Data processing method, device, electronic device and storage medium

Abstract

The embodiment of the present invention discloses a data processing method, device, electronic device and storage medium, the method comprising: obtaining original basic data based on a data pointer, and determining target screening data from the original basic data based on at least one indicator item; determining at least one aggregation identifier based on the data content of the target indicator item in at least one indicator item; and updating the historical storage data in the database based on the at least one aggregation identifier and the corresponding target screening data to obtain the target data. The technical solution of the embodiment of the present invention solves the problem in the prior art that when the indicator business requirements change, a large amount of code needs to be modified to implement the function, the system has poor flexibility and maintainability, and leads to high time and labor costs, and realizes the flexible loading of basic data according to the aggregation pointer, thereby achieving the technical effect of saving labor and time costs.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of computer technology, and in particular to a data processing method, device, electronic device, and storage medium. Background Art

[0002] Currently, after obtaining the basic data corresponding to customer service consultations, the existing customer service monitoring system can determine the aggregation dimension according to business needs, and then aggregate the aggregation dimension into a single indicator by executing scheduling tasks.

[0003] When implementing the technical solution based on the above method, the inventors found the following problems:

[0004] All data metric aggregation logic required customized development based on requirements, resulting in rigid dimensionality and low reusability. Changing business requirements for the same metric required extensive code modifications to implement functionality, leading to poor system flexibility and maintainability. The lack of a mechanism to ensure flexible control of aggregation computing tasks meant that any errors or data changes in a single calculation required a full recalculation, resulting in wasted time, computing power, and high costs. Summary of the Invention

[0005] The present invention provides a data processing method, device, electronic device and storage medium to achieve the technical effect of facilitating data processing.

[0006] In a first aspect, an embodiment of the present invention provides a data processing method, the method comprising:

[0007] Acquiring original basic data based on the data pointer, and determining target screening data from the original basic data based on at least one indicator item;

[0008] Determining at least one aggregation identifier based on data content of a target indicator item in the at least one indicator item;

[0009] Based on the at least one aggregation identifier and the corresponding target screening data, the historical stored data in the database is updated to obtain target data.

[0010] Furthermore, the obtaining of original basic data based on the data pointer includes: determining a current data pointer from a pointer record table based on the received scheduled task; and obtaining original basic data corresponding to the scheduled task from a basic data table based on the current data pointer.

[0011] Furthermore, the target filtering data is determined from the original basic data based on at least one indicator item, including: obtaining a target aggregation key consistent with the task identifier of the scheduled task from an aggregation key table; wherein the target aggregation key includes at least one indicator item, and the indicator item corresponds to the data dimension.

[0012] Furthermore, the target screening data includes data content corresponding to at least one target indicator item, and determining at least one aggregation identifier based on the data content of the target indicator item in the at least one indicator item includes: determining at least one target indicator item with fixed data content from the at least one indicator item; determining at least one first identifier based on the data content corresponding to the at least one target indicator item; classifying and processing the at least one first identifier to determine at least one aggregation identifier; wherein each aggregation identifier corresponds to at least one first identifier.

[0013] Furthermore, the updating of historical storage data in the database based on the at least one aggregation identifier and the corresponding target screening data to obtain target data includes: for each aggregation identifier, if the database includes the current aggregation identifier, determining the target data based on the target screening data corresponding to the current aggregation identifier and the historical storage data corresponding to the current aggregation identifier stored in the database.

[0014] Furthermore, based on the at least one aggregation identifier and the corresponding target screening data, the historical storage data in the database is updated to obtain the target data, including: for each aggregation identifier, if the current aggregation identifier is not included in the database, the target screening data corresponding to the current aggregation identifier is updated as the target data into the database.

[0015] Furthermore, the method also includes: determining a data pointer to be updated based on the original basic data obtained, and updating the data pointer to be updated in the pointer record table, so that when a scheduled task is received, the corresponding original basic data is obtained based on the data pointer in the pointer record table.

[0016] In a second aspect, an embodiment of the present invention further provides a data processing device, the device comprising:

[0017] a target screening data determination module, configured to obtain original basic data based on a data pointer, and determine target screening data from the original basic data based on at least one indicator item;

[0018] an aggregation identifier determination module, configured to determine at least one aggregation identifier based on data content of a target indicator item in the at least one indicator item;

[0019] The target data determination module is used to update the historical storage data in the database based on the at least one aggregation identifier and the corresponding target screening data to obtain the target data.

[0020] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising:

[0021] one or more processors;

[0022] a storage device for storing one or more programs,

[0023] When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method as described in any one of the embodiments of the present invention.

[0024] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer-executable instructions, which, when executed by a computer processor, are used to execute the data processing method as described in any one of the embodiments of the present invention.

[0025] The technical solution of the embodiment of the present invention obtains the original basic data based on the data pointer, and determines the corresponding target screening data from the original basic data according to at least one indicator item, and then determines at least one aggregation identifier according to the data content of the target indicator item in the at least one indicator item. After determining the aggregation identifier, the historical storage data in the database is updated based on the at least one aggregation identifier and the corresponding target screening data to obtain the target data. This solves the problem in the prior art that when the indicator business requirements change, a large amount of code needs to be modified to implement the function, and there are problems such as poor system elasticity, poor maintainability, and high labor and time costs. This achieves the technical effect of configuring various indicators, improving data processing efficiency and reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings introduced here only illustrate some of the embodiments to be described by the present invention, and are not exhaustive. A person skilled in the art can derive other drawings based on these drawings without inventive effort.

[0027] Figure 1 A flowchart of a data processing method provided by an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of generating indicators provided by an embodiment of the present invention;

[0029] Figure 3 A schematic diagram of a data processing method according to an embodiment of the present invention;

[0030] Figure 4 A schematic diagram of a data acquisition method provided by an embodiment of the present invention;

[0031] Figure 5 A schematic structural diagram of a data processing device provided in an embodiment of the present invention;

[0032] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0034] Before introducing this technical solution, we can first give an example of the application scenario. When processing data, the existing data processing methods often obtain the basic data stored in the system, and then aggregate the data aggregation dimensions into a single indicator based on a specific scheduling task. For example, when it is necessary to aggregate indicators such as customer service satisfaction, it is necessary to develop the corresponding program code and test it before it can be put into use. There are problems with long development cycles and high time costs. Furthermore, when the aggregation dimension changes, the corresponding program code needs to be redeveloped, which has the problems of high cost and long cycle.

[0035] Figure 1 A flow chart of a data processing method provided for an embodiment of the present invention. This embodiment is applicable to situations where at least one reference indicator is determined based on a configuration item to obtain data corresponding to the corresponding reference indicator. The method can be executed by a data processing device, which can be implemented in the form of software and / or hardware. The hardware can be an electronic device for processing data. The electronic device can execute the data processing method provided by the embodiment of the present disclosure to extract target screening data from basic data according to the task set by the user, and obtain corresponding target data according to the aggregation identifier.

[0036] It should be noted that the device for executing the data processing method provided in the embodiments of the present disclosure can be integrated into application software that supports data processing, and the application software can be installed in an electronic device. Optionally, the electronic device can be a mobile terminal or a PC. The application software can be any type of software that requires data processing. The specific application software will not be described here one by one, as long as it can realize data processing. Alternatively, it can be integrated into the corresponding page, and the user can realize data processing through the page integrated in the PC.

[0037] like Figure 1 As shown, the method includes:

[0038] S110 . Acquire original basic data based on a data pointer, and determine target screening data from the original basic data based on at least one indicator item.

[0039] The data pointer can be understood as the location of data within a data table. The original basic data refers to all customer service data collected by the customer service monitoring system. This customer service data includes at least one of the following: conversation data between users and customer service representatives, user reviews of customer service representatives, user ratings of customers, and satisfaction ratings. The original basic data also includes at least one source, which can be considered as different data entry channels. Optionally, the data entry dimension can include at least one of a channel dimension, a portal dimension, a skill group dimension, a customer service dimension, and a time period dimension. The channel dimension can be understood as the source of data. For example, data redirected from different applications or pages to a specific page can be considered a channel dimension. The portal dimension can be understood as the entry point to a functional interface. For example, when a user needs to consult customer service, they can access the customer service interface through different interfaces. They can access the customer service interface through an interactive button on the "My Information" page or through an interactive button on the "After-Sales Service" page. These methods of accessing the customer service interface can be considered entry channels. The customer service dimension can be the type of customer service representative, which can be either human or intelligent. At the same time, there are certain differences in the content of services provided by different manual customer service or intelligent customer service. For example, manual customer service A mainly provides pre-sales information, while manual customer service B mainly provides after-sales information. For example, different customer service number ranges correspond to different tasks. Customer service numbers 1-10 can be responsible for answering questions, and customer service numbers 11-20 can be responsible for after-sales services, etc. The corresponding intelligent customer service can also have different responsibilities. The time period dimension can be the statistical time period, for example, data is counted every half an hour, etc., that is, the time dimension mainly refers to the statistical time period. The indicator items are related to the target data to be obtained. For example, if the problem-solving rate of customer service is to be counted, the problem-solving rate indicator can be included in the indicator items. Target screening data can be understood as statistical data determined from the original basic data based on the indicator items.

[0040] Specifically, the data identifier corresponding to the current pointer data is n. At this time, it can be considered that the data identified as n is the starting point for obtaining the original basic data. Furthermore, the original basic data can be filtered according to at least one indicator item to determine the target screening data. For example, when it is necessary to investigate the praise rate of customer service within a time period, at this time, taking 10:00 a.m. to 10:30 a.m. as an example, the data between 10:00 a.m. and 10:30 a.m. is extracted based on the pointer data and used as the original basic data. Since it is necessary to investigate the praise rate indicator, the original basic data can be filtered based on the praise rate indicator and the customer service indicator. Assuming that the extracted original basic data is 1,000 data, and the data obtained after filtering based on the praise rate indicator and the customer service indicator is 100, the 100 data obtained after filtering will be used as the target screening data.

[0041] For example, Figure 2 As shown, the sources of large amounts of basic data (raw basic data) can be divided into different statistical dimensions. Accordingly, data can be grouped together based on business needs, using multiple conditions, and indicators can be generated through logical operations. Logical operations may include summation, maximum value, minimum value, and so on. When different indicator business requirements require different grouping logic, the same underlying code can be used to implement different grouping logic requirements by simply configuring different aggregation keys.

[0042] Based on the above scheme, the acquisition of original basic data based on data pointer includes: determining the current data pointer from the pointer record table based on the received scheduled task; and acquiring the original basic data corresponding to the scheduled task from the basic data table based on the current data pointer.

[0043] Among them, a scheduled task can be a task that the user pre-sets to be executed. It is understandable that the user can set multiple scheduled tasks according to needs. The content to be executed by different scheduled tasks can be the same or different. The user can set it according to needs, and the scheduled task can include information such as task identification, data acquisition step size, data acquisition interval, task duration, etc. The pointer record table can be understood as a table used to record the location information of the data pointer and historical data records. The basic data table can be a table used to record basic data. It should be noted that the basic data table can be a system log or a storage space that records all data processed by the system.

[0044] Specifically, according to the timed task set by the user, the position pointed to by the data pointer at this moment is obtained from the pointer record table, and the corresponding original basic data is obtained from the basic data table according to the current data pointer and the timed task set by the user.

[0045] For example, if the operator needs to inspect the customer service data from 10:00 AM to 10:30 AM, the operator can pre-set the task duration to 10:00 AM to 10:30 AM, set the step size of data acquisition to 100, and set the data acquisition interval to 3 minutes. Then, when the time is 10:00 AM, data will be pulled every 3 minutes, and 100 data will be pulled from the basic data each time until the end condition is met. For specific methods of pulling data, please refer to Figure 4 ,like Figure 4As shown, according to the data pointer corresponding to the data id, the next N data in the basic data table are loaded as the original basic data for this task statistics. The value of the number of acquisitions N can be flexibly changed according to business needs. If the remaining basic data is less than N, all the remaining basic data are loaded. Furthermore, if the purpose of pulling data is to obtain the customer service's praise rate, the task identifier of the scheduled task can be set to the praise rate inspection, and according to the task duration and the data acquisition interval, it can be known that 10 times of data need to be obtained. According to the data acquisition step size and the number of data acquisition times, it can be known that the final data pulled is 1000. The 1000 pieces of data pulled are used as the original basic data corresponding to the scheduled task executed this time.

[0046] The technical solution provided by the embodiment of the present disclosure determines the current pointer data from the pointer record table according to the received scheduled task, and obtains the original basic data corresponding to the scheduled task, thereby ensuring the correspondence between the obtained data and the task and improving the data acquisition efficiency.

[0047] On the basis of the above solution, the determining target screening data from the original basic data based on at least one indicator item includes: obtaining a target aggregation key consistent with the task identifier of the scheduled task from an aggregation key table.

[0048] Among them, the aggregation key table can be a table for storing pre-set aggregation keys. The task identifier can be an identifier for identifying the current scheduled task, and the task identifier can also be used to characterize what indicators need to be used to filter the data. The target aggregation key includes at least one indicator item, and the indicator item corresponds to the data dimension. The target aggregation key can be an aggregation key with a consistent identifier obtained from the aggregation key table based on the task identifier. It can be understood that different types of tasks need to obtain different target filtering data, so different types of tasks require corresponding different aggregation keys. Furthermore, users may need to examine multiple task items at the same time when examining the business. Therefore, the aggregation key may contain multiple indicator items.

[0049] Specifically, based on the task identifier contained in the scheduled task, a target aggregation key that matches the task identifier is obtained from the aggregation key table. For example, the aggregation key and the task identifier can be stored in a corresponding manner in the aggregation key table. That is, when it is necessary to find the corresponding aggregation key based on the task identifier, the task identifier and the task identifiers stored in the aggregation key table are matched one by one. When a match is successful, the corresponding aggregation key is determined to be the target aggregation key.

[0050] For example, see Figure 3Before processing data, users can set parameters in the scheduled task based on their needs. These parameters can include "investigating customer service satisfaction and praise rates between 10:00 AM and 10:30 AM," and can be set to retrieve basic data every three minutes, with 100 data items queried each time. Based on the set scheduled task, the starting position of the data pointer and the dimensions of the original basic data to be queried can be obtained. The corresponding original basic data can then be retrieved from the basic data table according to the query step size set by the user.

[0051] The technical solution provided by the embodiment of the present disclosure obtains the target aggregation key consistent with the task identifier of the scheduled task from the aggregation key table, so that the user can set the corresponding aggregation key according to the needs and select the corresponding aggregation key according to the scheduled task, thereby improving the efficiency of selecting the aggregation key and ensuring the correctness of the selection.

[0052] S120. Determine at least one aggregation identifier based on the data content of the target indicator item in the at least one indicator item.

[0053] Among them, the target indicator item can be an indicator item that needs to generate an aggregate identifier. Suppose you want to examine the comprehensive data of customer service. Since there are different customer services, you can generate corresponding aggregate identifiers for different customer services. For example, you can use the customer's label as the target indicator item, then for "Customer Service 1", "Customer Service 2", "Customer Service 3", etc., you need to generate corresponding aggregate identifiers. The number of aggregate identifiers can be one, two or more. For example, the number of aggregate identifiers can correspond to the number of customer services. The method of generating the aggregate identifier can be the identifier obtained by processing the customer service label through a hash algorithm, or it can be the corresponding aggregate identifier obtained through a snowflake algorithm, etc. The data content can be the basic data that needs to be obtained in the target indicator item. The aggregate identifier can be understood as the identification information generated by the content of different target indicator items.

[0054] Specifically, at least one aggregation identifier is determined based on the data content in the target indicator item in at least one indicator item. For example, when the operation personnel need to examine the two indicator items of the customer service's praise rate and problem solving rate, the corresponding aggregation identifier can be generated based on the praise rate, the problem solving rate and the corresponding customer service identifier. Furthermore, in specific applications, the operation personnel can also select the number of customer services that need to be evaluated according to needs. For example, if "Customer Service 1" and "Customer Service 3" need to be evaluated, the corresponding aggregation identifier can be determined based on the data content corresponding to "Customer Service 1" and "Customer Service 3".

[0055] On the basis of the above technical solution, the target screening data includes data content corresponding to at least one target indicator item, and the method of determining at least one aggregation identifier based on the data content of the target indicator item in the at least one indicator item includes: determining at least one target indicator item with fixed data content from the at least one indicator item; determining at least one first identifier based on the data content corresponding to the at least one target indicator item; and classifying and processing the at least one first identifier to determine at least one aggregation identifier.

[0056] Among them, the first identifier is determined based on the target indicator item. The target indicator item is determined based on the data content of each indicator item. For example, if the indicator item includes a customer service indicator item, then the aggregation identifier, i.e., the first identifier, can be determined based on the specific information in the customer service indicator item. The number of aggregation identifiers can be one or more. It is understandable that when it is necessary to evaluate the data of a customer service representative, since the customer service representative may have multiple dimensions of original basic data, such as conversation data, evaluation data, etc., each aggregation identifier corresponds to at least one first identifier.

[0057] Specifically, based on the data content corresponding to each indicator item, it is possible to determine which indicator items have limited data content and predictable changes. Based on this, such indicator items can be used as target indicator items. Accordingly, corresponding aggregation identifiers can be generated based on the data content corresponding to the target indicator items. At this time, the number of aggregation identifiers can be one or more, and at the same time, the aggregation identifiers can be the same or different. In order to process the data as a whole, the data with the same aggregation identifier can be classified, that is, when the aggregation identifier is the same, it means that the data corresponds to a customer service or user. Classification processing can be understood as associating all data contents with the same aggregation identifier with the same aggregation identifier to obtain data similar to a tree structure.

[0058] The technical solution provided by the embodiment of the present disclosure ensures the correctness of data classification and improves the efficiency of data classification by determining the corresponding first identifier based on the data content in the target indicator item and classifying the first identifier.

[0059] S130: Based on the at least one aggregation identifier and corresponding target screening data, update the historical storage data in the database to obtain target data.

[0060] The historical stored data can be data stored after the execution of a scheduled task. That is, the target filtered data stored in the database before the current moment is the historical stored data. The target data can be understood as the data that needs to be stored in the database after the current scheduled task is executed.

[0061] Specifically, historical data stored in the database is updated based on the at least one determined aggregation identifier and the corresponding target screening data, thereby obtaining the corresponding target data. For example, the obtained target screening data is aggregated based on the aggregation identifier to obtain corresponding indicator data, and the database is searched based on the aggregation identifier to determine whether the indicator data corresponding to the aggregation identifier exists. If so, the data is updated to obtain the corresponding target data.

[0062] On the basis of the above scheme, the historical storage data in the database is updated based on the at least one aggregation identifier and the corresponding target screening data to obtain the target data, including: for each aggregation identifier, if the database includes the current aggregation identifier, the target data is determined based on the target screening data corresponding to the current aggregation identifier and the historical storage data corresponding to the current aggregation identifier stored in the database.

[0063] Specifically, you can combine Figure 3 To illustrate, after the indicator data corresponding to the aggregation identifier is statistically obtained, the database is queried based on the indicator data to see whether there is historical storage data corresponding to the aggregation identifier. If there is historical storage data corresponding to the aggregation identifier in the database, the indicator data currently determined based on the aggregation identifier will be used to update the data stored in the database.

[0064] On the basis of the above scheme, the historical storage data in the database is updated based on the at least one aggregation identifier and the corresponding target screening data to obtain the target data, including: for each aggregation identifier, if the current aggregation identifier is not included in the database, the target screening data corresponding to the current aggregation identifier is updated as the target data into the database.

[0065] Specifically, matching is performed in the database according to each aggregation identifier. If the indicator data corresponding to the aggregation identifier does not exist in the database, new indicator data is created in the database based on the current aggregation identifier, and the indicator data obtained based on the current aggregation identifier and the target screening data is stored in the newly created data item, thereby obtaining the target data.

[0066] For example, Figure 3As shown in the figure, during the data aggregation phase, the aggregation key (the entity class used to distinguish multi-dimensional data in the aggregation task) is obtained. The basic data is grouped according to the multiple dimensions in the aggregation key. Dimensions typically include channel, portal, skill group, customer service representative, and time period. The grouped basic data is traversed, and the satisfaction ratings for different conversations are summed to generate aggregated indicator data for the group. Finally, the aggregated indicator data is compared with existing aggregated indicator data in the database to determine whether the data already exists. If not, the aggregated indicator data is saved; if so, the ratings are accumulated and the aggregated indicator data is updated.

[0067] Based on the above scheme, it also includes: determining the data pointer to be updated based on the original basic data obtained, and updating the data pointer to be updated in the pointer record table, so that when a scheduled task is received, the corresponding original basic data is obtained based on the data pointer in the pointer record table.

[0068] The data pointer to be updated may be the location in the basic data table where the data pointer exists after the original basic data is obtained.

[0069] Specifically, after collecting data from the basic data table according to the user-defined data collection step, the data pointer after the collection is updated in the pointer record table to record the current data pointer position. Then, the next time data is captured, the original basic data can be correctly obtained based on the content recorded in the pointer record table. In other words, the next time a scheduled task is received, the current pointer position can be obtained from the pointer record table based on the received scheduled task and the corresponding data can be retrieved.

[0070] For example, Figure 3 As shown, in the pointer update phase, after each aggregation indicator data generation, the position of the last basic data loaded in this task in the basic data table is recorded in the basic data table, and it is used as the position of the current data pointer, so that the data in the basic data table can be obtained the next time the task is executed. Furthermore, when the pointer record table records the position pointed to by the data pointer, the corresponding aggregation indicator of this time can also be stored in the pointer record table. Then, when an error in the aggregation indicator data is found, the original erroneous data can be overwritten by the indicator callback.

[0071] The technical solution of the embodiment of the present invention obtains the original basic data based on the data pointer, and determines the corresponding target screening data from the original basic data according to at least one indicator item, and then determines at least one aggregation identifier according to the data content of the target indicator item in the at least one indicator item. After determining the aggregation identifier, the historical storage data in the database is updated based on the at least one aggregation identifier and the corresponding target screening data to obtain the target data. This solves the problem in the prior art that when the indicator business requirements change, a large amount of code needs to be modified to implement the function, and there are problems such as poor system elasticity, poor maintainability, and high labor and time costs. This achieves the technical effect of configuring various indicators, improving data processing efficiency and reducing costs.

[0072] Figure 5 A schematic diagram of the structure of a data processing device provided by an embodiment of the present invention is shown in FIG. Figure 5 As shown, the device includes: a target screening data determination module 510, an aggregation identifier determination module 520 and a target data determination module 530.

[0073] a target screening data determination module 510, configured to obtain original basic data based on a data pointer, and determine target screening data from the original basic data based on at least one indicator item;

[0074] An aggregation identifier determination module 520 is configured to determine at least one aggregation identifier based on data content of a target indicator item in the at least one indicator item;

[0075] The target data determination module 530 is configured to update the historical storage data in the database based on the at least one aggregation identifier and the corresponding target screening data to obtain target data.

[0076] On the basis of the above technical solution, the target screening data determination module further includes:

[0077] a data pointer determining unit, configured to determine a current data pointer from a pointer record table based on a received timing task;

[0078] The original basic data determining unit is used to obtain the original basic data corresponding to the scheduled task from the basic data table based on the current data pointer.

[0079] Based on the above technical solution, the target screening data determination module includes:

[0080] An aggregation key determination unit is used to obtain a target aggregation key consistent with the task identifier of the scheduled task from the aggregation key table; wherein the target aggregation key includes at least one indicator item, and the indicator item corresponds to the data dimension.

[0081] Based on the above technical solution, the aggregation identifier determination module is specifically used to: determine at least one target indicator item with fixed data content from the at least one indicator item; determine at least one first identifier based on the data content corresponding to the at least one target indicator item; classify and process the at least one first identifier to determine at least one aggregation identifier; wherein each aggregation identifier corresponds to at least one first identifier.

[0082] Based on the above technical solution, the target data determination module includes:

[0083] The target data updating unit is configured to determine the target data for each aggregation identifier based on the target screening data corresponding to the current aggregation identifier and the historical storage data corresponding to the current aggregation identifier stored in the database if the database includes the current aggregation identifier.

[0084] On the basis of the above technical solution, the target data determination module further includes:

[0085] The target data creating unit is configured to update the target screening data corresponding to each aggregation identifier as the target data into the database if the database does not include the current aggregation identifier.

[0086] On the basis of the above technical solution, the device further includes:

[0087] The data pointer update module is used to determine the data pointer to be updated based on the original basic data obtained, and update the data pointer to be updated in the pointer record table, so as to obtain the corresponding original basic data based on the data pointer in the pointer record table when receiving a scheduled task.

[0088] The technical solution of the embodiment of the present invention obtains the original basic data based on the data pointer, and determines the corresponding target screening data from the original basic data according to at least one indicator item, and then determines at least one aggregation identifier according to the data content of the target indicator item in the at least one indicator item. After determining the aggregation identifier, the historical storage data in the database is updated based on the at least one aggregation identifier and the corresponding target screening data to obtain the target data. This solves the problem in the prior art that when the indicator business requirements change, a large amount of code needs to be modified to implement the function, and there are problems such as poor system elasticity, poor maintainability, and high labor and time costs. This achieves the technical effect of configuring various indicators, improving data processing efficiency and reducing costs.

[0089] The data processing device provided by the embodiment of the present invention can execute the data processing method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0090] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the embodiments of the present invention.

[0091] Figure 6 A schematic structural diagram of an electronic device provided in an embodiment of the present invention. Figure 6 A block diagram of an exemplary electronic device 60 suitable for implementing exemplary embodiments of the present invention is shown. Figure 6 The electronic device 60 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present invention.

[0092] like Figure 6 As shown, electronic device 60 is a general-purpose computing device. Components of electronic device 60 may include, but are not limited to, one or more processors or processing units 601, system memory 602, and a bus 603 connecting various system components (including system memory 602 and processing unit 601).

[0093] Bus 603 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0094] The electronic device 60 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 60, including volatile and non-volatile media, removable and non-removable media.

[0095] The system memory 602 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 604 and / or cache memory 605. The electronic device 60 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 606 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 Not shown, often called a "hard drive"). Although Figure 6Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 603 via one or more data medium interfaces. Memory 602 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0096] A program / utility 608 having a set (at least one) of program modules 607 may be stored, for example, in memory 602. Such program modules 607 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 607 generally perform the functions and / or methods of the embodiments described herein.

[0097] The electronic device 60 may also communicate with one or more external devices 609 (e.g., keyboard, pointing device, display 610, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 60, and / or communicate with any device that enables the electronic device 60 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input / output (I / O) interface 611. Furthermore, the electronic device 60 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 612. As shown, the network adapter 612 communicates with other modules of the electronic device 60 via the bus 603. It should be understood that although Figure 6 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 60, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0098] The processing unit 601 executes various functional applications and data processing by running programs stored in the system memory 602, such as implementing the data processing method provided by the embodiment of the present invention.

[0099] An embodiment of the present invention further provides a storage medium comprising computer-executable instructions, wherein the computer-executable instructions are used to perform a data processing method when executed by a computer processor.

[0100] The method includes:

[0101] Acquiring original basic data based on the data pointer, and determining target screening data from the original basic data based on at least one indicator item;

[0102] Determining at least one aggregation identifier based on data content of a target indicator item in the at least one indicator item;

[0103] Based on the at least one aggregation identifier and the corresponding target screening data, the historical stored data in the database is updated to obtain target data.

[0104] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0105] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0106] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0107] The computer program code for performing the operations of the embodiments of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0108] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A data processing method, characterized in that: include: Acquiring original basic data based on the data pointer, and determining target screening data from the original basic data based on at least one indicator item; Determining at least one aggregation identifier based on data content of a target indicator item in the at least one indicator item; Based on the at least one aggregation identifier and the corresponding target screening data, the historical stored data in the database is updated to obtain target data.

2. The method according to claim 1, characterized in that The obtaining of original basic data based on the data pointer includes: Based on the received timed task, determine the current data pointer from the pointer record table; Based on the current data pointer, original basic data corresponding to the scheduled task is obtained from the basic data table.

3. The method according to claim 2, characterized in that The determining target screening data from the original basic data based on at least one indicator item includes: Obtaining a target aggregation key consistent with the task identifier of the scheduled task from the aggregation key table; The target aggregation construction includes at least one indicator item, and the indicator item corresponds to a data dimension.

4. The method according to claim 1, wherein The target screening data includes data content corresponding to at least one target indicator item, and determining at least one aggregation identifier based on the data content of the target indicator item in the at least one indicator item includes: Determining at least one target indicator item with fixed data content from the at least one indicator item; Determining at least one first identifier based on data content corresponding to the at least one target indicator item; Classify and process the at least one first identifier to determine at least one aggregate identifier; Each aggregation identifier corresponds to at least one first identifier.

5. The method according to claim 1, characterized in that The updating of historical stored data in a database based on the at least one aggregation identifier and corresponding target screening data to obtain target data includes: For each aggregation identifier, if the database includes a current aggregation identifier, the target data is determined based on the target screening data corresponding to the current aggregation identifier and the historical storage data corresponding to the current aggregation identifier stored in the database.

6. The method according to claim 1, wherein The updating of historical stored data in a database based on the at least one aggregation identifier and corresponding target screening data to obtain target data includes: For each aggregation identifier, if the database does not include the current aggregation identifier, the target screening data corresponding to the current aggregation identifier is updated into the database as the target data.

7. The method according to claim 1, characterized in that Also includes: The data pointer to be updated is determined based on the acquired original basic data, and the data pointer to be updated is updated in the pointer record table, so that when a scheduled task is received, the corresponding original basic data is acquired based on the data pointer in the pointer record table.

8. A data processing device, characterized in that: include: a target screening data determination module, configured to obtain original basic data based on a data pointer, and determine target screening data from the original basic data based on at least one indicator item; an aggregation identifier determination module, configured to determine at least one aggregation identifier based on data content of a target indicator item in the at least one indicator item; The target data determination module is used to update the historical storage data in the database based on the at least one aggregation identifier and the corresponding target screening data to obtain the target data.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method according to any one of claims 1 to 7.

10. A storage medium comprising computer executable instructions, wherein the computer executable instructions are used to perform the data processing method according to any one of claims 1 to 7 when executed by a computer processor.

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