Data processing method and device, computer readable storage medium and electronic device

Abstract

The present application relates to the field of data processing technology, and specifically to a data processing method and a data processing device, as well as a computer-readable storage medium and an electronic device, which solves the problem that the list records are messy and it is impossible to judge the number of times a certain data in the list continuously meets the conditions or the number of times it continuously does not meet the conditions based on the list. The data processing method first generates a condition column, and then determines the first continuous number of times or the second continuous number of times corresponding to each row of data in the list of data to be processed based on N dimension columns, condition columns and date columns, so that the user can directly see the number of times the target value corresponding to the N dimensional content of each row of data in the list of data to be processed continuously meets the preset continuous condition at a time node, or the number of times the target value corresponding to the N dimensional content continuously does not meet the preset continuous condition at a time node.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data processing method and a data processing device, as well as a computer-readable storage medium and an electronic device. Background Art

[0002] Pharmaceutical factories, pharmacies and other institutions generally use lists to record sales. However, there are many sales representatives, many types of drugs sold, and many quantities of drugs sold, which leads to messy sales lists. It is impossible to directly see how many consecutive days a sales representative's sales are greater than a certain value, or how many consecutive days a sales representative's sales are less than a certain value, so as to judge the sales ability of the sales representative.

[0003] In addition, hospitals generally use lists to record patients' medication reactions. However, the hospital has a large number of patients and many medication times, which results in a messy list of patients' medication reactions. It is impossible to directly see the number of times a patient's medication reactions are normal in a row, or the number of times a patient's medication reactions are abnormal in a row.

[0004] In addition, hospitals generally use lists to record whether a patient's indicator meets the standard. However, the hospital has a large number of patients and uses medications frequently, which results in a messy list of patient indicator compliance status. It is impossible to directly see how many times a certain patient's indicator has met the conditions consecutively, or how many times a certain patient's indicator has not met the conditions consecutively. Summary of the invention

[0005] In view of this, an embodiment of the present application provides a data processing method and a data processing device, as well as a computer-readable storage medium and an electronic device, to solve the problem that the list records are messy and it is impossible to judge based on the list how many times a certain data in the list continuously meets a condition or the number of times it continuously does not meet the condition.

[0006] In a first aspect, a data processing method provided by an embodiment of the present application includes: determining a list of data to be processed comprising N dimension columns, a date column and a target value column, wherein each cell in the N dimension columns corresponds to a dimension content, each cell in the date column corresponds to a time node, each cell in the target value column corresponds to a target value, and each row of data in the list of data to be processed corresponds to N dimension contents, a time node and a target value; based on a preset continuous condition and the target value column, generating a condition column in the list of data to be processed, the condition column being used to characterize whether the target value column satisfies the preset continuous condition; based on the N dimension columns, the condition column and the date column, determining a first continuous number or a second continuous number corresponding to each row of data in the list of data to be processed, wherein the first continuous number is used to characterize the number of times at a time node, the target values ​​corresponding to the N dimension contents continuously meet the preset continuous condition, and the second continuous number is used to characterize the number of times at a time node, the target values ​​corresponding to the N dimension contents continuously do not meet the preset continuous condition.

[0007] In combination with the first aspect of the present application, in some embodiments, determining a list of data to be processed that includes N dimension columns, a date column, and a target value column includes: determining an initial list of data to be processed that includes N dimension columns, a date column, and a target value column; based on at least one dimension column among the N dimension columns, performing a grouping operation on the initial list of data to be processed to obtain a first grouping list, the first grouping list includes multiple groups of data, and the value content corresponding to at least one dimension column in each group of data is the same; based on a preset date dimension table, performing a date completion operation on the first grouping list to obtain a completion list; and associating the completion list with the first grouping list to obtain a list of data to be processed.

[0008] In combination with the first aspect of the present application, in some embodiments, based on preset continuous conditions and target value columns, a condition column is generated in a list of data to be processed, including: for the target value of each row of the target value column, judging whether the target value satisfies the preset continuous condition; if so, generating first result data corresponding to the target value, the first result data being used to characterize that the target value satisfies the preset continuous condition; if not, generating second result data corresponding to the target value, the second result data being used to characterize that the target value does not satisfy the preset continuous condition; based on the first result data and the second result data, generating a condition column.

[0009] In combination with the first aspect of the present application, in some embodiments, based on N dimension columns, condition columns and date columns, the first consecutive number or the second consecutive number corresponding to each row of data in the list of data to be processed is determined, including: based on at least one dimension column, condition column and date column among the N dimension columns, the data list to be processed is grouped and sorted to obtain a first sorted list; based on the date column, a sorting column is generated in the first sorted list; subtraction calculation is performed on the date column and the sorting column to generate a continuous grouping column; based on at least one dimension column, condition column and continuous grouping column among the N dimension columns, the first consecutive number or the second consecutive number corresponding to each row of data in the list of data to be processed is determined.

[0010] In combination with the first aspect of the present application, in some embodiments, the grouping and sorting operation includes a grouping operation and a sorting operation; based on at least one dimension column, a condition column and a date column among N dimension columns, a grouping and sorting operation is performed on the list of data to be processed to obtain a first sorted list, including: based on at least one dimension column and a condition column among the N dimension columns, a grouping operation is performed on the list of data to be processed to obtain a second grouping list containing multiple groups of data; for each group of data in the multiple groups of data in the second grouping list, a sorting operation is performed based on the date column to obtain a first sorted list.

[0011] In combination with the first aspect of the present application, in some embodiments, based on the condition column and the continuous grouping column, the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed is determined, including: based on the condition column and the continuous grouping column, generating a continuous number sequence; based on the continuous number sequence, determining the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed.

[0012] In combination with the first aspect of the present application, in some embodiments, based on a continuous number series, the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed is determined, including: determining the first continuous number or the second continuous number based on the positive and negative signs and values ​​of the data in the continuous number series.

[0013] In a second aspect, an embodiment of the present application provides a data processing device, comprising: a first determination module, configured to determine a list of data to be processed comprising N dimension columns, a date column and a target value column, wherein each cell in the N dimension columns corresponds to a dimension content, each cell in the date column corresponds to a time node, each cell in the target value column corresponds to a target value, and each row of data in the list of data to be processed corresponds to N dimension contents, a time node and a target value; a generation module, configured to generate a condition column in the list of data to be processed based on a preset continuous condition and a target value column, the condition column being used to characterize whether the target value column satisfies the preset continuous condition; a second determination module, configured to determine the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed based on the N dimension columns, the condition column and the date column, wherein the first continuous number is used to characterize the number of times at a time node that the target values ​​corresponding to the N dimension contents continuously meet the preset continuous condition, and the second continuous number is used to characterize the number of times at a time node that the target values ​​corresponding to the N dimension contents continuously do not meet the preset continuous condition.

[0014] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed by a processor of an electronic device, the electronic device can execute the data processing method mentioned in the first aspect above.

[0015] In a fourth aspect, an embodiment of the present application provides an electronic device, the electronic device comprising: a processor; a memory for storing computer-executable instructions; a processor, for executing computer-executable instructions to implement the data processing method mentioned in the first aspect above.

[0016] A data processing method provided by an embodiment of the present application first determines a list of data to be processed that includes N dimension columns, a date column, and a target value column, then generates a condition column in the list of data to be processed based on a preset continuous condition and the target value column, the condition column is used to characterize whether the target value column satisfies the preset continuous condition, and finally determines the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed based on the N dimension columns, the condition column, and the date column, so that a user can directly see the number of times that the target values ​​corresponding to the N dimensional contents in each row of data in the list of data to be processed continuously meet the preset continuous condition at a time node, or the number of times that the target values ​​corresponding to the N dimensional contents continuously do not meet the preset continuous condition at a time node. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a schematic diagram of an application scenario of a data processing method provided in one embodiment of the present application.

[0018] Figure 2The figure is a flow chart of a data processing method provided in one embodiment of the present application.

[0019] Figure 3 Shown is a flow chart of a data processing method provided in another embodiment of the present application.

[0020] Figure 4 Shown is a flow chart of a data processing method provided in another embodiment of the present application.

[0021] Figure 5 Shown is a flow chart of a data processing method provided in another embodiment of the present application.

[0022] Figure 6 Shown is a flow chart of a data processing method provided in another embodiment of the present application.

[0023] Figure 7 Shown is a flow chart of a data processing method provided in another embodiment of the present application.

[0024] Figure 8 Shown is a flow chart of a data processing method provided in another embodiment of the present application.

[0025] Fig. 9 Shown is a schematic diagram of the structure of a data processing device provided in one embodiment of the present application.

[0026] Fig.10 Shown is a schematic structural diagram of a data processing device provided in another embodiment of the present application.

[0027] Fig.11 Shown is a schematic structural diagram of a data processing device provided in another embodiment of the present application.

[0028] Fig.12 Shown is a schematic structural diagram of a data processing device provided in another embodiment of the present application.

[0029] Fig.13 Shown is a schematic structural diagram of a data processing device provided in another embodiment of the present application.

[0030] Fig.14 Shown is a schematic structural diagram of a data processing device provided in another embodiment of the present application.

[0031] Fig.15 Shown is a schematic structural diagram of a data processing device provided in another embodiment of the present application.

[0032] Fig.16 Shown is a schematic structural diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only 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 creative work are within the scope of protection of this application.

[0034] Example scenarios

[0035] Figure 1 The figure is a schematic diagram of an application scenario of a data processing method provided in one embodiment of the present application. Figure 1 The illustrated scenario includes a server 110 and a user terminal 120 that is communicatively connected to the server 110. Specifically, the server 110 is used to determine a list of data to be processed that includes N dimension columns, a date column, and a target value column, wherein each cell in the N dimension columns corresponds to a dimension content, each cell in the date column corresponds to a time node, each cell in the target value column corresponds to a target value, and each row of data in the list of data to be processed corresponds to N dimension content, a time node, and a target value; based on a preset continuous condition and a target value column, a condition column is generated in the list of data to be processed, and the condition column is used to characterize whether the target value column meets the preset continuous condition; based on the N dimension columns, the condition column, and the date column, the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed is determined, wherein the first continuous number is used to characterize the number of times at a time node, the target values ​​corresponding to the N dimension contents continuously meet the preset continuous condition, and the second continuous number is used to characterize the number of times at a time node, the target values ​​corresponding to the N dimension contents continuously do not meet the preset continuous condition. The client 120 is used to send a list of data to be processed to the server 110 so that the server 110 can perform the above operations.

[0036] Specifically, the user end 120 may be a pharmaceutical factory, a drug store, a hospital, etc. In practical applications, the above data processing method may be executed on the server 110 by programming using Structured Query Language (SQL).

[0037] Specifically, the server 110 may be a computer, a server, or a cloud computing service center, etc.

[0038] Another scenario of the present application may also include multiple servers 110, each server 110 is used to process a portion of the data in the list of data to be processed, and then merge the results processed by each server 110 together. Exemplarily, the list of data to be processed may contain 1 million rows of data, each server may process 10,000 rows of data, and then merge the results processed by each server 110 together. Exemplarily, the data processed by each server 110 can be allocated according to the dimension column. For example, the dimension column is the name column, that is, the list of data to be processed may include sales data of 5,000 people, each server 110 may process sales data of 10 people, and then merge the results processed by each server 110 together. The distribution method of the data processed by each server 110 can be set according to actual needs, and this application does not make specific restrictions. The number of data processed by each server 110 can also be selected according to actual needs, and this application does not make specific restrictions.

[0039] Exemplarily, the list of data to be processed may also be a list of patients' medication reaction conditions, wherein the dimension columns may be name columns, age columns, etc., and the target value column may be a column for recording medication reaction grade values.

[0040] Exemplarily, the list of data to be processed may also be a list of the compliance status of a certain indicator of the patient, wherein the dimension column may be a name column, an age column, etc., and the target value column may be a column recording the value of a certain indicator.

[0041] Exemplary Methods

[0042] Figure 2 FIG. 1 is a flow chart of a data processing method provided by an embodiment of the present application. Figure 2 As shown, the data processing method includes the following steps.

[0043] Step 210, determining a list of data to be processed including N dimension columns, a date column and a target value column.

[0044] Specifically, the list of data to be processed contains multiple rows of data. N is a positive integer. Each cell in the N dimension columns corresponds to a dimension content, each cell in the date column corresponds to a time node, each cell in the target value column corresponds to a target value, and each row of data in the list of data to be processed corresponds to N dimension content, a time node and a target value.

[0045] Specifically, the dimension column can be a name column, or a gender column, an age column, a position column, etc. This application does not specifically limit the content of the dimension column. The content of the date column can be a certain day, for example, 2021 / 9 / 1. The content of the date column can also be a certain month, for example, 2021 / 9. This application does not specifically limit the content of the date column. The format of the date column can be "2021 / 9 / 1", "September 1, 2021", or "2012-09-01". This application does not specifically limit the format of the date column. The target value column is a column of data in the list of data to be processed that needs to be calculated as "the number of consecutive times that the preset continuous condition is met or the number of consecutive times that the preset continuous condition is not met". The target value column can be sales volume, sales quantity, or other content. This application does not specifically limit the content of the target value column.

[0046] Step 220: Generate a condition column in the list of data to be processed based on the preset continuous condition and the target value column.

[0047] Specifically, the preset continuous condition is a condition that the preset target value column needs to meet. For example, if the target value column is sales, the preset continuous condition can be that the sales are greater than 0, or that the sales are greater than 10. The specific content and specific value of the preset continuous condition can be set according to actual needs, and this application does not make specific limitations.

[0048] Specifically, the condition column is used to indicate whether the target value column meets the preset continuous condition. For example, if the target value column in a row of data meets the preset continuous condition, the content of the row of data corresponding to the condition column can be represented by 1. If the target value column in a row of data does not meet the preset continuous condition, the content of the row of data corresponding to the condition column can be represented by -1.

[0049] Step 230 : Determine the first consecutive number or the second consecutive number corresponding to each row of data in the list of data to be processed based on the N dimension columns, the condition column, and the date column.

[0050] Specifically, the first continuous number is used to represent the number of times at a time node, the target values ​​corresponding to the N dimensional contents continuously meet the preset continuous condition. The second continuous number is used to represent the number of times at a time node, the target values ​​corresponding to the N dimensional contents continuously do not meet the preset continuous condition.

[0051] Specifically, the list of data to be processed can be grouped according to the dimension column and the condition column, and the rows with the same content in the dimension column and the same content in the condition column can be grouped into a group of data. Then, each group of data can be sorted according to the date column to sort the list of data to be processed, so as to determine the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed. In this way, the user can directly see the number of times that the target values ​​corresponding to the N dimensional contents in each row of data in the list of data to be processed continuously meet the preset continuous conditions at a time node, or the number of times that the target values ​​corresponding to the N dimensional contents continuously do not meet the preset continuous conditions at a time node.

[0052] In actual applications, if the list of data to be processed is a sales list, the first consecutive number or the second consecutive number corresponding to each row of data in the list of data to be processed can be used to intuitively see the number of times a certain medical representative has continuously met the sales quantity, or the number of times a certain medical representative has continuously failed to meet the sales quantity, thereby intuitively and accurately determining the sales ability of a certain medical representative.

[0053] In actual applications, if the list of data to be processed is a list of patients' medication reactions, the first consecutive number of times or the second consecutive number of times corresponding to each row of data in the list of data to be processed can be used to intuitively see the number of times a patient's medication reactions are consecutively normal, or the number of times a patient's medication reactions are consecutively abnormal, thereby intuitively and accurately determining a patient's medication reaction situation.

[0054] In actual applications, if the list of data to be processed is a list of compliance status of a certain indicator of a patient, the first consecutive number or the second consecutive number corresponding to each row of data in the list of data to be processed can be used to intuitively see the number of times a certain indicator of a certain patient has continuously met the conditions, or the number of times a certain indicator of a certain patient has continuously failed to meet the conditions, thereby intuitively and accurately determining the compliance status of a certain indicator of a certain patient.

[0055] Figure 3 FIG. 1 is a flow chart of a data processing method provided by another embodiment of the present application. Figure 2 Based on the embodiment shown, Figure 3 The embodiment shown is described below in detail. Figure 3 The embodiment shown and Figure 2 The differences and similarities of the illustrated embodiments are not described in detail.

[0056] like Figure 3 As shown, in an embodiment of the present application, the step of determining a list of data to be processed including N dimension columns, a date column and a target value column includes the following steps.

[0057] Step 310: determine an initial list of data to be processed that includes N dimension columns, a date column, and a target value column.

[0058] The initial list of data to be processed contains multiple rows of data. The names of adjacent rows can be the same or different. For example, the name of the first row can be "Zhang San", the name of the second row can be "Li Si", the name of the third row can also be "Zhang San", and the name of the fourth row can be "Wang Wu". That is, the content of the multiple rows of data contained in the list of data to be processed can be disorganized and there is no rule to find.

[0059] In practical applications, the initial list of data to be processed contains a massive amount of data, for example, tens of millions of rows of data.

[0060] For example, Table 1 is a list of initial data to be processed provided in an embodiment of the present application. As shown in Table 1, the dimension column is the name column, and the target value column is the sales volume. The unit of sales volume can be RMB or 10,000 yuan, which is not specifically limited in the present application.

[0061] Table 1

[0062] name date Sales Zhang San 2021 / 9 / 1 29.00 Zhang San 2021 / 9 / 3 31.00 Li Si 2021 / 9 / 11 25.30 Li Si 2021 / 9 / 12 26.30 Li Si 2021 / 9 / 13 27.30 Zhang San 2021 / 9 / 14 42.00 Zhang San 2021 / 9 / 15 12.00 Li Si 2021 / 9 / 1 15.30 Li Si 2021 / 9 / 2 16.30 Li Si 2021 / 9 / 3 17.30 Li Si 2021 / 9 / 4 18.30 Li Si 2021 / 9 / 8 22.30 Li Si 2021 / 9 / 9 2.00 Li Si 2021 / 9 / 10 3.00 Zhang San 2021 / 9 / 4 22.00 Zhang San 2021 / 9 / 5 13.00 Zhang San 2021 / 9 / 13 41.00

[0063] Step 320: performing a grouping operation on the initial list of data to be processed based on at least one dimension column among the N dimension columns to obtain a first grouping list.

[0064] Specifically, the first grouping list includes multiple groups of data, and the value content corresponding to at least one dimension column in each group of data is the same.

[0065] Illustratively, Table 2 is a first grouping list provided in an embodiment of the present application. As shown in Table 2.

[0066] Table 2

[0067]

[0068]

[0069] Step 330: Based on the preset date dimension table, perform a date completion operation on the first grouping list to obtain a completion list.

[0070] Specifically, the preset date dimension table may be a list of all days of a month or a list of all months of a year. The preset date dimension table may be set according to actual needs, and the present application does not specifically limit the content of the preset date dimension table.

[0071] Specifically, the date completion operation is performed on the first grouping list, and the Cartesian product calculation may be performed on the dimension column in the first grouping list and the preset date dimension table, so as to obtain the completion list.

[0072] Exemplarily, Table 3 is a preset date dimension table provided in an embodiment of the present application. As shown in Table 3, Table 3 includes all dates from September 1, 2021 to September 30, 2021, and the "..." in Table 3 represents the dates omitted here.

[0073] Table 3

[0074]

[0075]

[0076] For example, Table 4 is a complete list provided in an embodiment of the present application. As shown in Table 4, "..." in Table 4 indicates that the display is omitted here.

[0077] Table 4

[0078] name date Sales Zhang San 2021 / 9 / 1 Zhang San 2021 / 9 / 2 Zhang San 2021 / 9 / 3 Zhang San 2021 / 9 / 4 Zhang San 2021 / 9 / 5 … … … Zhang San 2021 / 9 / 30 Li Si 2021 / 9 / 1 Li Si 2021 / 9 / 2 Li Si 2021 / 9 / 3 Li Si 2021 / 9 / 4 Li Si 2021 / 9 / 5 … … … Li Si 2021 / 9 / 30

[0079] Step 340: associate the completion list with the first grouping list to obtain a list of data to be processed.

[0080] Specifically, for each row of data in the completion list, the first grouping list is searched for rows with the same content as the name column and date column according to the content of the name column and date column in the completion list. If a row with the same content as the name column and date column of the row can be found in the first grouping list, the sales in the row are associated with the completion list. If a row with the same content as the name column and date column of the row cannot be found in the first grouping list, the sales in the row in the completion list is set to 0.

[0081] For example, Table 5 is a list of data to be processed provided by an embodiment of the present application. As shown in Table 5, "..." in Table 5 means that the display is omitted here.

[0082] Table 5

[0083] name date Sales Zhang San 2021 / 9 / 1 29.00 Zhang San 2021 / 9 / 2 0 Zhang San 2021 / 9 / 3 31.00 Zhang San 2021 / 9 / 4 22.00 Zhang San 2021 / 9 / 5 13.00 … … … Zhang San 2021 / 9 / 30 0 Li Si 2021 / 9 / 1 15.30 Li Si 2021 / 9 / 2 16.30 Li Si 2021 / 9 / 3 17.30 Li Si 2021 / 9 / 4 18.30 Li Si 2021 / 9 / 5 0 … … … Li Si 2021 / 9 / 30 0

[0084] Since the data in the initial list of data to be processed can be the initial list provided by the pharmaceutical factory, only the sales on the date of sale are recorded, and no sales will be recorded. For example, if no product is sold on 2021 / 9 / 2, the sales on 2021 / 9 / 2 will not be recorded in the initial list of data to be processed. Therefore, the initial list of data to be processed is grouped, and the first group list obtained will not include the sales on 2021 / 9 / 2. Therefore, the content of the sales volume corresponding to the row with the date column of 2021 / 9 / 2 in the completion list can be set to 0 to obtain a list of data to be processed.

[0085] The list of data to be processed is obtained by processing the initial list of data to be processed from step 310 to step 340, which reduces the clutter of the list of data to be processed and provides a good data basis for subsequent calculations.

[0086] Figure 4 FIG. 1 is a flow chart of a data processing method provided by another embodiment of the present application. Figure 2 Based on the embodiment shown, Figure 4 The embodiment shown is described below in detail. Figure 4 The embodiment shown and Figure 2 The differences and similarities of the illustrated embodiments are not described in detail.

[0087] like Figure 4 As shown, in an embodiment of the present application, the step of generating a condition column in a list of data to be processed based on preset continuous conditions and a target value column includes the following steps.

[0088] Step 410 , for the target value of each row in the target value column, determine whether the target value satisfies a preset continuity condition.

[0089] Specifically, for the target value of each row of the target value column, it is determined whether the target value meets the preset continuity condition. If so, step 420 is executed; if not, step 430 is executed.

[0090] Step 420, generating first result data corresponding to the target value.

[0091] Specifically, the first result data is used to indicate that the target value satisfies a preset continuity condition.

[0092] Step 430, generating second result data corresponding to the target value.

[0093] Specifically, the second result data is used to indicate that the target value does not satisfy a preset continuity condition.

[0094] Step 440: Generate a condition column based on the first result data and the second result data.

[0095] Specifically, each row of data in the list of data to be processed corresponds to a first result data or a second result data. Based on the first result data and the second result data, a condition column is generated, that is, the content contained in the cell in the condition column is the first result data or the second result data.

[0096] For example, Table 6 is a list after generating a condition column provided in an embodiment of the present application. The preset continuous condition of generating the condition column is: sales is greater than 10. The first result data is 1, and the second result data is -1.

[0097] Table 6

[0098] name date Sales Sales volume greater than 10 Zhang San 2021 / 9 / 1 29.00 1 Zhang San 2021 / 9 / 2 0.00 -1 Zhang San 2021 / 9 / 3 31.00 1 Zhang San 2021 / 9 / 4 22.00 1 Zhang San 2021 / 9 / 5 13.00 1 Zhang San 2021 / 9 / 6 0.00 -1 Zhang San 2021 / 9 / 7 0.00 -1 Zhang San 2021 / 9 / 8 0.00 -1 Zhang San 2021 / 9 / 9 0.00 -1 Zhang San 2021 / 9 / 10 0.00 -1 Zhang San 2021 / 9 / 11 0.00 -1 Zhang San 2021 / 9 / 12 0.00 -1 Zhang San 2021 / 9 / 13 41.00 1 Zhang San 2021 / 9 / 14 42.00 1 Zhang San 2021 / 9 / 15 12.00 1

[0099] In practical applications, the first result data and the second result data are different, so that the first result data and the second result data are subsequently used to group the list of data to be processed.

[0100] Figure 5 FIG. 1 is a flow chart of a data processing method provided by another embodiment of the present application. Figure 2 Based on the embodiment shown, Figure 5 The embodiment shown is described below in detail. Figure 5 The embodiment shown and Figure 2 The differences and similarities of the illustrated embodiments are not described in detail.

[0101] like Figure 5 As shown, in an embodiment of the present application, based on N dimension columns, condition columns and date columns, the step of determining the first consecutive number or the second consecutive number corresponding to each row of data in the list of data to be processed includes the following steps.

[0102] Step 510: Based on at least one dimension column among the N dimension columns, the condition column and the date column, a grouping and sorting operation is performed on the list of data to be processed to obtain a first sorted list.

[0103] Specifically, a grouping operation may be performed based on at least one dimension column and a condition column, and then a sorting operation may be performed according to the date column for a group of data with the same contents in the dimension column and the same contents in the condition column, thereby obtaining a first sorted list.

[0104] For example, Table 7 is a first sorting list provided in an embodiment of the present application, wherein the preset continuous condition for generating the condition column is: sales is greater than 10. The first result data is 1, and the second result data is -1.

[0105] Table 7

[0106]

[0107]

[0108] Step 520 , generating a sort column in the first sort list based on the date column.

[0109] Specifically, for a group of data having the same content in the dimension column and the same content in the condition column, the content of the sorting column is generated.

[0110] Illustratively, Table 8 is a list after generating a sort column provided in an embodiment of the present application.

[0111] Table 8

[0112] name date Sales Sales volume greater than 10 Sorting Zhang San 2021 / 9 / 2 0.00 -1 1 Zhang San 2021 / 9 / 6 0.00 -1 2 Zhang San 2021 / 9 / 7 0.00 -1 3 Zhang San 2021 / 9 / 8 0.00 -1 4 Zhang San 2021 / 9 / 9 0.00 -1 5 Zhang San 2021 / 9 / 10 0.00 -1 6 Zhang San 2021 / 9 / 11 0.00 -1 7 Zhang San 2021 / 9 / 12 0.00 -1 8 Zhang San 2021 / 9 / 1 29.00 1 1 Zhang San 2021 / 9 / 3 31.00 1 2 Zhang San 2021 / 9 / 4 22.00 1 3 Zhang San 2021 / 9 / 5 13.00 1 4 Zhang San 2021 / 9 / 13 41.00 1 5 Zhang San 2021 / 9 / 14 42.00 1 6 Zhang San 2021 / 9 / 15 12.00 1 7

[0113] Step 530: perform subtraction calculation on the date column and the sort column to generate a continuous grouping column.

[0114] Specifically, for each row of data, the content in the sorting column is subtracted from the content in the date column of the row of data to generate the content in the continuous grouping column corresponding to the row. For example, the content in the date column of a row of data is: 2021 / 9 / 2, and the content in the sorting column of a row of data is: 1. After subtraction, the content of the continuous grouping column in the row of data is: 2021 / 9 / 1.

[0115] Illustratively, Table 9 is a list after generating continuous grouping columns provided in an embodiment of the present application.

[0116] Table 9

[0117]

[0118] Step 540 : Determine the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed based on at least one dimension column among the N dimension columns, the condition column, and the continuous grouping column.

[0119] In practical applications, the number of consecutive times can be determined based on whether the content in the continuous grouping column is the same in consecutive rows, and then whether the preset continuous condition is met can be determined based on the positive and negative signs of the content in the condition column.

[0120] By generating a continuous grouping column, the number of consecutive times can be directly determined based on the continuous grouping column, which is convenient for users to directly determine the number of consecutive times, thereby improving the user experience.

[0121] Figure 6 FIG. 1 is a flow chart of a data processing method provided by another embodiment of the present application. Figure 5 Based on the embodiment shown, Figure 6 The embodiment shown is described below in detail. Figure 6 The embodiment shown and Figure 5 The differences and similarities of the illustrated embodiments are not described in detail.

[0122] like Figure 6 As shown, in an embodiment of the present application, based on at least one dimension column, a condition column and a date column among N dimension columns, a grouping and sorting operation is performed on the data list to be processed to obtain a first sorted list, which includes the following steps.

[0123] Step 610: performing a grouping operation on the data list to be processed based on at least one dimension column and the condition column among the N dimension columns to obtain a second grouping list containing multiple groups of data.

[0124] Specifically, the grouping and sorting operation includes a grouping operation and a sorting operation.

[0125] Step 620: For each group of data in the plurality of groups of data in the second grouping list, a sorting operation is performed based on the date column to obtain a first sorting list.

[0126] In actual applications, the grouping operation is performed first, and then each group of data obtained after the grouping operation is sorted. The content of the dimension column and the content of the condition column of each group of data are the same, and they are arranged in date order.

[0127] The grouping operation is performed first, and then the sorting operation is performed, which improves the efficiency of processing the data list to be processed.

[0128] Figure 7 FIG. 1 is a flow chart of a data processing method provided by another embodiment of the present application. Figure 5 Based on the embodiment shown, Figure 7 The embodiment shown is described below in detail. Figure 7 The embodiment shown and Figure 5 The differences and similarities of the illustrated embodiments are not described in detail.

[0129] like Figure 7 As shown, in an embodiment of the present application, based on the condition column and the continuous grouping column, the step of determining the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed includes the following steps.

[0130] Step 710: Generate a continuous number sequence based on the condition column and the continuous grouping column.

[0131] Specifically, a cumulative algorithm for finding the number can be used to generate a continuous sequence based on a condition column and a continuous grouping column. The numerical value of the data in the continuous sequence can be used to characterize the number of consecutive times. The sign of the data in the continuous sequence can be used to characterize whether the preset continuous condition is met. The sign of the data in the continuous sequence can be positive or negative, or two other different symbols, which is not specifically limited in this application. For example, "#" can be used to indicate that the preset continuous condition is met, and "&" can be used to indicate that the preset continuous condition is not met. For another example, "a" can be used to indicate that the preset continuous condition is met, and "b" can be used to indicate that the preset continuous condition is not met.

[0132] Exemplarily, Table 10 is a list after generating a continuous sequence provided by an embodiment of the present application. The value of the data in the continuous sequence can be calculated according to the number of times the same content appears in the continuous grouping column. Taking a row of data with a date content of 2021 / 9 / 7 in Table 10 as an example, the content of the continuous grouping column in this row of data is 2021 / 9 / 4, and the content of the continuous grouping column in the previous row of this row is also 2021 / 9 / 4, that is, the content of the continuous grouping column in this row of data appears twice in a row, therefore, the value of the data in the continuous sequence in this row of data is 2. The positive and negative signs of the data in the continuous sequence can be determined according to the positive and negative signs of the conditional column, that is, if the sign of the data in the conditional column is positive, the sign of the data in the continuous sequence can be positive; if the sign of the data in the conditional column is negative, the sign of the data in the continuous sequence can be negative.

[0133] Table 10

[0134]

[0135] Step 720: Determine the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed based on the continuous number series.

[0136] Exemplarily, the first consecutive number or the second consecutive number corresponding to each row of data in the list of data to be processed can be determined through the data values ​​and data symbols of the continuous number series, thereby improving the intuitiveness and readability of the list of data to be processed.

[0137] Figure 8 FIG. 1 is a flow chart of a data processing method provided by another embodiment of the present application. Figure 7 Based on the embodiment shown, Figure 8 The embodiment shown is described below in detail. Figure 8 The embodiment shown and Figure 7 The differences and similarities of the illustrated embodiments are not described in detail.

[0138] like Figure 8 As shown, in an embodiment of the present application, based on a continuous number series, the step of determining the number of consecutive times that a preset continuous condition is satisfied or the number of consecutive times that a preset continuous condition is not satisfied corresponding to each row in the list of data to be processed includes the following steps.

[0139] Step 810, determining a first consecutive number or a second consecutive number based on the positive and negative signs and values ​​of the data in the consecutive number series.

[0140] Specifically, whether the preset continuity condition is met can be determined by the positive and negative signs of the data in the continuous sequence. The number of consecutive times can be determined by the values ​​of the data in the continuous sequence.

[0141] For example, as shown in Table 10, a row of data with the data "-1" in the continuous sequence can indicate that on September 2, 2021, Zhang San's sales volume did not meet the preset continuous condition for 1 time in a row, that is, September 2, 2021 was the first day that Zhang San's sales volume was less than or equal to 10; a row of data with the data "-3" in the continuous sequence can indicate that on September 8, 2021, Zhang San's sales volume did not meet the preset continuous condition for 3 times in a row, that is, September 8, 2021 was Zhang San's consecutive sales volume. The third day when Zhang San’s sales amount is less than or equal to 10; a row of data with the data "2" in the continuous number series indicates that on September 4, 2021, Zhang San’s sales volume continuously met the preset continuous conditions for 2 times, that is, September 4, 2021 is the second day in a row that Zhang San’s sales volume is greater than 10; a row of data with the data "3" in the continuous number series indicates that on September 5, 2021, Zhang San’s sales volume continuously met the preset continuous conditions for 3 times, that is, September 5, 2021 is the third day in a row that Zhang San’s sales volume is greater than 10.

[0142] In actual applications, users are generally not satisfied with the negative sign, but are generally satisfied with the positive sign. Therefore, by using positive and negative symbols to represent whether the preset continuity conditions are met, it is more in line with the user's cognition of whether the conditions are met, further improving the intuitiveness and readability of the list of data to be processed.

[0143] Combination of the above Figures 1 to 8 , describes the method embodiment of the present application in detail, and the following is combined with Figures 9 to 15 , describes the device embodiment of the present application in detail. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment, so the parts not described in detail can refer to the previous method embodiment.

[0144] Exemplary Devices

[0145] Fig. 9 FIG. 1 is a schematic diagram of the structure of a data processing device provided by an embodiment of the present application. Fig. 9 As shown, the data processing device 900 of the embodiment of the present application includes a first determination module 910 , a generation module 920 , and a second determination module 930 .

[0146] Specifically, the first determination module 910 is configured to determine a list of data to be processed that includes N dimension columns, a date column, and a target value column, wherein each cell in the N dimension columns corresponds to a dimension content, each cell in the date column corresponds to a time node, each cell in the target value column corresponds to a target value, and each row of data in the list of data to be processed corresponds to N dimension content, a time node, and a target value. The generation module 920 is configured to generate a condition column in the list of data to be processed based on a preset continuous condition and a target value column, and the condition column is used to characterize whether the target value column meets the preset continuous condition. The second determination module 930 is configured to determine the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed based on the N dimension columns, the condition column, and the date column, wherein the first continuous number is used to characterize the number of times at a time node, the target value corresponding to the N dimension content continuously meets the preset continuous condition, and the second continuous number is used to characterize the number of times at a time node, the target value corresponding to the N dimension content continuously does not meet the preset continuous condition.

[0147] Fig.10 FIG. 1 is a schematic diagram of the structure of a data processing device provided by another embodiment of the present application. Fig. 9 Based on the embodiment shown, Fig.10 The embodiment shown is described below in detail. Fig.10 The embodiment shown and Fig. 9 The differences and similarities of the illustrated embodiments are not described in detail.

[0148] like Fig.10 As shown, the first determination module 910 of the embodiment of the present application includes an initial list determination unit 911, a first grouping unit 912, a completion unit 913 and an association unit 914.

[0149] Specifically, the initial list determination unit 911 is configured to determine an initial list of data to be processed that includes N dimension columns, a date column, and a target value column. The first grouping unit 912 is configured to perform a grouping operation on the initial list of data to be processed based on at least one dimension column among the N dimension columns to obtain a first grouping list, wherein the first grouping list includes multiple groups of data, and the value content corresponding to at least one dimension column in each group of data is the same. The completion unit 913 is configured to perform a date completion operation on the first grouping list based on a preset date dimension table to obtain a completion list. The association unit 914 is configured to associate the completion list with the first grouping list to obtain a list of data to be processed.

[0150] Fig.11 FIG. 1 is a schematic diagram of the structure of a data processing device provided by another embodiment of the present application. Fig. 9 Based on the embodiment shown, Fig.11 The embodiment shown is described below in detail. Fig.11 The embodiment shown and Fig. 9 The differences and similarities of the illustrated embodiments are not described in detail.

[0151] like Fig.11 As shown, the generation module 920 of the embodiment of the present application includes a judgment unit 921, a first result generation unit 922, a second result generation unit 923 and a condition column generation unit 924.

[0152] Specifically, the judgment unit 921 is configured to judge, for the target value of each row of the target value column, whether the target value satisfies the preset continuity condition. The first result generation unit 922 is configured to, if yes, generate first result data corresponding to the target value, the first result data is used to characterize that the target value satisfies the preset continuity condition. The second result generation unit 923 is configured to, if no, generate second result data corresponding to the target value, the second result data is used to characterize that the target value does not satisfy the preset continuity condition. The condition column generation unit 924 is configured to generate a condition column based on the first result data and the second result data.

[0153] Fig.12 FIG. 1 is a schematic diagram of the structure of a data processing device provided by another embodiment of the present application. Fig. 9 Based on the embodiment shown, Fig.12 The embodiment shown is described below in detail. Fig.12 The embodiment shown and Fig. 9 The differences and similarities of the illustrated embodiments are not described in detail.

[0154] like Fig.12 As shown, the second determination module 930 of the embodiment of the present application includes a group sorting unit 931 , a sorting column generating unit 932 , a continuous group column generating unit 933 , and a number determining unit 934 .

[0155] Specifically, the grouping and sorting unit 931 is configured to perform a grouping and sorting operation on the list of data to be processed based on at least one dimension column, the condition column and the date column among the N dimension columns to obtain a first sorted list. The sorting column generating unit 932 is configured to generate a sorting column in the first sorted list based on the date column. The continuous grouping column generating unit 933 is configured to perform a subtraction calculation on the date column and the sorting column to generate a continuous grouping column. The number determination unit 934 is configured to determine the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed based on at least one dimension column, the condition column and the continuous grouping column among the N dimension columns.

[0156] Fig.13 FIG. 1 is a schematic diagram of the structure of a data processing device provided by another embodiment of the present application. Fig.12 Based on the embodiment shown, Fig.13The embodiment shown is described below in detail. Fig.13 The embodiment shown and Fig.12 The differences and similarities of the illustrated embodiments are not described in detail.

[0157] like Fig.13 As shown, the grouping sorting unit 931 of the embodiment of the present application includes a second grouping subunit 1310 and a first sorting subunit 1320 .

[0158] Specifically, the second grouping subunit 1310 is configured to perform a grouping operation on the list of data to be processed based on at least one dimension column and the condition column among the N dimension columns to obtain a second grouping list containing multiple groups of data. The first sorting subunit 1320 is configured to perform a sorting operation on each group of data in the multiple groups of data in the second grouping list based on the date column to obtain a first sorting list.

[0159] Fig.14 FIG. 1 is a schematic diagram of the structure of a data processing device provided by another embodiment of the present application. Fig.12 Based on the embodiment shown, Fig.14 The embodiment shown is described below in detail. Fig.14 The embodiment shown and Fig.12 The differences and similarities of the illustrated embodiments are not described in detail.

[0160] like Fig.14 As shown, the number determination unit 934 of the embodiment of the present application includes a continuous number series generation subunit 1410 and a continuous number determination subunit 1420.

[0161] Specifically, the continuous number sequence generating subunit 1410 is configured to generate a continuous number sequence based on the condition column and the continuous grouping column. The continuous number determination subunit 1420 is configured to determine the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed based on the continuous number sequence.

[0162] Fig.15 FIG. 1 is a schematic diagram of the structure of a data processing device provided by another embodiment of the present application. Fig.14 Based on the embodiment shown, Fig.15 The embodiment shown is described below in detail. Fig.15 The embodiment shown and Fig.14 The differences and similarities of the illustrated embodiments are not described in detail.

[0163] like Fig.15 As shown, the continuous number determining subunit 1420 of the embodiment of the present application includes a first or second number determining subunit 1421.

[0164] Specifically, the first or second number determination subunit 1421 is configured to determine the first consecutive number or the second consecutive number based on the positive and negative signs and values ​​of the data in the consecutive number series.

[0165] Exemplary Electronic Devices

[0166] Fig.16 FIG. 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Fig.16 As shown, the electronic device 160 includes: one or more processors 1601 and a memory 1602; and computer program instructions stored in the memory 1602, and when the computer program instructions are executed by the processor 1601, the processor 1601 executes a data processing method as in any of the above embodiments.

[0167] The processor 1601 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0168] The memory 1602 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 1601 may execute the program instructions to implement the steps in the data processing method of each embodiment of the present application described above and / or other desired functions.

[0169] In one example, the electronic device 160 may further include: an input device 1603 and an output device 1604, which are connected via a bus system and / or other forms of connection mechanisms ( Fig.16 ) interconnected.

[0170] In addition, the input device 1603 may also include, for example, a keyboard, a mouse, a microphone, etc.

[0171] The output device 1604 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0172] Of course, to simplify, Fig.16Only some of the components related to the present application in the electronic device 160 are shown, and components such as a bus, an input device / output interface, etc. are omitted. In addition, according to specific application conditions, the electronic device 160 may also include any other appropriate components.

[0173] Exemplary computer-readable storage media

[0174] In addition to the above methods and devices, the embodiments of the present application may also be a computer program product, including computer program instructions, which, when executed by a processor, enable the processor to execute the steps in the data processing method of any of the above embodiments.

[0175] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0176] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the data processing method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0177] Computer readable storage media can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0178] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0179] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0180] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0181] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0182] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

[0183] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A data processing method, characterized in that: include: Determine a list of data to be processed that includes N dimension columns, a date column, and a target value column, wherein each cell in the N dimension columns corresponds to a dimension content, each cell in the date column corresponds to a time node, each cell in the target value column corresponds to a target value, and each row of data in the list of data to be processed corresponds to N dimension contents, one time node, and one target value; Based on the preset continuous condition and the target value column, a condition column is generated in the list of data to be processed, and the condition column is used to indicate whether the target value column satisfies the preset continuous condition; Based on the N dimension columns, the condition column and the date column, determine the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed, wherein the first continuous number is used to represent the number of times that the target values ​​corresponding to the N dimension contents continuously meet the preset continuous condition at the time node, and the second continuous number is used to represent the number of times that the target values ​​corresponding to the N dimension contents continuously do not meet the preset continuous condition at the time node; The determining, based on the N dimension columns, the condition column, and the date column, the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed includes: Based on at least one dimension column among the N dimension columns, the condition column and the date column, the to-be-processed data list is grouped and sorted to obtain a first sorted list; Based on the date column, generating a sort column in the first sort list; Perform subtraction calculation on the date column and the sort column to generate a continuous grouping column; Based on at least one of the N dimension columns, the condition column and the continuous grouping column, a first continuous number or a second continuous number corresponding to each row of data in the list of data to be processed is determined.

2. The data processing method according to claim 1, characterized in that: The step of determining a list of data to be processed that includes N dimension columns, a date column, and a target value column includes: Determine an initial list of data to be processed that includes the N dimension columns, the date column, and the target value column; Based on at least one of the N dimension columns, a grouping operation is performed on the initial list of data to be processed to obtain a first grouping list, wherein the first grouping list includes multiple groups of data, and the value content corresponding to the at least one dimension column in each group of data is the same; Based on the preset date dimension table, performing a date completion operation on the first grouping list to obtain a completion list; The completion list is associated with the first grouping list to obtain the list of data to be processed.

3. The data processing method according to claim 1 or 2, characterized in that: The generating of a condition column in the list of data to be processed based on the preset continuous condition and the target value column comprises: For the target value in each row of the target value column, determining whether the target value satisfies the preset continuity condition; If yes, generating first result data corresponding to the target value, wherein the first result data is used to indicate that the target value satisfies the preset continuity condition; If not, generating second result data corresponding to the target value, the second result data being used to indicate that the target value does not satisfy the preset continuity condition; The condition column is generated based on the first result data and the second result data.

4. The data processing method according to claim 1, characterized in that: The grouping and sorting operation includes a grouping operation and a sorting operation; the grouping and sorting operation is performed on the list of data to be processed based on at least one dimension column among the N dimension columns, the condition column and the date column to obtain a first sorted list, including: Performing a grouping operation on the list of data to be processed based on at least one of the N dimension columns and the condition column to obtain a second grouping list containing multiple groups of data; For each group of data in the plurality of groups of data in the second grouping list, a sorting operation is performed based on the date column to obtain a first sorting list.

5. The data processing method according to claim 1, characterized in that: The determining, based on the condition column and the continuous grouping column, the first continuous number or the second continuous number corresponding to each row of data in the to-be-processed data list comprises: Generate a continuous number sequence based on the condition column and the continuous grouping column; Based on the continuous number series, a first continuous number or a second continuous number corresponding to each row of data in the list of data to be processed is determined.

6. The data processing method according to claim 5, characterized in that: The determining, based on the continuous number series, a first continuous number or a second continuous number corresponding to each row of data in the list of data to be processed includes: The first consecutive number or the second consecutive number is determined based on the positive and negative signs and values ​​of the data in the consecutive number series.

7. A data processing device, characterized in that: include: A first determination module is configured to determine a list of data to be processed comprising N dimension columns, a date column and a target value column, wherein each cell in the N dimension columns corresponds to a dimension content, each cell in the date column corresponds to a time node, each cell in the target value column corresponds to a target value, and each row of data in the list of data to be processed corresponds to N dimension contents, one time node and one target value; A generating module, configured to generate a condition column in the to-be-processed data list based on a preset continuous condition and the target value column, wherein the condition column is used to indicate whether the target value column satisfies the preset continuous condition; A second determination module is configured to determine, based on the N dimension columns, the condition column and the date column, a first continuous number or a second continuous number corresponding to each row of data in the list of data to be processed, wherein the first continuous number is used to represent the number of times at the time node, the target values ​​corresponding to the N dimension contents continuously meet the preset continuous condition, and the second continuous number is used to represent the number of times at the time node, the target values ​​corresponding to the N dimension contents continuously do not meet the preset continuous condition; The determining, based on the N dimension columns, the condition column, and the date column, the first continuous number or the second continuous number corresponding to each row of data in the list of data to be processed includes: Based on at least one dimension column among the N dimension columns, the condition column and the date column, the to-be-processed data list is grouped and sorted to obtain a first sorted list; Based on the date column, generating a sort column in the first sort list; Perform subtraction calculation on the date column and the sort column to generate a continuous grouping column; Based on at least one of the N dimension columns, the condition column and the continuous grouping column, a first continuous number or a second continuous number corresponding to each row of data in the list of data to be processed is determined.

8. A computer-readable storage medium, characterized in that: The storage medium stores instructions, and when the instructions are executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method described in any one of claims 1 to 6.

9. An electronic device, characterized in that: The electronic device comprises: processor; memory for storing computer executable instructions; The processor is used to execute the computer executable instructions to implement the data processing method described in any one of claims 1 to 6 above.

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