Data processing method, device, electronic device and storage medium

Abstract

The present disclosure provides a data processing method, device, electronic device and readable storage medium, which can be applied to the field of data processing technology. The method includes: responding to a data update instruction, updating the data information of M first databases within a first time period, each first database containing N data categories; responding to a first data query instruction, performing a first processing on the N data categories of each first database according to the first data category among the N data categories obtained from each first database, and generating first summary information; verifying the task status of the first summary information, and importing the verified first summary information into a second database; performing a second processing on the first summary information in the second database according to the second data category among the N data categories obtained from each first database, and generating second summary information, the second summary information being associated with the second data category. This method can effectively improve the efficiency of data processing.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and more specifically to a data processing method, device, electronic device, and readable storage medium. Background Art

[0002] With the development of information technology, the amount of data held by financial institutions is growing. Acquiring this data consumes considerable time and effort. In related technologies, the process of acquiring financial institution data and generating reports based on this data requires acquiring and processing the source data to obtain the required data. However, due to limitations in device performance, the time required to read and process files is long, resulting in low efficiency in generating the required data results. Summary of the Invention

[0003] In view of the above problems, the present disclosure provides a data processing method, device, electronic device and readable storage medium, which can effectively improve the efficiency of data processing.

[0004] According to a first aspect of the present disclosure, a data processing method is provided, comprising: responding to a data update instruction, updating data information of M first databases within a first time period, each first database containing N data categories, where M and N are integers greater than 1; responding to a first data query instruction, performing a first processing on the N data categories of each first database according to a first data category among the N data categories obtained from each first database, to generate first summary information; verifying the task status of the first summary information, and importing the first summary information that has passed the verification into a second database; performing a second processing on the first summary information in the second database according to a second data category among the N data categories obtained from each first database, to generate second summary information, wherein the second summary information is associated with the second data category.

[0005] In some exemplary embodiments of the present disclosure, the response to the first data query instruction, performing a first processing on the N data categories of each first database according to the first data category among the N data categories obtained from each first database, and generating first summary information, includes: responding to the first data query instruction, generating a data information detail table according to the N data categories obtained from each first database; processing the data information detail table according to the first data category among the N data categories obtained from each first database, and generating first summary information, wherein the first summary information includes first summary sub-information of the first time period and second summary sub-information of the second time period.

[0006] In some exemplary embodiments of the present disclosure, verifying the task status of the first summary information and importing the verified first summary information into a second database includes: obtaining a task status table; determining the task status associated with the first summary information from the task status table based on the first summary information; verifying whether the task status meets a preset condition; and importing the first summary information whose task status meets the preset condition into the second database.

[0007] In some exemplary embodiments of the present disclosure, the data processing method further includes: after importing the first summary information of the task status that meets the preset conditions into the second database, obtaining a verification file associated with the first summary information; and verifying the first summary information in the second database according to the verification file.

[0008] In some exemplary embodiments of the present disclosure, the data processing method further includes: after verifying the first summary information in the second database according to the verification file, after the verification passes, updating the task status of the first summary information that has been imported into the second database in the task status table.

[0009] In some exemplary embodiments of the present disclosure, the second summary information includes third summary sub-information of the first time period and fourth summary sub-information of the second time period, the third summary sub-information is associated with the first summary sub-information, and the fourth summary sub-information is associated with the second summary sub-information.

[0010] In some exemplary embodiments of the present disclosure, the data processing method further includes: responding to a second data query instruction, acquiring second summary information associated with the second data query instruction from the second database, and generating target data content.

[0011] According to a second aspect of the present disclosure, a data processing device is provided, including: an update module, configured to respond to a data update instruction, update data information of M first databases within a first time period, each first database containing N data categories, M and N being integers greater than 1; a processing module, configured to respond to a first data query instruction, perform a first processing on the N data categories of each first database according to a first data category among the N data categories obtained from each first database, and generate first summary information; a verification module, configured to verify the task status of the first summary information, and import the first summary information that has passed the verification into a second database; a generation module, configured to perform a second processing on the first summary information in the second database according to a second data category among the N data categories obtained from each first database, and generate second summary information, wherein the second summary information is associated with the second data category.

[0012] In some exemplary embodiments of the present disclosure, the processing module includes a processing sub-module, which is configured to: respond to a first data query instruction, generate a data information detail table based on the N data categories obtained from each first database; process the data information detail table based on the first data category among the N data categories obtained from each first database, and generate first summary information, wherein the first summary information includes first summary sub-information of the first time period and second summary sub-information of the second time period.

[0013] In some exemplary embodiments of the present disclosure, the verification module includes a verification sub-module, and the verification sub-module is configured to: obtain a task status table; determine the task status associated with the first summary information from the task status table based on the first summary information; verify whether the task status meets the preset conditions; and import the first summary information whose task status meets the preset conditions into the second database.

[0014] In some exemplary embodiments of the present disclosure, the processing device also includes a verification module, which is configured to: after importing the first summary information of the task status that meets the preset conditions into the second database, obtain a verification file associated with the first summary information; and verify the first summary information in the second database according to the verification file.

[0015] In some exemplary embodiments of the present disclosure, the processing device also includes a task status update module, and the task status update module is configured to: after verifying the first summary information in the second database according to the verification file, update the task status of the first summary information that has been imported into the second database in the task status table after the verification passes.

[0016] In some exemplary embodiments of the present disclosure, the processing device further includes a target data content generation module, which is configured to: respond to a second data query instruction, obtain second summary information associated with the second data query instruction from the second database, and generate target data content.

[0017] According to a third aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; and a storage device for storing executable instructions, wherein the executable instructions, when executed by the processors, implement the method described above.

[0018] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which executable instructions are stored. When the instructions are executed by a processor, the method described above is implemented.

[0019] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method described above is implemented.

[0020] According to an embodiment of the present disclosure, the N data categories contained in each of the M first databases are processed for the first time, and the first summary information imported into the second database is processed for the second time to obtain the second summary information. The first summary information is data that has been appropriately processed, and thus when the second summary information is generated, the data query speed can be guaranteed. In addition, before the first summary information is imported into the second database, the task status of the first summary information is verified to ensure the accuracy of the data query. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0022] Figure 1 A schematic diagram schematically illustrates a system architecture to which the data processing method according to an embodiment of the present disclosure may be applied;

[0023] Figure 2 The following schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure;

[0024] Figure 3 Schematically shows a flow chart of operation S220 of the data processing method according to an embodiment of the present disclosure;

[0025] Figure 4 Schematically shows a flow chart of operation S230 of the data processing method according to an embodiment of the present disclosure;

[0026] Figure 5 Schematically shows a flow chart of operation S250 of the data processing method according to an embodiment of the present disclosure;

[0027] Figure 6 Schematically shows a flow chart of operation S260 of the data processing method according to an embodiment of the present disclosure;

[0028] Figure 7 Schematically shows a flow chart of operation S270 of the data processing method according to an embodiment of the present disclosure;

[0029] Figure 8 A diagram schematically illustrates an execution process of a data processing method according to an embodiment of the present disclosure;

[0030] Figure 9 The following schematically shows a structural block diagram of a data processing device according to an embodiment of the present disclosure;

[0031] Figure 10 The block diagram schematically shows an electronic device suitable for implementing the data processing method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0033] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0034] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0035] When expressions such as "at least one of A, B and C, etc." are used, they should generally be interpreted in accordance with the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0036] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0037] In order to solve the problem in the related art that it takes a long time to directly read the original file from the source database for processing to obtain the final data, the present disclosure provides a data processing method, device, electronic device and readable storage medium, which can effectively improve the efficiency of data processing. The data processing method includes but is not limited to: responding to a data update instruction, updating the data information of M first databases within a first time period, each first database contains N data categories, M and N are integers greater than 1; responding to a first data query instruction, performing a first processing on the N data categories of each first database according to the first data category of the N data categories obtained from each first database, and generating first summary information; verifying the task status of the first summary information, and importing the first summary information that has passed the verification into a second database; performing a second processing on the first summary information in the second database according to the second data category of the N data categories obtained from each first database, and generating second summary information, wherein the second summary information is associated with the second data category.

[0038] According to an embodiment of the present disclosure, by performing a first processing on the N data categories contained in each of M first databases and then performing a second processing on the first summary information imported into the second database to obtain the second summary information, the first summary information is appropriately processed data, thereby ensuring data query speed when generating the second summary information. In addition, before the first summary information is imported into the second database, the task status of the first summary information is verified to ensure data query accuracy. By performing different preprocessing on the data in different databases, the steps of data processing in the second database are reduced, thereby improving data processing speed.

[0039] Figure 1 The following schematically illustrates a system architecture to which the data processing method of the embodiment of the present disclosure can be applied. Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure can be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments, or scenarios. It should be noted that the data processing methods and data processing devices provided by the embodiments of the present disclosure can be used in the fields of data processing technology and related aspects of the financial field, and can also be used in any field other than the financial field. The data processing methods and data processing devices provided by the embodiments of the present disclosure are not limited to the fields of application.

[0040] like Figure 1As shown, an exemplary system architecture 100 to which the data processing method according to an embodiment of the present disclosure can be applied may include terminal devices 101, 102, and 103, a network 104, and a server 105. The network 104 is a medium for providing a communication link between the terminal devices 101, 102, and 103 and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0041] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0042] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0043] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process received data such as user requests, and feed back processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0044] It should be noted that the data processing method provided in the embodiments of the present disclosure can generally be executed by the server 105. Accordingly, the data processing apparatus provided in the embodiments of the present disclosure can generally be set in the server 105. The data processing method provided in the embodiments of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the data processing apparatus provided in the embodiments of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0045] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0046] The following will be passed Figures 2 to 8 The data processing method of the disclosed embodiment is described in detail.

[0047] Figure 2The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0048] like Figure 2 As shown, the data processing method 200 of the embodiment of the present disclosure includes operations S210 to S240.

[0049] In operation S210 , in response to a data update instruction, data information of M first databases within a first time period is updated, where each first database includes N data categories, and M and N are integers greater than 1.

[0050] In an exemplary embodiment of the present disclosure, the first database is, for example, a basic database for storing data. For example, there may be M first databases, each of which contains N data categories, where M and N are integers greater than 1. When storing data information, a storage method of sub-libraries and sub-tables is adopted. For example, the N data categories may include a first data category for storing personnel information, a second data category for storing information about the organization to which the personnel belong, and a third data category for storing details of the personnel's daily business operations, etc., and may also include other more data categories. For example, each data category may contain multiple sub-tables, so that all data information is evenly distributed in multiple sub-tables.

[0051] The data update instruction may be, for example, a preset conditional update instruction, such as executing the update instruction when a set time is met, or executing the update instruction when a set condition is met.

[0052] Exemplarily, the data update instruction is, for example, updating the data information of N data categories in M ​​first databases at a specified time every day, for example, at 24:00 every day.

[0053] In the embodiments of the present disclosure, updating the data information in the first database may include updating the data information within a first time period, such as updating the data information for the current day or updating the data information for a month. The first time period may be adjusted based on actual needs. When updating data, updating the data information in the first database within the first time period may update part of the data, thereby improving the speed and efficiency of data updates.

[0054] In operation S220 , in response to the first data query instruction, the N data categories of each first database are first processed according to the first data category among the N data categories obtained from each first database to generate first summary information.

[0055] In an embodiment of the present disclosure, the first data query instruction may include, for example, a database query statement. According to the first data query instruction and the first data category among the N data categories, the other data categories are processed for the first time to generate first summary information.

[0056] For example, data information in N data categories in each first database is obtained, and based on the first data query instruction, the data information in N data categories in each first database is connected and queried to generate a data information detail table. The data information detail table has the association relationship of specific data information in N data categories. Figure 3 Operation S220 will be described in detail.

[0057] Through the embodiments of the present disclosure, by initially processing the data in the M first databases to generate first summary information, the M first databases can be processed synchronously during the generation of the first summary information, thereby increasing the speed of data processing. This first summary information serves as intermediate data for subsequent operations and is not the final required data. When determining the final required data information based on a user query instruction, the final required data information can be generated based on this first summary information, thereby reducing the steps in the subsequent data processing process and improving data processing efficiency.

[0058] In operation S230 , the task status of the first summary information is verified, and the verified first summary information is imported into the second database.

[0059] In an embodiment of the present disclosure, after the data information in the first database is processed for the first time and the first summary information is generated, the task status of the first summary information is verified, and the first summary information that has passed the verification is imported into the second database. By verifying the task status of the first summary information, it is possible to obtain whether the first summary information has been imported into the second database, thereby preventing the first summary information from failing to be imported into the second database due to unexpected circumstances (for example, network disconnection, power outage, etc.). Figure 4 Operation S230 will be described in detail.

[0060] In an embodiment of the present disclosure, there is one second database, and by importing the first summary information generated from each first database into the second database, the first summary information can be reprocessed in the second database.

[0061] In operation S240 , the first summary information in the second database is processed a second time according to the second data category among the N data categories obtained in each first database to generate second summary information, where the second summary information is associated with the second data category.

[0062] Exemplarily, the second data category may include information about the organization to which the personnel belong, and the first summary information in the second database is processed a second time based on the information about the organization to which the personnel belong. For example, the first summary information may include performance summary information of each personnel, and the performance summary information of the personnel may be the performance summary information of the personnel for the day or the monthly performance summary information of the personnel as of the day.

[0063] The second processing may, for example, be obtaining the information on the institutions to which personnel in the N data categories of each first database belong, extracting and classifying the first summary information, and thereby generating second summary information, wherein the second summary information may, for example, be an institution performance summary table, which includes the daily institution performance summary information of each institution and the monthly institution performance summary information as of that day.

[0064] In the embodiment of the present disclosure, the second summary information being associated with the second data category may indicate that the second summary information is generated according to the second data category. When the second data category is different, the generated second summary information is also different.

[0065] For example, if the second data category is institutional information, the generated second summary information is the institutional performance summary information of the day corresponding to different institutions with institutional information as the dimension and the monthly institutional performance summary information as of the day.

[0066] For another example, if the second data category is geographic location information, the generated second summary information is the regional performance summary information of the day corresponding to different regions based on geographic location information and the monthly regional performance summary information as of the day.

[0067] In other optional embodiments, the second data category can be adjusted according to actual needs and usage scenarios.

[0068] Figure 3 The flowchart of operation S220 of the data processing method according to an embodiment of the present disclosure is schematically shown.

[0069] like Figure 3 As shown, operation S220 of the embodiment of the present disclosure includes operation S221 to operation S222.

[0070] In operation S221 , in response to the first data query instruction, a data information detail table is generated according to the N data categories obtained from each first database.

[0071] In an embodiment of the present disclosure, the first data query instruction may be, for example, a database query statement. N data categories in each first database are stored in different tables using a categorized storage method. The database query statement may be used to connect and query data information in the N data categories, thereby generating a data information detail table.

[0072] In operation S222, the data information detail table is processed according to the first data category among the N data categories obtained from each first database to generate first summary information, which includes first summary sub-information of the first time period and second summary sub-information of the second time period.

[0073] In an embodiment of the present disclosure, a first data category is obtained from N data categories. The first data category may be, for example, personnel information. Based on the first data category, a data information detail table is processed to generate first summary information. For example, based on the personnel information, the performance information of each person in the data information detail table is extracted to generate a personnel performance summary table.

[0074] In an embodiment of the present disclosure, the first time period may be, for example, a natural day, and the second time period may be, for example, a month (e.g., including 30 natural days). The first summary information includes first summary sub-information for the first time period and second summary sub-information for the second time period. The first summary information may include, for example, the daily performance information of each person and the monthly performance information of each person as of that day, i.e., the first summary sub-information may be the daily performance information of the person, and the second summary sub-information may be the monthly performance information of the person as of that day.

[0075] According to an embodiment of the present disclosure, by performing a first processing on N data categories according to a first data category, first summary information is generated to implement classified statistics of data information. For example, by performing statistical aggregation on the data information in the first database according to the first data category, the number of times the data information is processed in the second database can be reduced, and users can effectively improve efficiency when obtaining data information from the second database.

[0076] Figure 4 The flowchart of operation S230 of the data processing method according to an embodiment of the present disclosure is schematically shown.

[0077] like Figure 4 As shown, operation S230 of the embodiment of the present disclosure includes operations S231 to S234.

[0078] In an embodiment of the present disclosure, after the first summary information is generated, it is necessary to import the first summary information from the first database into the second database to facilitate subsequent users to query the data. However, in the process of importing the first summary information from the first database into the second database. Due to unexpected situations such as network disconnection, power outage, database crash, etc., the first summary information is not completely imported into the second database. If the import is repeated, it will take time and occupy resources. If the import is continued, there may be problems such as data corruption or data errors. In this regard, in operation S230, the task status of the first summary information is verified to ensure that the information imported into the second database is accurate.

[0079] In operation S231 , a task status table is acquired.

[0080] In an embodiment of the present disclosure, a task status table may, for example, record the task status of the first summary information imported from the first database to the second database. For example, a task status table corresponding to the first summary information may be retrieved through a keyword search. The task status table may, for example, include the task statuses of multiple pieces of first summary information. After obtaining the task status table, the task status of a specific piece of first summary information may be queried in the task status table.

[0081] In operation S232 , a task status associated with the first summary information is determined from a task status table according to the first summary information.

[0082] After the task status table is acquired, the task status associated with the first summary information is further determined from the determined task status table according to the first summary information.

[0083] For example, the task status of the specific first summary information is determined from the task status table based on the keyword corresponding to the first summary information. For example, if the first summary information is the performance information of person A1 on that day, the task status table can be determined based on the keyword A1, and the task status of the first summary information corresponding to the keyword A1 in the task status table can be further determined. The task status includes whether the first summary information was successfully imported, whether the first summary information was failed to be imported, or whether the first summary information was not imported.

[0084] In operation S233 , it is verified whether the task status satisfies a preset condition.

[0085] In the embodiments of the present disclosure, the preset condition may be, for example, a failure to import the first summary information or a failure to import the first summary information. For example, if the first summary information is successfully imported, it indicates that the first summary information has already been imported from the first database into the second database and does not need to be imported again. If the first summary information fails to be imported or is not imported, it indicates that the first summary information does not exist in the second database and needs to be re-imported.

[0086] In operation S234 , the first summary information of the task status that meets the preset condition is imported into the second database.

[0087] In an embodiment of the present disclosure, the task status meeting the preset condition may mean that the task status of the first summary information is that the first summary information import fails or the first summary information is not imported. In this case, the first summary information in this state is imported into the second database, thereby completing the data import process.

[0088] Figure 5 The flowchart of operation S250 of the data processing method according to an embodiment of the present disclosure is schematically shown.

[0089] like Figure 5 As shown, operation S250 of the embodiment of the present disclosure includes operation S251 to operation S252.

[0090] In an embodiment of the present disclosure, after the first summary information of the task status that meets the preset conditions is imported into the second database, it is necessary to verify the first summary information in the second database to ensure the accuracy of the information imported from the first database to the second database. Exemplarily, this includes operations S251 and S252.

[0091] In operation S251 , a verification file associated with the first summary information is obtained.

[0092] Exemplarily, the verification file may have the same table structure and other contents as the first summary information. The verification file may be obtained, for example, through a keyword or through other methods.

[0093] In operation S252 , the first summary information in the second database is verified according to the verification file.

[0094] After obtaining the verification file, the first summary information that has been imported into the second database is verified. If the verification passes, it indicates that the first summary information has been successfully imported into the second database. If the verification fails, it indicates that the first summary information has not been successfully imported into the second database. In this case, the first summary information can be imported into the second database again.

[0095] According to an embodiment of the present disclosure, by verifying the first summary information in the second database according to the verification file, the accuracy of data import can be improved, and problems such as data errors caused by unexpected situations during the import of the first summary information into the second database can be prevented.

[0096] Figure 6 The flowchart of operation S260 of the data processing method according to an embodiment of the present disclosure is schematically shown.

[0097] In operation S260 , after the first summary information in the second database is verified according to the verification file, if the verification passes, the task status of the first summary information that has been imported into the second database in the task status table is updated.

[0098] For example, after verification passes, the first summary information has been imported into the second database, and the first summary information in the second database has passed verification, indicating that the data is consistent. At this point, the task status of the first summary information imported into the second database is updated in the task status table. This improves the accuracy of data import and effectively avoids data errors caused by unexpected situations.

[0099] Figure 7 The flowchart of operation S270 of the data processing method according to an embodiment of the present disclosure is schematically shown.

[0100] In operation S270 , in response to the second data query instruction, second summary information associated with the second data query instruction is acquired from the second database, and target data content is generated.

[0101] Exemplarily, the second data query instruction may be an instruction sent by a user through a terminal device (such as a computer, etc.), for example, the query instruction may be input through a browser, client, etc. of the terminal device. After receiving the data query instruction, the second database processes the second summary information again, generates target data content, sends it to the client and displays it to the user.

[0102] For example, the target data content may be a report in a set format, and the user may download the target data content through a terminal device, for example, to generate a data file in an Excel format.

[0103] In an embodiment of the present disclosure, the second summary information includes third summary sub-information of the first time period and fourth summary sub-information of the second time period, the third summary sub-information is associated with the first summary sub-information, and the fourth summary sub-information is associated with the second summary sub-information.

[0104] For example, the second summary information may be, for example, an institutional performance summary table, the first time period may be, for example, each natural day, and the second time period may be, for example, a month (e.g., including 30 natural days). The third summary sub-information of the first time period may be, for example, the institutional performance summary information for that day, and the fourth summary sub-information of the second time period may be, for example, the monthly institutional performance summary information as of that day. The institutional performance summary information for that day is associated with the personnel performance summary information for that day, and the monthly institutional performance summary information as of that day is associated with the monthly personnel performance summary information as of that day.

[0105] Figure 8 The diagram schematically shows an execution process diagram of the data processing method according to an embodiment of the present disclosure.

[0106] like Figure 8 As shown, the execution process 300 of the data processing method according to the embodiment of the present disclosure includes operations S210 to S240 , as well as a first database 310 , a second database 320 , first summary information 330 and a target end 340 .

[0107] In the first database 310 , operation S210 is executed to respond to the data update instruction and update data information of M first databases within the first time period. Each first database includes N data categories, where M and N are integers greater than 1.

[0108] After the data information in the first database is updated, operation S220 is executed to respond to the first data query instruction and perform a first processing on the N data categories of each first database according to the first data category among the N data categories obtained from each first database to generate first summary information 330.

[0109] After the first summary information 330 is generated, operation S230 is performed to verify the task status of the first summary information 330 and import the verified first summary information 330 into the second database 320 .

[0110] After the first summary information 330 is imported into the second database 320 , operation S240 is performed to perform a second processing on the first summary information in the second database according to the second data category among the N data categories obtained from each first database to generate second summary information.

[0111] Next, according to the second data query instruction input by the user at the target end 340 , the second summary information is obtained from the second database 320 , and target data content is generated. The target data content can be displayed at the target end 340 or downloaded.

[0112] According to the embodiments of the present disclosure, by performing a first processing of the N data categories contained in each of the M first databases to generate first summary information, and then performing a second processing of the first summary information in each of the second databases to generate second summary information, different data can be processed step by step in different databases, thereby ensuring the speed of data query. In addition, before the first summary information is imported into the second database, the task status of the first summary information is verified to ensure the accuracy of data query.

[0113] Figure 9 The structural block diagram of the data processing device according to an embodiment of the present disclosure is schematically shown.

[0114] like Figure 9 As shown, the data processing device 400 of the embodiment of the present disclosure includes an updating module 410 , a processing module 420 , a verification module 430 and a generating module 440 .

[0115] The update module 410 is configured to respond to the data update instruction and update the data information of M first databases within a first time period, where each first database includes N data categories, and M and N are integers greater than 1. In one embodiment, the update module 410 can be used to perform the operation S210 described above, which will not be repeated here.

[0116] Processing module 420 is configured to, in response to the first data query instruction, perform a first processing on the N data categories of each first database based on the first data category of the N data categories obtained from each first database, thereby generating first summary information. In one embodiment, processing module 420 may be configured to perform operation S220 described above, which is not further described here.

[0117] The verification module 430 is configured to verify the task status of the first summary information and import the verified first summary information into the second database. In one embodiment, the verification module 430 can be used to perform the operation S230 described above, which will not be repeated here.

[0118] Generating module 440 is configured to perform a second processing on the first summary information in the second database based on the second data category among the N data categories obtained from each first database to generate second summary information, where the second summary information is associated with the second data category. In one embodiment, generating module 440 can be used to perform operation S240 described above, and will not be further described here.

[0119] In some exemplary embodiments of the present disclosure, the processing module includes a processing sub-module, which is configured to: respond to a first data query instruction, generate a data information detail table based on the N data categories obtained from each first database; process the data information detail table based on the first data category among the N data categories obtained from each first database, and generate first summary information, wherein the first summary information includes first summary sub-information of the first time period and second summary sub-information of the second time period.

[0120] In some exemplary embodiments of the present disclosure, the verification module includes a verification sub-module, and the verification sub-module is configured to: obtain a task status table; determine the task status associated with the first summary information from the task status table based on the first summary information; verify whether the task status meets the preset conditions; and import the first summary information whose task status meets the preset conditions into the second database.

[0121] In some exemplary embodiments of the present disclosure, the processing device also includes a verification module, which is configured to: after importing the first summary information of the task status that meets the preset conditions into the second database, obtain a verification file associated with the first summary information; and verify the first summary information in the second database according to the verification file.

[0122] In some exemplary embodiments of the present disclosure, the processing device also includes a task status update module, and the task status update module is configured to: after verifying the first summary information in the second database according to the verification file, update the task status of the first summary information that has been imported into the second database in the task status table after the verification passes.

[0123] In some exemplary embodiments of the present disclosure, the processing device further includes a target data content generation module, which is configured to: respond to a second data query instruction, obtain second summary information associated with the second data query instruction from the second database, and generate target data content.

[0124] In an embodiment of the present disclosure, any multiple modules in the update module 410, the processing module 420, the verification module 430, the generation module 440, the processing submodule, the verification submodule, the check module, the task status update module, and the target data content generation module can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the update module 410, the processing module 420, the verification module 430, the generation module 440, the processing submodule, the verification submodule, the check module, the task status update module, and the target data content generation module can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of the update module 410, processing module 420, verification module 430, generation module 440, processing sub-module, verification sub-module, verification module, task status update module, and target data content generation module can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0125] Figure 10 The block diagram schematically shows an electronic device suitable for implementing the data processing method according to an embodiment of the present disclosure. Figure 10 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0126] like Figure 10 As shown, the electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include an onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0127] Various programs and data required for the operation of the electronic device 500 are stored in the RAM 503. The processor 501, ROM 502, and RAM 503 are connected to each other via a bus 504. The processor 501 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than the ROM 502 and RAM 503. The processor 501 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0128] According to an embodiment of the present disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to the bus 504. The electronic device 500 may further include one or more of the following components connected to the I / O interface 505: an input portion 506 including a keyboard, a mouse, etc.; an output portion 507 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage portion 508 including a hard disk; and a communication portion 509 including a network interface card such as a LAN card or a modem. The communication portion 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed in the drive 510 as needed, so that a computer program read therefrom can be installed into the storage portion 508 as needed.

[0129] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the data processing method according to the embodiments of the present disclosure.

[0130] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: 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), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, 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, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 502 and / or RAM 503 described above and / or one or more memories other than ROM 502 and RAM 503.

[0131] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the data processing method provided by the embodiments of the present disclosure.

[0132] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the computer program is executed by the processor 501. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0133] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 509, and / or installed from a removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0134] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 509, and / or installed from a removable medium 511. When the computer program is executed by the processor 501, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0135] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0137] Those skilled in the art will appreciate that the features described in the various embodiments and / or claims of this disclosure may be combined and / or coupled in various ways, even if such combinations and / or couplings are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or couplings are intended to fall within the scope of this disclosure.

[0138] The embodiments of the present disclosure are described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data processing method, comprising: In response to the data update instruction, update data information of M first databases within a first time period, each first database including N data categories, where M and N are integers greater than 1, and each data category includes multiple sub-tables; In response to the first data query instruction, performing a first processing on the N data categories of each first database according to a first data category among the N data categories obtained from each first database to generate first summary information; Obtaining a task status table, verifying the task status of the first summary information, and importing the verified first summary information into a second database; Obtaining a verification file associated with the first summary information, and verifying the first summary information in the second database according to the verification file; performing a second processing on the first summary information in the second database according to a second data category among the N data categories obtained from each first database to generate second summary information, wherein the second summary information is associated with the second data category; The step of responding to the first data query instruction and performing a first processing on the N data categories of each first database according to the first data category of the N data categories obtained from each first database to generate first summary information includes: In response to the first data query instruction, the data information in the N data categories of each first database is connected and queried to generate a data information detailed table; According to the first data category among the N data categories obtained from each first database, the data information detailed table is processed to generate first summary information.

2. The data processing method according to claim 1, wherein: The first summary information includes first summary sub-information of a first time period and second summary sub-information of a second time period.

3. The data processing method according to claim 1, wherein: The obtaining of the task status table, verifying the task status of the first summary information, and importing the verified first summary information into the second database includes: Get the task status table; determining, from the task status table according to the first summary information, a task status associated with the first summary information; Verify whether the task status meets the preset conditions; Importing the first summary information that the task status meets the preset condition into the second database.

4. The data processing method according to claim 1, wherein: Also includes: After verifying the first summary information in the second database according to the verification file, After the verification is passed, the task status of the first summary information that has been imported into the second database in the task status table is updated.

5. The data processing method according to claim 2, wherein: The second summary information includes the third summary sub-information of the first time period and the fourth summary sub-information of the second time period, The third summary sub-information is associated with the first summary sub-information, The fourth summary sub-information is associated with the second summary sub-information. The data processing method according to claim 1 , wherein: Also includes: In response to the second data query instruction, second summary information associated with the second data query instruction is acquired from the second database, and target data content is generated.

7. A data processing device comprising: an update module configured to respond to a data update instruction and update data information of M first databases within a first time period, each first database including N data categories, where M and N are integers greater than 1, and each data category includes multiple sub-tables; a processing module configured to, in response to the first data query instruction, perform a first processing on the N data categories of each first database according to the first data category of the N data categories obtained from each first database to generate first summary information; a verification module configured to obtain a task status table, verify the task status of the first summary information, and import the verified first summary information into a second database; a verification module configured to obtain a verification file associated with the first summary information, and verify the first summary information in the second database according to the verification file; a generating module configured to perform a second processing on the first summary information in each of the second databases based on a second data category among the N data categories obtained from the first database to generate second summary information, wherein the second summary information is associated with the second data category; The step of responding to the first data query instruction and performing a first processing on the N data categories of each first database according to the first data category of the N data categories obtained from each first database to generate first summary information includes: In response to the first data query instruction, the data information in the N data categories of each first database is connected and queried to generate a data information detailed table; According to the first data category among the N data categories obtained from each first database, the data information detailed table is processed to generate first summary information.

8. An electronic device comprising: one or more processors; A storage device for storing executable instructions, wherein when the executable instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, implement the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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