displaying a table content editing page corresponding to a target table (a certain spreadsheet), when the electronic device receives a formula editing operation triggered on the table content editing page, it will determine the overall complexity expression data of the target formula corresponding to the formula editing operation, and display the overall complexity expression data of the target formula on the table content editing page, allowing the user to query the overall complexity expression data of the target formula as quickly as possible after editing the target formula, thereby meeting the user's need to understand the complexity of the target formula, which is beneficial to improving the user experience.

[0096] Furthermore, since the overall complexity expression data of the target formula is obtained by theoretically deriving the complexity expression data of the smallest calculation unit related to the target formula, there is no need to complete the execution process for the target formula when obtaining the overall complexity expression data of the target formula. In this way, adverse effects (e.g., time consumption) caused by the execution of the target formula can be effectively avoided. This is advantageous in improving the efficiency of identifying the overall complexity expression data of the target formula, and further advantageous in improving the real-time performance of displaying the overall complexity expression data, thereby better improving the user experience.

[0097] The embodiments of the present disclosure do not limit the entity that performs the interaction method. For example, the interaction method according to the embodiments of the present disclosure may be applied to a data processing device such as a terminal device or a server. Furthermore, for example, the interaction method according to the embodiments of the present disclosure may be implemented via a data communication process between the terminal device and the server. Here, the terminal device may be a smartphone, a computer, a personal digital assistant (PDA), a tablet PC, etc. The server may be a standalone server, a cluster server, or a cloud server.

[0098] In fact, in order to better improve the user experience, the present disclosure further provides one possible embodiment of the above interaction method, in which the interaction method may include the following steps 31 to 33:

[0099] Step 31: Display the table content editing page corresponding to the target table.

[0100] For details related to step 31, please refer to the details related to S1 above.

[0101] Step 32: In response to a formula editing operation triggered on the table content editing page, identify the overall complexity expression data of the target formula corresponding to the formula editing operation, display the overall complexity expression data of the target formula on the table content editing page, and display adjustment advice prompt information corresponding to the target formula.

[0102] For the relevant content of the above step of "in response to a formula editing operation triggered on the table content editing page, identify the overall complexity expression data of the target formula corresponding to the formula editing operation, and display the overall complexity expression data of the target formula on the table content editing page," please refer to the relevant content of S2 above.

[0103] The "adjustment advice prompt information corresponding to the target formula" is used to inform the user how to adjust the target formula so as to reduce the complexity of the target formula.

[0104] Furthermore, the embodiments of the present disclosure are not limited to the process of identifying the above-mentioned "adjustment advice prompt information corresponding to the target formula," and may be implemented, for example, by adopting a pre-built calculation unit having an adjustment advice generation function (e.g., a machine learning model, a mapping relationship built based on a large number of formulas and their corresponding adjustment advice, a search library built based on pre-set formula adjustment advice generation rules, etc.).

[0105] Step 33: In response to a trigger operation on the adjustment advice prompt information, display a formula adjustment guide interface corresponding to the target formula.

[0106] Here, the formula adjustment guide interface is used to guide a user to perform a complexity optimization process on the target formula, and the present disclosure does not limit the formula adjustment guide interface. For example, the formula adjustment guide interface may have at least a formula editing function and an adjustment advice display function.

[0107] Furthermore, the present application does not limit the embodiment of the "trigger operation for the adjustment advice prompt information," and may be, for example, a click operation.

[0108] As can be seen from the relevant content of steps 31 to 33 above, for a client having a spreadsheet processing function, when the client receives a formula input for the spreadsheet, not only will the client immediately display the overall complexity representation data of the formula, but also display the adjustment advice prompt information corresponding to the formula, thereby enabling the user to optimize the complexity status of the formula based on the adjustment advice prompt information, which is beneficial to improving the user's formula editing experience.

[0109] According to the interaction method of the embodiment of the present disclosure, the embodiment of the present disclosure further provides an interaction device, which will be described below with reference to FIG. 4. Here, FIG. 4 is a schematic diagram of the structure of the interaction device according to the embodiment of the present disclosure. For technical details of the interaction device according to the embodiment of the present disclosure, please refer to the relevant content of the above interaction method.

[0110] As shown in FIG. 4, an interaction device 400 according to an embodiment of the present disclosure includes: a first display module 401 for displaying a table content edit page corresponding to the target table; and a second display module 402 for, in response to a formula editing operation triggered on the table content editing page, identifying overall complexity expression data of a target formula corresponding to the formula editing operation and displaying the overall complexity expression data of the target formula on the table content editing page, wherein the overall complexity expression data is identified based on the complexity expression data of the smallest calculation unit in the target formula.

[0111] In one possible embodiment, the second display module 402 comprises: a formula analysis submodule for performing a minimum calculation unit analysis process on the target formula to obtain at least one calculation unit to be used; a first specifying sub-module for specifying the complexity expression data of the target formula itself based on the complexity expression data of the at least one computing unit to be used; and a second specifying sub-module for specifying overall complexity expression data of the target mathematical formula based on the self-complexity expression data of the target mathematical formula.

[0112] In one possible embodiment, the second specific sub-module is specifically If there is a predetermined dependency relationship between the target formula and at least one reference formula in the target table, add the complexity expression data of the target formula itself and the complexity expression data of the at least one reference formula itself to obtain the overall complexity expression data of the target formula; If there is no predetermined dependency between the target formula and each formula in the target table, the self-complexity expression data of the target formula is used to identify the overall complexity expression data of the target formula.

[0113] In one possible embodiment, the first identification submodule is specifically used to identify the complexity expression data of the target formula itself based on the complexity expression data of the at least one computing unit to be used and the unit type of the at least one computing unit to be used.

[0114] In one possible embodiment, the first specific sub-module specifically comprises: Selecting at least one function calculation unit belonging to a predetermined unit type from the at least one calculation unit to be used based on a unit type of the at least one calculation unit to be used; The sum of the complexity expression data of all the function calculation units is used to specify the complexity expression data of the target formula itself.

[0115] In one possible embodiment, the interaction device 400 comprises: a data acquisition module for acquiring magnitude representation data of the input parameters of the function calculation unit; a data identifying module for identifying a complexity prediction unit corresponding to the function calculation unit based on a unit identifier of the function calculation unit; The method further includes a data prediction module for inputting the magnitude representation data to the complexity prediction unit to obtain complexity representation data of the function calculation unit output from the complexity prediction unit.

[0116] In one possible embodiment, the complexity prediction unit is obtained by performing a fitting process based on a fitting reference data set corresponding to the function calculation unit and a curve to be fitted corresponding to the function calculation unit, wherein the fitting reference data set includes at least one first input parameter magnitude representation data corresponding to the function calculation unit and actual complexity representation data corresponding to the at least one first input parameter magnitude representation data; Or, The complexity prediction unit is obtained by performing a training process based on a training data set corresponding to the function calculation unit and a model to be trained corresponding to the function calculation unit, and the training data set includes at least one second input parameter magnitude representation data corresponding to the function calculation unit and actual complexity representation data corresponding to the at least one second input parameter magnitude representation data.

[0117] In one possible embodiment, if there is an upstream calculation unit corresponding to the function calculation unit in the target formula, the magnitude representation data of the input parameters of the function calculation unit is determined based on the magnitude representation data of the output result of the upstream calculation unit.

[0118] In one possible embodiment, the magnitude representation data of the output result of the upstream computing unit is specified based on the unit type of the upstream computing unit.

[0119] In one possible embodiment, the second display module 402 is specifically used to, in response to a formula editing operation triggered on the table content editing page, identify overall complexity expression data of a target formula corresponding to the formula editing operation, display the overall complexity expression data of the target formula on the table content editing page, and display adjustment advice prompt information corresponding to the target formula.

[0120] The interaction device 400 includes: The adjustment advice prompt information further includes a third display module for displaying a formula adjustment guide interface corresponding to the target formula in response to a trigger operation on the adjustment advice prompt information.

[0121] As can be seen from the relevant content of the above interaction device 400, for the interaction device 400 according to the embodiment of the present disclosure, when the interaction device 400 displays a table content editing page corresponding to a target table (a certain spreadsheet), when the interaction device 400 receives a formula editing operation triggered on the table content editing page, the interaction device 400 includes: determining overall complexity expression data of the target formula corresponding to the formula editing operation, and displaying the overall complexity expression data of the target formula on the table content editing page, so that the user can query the overall complexity expression data of the target formula as quickly as possible after editing the target formula, thereby meeting the user's need to understand the complexity of the target formula, which is beneficial to improving the user experience.

[0122] Furthermore, since the overall complexity expression data of the target formula is obtained by theoretically deriving the complexity expression data of the smallest calculation unit related to the target formula, there is no need to complete the execution process for the target formula when obtaining the overall complexity expression data of the target formula. Thus, it is possible to effectively avoid adverse effects (e.g., time consumption) caused by the execution of the target formula. This is advantageous in improving the efficiency of identifying the overall complexity expression data of the target formula and further advantageous in improving the real-time performance of displaying the overall complexity expression data, thereby better improving the user experience.

[0123] Furthermore, an embodiment of the present disclosure further provides an electronic device, the device including a processor and a memory, the memory being used to store instructions or computer programs, and the processor being used to execute the instructions or computer programs in the memory to cause the electronic device to perform any one embodiment of an interaction method according to an embodiment of the present disclosure.

[0124] Referring to Figure 5, a schematic diagram of the structure of an electronic device 500 suitable for implementing an embodiment of the present disclosure is shown. Terminal devices in the embodiment of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable media players), in-vehicle terminals (e.g., in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs and desktop computers, etc. The electronic device shown in Figure 5 is merely an example and is not intended to limit the functionality and scope of use of the embodiment of the present disclosure.

[0125] 5, electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processor, etc.) 501 that can perform various types of appropriate operations and processes in accordance with a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. RAM 503 also stores various types of programs and data necessary for the operation of electronic device 500. Processing unit 501, ROM 502, and RAM 503 are connected to one another via a bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0126] Typically, input devices 506, including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507, including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 508, including, for example, a magnetic tape, hard disk, etc.; and communication devices 509 may be connected to the I / O interface 505. The communication devices 509 may allow the electronic device 500 to communicate wirelessly or via wires with other devices to exchange data. While FIG. 7 illustrates the electronic device 500 having various devices, it should be understood that it need not embody or include all of the devices shown. More or fewer devices may alternatively be implemented or included.

[0127] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product including a computer program embodied on a non-transitory computer-readable medium, the computer program including program code for performing the methods illustrated in the flowcharts. In such embodiments, the computer program may be downloaded and installed over a network via the communication device 509, installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the functions defined above in the methods of the embodiments of the present disclosure are performed.

[0128] The electronic device according to the embodiment of the present disclosure belongs to the same inventive concept as the method according to the above embodiment, and for technical details not described in detail in this embodiment, reference may be made to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0129] An embodiment of the present disclosure further provides a computer-readable medium having instructions or a computer program stored thereon, the instructions or computer program being operable, when executed on a device, to cause the device to perform any one of the embodiments of the interaction method according to the embodiment of the present disclosure.

[0130] It should be noted that the computer-readable medium described above in this disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the above. The computer-readable storage medium may be, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM, ROM, an Erasable Programmable Read Only Memory (EPROM) or flash memory, an optical fiber, a portable Compact Disc Read Only Memory (CD-ROM), an optical memory device, a magnetic memory device, or any suitable combination of the above. In this disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in combination with an instruction execution system, apparatus, or device. Meanwhile, in this disclosure, a computer-readable signal medium may include a data signal, propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take various forms, including, but not limited to, an electromagnetic signal, an optical signal, or any suitable combination of the above. Furthermore, a computer-readable signal medium may be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.The program code contained in the computer readable medium may be transmitted over any suitable medium, including, but not limited to, wire, optical cable, radio frequency (RF), or the like, or any suitable combination of the above.

[0131] In some embodiments, clients and servers may communicate using any network protocol now known or later developed, such as HyperText Transfer Protocol (HTTP), and may be connected to each other via any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include local area networks ("LANs"), wide area networks ("WANs"), the World Wide Web (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any networks now known or later developed.

[0132] The computer-readable medium may be included in the electronic device described above, or may exist separately from the electronic device.

[0133] The computer-readable medium may include one or more programs that, when executed by the electronic device, can cause the electronic device to perform the method.

[0134] Computer program code for carrying out the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, etc., as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may run entirely on the user computer, partially on the user computer, as a separate software package, partially on the user computer and partially on a remote computer, or entirely on a remote computer or server. When remote computers are involved, the remote computers may be connected to the user computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., connected via the Internet using an Internet Service Provider).

[0135] The flowcharts and block diagrams in the figures illustrate the architecture, functions, and operations that can be implemented in accordance with systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or portion of code, including one or more executable instructions for implementing a given logical function. It should also be noted that in some implementations, the functions noted in the blocks may occur in a different order than that shown in the figures. For example, two blocks shown in succession may actually be executed substantially in parallel or may be executed in the reverse order depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented in a dedicated hardware-based system that performs a given function or operation, or in a combination of dedicated hardware and computer instructions.

[0136] The units mentioned in the description of the embodiments of the present disclosure may be implemented in a software manner or a hardware manner, and the names of the units / modules in some cases do not limit the units themselves.

[0137] The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific general purpose products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), etc.

[0138] In the context of the disclosure, a machine-readable medium may be a tangible medium that can contain or store a program used by or in connection with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the above. More specific examples of machine-readable storage media include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0139] In addition, each embodiment in this specification is described sequentially, and the main points described in each embodiment are the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the systems or devices disclosed in the embodiments, the description is simplified to correspond to the methods disclosed in the embodiments, and for related parts, please refer to the description of the method part.

[0140] It should be understood that in this disclosure, "at least one" means one or more, and "plurality" means two or more. "And / or" is used to describe a relationship between related objects and indicates that three relationships are possible. For example, "A and / or B" may represent three cases: A only exists, B only exists, or both A and B exist simultaneously, where A and B may be singular or plural. The character " / " generally indicates that the related objects before and after it are in an "or" relationship. "At least one of" or similar expressions refers to any combination of these items, including any combination of single or multiple items. For example, "at least one of a, b, or c" can represent a, b, c, "a and b," "a and c," "b and c," or "a, b, and c," where a, b, and c may be singular or plural.

[0141] It should be noted that, in this specification, relational terms such as "first" and "second," etc., are merely used to distinguish one entity or operation from another and do not require or imply the existence of any actual relationship or ordering between those entities or operations. Furthermore, the terms "comprise," "include," "includes," or any other variation thereof are intended to cover the non-exclusive "comprise," whereby a process, method, article, or apparatus comprising a set of elements not only includes those elements, but also includes other elements not expressly listed or inherent in such process, method, article, or apparatus. Unless further limited, an element defined by the phrase "comprising one of" does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising that element.

[0142] The steps of a method or algorithm described with reference to the embodiments disclosed herein may be embodied directly in hardware, by software modules executed by a processor, or by a combination of the two. The software modules may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), Electrically Programmable ROM, Electrically Erasable Programmable ROM, registers, hard disk, removable magnetic disk, CD-ROM, or any other form of storage medium well known in the art.

[0143] The above description of the disclosed embodiments will enable those skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. Displaying a table content edit page corresponding to the target table; in response to a formula editing operation triggered on the table content editing page, identifying overall complexity representation data of a target formula corresponding to the formula editing operation, and displaying the overall complexity representation data of the target formula on the table content editing page; An interaction method, characterized in that the overall complexity representation data is identified based on complexity representation data of a smallest calculation unit in the target mathematical formula.

2. The process of identifying the overall complexity expression data of the target mathematical formula includes: performing a minimum calculation unit analysis process on the target formula to obtain at least one calculation unit to be used; Identifying the complexity expression data of the target formula itself based on the complexity expression data of the at least one calculation unit to be used; and determining overall complexity representation data of the target formula based on the self-complexity representation data of the target formula.

3. Determining overall complexity expression data of the target formula based on the self complexity expression data of the target formula, If there is a predetermined dependency between the target formula and at least one reference formula in the target table, add the complexity expression data of the target formula itself and the complexity expression data of the at least one reference formula itself to obtain the overall complexity expression data of the target formula; 3. The method of claim 2, further comprising: if there is no predetermined dependency between the target formula and each formula in the target table, identifying the target formula's own complexity representation data as the target formula's overall complexity representation data.

4. Identifying the complexity expression data of the target formula itself based on the complexity expression data of the at least one calculation unit to be used, 3. The method of claim 2, further comprising: determining a complexity representation data of the target formula based on the complexity representation data of the at least one calculation unit to be used and a unit type of the at least one calculation unit to be used.

5. Identifying the complexity expression data of the target formula based on the complexity expression data of the at least one calculation unit to be used and the unit type of the at least one calculation unit to be used includes: selecting at least one function calculation unit belonging to a predetermined unit type from the at least one calculation unit to be used based on a unit type of the at least one calculation unit to be used; The method according to claim 4, further comprising: specifying a sum of the complexity representation data of all function calculation units as the complexity representation data of the target formula itself.

6. The process of identifying the complexity expression data of the function calculation unit includes: Obtaining magnitude representation data of the input parameters of the function calculation unit; Identifying a complexity prediction unit corresponding to the function calculation unit based on a unit identifier of the function calculation unit; 6. The method of claim 5, further comprising: inputting the magnitude representation data into the complexity prediction unit to obtain complexity representation data of the function calculation unit output from the complexity prediction unit.

7. The complexity prediction unit is obtained by performing a fitting process based on a fitting reference data set corresponding to the function calculation unit and a curve to be fitted corresponding to the function calculation unit, and the fitting reference data set includes at least one first input parameter magnitude representation data corresponding to the function calculation unit and actual complexity representation data corresponding to the at least one first input parameter magnitude representation data; Or, 7. The method of claim 6, wherein the complexity prediction unit is obtained by performing a training process based on a training data set corresponding to the function calculation unit and a model to be trained corresponding to the function calculation unit, and the training data set includes at least one second input parameter magnitude representation data corresponding to the function calculation unit and actual complexity representation data corresponding to the at least one second input parameter magnitude representation data.

8. 7. The method according to claim 6, wherein, when there is an upstream calculation unit corresponding to the function calculation unit in the target formula, the magnitude representation data of the input parameters of the function calculation unit is determined based on the magnitude representation data of the output result of the upstream calculation unit.

9. 9. The method of claim 8, wherein the magnitude representation data of the output result of the upstream computing unit is specified based on the unit type of the upstream computing unit.

10. displaying adjustment advice prompt information corresponding to the target formula in response to a formula editing operation triggered on the table content editing page; 2. The method of claim 1, further comprising: displaying a formula adjustment guide interface corresponding to the target formula in response to a trigger operation on the adjustment advice prompt information.

11. a first display module for displaying a table content edit page corresponding to the target table; a second display module for, in response to a formula editing operation triggered on the table content editing page, identifying overall complexity representation data of a target formula corresponding to the formula editing operation and displaying the overall complexity representation data of the target formula on the table content editing page; An interaction device, characterized in that the overall complexity representation data is identified based on complexity representation data of a minimum calculation unit in the target mathematical formula.

12. An electronic device, a processor and a memory, the memory is used to store instructions or computer programs; The electronic device, characterized in that the processor is used to execute the instructions or computer programs in the memory to cause the electronic device to perform the method of any one of claims 1 to 10.

13. 1. A computer-readable medium, comprising: The computer-readable medium has stored thereon instructions or a computer program that, when executed on an apparatus, causes the apparatus to carry out the method of any one of claims 1 to 10.

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