Data processing method and related equipment

Abstract

The present disclosure provides a data processing method and related equipment, the method comprising: obtaining a first business data set of a target object within a first time period, the first business data set comprising first business data; determining a second time period of missing data in the first time period according to a field value of a time field of the first business data; determining a field value of a time field of the second business data of the target object within the second time period according to a time value included in the second time period; generating a field value of a business field of the second business data through a corresponding repair method according to a data set category of the first business data set, the second business data comprising a business field and a time field; adding the second business data to the first business data set to obtain a second business data set. The embodiments of the present disclosure can improve the accuracy of data missing repair.

Description

Technical Field

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

[0002] Abnormal missing data is usually random and unpredictable, so it is even more important to detect and automatically supplement missing data. The accuracy of data missing repair in related technologies is low. Summary of the invention

[0003] The present disclosure provides a data processing method and device, an electronic device, a computer-readable storage medium, and a computer program product.

[0004] In a first aspect, the present disclosure provides a data processing method, the data processing method comprising:

[0005] Acquire a first business data set of a target object within a first time period, the first business data set including first business data; determine a second time period of missing data in the first time period according to a field value of a time field of the first business data; determine a field value of a time field of second business data of the target object within the second time period according to a time value included in the second time period; generate a field value of a business field of the second business data by a corresponding repair method according to a data set category of the first business data set, the second business data including a business field and a time field; add the second business data to the first business data set to obtain a second business data set.

[0006] In a second aspect, the present disclosure provides a data processing device, the data processing device comprising:

[0007] A data set acquisition module, used to acquire a first business data set of a target object within a first time period, wherein the first business data set includes first business data;

[0008] a time period determination module, configured to determine a second time period in which data is missing in the first time period according to a field value of a time field of the first business data;

[0009] A time field value determination module, used to determine the field value of the time field of the second business data of the target object within the second time period according to the time value included in the second time period;

[0010] A business field value determination module, configured to generate a field value of a business field of the second business data by a corresponding repair method according to a data set category of the first business data set, wherein the second business data includes a business field and a time field;

[0011] The data adding module is used to add the second business data to the first business data set to obtain a second business data set.

[0012] In a third aspect, the present disclosure provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores one or more computer programs executable by the at least one processor, and one or more of the computer programs are executed by the at least one processor so that the at least one processor can perform the above-mentioned data processing method.

[0013] In a fourth aspect, the present disclosure provides a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying a computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned data processing method.

[0014] The data processing method provided by the embodiment of the present disclosure can determine the time period of missing data according to the time field of the business data in the business data set of the object, and determine the time value of the missing business data according to the time value included in the time period, thereby fully and quantitatively detecting the missing business data and improving the accuracy of data missing detection. Then, according to the data set category of the business data set, the business field of the missing business data is generated through the corresponding repair method; and the repaired missing business data is added to the business data set, thereby differentially repairing different categories of missing data and improving the accuracy of data missing repair.

[0015] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. The above and other features and advantages will become more apparent to those skilled in the art by describing detailed example embodiments with reference to the accompanying drawings, in which:

[0017] Figure 1 It is a schematic diagram of the process from the generation of business data to the storage in the data warehouse in the related technology;

[0018] Figure 2 A schematic diagram of a data table provided for an embodiment of the present disclosure;

[0019] Figure 3A flowchart of a data processing method provided by an embodiment of the present disclosure;

[0020] Figure 4 A schematic diagram of grouping and sorting a data table provided in an embodiment of the present disclosure;

[0021] Figure 5 A schematic diagram of a first business data set provided in an embodiment of the present disclosure;

[0022] Figure 6 A schematic diagram of subtracting first service data from adjacent service data provided in an embodiment of the present disclosure;

[0023] Figure 7 A schematic diagram of a missing period provided for an embodiment of the present disclosure;

[0024] Figure 8 A schematic diagram of a missing period provided for an embodiment of the present disclosure;

[0025] Fig. 9 A schematic diagram of an example time dimension table provided for an embodiment of the present disclosure;

[0026] Fig.10 A schematic diagram of the field value of the time field in the second business data provided by an embodiment of the present disclosure;

[0027] Fig.11 A schematic diagram of second business data corresponding to a second time period provided in an embodiment of the present disclosure;

[0028] Fig.12 A schematic diagram of a pipeline data set provided for an embodiment of the present disclosure;

[0029] Fig.13 A block diagram of a data processing device provided in an embodiment of the present disclosure;

[0030] Fig.14 A block diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0032] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0033] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0034] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a" and "the" are also intended to include plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "including" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded. "Connected" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0035] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.

[0036] In the technical solution disclosed in this disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution complies with relevant national laws and regulations (for example, the "Information Security Technology Personal Information Security Specification", etc.). For example: corresponding prescribed measures are taken for personal information access control; prescribed restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; when using personal information, clear identity orientation is eliminated to avoid precise positioning of specific individuals.

[0037] Figure 1 It is a schematic diagram of the business data from generation to storage in the data warehouse in the related technology. Figure 1In the process of business data being generated from the business system to finally being stored in the data warehouse, there will be data transfer at multiple nodes in the middle, and any abnormality at any node may lead to data loss. The specific data transfer steps include: data is generated in the business system, specifically, the data can be stored in a relational database in the business system (①); the data is processed by ETL and sent to the data warehouse to achieve ETL warehousing (②), where ETL refers to the process of extracting, transforming, and loading data from the source end to the destination end; data is processed in the data warehouse (③), specifically, data processing can be performed using hive or spark, hive is a data warehouse tool used for data extraction, transformation, and loading, spark is a fast and general computing engine designed for large-scale data processing; data is stored (④) in the HDFS (Hadoop Distributed File System) in the data warehouse. Specific scenarios of data loss include but are not limited to the following: 1) abnormal data deletion in the business system; 2) errors in the ETL warehousing process, resulting in data omission; 3) abnormal data processing process, resulting in data being mistakenly deleted; 4) cluster abnormality, the underlying storage HDFS block is lost.

[0038] Figure 2 A schematic diagram of a data table provided in an embodiment of the present disclosure. In the related art, the business data is sorted according to the time field, and for the business data corresponding to two numerically adjacent field values ​​in the time field, it is determined whether the value of the object identification field exists in the business data corresponding to the field value with a smaller value in the time field, but does not exist in the business data corresponding to the field value with a larger value in the time field. If this is the case, it indicates that there is data missing. For example, Figure 2In the data, the values ​​of the object identification field in the business data corresponding to 2020-01-01 include A and B; the values ​​of the object identification field in the business data corresponding to 2020-01-02 include A, B, and C; the values ​​of the object identification field in the business data corresponding to 2020-01-03 include B, C, D, and E; the values ​​of the object identification field in the business data corresponding to 2020-01-04 include B, C, and D; and the values ​​of the object identification field in the business data corresponding to 2020-01-05 include A, B, and D. The value of the object identification field in the business data corresponding to 2020-01-02 is compared with the value of the object identification field in the business data corresponding to 2020-01-01. The value C of the object identification field is missing, that is, the business data corresponding to the time field value of 2020-01-02 and the value of the object identification field C is missing; the value of the object identification field in the business data corresponding to 2020-01-03 is compared with the value of the object identification field in the business data corresponding to 2020-01-02. The value A of the object identification field is missing, that is, the business data corresponding to the time field value of 2020-01-03 and the value of the object identification field A is missing; Comparing the value of the object identification field in the business data corresponding to 2020-01-04 with the value of the object identification field in the business data corresponding to 2020-01-03, the value E of the object identification field is missing, that is, the business data corresponding to the time field value of 2020-01-04 and the value of the object identification field is E is missing; comparing the value of the object identification field in the business data corresponding to 2020-01-05 with the value of the object identification field in the business data corresponding to 2020-01-04, the value C of the object identification field is missing, that is, the business data corresponding to the time field value of 2020-01-05 and the value of the object identification field is C is missing. However, by detecting data missing in this way, it can be found that Figure 2 In the example, the business data corresponding to the value of the object identification field A is missing not only when the value of the time field is 2020-01-03, but also when the value of the time field is 2020-01-04. When the business data of the same object is missing at multiple consecutive values ​​of the time field, it cannot be detected. The accuracy of data missing detection in the related art is low. Since data missing repair needs to be performed on the premise that data missing is detected, the accuracy of data missing repair in the related art is low.

[0039] In order to solve the problem of low accuracy of data missing repair in the related art, the present disclosure provides a data processing method and device, an electronic device, and a computer program product.

[0040] The data processing method according to the embodiment of the present disclosure can be executed by an electronic device such as a terminal device or a server, and the terminal device can be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be an independent physical server, a server cluster composed of multiple physical servers, or a cloud server capable of cloud computing. The method can be implemented by a processor calling a computer-readable program instruction stored in a memory.

[0041] Figure 3 A flowchart of a data processing method provided by an embodiment of the present disclosure. Figure 3 , the method comprising:

[0042] Step 301: Acquire a first business data set of a target object in a first time period, where the first business data set includes first business data.

[0043] For example, the data tables in the business database can be split according to the objects to obtain the business data sets of the objects. Figure 2 The data table shown includes business data of multiple objects. Each business data includes an object identification field, a time field, and a business field. The object identification field is used to indicate the corresponding object. Figure 2 The object identifier in the time field is used to indicate the time when the object handles the business or the cross-sectional time when the data warehouse of the business system extracts business data. Figure 2 The business field is used to indicate the business content handled by the object. Figure 2 Each object has an object identifier, such as A, B, C, etc. The object can be an entity such as an order, a user, or a product. The present disclosure does not limit the type and quantity of objects in the data table, the specific type of business data, etc.

[0044] Figure 4 A schematic diagram of grouping and sorting a data table provided in an embodiment of the present disclosure. Figure 4 As shown, it can be based on the object and time Figure 4 Group and sort the business data in the data table on the left to obtain Figure 4 The data table after grouping and sorting on the right. Figure 4There are three values ​​of the object identification field, namely A, B, and C. First, the business data in the data table is grouped according to the value of the object identification field to obtain the data set of object A, the data set of object B, and the data set of object C. Then, the business data in the data set of object A is sorted according to the value of the time field, the business data in the object data set of object B is sorted according to the value of the time field, and the business data in the data set of object C is sorted according to the value of the time field to obtain the sorted business data sets.

[0045] The first business data in the object data set may be sorted in ascending order or in descending order according to the time field, and the present disclosure does not impose any limitation on this.

[0046] In some possible implementations, any object in the data table can be used as a target object to obtain a first business data set of the target object in a first period. The first period can be any historical period in which business data is to be processed, for example, the first period is [2020-01-01, 2020-01-31]; the first business data set includes the first business data of the target object in the first period. The first business data set can be a full data set in the first period.

[0047] Figure 5 Schematic diagram of a first business data set provided by an embodiment of the present disclosure. Figure 5 As shown, the target object is object A, the first period is [2020-01-01, 2020-01-31], the field value of the time field is the time value of the first business data, in dates; there are multiple business fields, namely, loan amount, principal balance and settlement date.

[0048] Step 302: Determine a second time period in which data is missing in the first time period according to the field value of the time field of the first business data.

[0049] That is, after sorting the first business data in the first business data set, it is possible to determine whether there is missing business data between two adjacent first business data according to the difference between the field values ​​of the time fields of the two adjacent first business data. Figure 5For the first and second first business data in the data, the field values ​​of the time field are "2020-01-01" and "2020-01-02" respectively, and the difference is 1. There is no missing business data between the first and second first business data; and for the two adjacent first business data with the field values ​​of the time field being "2020-01-05" and "2020-01-11", the difference is 6, which is greater than 1. There is missing business data between the two first business data, and the time period of the missing data is [2020-01-06, 2020-01-10], that is, the time period corresponding to the field value of the time field of the previous first business data plus 1 to the field value of the time field of the next first business data minus 1.

[0050] In this way, by processing each group of adjacent first service data, all time periods with missing data in the first time period can be determined to obtain the second time period.

[0051] Step 303: Determine the field value of the time field of the second business data of the target object within the second time period according to the time value included in the second time period.

[0052] That is to say, the business data in the second time period can be called the second business data, and each piece of the second business data includes a business field and a time field. For the time field of the second business data, the field value of the time field of the second business data can be determined according to the time value included in the second time period. For example, if one of the time periods in the second time period is [2020-01-06, 2020-01-10], there are 5 corresponding second business data, and the field values ​​of the time field of each piece of the second business data are 2020-01-06, 2020-01-07, 2020-01-08, 2020-01-09, and 2020-01-10, respectively.

[0053] In this way, the second service data missing in the second time period can be supplemented and the field value of the time field of the second service data can be determined, thereby repairing the service field of the second service data.

[0054] Step 304: Generate a field value of a business field of the second business data by a corresponding repair method according to the data set category of the first business data set.

[0055] For example, data set categories may include flow categories and status categories. The flow category corresponds to the flow table. The business data in the data set of the flow category remains unchanged after generation. The business data in the data set of the flow category records business events, such as repayment events, loan events, collection events, etc. Once they occur, the field values ​​of all business fields will no longer change. The status category corresponds to the status table. The business data in the data set of the status category changes with business changes after generation. The business data in the data set of the flow category records the state of the object at a certain time, which will change with changes in the business, such as the customer's loan balance. As the customer repays, the balance value will gradually decrease to 0. The present disclosure does not limit the specific content of the business data record.

[0056] In some possible implementations, the service field of the second service data may be repaired according to the data set category of the first service data set.

[0057] In the example, if the data set category of the first business data set is a flow category, since the business data remains unchanged after being generated, the field value of the business field of the first business data of the previous time value or the next time value of the missing period where the second business data is located can be directly used as the field value of the business field of the second business data. For example, for the second business data whose field value of the time field is 2020-01-06 and whose missing period is [2020-01-06, 2020-01-10], the field value of the business field of the first business data of 2020-01-05 or 2020-01-11 can be used as the field value of the business field of the second business data.

[0058] In the example, if the data set category of the first business data set is a status category, since the business data changes with business changes after it is generated, it can be processed differently according to the field category of each business field itself and the situation of the first business data before and after the missing period, so that the field value of the generated business field conforms to the actual changes.

[0059] Step S305: Add the second business data to the first business data set to obtain a second business data set.

[0060] That is, after the time field and the service field of the second service data are repaired, the second service data can be added to the first service data set to obtain a repaired second service data set.

[0061] According to the embodiments of the present disclosure, the time period of missing data can be determined based on the time field of the business data in the business data set of the object, and the time value of the missing business data can be determined based on the time value included in the time period, thereby fully and quantitatively detecting the missing business data and improving the accuracy of data missing detection. Then, according to the data set category of the business data set, the business field of the missing business data is generated through the corresponding repair method; and the repaired missing business data is added to the business data set, thereby differentially repairing different categories of missing data and improving the accuracy of data missing repair.

[0062] The data processing method according to the embodiment of the present disclosure is described below.

[0063] As mentioned above, a first business data set of the target object in the first time period can be obtained in step 301. The first business data set can be expressed as Figure 5 The data table shown includes multiple first business data, each of which includes a time field, an object identification field and a business field, for example, the first business data: time field "2020-01-01"; object identification field "A"; business field "1000; 1000; empty".

[0064] In step 302, a second period of missing data in the first period is determined based on the field value of the time field of the first service data. Whether there is missing service data between two adjacent first service data can be determined based on the difference between the field values ​​of the time fields of the two adjacent first service data.

[0065] In some possible implementations, step 302 may include:

[0066] For each piece of first business data, determine a first difference between a field value of a time field of the first business data and a field value of a time field of adjacent business data, where the adjacent business data is business data in the first business data set, whose time is before the first business data, and whose field value of a time field has the smallest difference with the field value of the time field of the first business data;

[0067] A second time period in which data is missing in the first time period is determined according to the first difference.

[0068] For example, the first difference value can be determined by subtracting the field value of the time field between each first business data and the adjacent business data, where the adjacent business data is the business data in the first business data set, before the first business data, and with the smallest difference between the field value of the time field and the field value of the time field of the first business data, that is, the previous business data of each first business data in the sorted data table.

[0069] Figure 6 Schematic diagram of subtracting first service data from adjacent service data provided in an embodiment of the present disclosure. Figure 6 As shown, Figure 5 The case where the first business data in the first business data set is subtracted from the adjacent business data. For any first business data, if the first business data is the first in the first business data set, the adjacent business data and the first difference are both empty; if the first business data is not the first in the first business data set, the field value of the time field of the previous adjacent business data is subtracted to obtain the first difference, for example, "2020-01-02" - "2020-01-01", and the first difference is 1.

[0070] In some possible implementations, if the first difference is equal to a preset value, it is considered that there is no missing data between the first service data and the adjacent service data; if the first difference is greater than the preset value, it is considered that there is missing data between the first service data and the adjacent service data, and the corresponding missing period can be determined. The start time of the corresponding missing period is the field value of the time field of the adjacent service data plus the preset value, and the end time is the field value of the time field of the first service data minus the preset value.

[0071] For example, Figure 6 The preset value of the first business data set is set to 1, that is, one day; for the first business data whose field value of the time field is "2020-01-11", the difference between it and the adjacent business data is "2020-01-11" - "2020-01-05", and the first difference is 6, which is greater than 1, then there is missing data, and the start time of the corresponding missing period is the field value of the time field of the adjacent business data + 1, and the end time is the field value of the time field of the first business data - 1, that is, the missing period is [2020-01-06, 2020-01-10].

[0072] In this way, by processing each first business data, one or more corresponding missing time periods can be obtained, which are collectively used as the second time period of the missing data in the first time period.

[0073] Figure 7 A schematic diagram of a missing period provided by an embodiment of the present disclosure. Figure 6 After calculating the difference for each first service data in the adjacent service data, the start time and end time of each missing period can be determined according to the field values ​​of the time fields of the corresponding first service data and the adjacent service data, such as Figure 7As shown, four missing time periods are obtained: [2020-01-06, 2020-01-10], [2020-01-16, 2020-01-16], [2020-01-21, 2020-01-22], and [2020-01-30, 2020-01-30], which together constitute the second time period with missing data in the first time period.

[0074] Figure 8 Schematic diagram of missing time periods provided by embodiments of the present disclosure. Figure 8 As shown, after intuitive representation, the missing time periods correspond to the four time periods on the coordinate axis [2020-01-06, 2020-01-10], 2020-01-16, [2020-01-21, 2020-01-22], and 2020-01-30. Among them, the two time periods of 2020-01-16 and 2020-01-30 have the same start time and end time, and there is only one time value.

[0075] In this way, missing data can be fully detected and the occurrence of missing periods being missed when detecting missing data can be reduced; determining the second period of missing data in the data set based on all missing periods can improve the completeness of the acquired second period and improve the accuracy of missing data detection.

[0076] In some possible implementations, in step 303, the field value of the time field of the second business data of the target object within the second time period may be determined according to the time value included in the second time period.

[0077] For the second business data in the second time period, the time value included in the second time period can be determined in combination with the time dimension table; and based on the time value included in the second time period, the field value of the time field of the second business data is determined.

[0078] Among them, each row in the time dimension table corresponds to a time. Specifically, the time dimension table refers to a dimension table that records time. The time dimension table includes all time and time-related information within the time range required for the business. The time dimension table is stored in the database by row, that is, each row in the time dimension table corresponds to a time. In the embodiment of the present disclosure, only the time corresponding to each row in the time dimension table is used, and time-related information is not used. The unit time can be one day, one hour, one month, or other values. Each row in the time dimension table can correspond to a specific date, a specific hour, a specific month, or other values. The present disclosure uses the example of each row in the time dimension table corresponding to a specific date for explanation, but does not limit this.

[0079] For example, Fig. 9As shown, it is a schematic diagram of an example time dimension table provided in an embodiment of the present disclosure. Fig. 9 In the time dimension table, the date and time related information is included. Fig. 9 The time-related information is represented by xxx, and the actual time-related information is not listed. The time-related information includes the year, month, quarter, week number, and day of the week to which the date belongs.

[0080] Since the time dimension table already includes the specific time, the time values ​​included in each missing time period in the second time period can be directly determined according to the time dimension table, thereby improving the efficiency of data missing repair.

[0081] For example, if a missing period in the second period is [2020-01-06, 2020-01-10], the time values ​​included are 2020-01-06, 2020-01-07, 2020-01-08, 2020-01-09, and 2020-01-10 respectively; there are 5 corresponding second business data, and the field values ​​of the time fields of each second business data are 2020-01-06, 2020-01-07, 2020-01-08, 2020-01-09, and 2020-01-10 respectively. In this way, by processing each missing period, the missing second business data in the second period can be supplemented.

[0082] Fig.10 A schematic diagram of the field value of the time field in the second business data provided by an embodiment of the present disclosure. Figure 7 The four missing time periods [2020-01-06, 2020-01-10], [2020-01-16, 2020-01-16], [2020-01-21, 2020-01-22], and [2020-01-30, 2020-01-30] in the second business data can be respectively determined to obtain the following: Fig.10 In the 9 pieces of second business data shown, the field values ​​of the time field are 2020-01-06, 2020-01-07, 2020-01-08, 2020-01-09, 2020-01-10, 2020-01-16, 2020-01-21, 2020-01-22, and 2020-01-30, respectively.

[0083] In some possible implementations, in step 304, the field value of the business field of the second business data may be generated by a corresponding repair method according to the data set category of the first business data set.

[0084] In some possible implementations, the data set categories may include flow categories and status categories. The flow category corresponds to the flow table. The business data in the data set of the flow category remains unchanged after generation. The business data in the data set of the flow category records business events, such as repayment events, loan events, collection events, etc. Once they occur, the field values ​​of all business fields will no longer change. The status category corresponds to the status table. The business data in the data set of the status category changes with the business after generation. The business data in the data set of the flow category records the state of the object at a certain time, which will change with the changes in the business, such as the customer's loan balance. As the customer repays, the balance value will gradually decrease to 0. The present disclosure does not limit the specific content of the business data record.

[0085] The business field of the second business data may be repaired according to the data set category of the first business data set.

[0086] In some possible implementations, the data set category includes a pipeline category, and the business data in the data set of the pipeline category remains unchanged after being generated; step S304 includes:

[0087] In the case where the data set category of the first business data set is a flow category, for each missing period of the second period, a field value of a business field of the third business data or a field value of a business field of the fourth business data adjacent to the missing period is determined as the field value of the business field of the second business data in the missing period,

[0088] The third service data is the first service data corresponding to the time value before the starting time value of the missing period, and the fourth service data is the first service data corresponding to the time value after the ending time value of the missing period.

[0089] For example, if the data set category of the first business data set is a flow category, since the business data remains unchanged after being generated, for any missing period, the field value of the business field of the third business data adjacent to the missing period or the field value of the business field of the fourth business data can be directly used as the field value of the business field of the second business data in the missing period. Among them, the third business data is the first business data corresponding to the time value before the starting time value of the missing period, and the fourth business data is the first business data corresponding to the time value after the ending time value of the missing period.

[0090] For example, for the missing period [2020-01-06, 2020-01-10], it includes 5 second business data, and the field values ​​of the time field are 2020-01-06, 2020-01-07, 2020-01-08, 2020-01-09, and 2020-01-10 respectively; the field value of the business field of the first business data of 2020-01-05 or 2020-01-11 can be used as the field value of the business field of these 5 second business data.

[0091] In this way, the business field of the second business data that is missing in the data set of the flow category can be quickly repaired, thereby improving the repair efficiency.

[0092] In some possible implementations, the data set category includes a state category, and the business data in the data set of the state category changes with business changes after being generated; step S304 includes:

[0093] In the case where the data set category of the first business data set is a state category, determining the field category of each business field of the second business data respectively, wherein the field category includes a static category and a dynamic category, wherein the business field of the static category is a business field that remains unchanged after being generated, and the business field of the dynamic category is a business field that changes with business changes after being generated;

[0094] For each missing period of the second period, the field value of each business field is determined respectively by a corresponding repair method according to the field category of each business field of the second business data in the missing period.

[0095] For example, in a data set of the flow category, the field values ​​of all business fields of the business data remain unchanged after the business occurs, while in a data set of the status category, the field values ​​of some business fields of the business data may remain unchanged after the business occurs, while the field values ​​of some business fields may change as the business changes after the business occurs.

[0096] In this case, if the data set category of the first business data set is a status category, it can be further processed differently based on the field category of each business field itself and the situation of the first business data before and after the missing period, so that the field value of the generated business field conforms to the actual changes.

[0097] In some possible implementations, the field categories include static categories and dynamic categories, wherein the business fields of the static category are business fields that remain unchanged after being generated, for example Figure 5 Business fields in: Loan amount, dynamic category business fields are business fields that change with business changes after generation, for example Figure 5Business fields in: principal balance and settlement date.

[0098] In some possible implementations, for each missing period of the second period, the field value of each business field can be determined respectively by a corresponding repair method according to the field category of each business field of the second business data in the missing period. This step includes:

[0099] For the first service field whose field category is a static category, the field value of the first service field of the third service data adjacent to the missing period or the field value of the first service field of the fourth service data is determined as the field value of the first service field of the second service data in the missing period, wherein the third service data is the first service data corresponding to the time value before the starting time value of the missing period, and the fourth service data is the first service data corresponding to the time value after the ending time value of the missing period;

[0100] For a second business field whose field category is a dynamic category, the field value of the second business field of the second business data in the missing period is determined according to the first field value of the second business field in the third business data and the second field value of the second business field in the fourth business data.

[0101] For example, for each business field of a second business data in the missing period, if the field category of the business field is a static category, it is called a first business field; since the first business field of the static category remains unchanged after generation, the field value of the first business field of the third business data adjacent to the missing period or the field value of the first business field of the fourth business data can be directly used as the field value of the first business field of the second business data.

[0102] Fig.11 This is a schematic diagram of the second service data corresponding to the second time period provided by the embodiment of the present disclosure. Fig.11 As shown, for the first business field of the second business data (the field value of the time field is 2020-01-06) whose field category is a static category: loan amount, the field value 1000 of the first business field (loan amount) of the first business data with the previous time value 2020-01-05 or the next time value 2020-01-11 of the corresponding missing period [2020-01-06, 2020-01-10] can be used as the field value of the first business field of the second business data to achieve the repair of the first business field of the static category.

[0103] In some possible implementations, for each business field of a second business data in the missing period, if the field category of the business field is a dynamic category, it is called a second business field; since the first business field of the dynamic category changes with business changes after it is generated, the field value of the second business field of the second business data can be determined based on the field value of the business field of the third business data adjacent to the missing period and the field value of the second business field of the fourth business data, as well as the difference between the field values ​​and other information.

[0104] In some possible implementations, the step of determining the field value of the second business field of the second business data in the missing period according to the first field value of the second business field in the third business data and the second field value of the second business field of the fourth business data includes:

[0105] When both the first field value and the second field value are numerical values ​​and the first field value is the same as the second field value, determining the first field value as the field value of the second service field of the second service data in the missing period;

[0106] When both the first field value and the second field value are numerical values ​​and the first field value is different from the second field value, determining the field value of the second business field of the second business data in the missing period according to the difference between the first field value and the second field value and the running data set corresponding to the first business data set;

[0107] When the first field value is a null value and the second field value is a null value or a time value, determining the field value of the second service field of the second service data in the missing period to be a null value;

[0108] In a case where the first field value is a time value and the second field value is a null value, the time value is determined as the field value of the second service field of the second service data in the missing period.

[0109] For example, for a second business field of the second business data in the missing period, if the first field value in the third business data and the second field value in the fourth business data are both numerical values, it means that the second business field is a numerical field, such as a dynamic category field such as "principal balance"; if the first field value is the same as the second field value, it indicates that the second business field has not changed in the missing period, and the first field value can be determined as the field value of the second business field of the second business data in the missing period.

[0110] In some possible implementations, if the first field value in the third business data and the second field value in the fourth business data are both numerical values, and the first field value is different from the second field value, it indicates that the second business field has changed during the missing period. In this case, the processing can be combined with the pipeline data set corresponding to the first business data set.

[0111] Fig.12 Schematic diagram of a flow data set provided by an embodiment of the present disclosure. Fig.12 As shown, the transaction data set includes the repayment business data of the target object A, including the time field "repayment time" and the business field "repayment amount".

[0112] In the example, for each piece of second service data in the missing period [2020-01-06, 2020-01-10] (see Fig.11 ), based on the first field value "1000" in the third business data (the field value of the time field is 2020-01-05) and the first field value "800" in the fourth business data (the field value of the time field is 2020-01-11), combined with Fig.12 For the business data (2020-01-07) whose time field "Repayment Time" falls in the missing period [2020-01-06, 2020-01-10], the business field "Repayment Amount" is "200", then it can be determined that the field value of the second business field "Principal Balance" of the second business data (2020-01-06) is "1000", indicating that no repayment occurred on 2020-01-06, and the "Principal Balance" remains unchanged; the field value of the second business field "Principal Balance" of the second business data (2020-01-07, 2020-01-08, 2020-01-09, 2020-01-10) is "800", indicating that after the repayment occurred on 2020-01-07, the "Principal Balance" changed to "800".

[0113] In this way, after processing each missing period, the service field repair can be achieved when the second service field is a numerical field.

[0114] In some possible implementations, if there are null values ​​and / or time values ​​in the first field value and the second field value, it means that the second business field is a non-numeric field, such as a dynamic field such as "settlement date"; if the first field value and the second field value are both null values, it indicates that the second business field has not changed during the missing period, and the field value of the second business field of the second business data in the missing period can be set to a null value; if the first field value is a null value and the second field value is a time value, it indicates that the second business field has changed after the missing period but has not changed during the missing period, and the field value of the second business field of the second business data in the missing period can also be set to a null value.

[0115] In some possible implementations, the first field value is a time value, and the second field value is a null value or a time value, indicating that a change occurred before the missing period but no change occurred during the missing period. The field value of the second business field of the second business data in the missing period can be set to the time value corresponding to the first field value.

[0116] Thus, for Fig.11 The second business data shown has repaired the business fields "loan amount" of the static category, "principal balance" (numeric field) and "settlement date" (non-numeric field) of the dynamic category, and obtained the second business data with complete business fields.

[0117] In this way, different repair methods can be configured according to whether the business field is a static category or a dynamic category, and based on the value type under the dynamic category, to generate different business fields of missing business data, thereby differentially repairing missing data of different categories and improving the accuracy of missing data repair.

[0118] In some possible implementations, in step 305, the second business data is added to the first business data set to obtain the second business data set. That is, after the time field and the business field of the second business data are repaired, the second business data can be added to the first business data set to obtain the repaired second business data set, which is the full data set of the target object.

[0119] In this way, the above processing method can be used to complete and repair the missing business data of the first business data set of each object respectively.

[0120] According to the data processing method of the embodiment of the present disclosure, the time period of missing data can be determined based on the time field of the business data in the business data set of the object, and the time value of the missing business data can be determined based on the time value included in the time period, thereby fully and quantitatively detecting the missing business data, reducing the situation where multiple business data of the same object are missing and the detection is incomplete, and improving the accuracy of data missing detection.

[0121] Furthermore, according to the data processing method of the embodiment of the present disclosure, it is possible to generate business fields of missing business data through corresponding repair methods according to the data set category of the business data set; and add the repaired missing business data to the business data set, thereby differentially repairing different categories of missing data, thereby further improving the accuracy of missing data repair on the basis of improving the accuracy of missing data detection.

[0122] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not repeat them. It can be understood by those skilled in the art that in the above-mentioned method of the specific implementation method, the specific execution order of each step should be determined according to its function and possible internal logic.

[0123] In addition, the present disclosure also provides a data processing device, an electronic device, and a computer program product, all of which can be used to implement any data processing method provided by the present disclosure. The corresponding technical solutions and descriptions are referred to in the corresponding records of the method part and will not be repeated here.

[0124] Fig.13 A block diagram of a data processing device provided in an embodiment of the present disclosure.

[0125] Reference Fig.13 , an embodiment of the present disclosure provides a data processing device, the data processing device comprising:

[0126] A data set acquisition module 131 is used to acquire a first business data set of a target object within a first period of time, wherein the first business data set includes first business data;

[0127] A time period determination module 132, configured to determine a second time period in which data is missing in the first time period according to a field value of a time field of the first business data;

[0128] A time field value determination module 133, configured to determine a field value of a time field of second business data of the target object within the second time period according to a time value included in the second time period;

[0129] A business field value determination module 134, configured to generate a field value of a business field of the second business data by a corresponding repair method according to the data set category of the first business data set, wherein the second business data includes a business field and a time field;

[0130] The data adding module 135 is used to add the second business data to the first business data set to obtain a second business data set.

[0131] In some possible implementations, the time period determination module 132 is configured to:

[0132] For each first business data, determine a first difference between the field value of the time field of the first business data and the field value of the time field of adjacent business data, where the adjacent business data is business data that is in the first business data set and is before the first business data, and whose field value of the time field has the smallest difference with the field value of the time field of the first business data; based on the first difference, determine the second time period in which data is missing in the first time period.

[0133] In some possible implementations, the data set category includes a pipeline category, and the business data in the data set of the pipeline category remains unchanged after being generated; the business field value determination module 134 is used to:

[0134] When the data set category of the first business data set is a flow category, for each missing period of the second time period, the field value of the business field of the third business data adjacent to the missing period or the field value of the business field of the fourth business data is determined as the field value of the business field of the second business data in the missing period, wherein the third business data is the first business data corresponding to the time value before the starting time value of the missing period, and the fourth business data is the first business data corresponding to the time value after the ending time value of the missing period.

[0135] In some possible implementations, the data set category includes a state category, and the business data in the data set of the state category changes with business changes after being generated; the business field value determination module 134 is used to:

[0136] When the data set category of the first business data set is a status category, the field category of each business field of the second business data is determined respectively, and the field category includes a static category and a dynamic category, wherein the business field of the static category is a business field that remains unchanged after generation, and the business field of the dynamic category is a business field that changes with business changes after generation; for each missing period of the second time period, the field value of each business field is determined respectively according to the field category of each business field of the second business data in the missing period through the corresponding repair method.

[0137] In some possible implementations, the business field value determination module 134 is configured to:

[0138] For the first business field whose field category is a static category, the field value of the first business field of the third business data adjacent to the missing period or the field value of the first business field of the fourth business data is determined as the field value of the first business field of the second business data in the missing period, wherein the third business data is the first business data corresponding to the time value before the start time value of the missing period, and the fourth business data is the first business data corresponding to the time value after the end time value of the missing period; for the second business field whose field category is a dynamic category, the field value of the second business field of the second business data in the missing period is determined according to the first field value of the second business field in the third business data and the second field value of the second business field of the fourth business data.

[0139] In some possible implementations, the business field value determination module 134 is configured to:

[0140] When both the first field value and the second field value are numerical values ​​and the first field value is the same as the second field value, determining the first field value as the field value of the second service field of the second service data in the missing period;

[0141] When both the first field value and the second field value are numerical values ​​and the first field value is different from the second field value, determining the field value of the second business field of the second business data in the missing period according to the difference between the first field value and the second field value and the running data set corresponding to the first business data set;

[0142] When the first field value is a null value and the second field value is a null value or a time value, determining the field value of the second service field of the second service data in the missing period to be a null value;

[0143] In a case where the first field value is a time value and the second field value is a null value or a time value, the time value is determined as the field value of the second service field of the second service data in the missing period.

[0144] Each module in the above data processing device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0145] Fig.14 A block diagram of an electronic device provided in an embodiment of the present disclosure.

[0146] Reference Fig.14An embodiment of the present disclosure provides an electronic device, which includes: at least one processor 1801; at least one memory 1802, and one or more I / O interfaces 1803, connected between the processor 1801 and the memory 1802; wherein the memory 1802 stores one or more computer programs that can be executed by the at least one processor 1801, and the one or more computer programs are executed by the at least one processor 1801, so that the at least one processor 1801 can execute the above-mentioned data processing method.

[0147] Each module in the above electronic device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0148] The present disclosure also provides a computer-readable storage medium on which a computer program is stored, wherein the computer program implements the above-mentioned data processing method when executed by a processor / processing core. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium.

[0149] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned data processing method.

[0150] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable storage medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium).

[0151] As is known to those of ordinary skill in the art, the term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable program instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technology, portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. In addition, it is known to those of ordinary skill in the art that communication media typically contain computer-readable program instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and may include any information delivery medium.

[0152] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0153] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's 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., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0154] The computer program product described herein may be implemented in hardware, software, or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK), etc.

[0155] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0156] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0157] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0158] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0159] Example embodiments have been disclosed herein, and although specific terms are employed, they are used and should be interpreted only in a general illustrative sense and not for limiting purposes. In some instances, it will be apparent to those skilled in the art that, unless otherwise expressly noted, features, characteristics, and / or elements described in conjunction with a particular embodiment may be used alone or in combination with features, characteristics, and / or elements described in conjunction with other embodiments. Therefore, those skilled in the art will appreciate that various changes in form and detail may be made without departing from the scope of the present disclosure as set forth in the appended claims.

Claims

1. A data processing method, characterized in that: include: Acquire a first business data set of a target object within a first time period, wherein the first business data set includes first business data; Determine, according to the field value of the time field of the first service data, a second time period in which data is missing in the first time period; Determining, according to the time value included in the second time period, a field value of a time field of the second business data of the target object within the second time period; generating, according to the data set category of the first business data set, a field value of a business field of the second business data by a corresponding repair method, wherein the second business data includes a business field and a time field; The second business data is added to the first business data set to obtain a second business data set.

2. The method according to claim 1, characterized in that The determining, according to the field value of the time field of the first service data, a second time period in which data is missing in the first time period comprises: For each piece of first business data, determine a first difference between a field value of a time field of the first business data and a field value of a time field of adjacent business data, where the adjacent business data is business data in the first business data set, whose time is before the first business data, and whose field value of a time field has the smallest difference with the field value of the time field of the first business data; A second time period in which data is missing in the first time period is determined according to the first difference.

3. The method according to claim 1, characterized in that The data set category includes a pipeline category, and the business data in the data set of the pipeline category remains unchanged after being generated; The generating, according to the data set category of the first business data set, a field value of a business field of the second business data by a corresponding repairing method includes: In the case where the data set category of the first business data set is a flow category, for each missing period of the second period, a field value of a business field of the third business data or a field value of a business field of the fourth business data adjacent to the missing period is determined as the field value of the business field of the second business data in the missing period, The third service data is the first service data corresponding to the time value before the starting time value of the missing period, and the fourth service data is the first service data corresponding to the time value after the ending time value of the missing period.

4. The method according to claim 1, characterized in that The data set categories include state categories, and the business data in the data set of the state category changes with business changes after being generated; The generating, according to the data set category of the first business data set, a field value of a business field of the second business data by a corresponding repairing method includes: In the case where the data set category of the first business data set is a state category, determining the field category of each business field of the second business data respectively, wherein the field category includes a static category and a dynamic category, wherein the business field of the static category is a business field that remains unchanged after being generated, and the business field of the dynamic category is a business field that changes with business changes after being generated; For each missing period of the second period, the field value of each business field is determined respectively by a corresponding repair method according to the field category of each business field of the second business data in the missing period.

5. The method according to claim 4, characterized in that According to the field category of the field value of each business field of the second business data in the missing period, the field value of each business field is determined respectively by a corresponding repair method, including: For the first service field whose field category is a static category, the field value of the first service field of the third service data adjacent to the missing period or the field value of the first service field of the fourth service data is determined as the field value of the first service field of the second service data in the missing period, wherein the third service data is the first service data corresponding to the time value before the starting time value of the missing period, and the fourth service data is the first service data corresponding to the time value after the ending time value of the missing period; For a second business field whose field category is a dynamic category, the field value of the second business field of the second business data in the missing period is determined according to the first field value of the second business field in the third business data and the second field value of the second business field in the fourth business data.

6. The method according to claim 5, characterized in that The determining, according to the first field value of the second service field in the third service data and the second field value of the second service field in the fourth service data, the field value of the second service field in the second service data in the missing period includes: When both the first field value and the second field value are numerical values ​​and the first field value is the same as the second field value, determining the first field value as the field value of the second service field of the second service data in the missing period; When both the first field value and the second field value are numerical values ​​and the first field value is different from the second field value, determining the field value of the second business field of the second business data in the missing period according to the difference between the first field value and the second field value and the running data set corresponding to the first business data set; When the first field value is a null value and the second field value is a null value or a time value, determining the field value of the second service field of the second service data in the missing period to be a null value; In a case where the first field value is a time value and the second field value is a null value or a time value, the time value is determined as the field value of the second service field of the second service data in the missing period.

7. A data processing device, characterized in that: include: A data set acquisition module, used to acquire a first business data set of a target object within a first time period, wherein the first business data set includes first business data; a time period determination module, configured to determine a second time period in which data is missing in the first time period according to a field value of a time field of the first business data; A time field value determination module, used to determine the field value of the time field of the second business data of the target object within the second time period according to the time value included in the second time period; A business field value determination module, configured to generate a field value of a business field of the second business data by a corresponding repair method according to a data set category of the first business data set, wherein the second business data includes a business field and a time field; The data adding module is used to add the second business data to the first business data set to obtain a second business data set.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor to enable the at least one processor to perform the data processing method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program implements the data processing method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the data processing method as described in any one of claims 1 to 6.

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