Data processing method and device, medium, equipment and product

Abstract

A data processing method and apparatus, a medium, a device and a product, the method comprising: determining a first data table corresponding to a time T, the first data table being used for storing execution data of a service node executed by a target incremental service within a first time period before the time T; according to the first data table and a second data table corresponding to the T-1 moment, a third data table corresponding to the T moment is determined, the second data table stores execution data of a service node executed by the first incremental service in a first specified time period before the T-1 moment, and the T-1 moment is taken as an ending moment in the first specified time period; the third data table is used for storing execution data of the service node executed by the second incremental service in the second specified time period before the T moment, and the T moment is taken as the ending moment in the second specified time period. Therefore, the first data table can reflect the complete execution data of the target incremental service, and the third data table is determined through the first data table, so that the resource consumption in the data processing process is reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular, to a data processing method, apparatus, medium, equipment, and product. Background Art

[0002] In the data processing field, common data modeling approaches include full-data modeling and incremental modeling. Incremental modeling records only the incremental data that has changed, while full modeling records all historical data. Currently, for data with long business lifecycles and a large number of business nodes, the volume of data increases as the business progresses. The data processing methods used in related technologies consume a lot of computing resources. Summary of the Invention

[0003] This summary is provided to briefly introduce concepts that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0004] In a first aspect, the present disclosure provides a data processing method, the method comprising: determining a first data table corresponding to time T, the first data table being used to store execution data of business nodes that have been executed before time T for a target incremental business within a first time period, the first time period being a preset time period with time T as the end time, and the incremental business including business with a changed status and / or a newly created business; determining a third data table corresponding to time T based on the first data table and the second data table corresponding to time T-1, wherein the second data table stores execution data of business nodes that have been executed before time T-1 for a first incremental business within a first specified time period, the first specified time period ending with time T-1, and the third data table being used to store execution data of business nodes that have been executed before time T for a second incremental business within a second specified time period, the second specified time period ending with time T.

[0005] In a second aspect, the present disclosure provides a data processing device, which includes: a first determination module, used to determine a first data table corresponding to time T, the first data table being used to store execution data of business nodes that have been executed before time T for a target incremental business within a first time period, the first time period being a preset time period with time T as the end time, and the incremental business including business with a changed status and / or a newly created business; a second determination module, used to determine a third data table corresponding to time T based on the first data table and the second data table corresponding to time T-1, wherein the second data table stores execution data of business nodes that have been executed before time T-1 for a first incremental business within a first specified time period, the first specified time period ending with time T-1, and the third data table being used to store execution data of business nodes that have been executed before time T for a second incremental business within a second specified time period, and the second specified time period ending with time T.

[0006] In a third aspect, the present disclosure provides a computer-readable medium having a computer program stored thereon, which, when executed by a processing device, implements the steps of the data processing method provided in the first aspect of the present disclosure.

[0007] In a fourth aspect, the present disclosure provides an electronic device comprising: a storage device storing a computer program; and a processing device for executing the computer program in the storage device to implement the steps of the data processing method provided in the first aspect of the present disclosure.

[0008] In a fifth aspect, the present disclosure provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the data processing method provided in the first aspect of the present disclosure.

[0009] Through the above technical solution, the first data table corresponding to time T is used to store the execution data of the business nodes that have been executed before time T for the target incremental business in the first time period, that is, the first data table corresponding to time T can store the execution data of all business nodes that have been executed before time T for the target incremental business. Through the first data table, not only can the state change and / or new creation of the target incremental business in the first time period be known, but also all historical execution conditions of the target incremental business can be known, that is, the complete execution data of the target incremental business. Since the first data table has the execution data of all business nodes that have been executed before time T for the target incremental business, the third data table corresponding to time T can be determined based on the first data table and the second data table corresponding to time T-1. The number of target incremental businesses in the first time period is less than the number of all businesses created in the first specified time period. Therefore, the amount of calculation required to determine the third data table corresponding to time T through the first data table is relatively small, thereby reducing resource consumption during data processing.

[0010] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale. In the drawings:

[0012] Figure 1 The figure is a flow chart showing a data processing method according to an exemplary embodiment.

[0013] Figure 2 It is a block diagram of a data processing device according to an exemplary embodiment.

[0014] Figure 3 A schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0015] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0016] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0017] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0018] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0019] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0020] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0021] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0022] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the operation requested will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operations of the disclosed technical solution based on the prompt message.

[0023] As an optional but non-limiting implementation, in response to receiving a user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. Furthermore, the pop-up window may also contain a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0024] It is understandable that the above notification and user authorization process are merely illustrative and do not limit the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0025] At the same time, it is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) must comply with the requirements of relevant laws, regulations and relevant provisions.

[0026] Figure 1 FIG. 1 is a flow chart showing a data processing method according to an exemplary embodiment. The method can be applied to an electronic device, such as a terminal or a server. Figure 1 As shown, the data processing method may include step 11 and step 12.

[0027] In step 11, a first data table corresponding to time T is determined.

[0028] Among them, the first data table is used to store the execution data of the business nodes that have been executed before time T for the target incremental business within the first time period. The first time period is a preset time period with time T as the end time. The incremental business includes business with changed status and / or newly created business.

[0029] For example, the preset time period is 1 day, and the first time period is the most recent day with time T as the end time. For example, time T is 24:00 on April 1, 2025, which is 00:00 on April 2, 2025. The first time period is April 1, 2025. The target incremental business within the first time period is the business whose status has changed within 24 hours on April 1, 2025 and / or the newly created business.

[0030] A business node is a key component in a business process. The execution data of a business node may be, for example, the time of executing the business node. The target incremental business may have one or more business nodes that have been executed before time T. This disclosure uses the logistics business as an example for explanation. The business nodes of the logistics business may include the package creation node, collection node, transportation node, receipt node, damage reporting node, etc. Table 1 is a schematic diagram of the first data table, which can be represented as Table D_1FD[T]. In this disclosure, the T in the square brackets representing the data table represents time T, the T-1 in the square brackets represents time T-1, and the TN in the square brackets represents time TN.

[0031] Table 1

[0032]

[0033] The first data table D_1FD[T] shown in Table 1 corresponds to time T, which is midnight on April 1, 2025. The package ID is the package's identifier, and each date represents the execution data for the service node. Specific time periods, such as the time the package was created, are also acceptable, without limitation. Empty fields in the data table indicate that the corresponding service node has not been executed, and the execution data is empty.

[0034] The target incremental business for April 1st includes Package 2, Package 3, and Package 4. Packages 2 and 3 are businesses with changed status, while Package 4 is a newly created business. Package 2's status changed on April 1st, meaning it was reported lost. Package 3's status changed on April 1st, meaning it began shipping on April 1st. Package 4 is a newly created business, and was both created and collected on April 1st.

[0035] As shown in Table 1, the service nodes executed for Package 2 before time T include the creation node, collection node, transportation node, damage reporting node, and loss reporting node. The service nodes executed for Package 3 before time T include the creation node, collection node, and transportation node. The service nodes executed for Package 4 before time T include the creation node and collection node.

[0036] Taking Table 1 as an example for explanation, in the related art, the incremental data corresponding to time T, for example, for package 3, can only reflect the event that package 3 began to be transported on April 1st. The information that package 3 was created on March 29th and collected on March 30th is not reflected in the incremental data corresponding to time T. Specifically, the creation of package 3 on March 29th is reflected in the incremental data corresponding to March 29th. In other words, through the incremental data corresponding to time T, the user can only know that package 3 began to be transported on April 1st, but cannot know the creation information and collection information of package 3, and thus cannot reflect the complete execution process of package 3. In the present disclosure, the first data table D_1FD[T] corresponding to time T can simultaneously reflect the information that package 3 was created on March 29th, collected on March 30th, and transported on April 1st. Therefore, the user can obtain the complete execution data of package 3 through this first data table. The same applies to other packages in Table 1.

[0037] In step 12, a third data table corresponding to time T is determined based on the first data table and the second data table corresponding to time T-1.

[0038] Among them, the second data table stores the execution data of the business nodes that have been executed before the T-1 moment of the first incremental business within the first specified time period, and the first specified time period ends at the T-1 moment. The third data table is used to store the execution data of the business nodes that have been executed before the T moment of the second incremental business within the second specified time period, and the second specified time period ends at the T moment.

[0039] For example, the second specified time period is longer than a preset time period. For example, the second specified time period is the last N consecutive days, where N may be greater than or equal to 2. Time T-1 is before time T, and the time interval between time T-1 and time T is a preset time period. For example, if the time interval between time T-1 and time T is 1 day and time T is midnight on April 1, then time T-1 is midnight on March 31.

[0040] Through the above technical solution, the first data table corresponding to time T is used to store the execution data of the business nodes that have been executed before time T for the target incremental business in the first time period, that is, the first data table corresponding to time T can store the execution data of all business nodes that have been executed before time T for the target incremental business. Through the first data table, not only can the state change and / or new creation of the target incremental business in the first time period be known, but also all historical execution conditions of the target incremental business can be known, that is, the complete execution data of the target incremental business. Since the first data table has the execution data of all business nodes that have been executed before time T for the target incremental business, the third data table corresponding to time T can be determined based on the first data table and the second data table corresponding to time T-1. The number of target incremental businesses in the first time period is less than the number of all businesses created in the first specified time period. Therefore, the amount of calculation required to determine the third data table corresponding to time T through the first data table is relatively small, thereby reducing resource consumption during data processing.

[0041] The following introduces a first implementation method of determining the first data table corresponding to time T in the present disclosure. In this implementation method, step 11 may include: determining the first data table according to fourth data tables corresponding to multiple first upstream data tables.

[0042] The fields in each first upstream data table include fields corresponding to some business nodes related to the business, and the fields in multiple first upstream data tables include fields corresponding to all business nodes related to the business.

[0043] For businesses with long business cycles and involving many business nodes, such as logistics business, especially logistics business with long transportation distances, there are many nodes involved from creation to receipt. The data of different business nodes can be stored in different upstream data tables. The upstream data table can be the incremental change data table on the data warehouse ODS (Operational Data Store) side.

[0044] For example, all business nodes related to the business include the package creation node, collection node, transportation node, signature node, damage reporting node, loss reporting node and rejection reporting node. The example of all nodes is for explanation only. In actual application, there is no limit on the number of business nodes, and it is not limited to the business nodes given in the example.

[0045] Taking the logistics business as an example, multiple first upstream data tables may include a first upstream data table for recording the trajectory of the package and a first upstream data table for recording the abnormal status of the package. Among them, the fields in the first upstream data table for recording the trajectory of the package include fields corresponding to some business nodes, namely the creation node, collection node, transportation node and receipt node, which are respectively used to indicate the creation date, collection date, transportation date and receipt date of the package. The fields in the first upstream data table for recording the abnormal status of the package include fields corresponding to some business nodes, namely the damage reporting node, loss reporting node and rejection reporting node, which are respectively used to indicate the damage reporting date, loss reporting date and rejection reporting date of the package. The fields in the multiple first upstream data tables include fields corresponding to all business nodes related to the business, that is, through the multiple first upstream data tables, the incremental data of all business nodes can be obtained.

[0046] The first upstream data table stores the execution data of the first business node that has been executed in the first time period of the first target incremental business. The first target incremental business includes all or part of the incremental business in the target incremental business, and the first business node includes at least one of the partial business nodes corresponding to the fields in the first upstream data table.

[0047] The first upstream data table for recording the package trajectory can be expressed as INPUT_A_DI[T]. Table 2 is an exemplary diagram of the first upstream data table INPUT_A_DI[T].

[0048] Table 2

[0049]

[0050] As shown in Table 2, the first upstream data table INPUT_A_DI[T] stores the execution data of package 3 and package 4 on April 1. The first target incremental business indicated by the first upstream data table INPUT_A_DI[T] includes package 3 and package 4. The first business node executed for package 3 on April 1 is the transportation node, and the first business node executed for package 4 on April 1 includes the creation node and the collection node.

[0051] The first upstream data table for recording the abnormal status of the package can be expressed as INPUT_B_DI[T]. Table 3 is an exemplary diagram of the first upstream data table INPUT_B_DI[T].

[0052] Table 3

[0053]

[0054] As shown in Table 3, the first upstream data table INPUT_B_DI[T] stores the execution data of package 2 on April 1. The first target incremental business indicated by the first upstream data table INPUT_B_DI[T] includes package 2, and the first business node executed by package 2 on April 1 is the loss reporting node.

[0055] The target incremental business includes multiple businesses whose status has changed as indicated by the first upstream data table and / or newly created businesses. Referring to the examples in Tables 2 and 3 above, the target incremental business includes package 2, package 3 and package 4. Assuming that the first upstream data table INPUT_A_DI[T] also indicates that the status of package 2 has changed, the first target incremental business indicated by the first upstream data table INPUT_A_DI[T] includes all incremental businesses in the target incremental business.

[0056] Before introducing the fourth data table, the sixth data tables corresponding to the plurality of first upstream data tables in the present disclosure are first introduced.

[0057] The sixth data table stores execution data of the first service node executed before time T-1 for a third target incremental service within the first specified time period. The third target incremental service includes all or part of the incremental service in the first incremental service. The sixth data table may include a third incremental data table and a third full data table corresponding to multiple first upstream data tables, respectively.

[0058] The first designated period corresponding to the third incremental data table is N consecutive preset time periods ending at time T-1. For example, if the preset time period is one day, the first designated period is the most recent N days ending at time T-1. For example, if time T-1 is midnight on March 31st, and N is 3, the first designated period is March 29th, March 30th, and March 31st. The third incremental data table corresponding to the first upstream data table INPUT_A_DI[T] used to record package trajectories can be represented as A_NFD[T-1]. Table 4 shows an example of table A_NFD[T-1].

[0059] Table 4

[0060]

[0061] As shown in Table 4, the third target incremental services include logistics services that were created and / or whose package trajectories changed in the last N days ending at time T-1, including Package 1, Package 2, and Package 3. The first service nodes executed for Package 2 before time T-1 include the creation node, the collection node, and the transport node. The first service nodes executed for Package 3 before time T-1 include the creation node and the collection node.

[0062] The third incremental data table corresponding to the first upstream data table INPUT_B_DI[T] for recording the abnormal status of the package can be expressed as B_NFD[T-1]. Table 5 is an exemplary table B_NFD[T-1].

[0063] Table 5

[0064]

[0065] As shown in Table 5, the third target incremental service includes logistics services for packages with abnormal status in the last N days ending at time T-1, including Package 1 and Package 2. The first service node executed before time T-1 for Package 1 includes the damage reporting node, and the first service node executed before time T-1 for Package 2 includes the damage reporting node.

[0066] The first specified time period corresponding to the third full data table is the period from the preset service start time to time T-1. This period can be considered the total number of days ending at time T-1. The third full data table corresponding to the first upstream data table INPUT_A_DI[T] can be represented as A_TFD[T-1]. Table 6 is an exemplary table A_TFD[T-1].

[0067] Table 6

[0068]

[0069] Referring to Tables 4 and 6, both are data tables related to package trajectories, and both end at time T-1. Table A_NFD[T-1] records data for services that have undergone status changes and / or been newly created in the last N days, while Table A_TFD[T-1] records data for services that have undergone status changes and / or been newly created for all days up to time T-1. Therefore, Table A_TFD[T-1] contains more records than Table A_NFD[T-1]. For example, Table A_NFD[T-1] also includes execution data for Package 0. Because Package 0 was created earlier, exceeding its lifespan of 180 days relative to time T-1, it does not belong to services that have undergone status changes and / or been newly created in the last N days. Therefore, Table A_NFD[T-1] does not contain information about Package 0. However, since Table A_TFD[T-1] stores data for all days up to time T-1, it does contain information about Package 0. The value of N can be determined based on the service lifecycle. For example, if the lifecycle of a logistics service is 180 days, N can be set to 180.

[0070] The third full data table corresponding to the first upstream data table INPUT_B_DI[T] can be expressed as B_TFD[T-1]. Table 7 is an exemplary table B_TFD[T-1].

[0071] Table 7

[0072]

[0073] Referring to Table 5 and Table 7, Table B_TFD[T-1] also includes the execution data of package 0.

[0074] The fourth data table in the present disclosure is described below. The fourth data table stores the execution data of the first service node that has executed the first target incremental service before time T. The fourth data table can be obtained by:

[0075] If the corresponding first upstream data table indicates that the first target incremental business is a business with a changed state, then the execution data of the first business node that has been executed before time T for the first target incremental business is determined based on the first upstream data table and the sixth data table corresponding to the first upstream data table.

[0076] As introduced above, the sixth data table corresponding to the first upstream data table includes the third incremental data table and / or the third full data table. The execution data of the business before time T can be determined based on the third incremental data table and / or the third full data table. For example, if the duration of the life cycle of a business is certain, and the business can be completed or ended within the life cycle, for example, the life cycle of a logistics business is 180 days, the execution data of the business before time T can be determined based on the third incremental data table. If the duration of the life cycle of the business is uncertain and there is no specified duration, the data in the third full data table is more comprehensive, and the execution data of the business before time T can be determined based on the third full data table. In addition, the third incremental data table has less data than the third full data table. If the timeliness of data output is higher, it can be calculated based on the third incremental data table with less data to increase the data processing speed.

[0077] Determining the execution data of the first service node that has been executed before time T for the first target incremental service based on the first upstream data table and the sixth data table corresponding to the first upstream data table may include:

[0078] Obtaining, from the first upstream data table, execution data of a first service node of a first target incremental service that has been executed within a first time period;

[0079] The execution data of the first service node of the first target incremental service that has been executed within the first specified time period is obtained from the sixth data table.

[0080] For example, the fourth data table corresponding to the first upstream data table INPUT_A_DI[T] is represented as A_1FD[T], and Table 8 is an exemplary table A_1FD[T].

[0081] Table 8

[0082]

[0083] In the example, table INPUT_A_DI[T] indicates that package 3 is a business with a changed status. Then, the execution data of package 3 at the transportation node is obtained from table INPUT_A_DI[T], that is, package 3 starts to be transported on April 1.

[0084] As introduced above, the sixth data table corresponding to INPUT_A_DI[T] includes the third incremental data table A_NFD[T-1] (Table 4) and / or the third full data table A_TFD[T-1] (Table 6). Both Table A_NFD[T-1] and Table A_TFD[T-1] store the execution data of package 3 in the first specified time period. Therefore, the execution data of package 3 at the creation node and the collection node can be obtained from Table A_NFD[T-1] and / or Table A_TFD[T-1], that is, package 3 was created on March 29 and collected on March 30.

[0085] If the corresponding first upstream data table indicates that the first target incremental business is a newly created business, the execution data of the first business node executed by the first target incremental business in the first upstream data table within the first time period will be used as the execution data of the first business node executed by the first target incremental business before time T.

[0086] Table INPUT_A_DI[T] indicates that package 4 is a newly created service. Therefore, the execution data of package 4 on April 1 is obtained from table INPUT_A_DI[T] as the execution data of the first service node executed by package 4 before time T.

[0087] For example, the fourth data table corresponding to the first upstream data table INPUT_B_DI[T] is expressed as B_1FD[T]. Table 9 is an exemplary table B_1FD[T].

[0088] Table 9

[0089]

[0090] Table INPUT_B_DI[T] indicates that package 2 is a service with a changed status. The execution data of package 2 at the loss reporting node is obtained from table INPUT_B_DI[T]. That is, package 2 was reported lost on April 1. The execution data of package 2 at the damage reporting node is obtained from table B_NFD[T-1] and / or table B_TFD[T-1].

[0091] In this way, for newly created businesses, the execution data within the first time period is obtained from the first upstream data table, for example, the execution data on April 1 is obtained from the first upstream data table. For businesses whose status has changed, the execution data on April 1 is obtained from the first upstream data table, and the execution data before April 1 is obtained from the corresponding sixth data table, so that the complete executed data of the businesses whose status has changed can be obtained.

[0092] In the present disclosure, determining the first data table according to the fourth data tables respectively corresponding to the plurality of first upstream data tables may include:

[0093] Performing a data merging operation on the plurality of fourth data tables according to the business identifier to obtain a fifth data table corresponding to time T, wherein the fields in the fifth data table include fields corresponding to all business nodes;

[0094] The first data table is determined according to the fifth data table.

[0095] The service identifier uniquely identifies a service. In this example, the package ID is the service identifier. A data merge operation is performed on multiple fourth data tables A_1FD[T] and B_1FD[T] based on the package ID. This data merge operation involves merging the fields corresponding to some service nodes in the multiple first upstream data tables, thereby reflecting the fields corresponding to all service nodes in the fifth data table. Furthermore, based on the package ID, the execution data for the same package ID is merged into one record (i.e., a row of data), while the execution data for different package IDs is stored in different records (i.e., rows of the table). The merged fifth data table contains all the data from the multiple fourth data tables.

[0096] The fifth data table is represented as tmp_D_1FD[T], for example. Table 10 shows tmp_D_1FD[T] obtained by merging Table A_1FD[T] (Table 8) and Table B_1FD[T] (Table 9).

[0097] Table 10

[0098]

[0099] In the present disclosure, determining the first data table according to the fifth data table may include:

[0100] If there is missing data in the fifth data table, obtaining the missing data in the fifth data table from the sixth data tables corresponding to the plurality of first upstream data tables;

[0101] The fifth data table is completed according to the acquired data to obtain the first data table.

[0102] Among them, the missing data is the execution data of the second business node of the second target incremental business within the first time period that has been executed before time T-1. There are multiple first upstream data tables in which the first target upstream data table does not indicate that the second target incremental business is a business with a changed status and / or a newly created business. The second business node includes at least one of the partial business nodes corresponding to the fields in the first target upstream data table.

[0103] As shown in Table 10, the cells represented by placeholders are missing data. The reason for the missing data is that table tmp_D_1FD[T] is obtained by merging tables A_1FD[T] and B_1FD[T]. Table A_1FD[T] does not contain data on package 3 and package 4 at abnormal status-related business nodes, and table B_1FD[T] does not contain data on package 2 at trajectory-related business nodes. Therefore, the cells represented by placeholders in Table 10 are missing data.

[0104] For example, in the first upstream data table INPUT_A_DI[T], it is not indicated that package 2 is a business with a changed status and / or a newly created business. Package 2 is used as the second target incremental business, and table INPUT_A_DI[T] is used as the first target upstream data table. The second business node includes the creation node, collection node, transportation node and receipt node, that is, the data of package 2 in these trajectory-related business nodes in table tmp_D_1FD[T] is missing.

[0105] The first upstream data table INPUT_B_DI[T] does not indicate that packages 3 and 4 are services with changed status and / or newly created services. For example, using package 3 as the second target incremental service, and table INPUT_B_DI[T] as the first target upstream data table, the second service nodes include the damage reporting node, the loss reporting node, and the rejection reporting node. This means that the data for package 3 in table tmp_D_1FD[T] at these abnormal status-related service nodes is missing. The same applies to the missing data for package 4.

[0106] The above embodiment is a case where there is missing data in table tmp_D_1FD[T]. If the first upstream data table INPUT_A_DI[T] also indicates that the status of package 2 has changed, then the data of package 2 at the trajectory-related nodes exists in table A_1FD[T]. Then, in the merged table tmp_D_1FD[T], the execution data of package 2 is not missing.

[0107] Missing data can be retrieved from the sixth data tables corresponding to the multiple first upstream data tables. Specifically, data for trajectory-related business nodes missing from table tmp_D_1FD[T] can be retrieved from table A_NFD[T-1] and / or table A_TFD[T-1]. For example, data for package 2 at the creation, collection, transportation, and receipt nodes can be retrieved. Data for abnormal status-related business nodes missing from table tmp_D_1FD[T] can be retrieved from table B_NFD[T-1] and / or table B_TFD[T-1]. For example, data for packages 3 and 4 at the damage report, loss report, and rejection report nodes can be retrieved.

[0108] The fifth data table tmp_D_1FD[T] is completed according to the acquired data to obtain the first data table D_1FD[T] as shown in Table 1 above.

[0109] In this way, the execution data of the target incremental business within the first time period, such as the execution data on April 1, is reflected in the fifth data table. The missing data in the fifth data table is the execution data of the business before time T-1. Therefore, the fifth data table can be supplemented with data through the sixth data tables corresponding to multiple first upstream data tables to obtain the first data table D_1FD[T] with complete information.

[0110] Among them, the meanings of empty and placeholder in Table 10 are different. If the field information is empty, it means that the corresponding business node has not been executed, and the execution data is empty. If the field information is a placeholder, it means that the corresponding data cannot be obtained from the fourth data table.

[0111] The above describes a first implementation method for determining the first data table corresponding to time T, where the execution data of all service nodes related to the service is stored in multiple first upstream data tables. The following describes a second implementation method for determining the first data table corresponding to time T, where the execution data of all service nodes related to the service is stored in a single upstream data table.

[0112] First, the second data table corresponding to time T-1 in the present disclosure is explained. The second data table may include a first incremental data table and a first full data table corresponding to time T-1.

[0113] Among them, the first specified time period corresponding to the first incremental data table is N consecutive preset time periods with T-1 as the end time, and the first incremental data table stores the first incremental business within N consecutive preset time periods with T-1 as the end time, and the execution data of the business nodes that have been executed before T-1.

[0114] The N consecutive preset time periods have been explained above. The first incremental data table can be expressed as D_NFD[T-1]. Table 11 is an example of the first incremental data table D_NFD[T-1].

[0115] Table 11

[0116]

[0117] The first incremental data table D_NFD[T-1] shown in Table 11 corresponds to time T-1, which is midnight on March 31st. As shown in Table 11, the first incremental service for the last N days ending at time T-1 includes packages 1, 2, and 3. The execution data for each service node executed for each package before time T-1 is shown in the table. Referring to Tables 1 and 11, package 3 had not yet been shipped on March 31st, so its package shipping date in Table 11 is empty. Package 4 had not yet been created on March 31st, so Table 11 does not contain any information about package 4.

[0118] Among them, the first specified time period corresponding to the first full data table is the period from the preset business start time to the T-1 time, and the first full data table stores the first incremental business in the period from the preset business start time to the T-1 time, and the execution data of the business nodes that have been executed before the T-1 time.

[0119] The preset service start time has been explained above. The first full data table can be expressed as D_TFD[T-1]. Table 12 is an example of the first full data table D_TFD[T-1].

[0120] Table 12

[0121]

[0122] Referring to Tables 11 and 12, since Package 0 was created relatively early and has a life cycle of more than 180 days, it does not belong to the business whose status has changed and / or has been newly created in the last N days. Therefore, there is no information about Package 0 in the first incremental data table shown in Table 11. Since the first full data table, Table 12, stores data for all days up to time T-1, there is information about Package 0.

[0123] Furthermore, the aforementioned third target incremental services include all or part of the incremental services in the first incremental services. The sixth data table includes the third incremental data table and the third full data table. For example, the third target incremental services included in the third incremental data table A_NFD[T-1] are services newly created within the last N days and / or whose service nodes related to package trajectory have changed. The first incremental services included in the first incremental data table D_NFD[T-1] also incorporate services with changes to service nodes related to package abnormal status. Therefore, the third target incremental services are all or part of the first incremental services. Similarly, the third target incremental services included in the third full data table A_TFD[T-1] are all or part of the first incremental services included in the first full data table D_TFD[T-1]. Similar procedures apply to the third incremental data table B_NFD[T-1] and the third full data table B_TFD[T-1].

[0124] In the present disclosure, determining the first data table corresponding to time T may include:

[0125] The first data table is determined according to the second upstream data table.

[0126] The fields in the second upstream data table include fields corresponding to all business nodes related to the business. The second upstream data table stores the execution data of the business nodes executed during the first time period for the target incremental business. For example, the second upstream data table also indicates that package 2 was reported lost on April 1, package 3 began shipping on April 1, and package 4 was created and collected on April 1.

[0127] In the present disclosure, determining the first data table according to the second upstream data table may include:

[0128] If the second upstream data table indicates that the target incremental service is a service with a changed state, the execution data of the service node that has been executed before time T for the target incremental service is determined based on the second upstream data table and the second data table.

[0129] Determining the execution data of the service node that has been executed before time T for the target incremental service based on the second upstream data table and the second data table may include:

[0130] Obtaining, from the second upstream data table, execution data of the service nodes of the target incremental service that have been executed within the first time period;

[0131] The execution data of the service nodes that have been executed during the first specified time period of the target incremental service are obtained from the second data table.

[0132] For example, if the target incremental service package 2 is a service with a changed status, the data reported as lost for package 2 on April 1 can be obtained from the second upstream data table. The second data table may include the first incremental data table D_NFD[T-1] and the first full data table D_TFD[T-1]. Both tables D_NFD[T-1] and D_TFD[T-1] store the execution data of the target incremental service before time T-1. Therefore, data on package 2 at the creation, collection, transportation, and damage reporting nodes can be obtained from tables D_NFD[T-1] and / or D_TFD[T-1].

[0133] If the service lifecycle duration is known, the data for the target incremental service before time T-1 can be obtained from the first incremental data table D_NFD[T-1]. If the service lifecycle duration is uncertain, the data for the target incremental service before time T-1 can be obtained from the first full data table D_TFD[T-1]. Furthermore, table D_NFD[T-1] contains less data than table D_TFD[T-1]. If data output timeliness is a priority, calculations can be performed based on the smaller first incremental data table D_NFD[T-1] to improve data processing speed.

[0134] If the second upstream data table indicates that the target incremental business is a newly created business, the execution data of the business nodes executed by the target incremental business in the second upstream data table within the first time period will be used as the execution data of the business nodes executed by the target incremental business before time T.

[0135] For example, the target incremental business package 4 is a newly created business. The data of the creation and collection of package 4 on April 1 can be obtained from the second upstream data table as the execution data of the business node that has been executed by package 4 before time T.

[0136] The first data table D_1FD[T] determined in the second embodiment can be referred to as shown in Table 1.

[0137] Through the above technical solution, the first data table corresponding to the T moment determined in the present disclosure, such as the execution data of the target incremental business in the last day, can reflect the complete data of the target incremental business that has been executed. It can not only support the user's demand for viewing the complete business execution process of the target incremental business, but also determine the third data table corresponding to the T moment based on the first data table, and the required resource consumption is relatively small.

[0138] The following describes an implementation of determining the third data table corresponding to time T in the present disclosure.

[0139] The second data table includes a first incremental data table D_NFD[T-1] corresponding to time T-1. The first designated time period is N consecutive preset time periods ending at time T-1. Correspondingly, the second designated time period is N consecutive preset time periods ending at time T. The third data table includes a second incremental data table corresponding to time T, for example, represented as D_NFD[T]. The second incremental data table D_NFD[T] is used to store execution data of the second incremental service within the N consecutive preset time periods ending at time T and the service nodes executed before time T.

[0140] As an example, if the first designated period is March 29, March 30, and March 31, then the second designated period is March 30, March 31, and April 1.

[0141] Step 12 may include:

[0142] A second incremental data table is determined based on the first data table, the first incremental data table, and a seventh data table corresponding to time TN. The seventh data table stores execution data of service nodes for the third incremental service executed before time TN within a second time period, where the second time period is a preset time period ending at time TN.

[0143] Determining the second incremental data table according to the first data table, the first incremental data table, and the seventh data table corresponding to the TN time may include:

[0144] According to the business identifier, a data merging operation is performed on the first data table and the first incremental data table.

[0145] The implementation method of the data merging operation can refer to the above introduction. For example, the first data table D_1FD[T] (Table 1) and the first incremental data table D_NFD[T-1] (Table 11) are merged, and the resulting data table is shown in Table 13.

[0146] Table 13

[0147]

[0148] Afterwards, if the result obtained from the data merging operation contains a record corresponding to the first business identifier, the record corresponding to the first business identifier is deleted to obtain a second incremental data table, where the first business identifier is the business identifier existing in the seventh data table.

[0149] Since the second incremental data table D_NFD[T] only needs to store data for N days, and data before N days is unnecessary, the record corresponding to the first service identifier can be deleted to obtain the second incremental data table. For example, assume that N is 3, time T is 24:00 on April 1, time T-1 is 24:00 on March 31, time T-2 is 24:00 on March 30, and time T-3 is 24:00 on March 29. The seventh data table D_1FD[TN] stores the execution data for all service nodes executed before time T-3 for the third incremental service on March 29th. For example, if package 5 was created on March 29th and received on March 31st, the seventh data table D_1FD[TN] contains a record of package 5's creation. The first incremental data table D_NFD[T-1] also contains records of package 5's creation, receipt, and other nodes. Therefore, after merging the data in the first data table D_1FD[T] and the first incremental data table D_NFD[T-1], the merged result includes a record for package 5. However, since the second incremental data table D_NFD[T] only needs to store data for April 1st, March 31st, and March 30th, the data for March 29th is three days beyond its lifecycle. Packages that have exceeded their lifecycle are typically received and the service process has ended, making them unnecessary data. Therefore, the record corresponding to package 5 can be deleted from the merged result. Since the incremental data before TN-1 has been deleted when determining table D_NFD[T-1], it is only necessary to delete the records according to table D_1FD[TN]. That is, for example, the incremental business before 24:00 on March 28 is not in table D_NFD[T-1], so it is only necessary to delete the execution data of the incremental business on March 29.

[0150] Returning to the embodiment of Table 13 above, for example, if Table 13 obtained from the data merge operation does not contain a record corresponding to the first service identifier, Table 13 obtained from the data merge operation can be used as the second incremental data table D_NFD[T]. As shown in Table 13, the second incremental service includes packages 1 through 4. Because the second incremental data table D_NFD[T] and the first data table A_1FD[T] correspond to the same end time, time T, the second incremental data table D_NFD[T] records data from the last N days, while the first data table A_1FD[T] records data from the last day. Therefore, the second incremental service can include the target incremental service, meaning that the target incremental service can be part of the second incremental service.

[0151] Among them, the second data table includes the first full data table D_TFD[T-1] corresponding to the T-1 moment, the first specified time period is the period from the preset business start time to the T-1 moment, and accordingly, the second specified time period is the period from the preset business start time to the T moment. The third data table includes the second full data table corresponding to the T moment, and the second full data table is used to store the second incremental business in the period from the preset business start time to the T-1 moment, and the execution data of the business nodes that have been executed before the T moment.

[0152] Step 12 may include: performing a data merging operation on the first data table and the first full data table according to the business identifier to obtain a second full data table.

[0153] For example, the first data table D_1FD[T] (Table 1) and the first full data table D_TFD[T-1] (Table 12) are merged to obtain the second full data table shown in Table 14. The second full data table can be expressed as D_TFD[T].

[0154] Table 14

[0155]

[0156] The data recorded in the second full data table is the most comprehensive, including data for all days ending at time T, as well as execution data for all services. As shown in Table 14, the second incremental service includes packages 0 through 4. In the embodiment where the first designated time period is from the preset service start time to time T-1, the first incremental service is part of the second incremental service, and the target incremental service is part of the second incremental service.

[0157] For businesses with long life cycles and many business nodes, data is generally stored in multiple upstream data tables. Taking the upstream data tables of logistics business, including tables INPUT_A_DI[T] and INPUT_B_DI[T], as an example, since the incremental data of the most recent day in related technologies can only reflect the new business creation and / or status changes within that day, but cannot reflect the complete business process that has been executed historically, when constructing full data in related technologies, the full data related to the package trajectory corresponding to time T (denoted as table A_TD[T]) and the full data related to the package abnormal status corresponding to time T (denoted as table B_TD[T]) are merged to obtain the data. If table A_TD[T] has Ma rows and table B_TD[T] has Mb rows, then the computational complexity of the merged full data corresponding to time T, namely D_TFD[T], is Ma*Mb, where * represents data association or merging operations.

[0158] In the present disclosure, when determining the second incremental data table D_NFD[T] and the second full data table D_TFD[T] corresponding to time T, there is no need to calculate the full data of time T corresponding to each upstream data table, that is, there is no need to calculate A_TD[T] and B_TD[T]. The second incremental data table and the second full data table are calculated through the first data table D_1FD[T], which can significantly reduce the amount of calculation.

[0159] For example, table A_1FD[T] has △Ma rows, table B_1FD[T] has △Mb rows, table A_TFD[T-1] has Ma rows, and table B_TFD[T-1] has Mb rows. Then the number of rows in table tmp_D_1FD[T] is less than or equal to △Ma+△Mb, and the calculation amount Cal_1FD of the first data table D_1FD[T] is determined to be ≤(△Ma+△Mb)*(Ma+Mb). Among them, the data table related to the package trajectory is used as the main table, that is, all packages must have a trajectory process such as creation and collection. However, some packages do not have abnormal conditions such as damage reporting. Therefore, the number of rows in table D_TFD[T-1] is also considered to be Ma rows. Table D_NFD[T-1] only has N days of data, and the number of rows is less than or equal to Ma rows. Therefore, the calculation amount of the second incremental data table D_NFD[T] is determined to be less than or equal to Ma+Cal_1FD, and the calculation amount of the second full data table D_TFD[T] is determined to be less than or equal to Ma+Cal_1FD.

[0160] The above embodiments are for illustrative purposes only, and the number of rows in the given data table is relatively small. In actual application scenarios, when the number of rows is relatively large, for example, assuming that Ma is one million rows and Mb is one million rows, and the amount of data changed in one day △Ma is ten thousand rows and △Mb is ten thousand rows, the amount of calculation and resource consumption can be significantly reduced.

[0161] It should be noted that the fields in the various data tables in the embodiments of the present disclosure do not mean that the data tables are only used to store information in these fields. The fields given in the data tables of the embodiments are for the convenience of explaining the data processing method of the present disclosure. For example, the data tables related to the package trajectory may also store information such as the sender's name, sender's phone number, recipient's name, recipient's phone number, etc. The data tables related to the package abnormal status may also store, for example, information describing the package abnormal status, such as a description of the package damage.

[0162] In addition, when determining the fifth data table tmp_D_1FD[T], the first data table D_1FD[T], the second incremental data table D_NFD[T], and the second full data table D_TFD[T] corresponding to time T, the data tables corresponding to time T-1, such as table A_TFD[T-1] and table D_TFD[T-1], are all known and stored in the electronic device. The method for determining each data table at time T-1 can refer to the method for determining the corresponding data table at time T. For example, the generation of table D_TFD[T-1] can refer to the method for determining table D_TFD[T]. In addition, the fourth data tables corresponding to the multiple first upstream data tables, such as A_1FD[T] and B_1FD[T], can be predetermined and stored in the electronic device before the execution process of the data processing method, or can be determined in real time during the execution process of the data processing method.

[0163] The embodiments of the present disclosure are illustrated using the logistics business as an example. The data processing method of the present disclosure is not limited to the scenarios of the logistics business. For example, in the product manufacturing business, the relevant business nodes may include raw material procurement, raw material quality inspection, production, finished product quality inspection, warehousing, sales and other nodes. It can also be applied to other businesses with long life cycles and many business nodes.

[0164] Furthermore, a preset time period of 1 day is only an example, and the length of a preset time period can be set according to the execution process of the business process, such as 1 hour, one week, etc.

[0165] Based on the same inventive concept, the present disclosure also provides a data processing device, Figure 2 is a block diagram of a data processing device according to an exemplary embodiment. Figure 2 As shown, the data processing device 20 may include:

[0166] The first determination module 21 is used to determine the first data table corresponding to time T, wherein the first data table is used to store the execution data of the business nodes that have been executed before time T for the target incremental business within the first time period, and the first time period is a preset time period with time T as the end time, and the incremental business includes the business with changed status and / or the newly created business; the second determination module 22 is used to determine the third data table corresponding to time T based on the first data table and the second data table corresponding to time T-1, wherein the second data table stores the execution data of the business nodes that have been executed before time T-1 for the first incremental business within the first specified time period, and the first specified time period ends with time T-1, and the third data table is used to store the execution data of the business nodes that have been executed before time T for the second incremental business within the second specified time period, and the second specified time period ends with time T.

[0167] Optionally, the first determination module 21 includes: a first determination sub-module, used to determine the first data table based on the fourth data table corresponding to multiple first upstream data tables; wherein, the fields in each of the first upstream data tables include fields corresponding to some business nodes related to the business, and the fields in the multiple first upstream data tables include fields corresponding to all business nodes related to the business; the first upstream data table stores the execution data of the first business node that has been executed by the first target incremental business within the first time period, the first target incremental business includes all or part of the incremental business in the target incremental business, and the first business node includes at least one of the some business nodes corresponding to the fields in the first upstream data table; the fourth data table stores the execution data of the first business node that has been executed by the first target incremental business before time T.

[0168] Optionally, the first determination submodule includes: a first merging submodule, used to perform data merging operations on multiple fourth data tables according to the business identifier to obtain a fifth data table corresponding to time T, and the fields in the fifth data table include fields corresponding to all the business nodes; a second determination submodule, used to determine the first data table based on the fifth data table.

[0169] Optionally, the second determination submodule includes: a first acquisition submodule, which is used to obtain the missing data in the fifth data table from the sixth data table corresponding to the multiple first upstream data tables if there is missing data in the fifth data table; a data completion submodule, which is used to complete the data of the fifth data table according to the acquired data to obtain the first data table; wherein, the missing data is the execution data of the second business node that has been executed before the T-1 moment for the second target incremental business within the first time period, and there is a first target upstream data table in the multiple first upstream data tables that does not indicate that the second target incremental business is a business with a changed status and / or a newly created business, and the second business node includes at least one of the partial business nodes corresponding to the fields in the first target upstream data table; the sixth data table stores the execution data of the first business node that has been executed before the T-1 moment for the third target incremental business, and the third target incremental business includes all or part of the incremental business in the first incremental business.

[0170] Optionally, the fourth data table is obtained through the following modules: a third determination module, which is used to determine the execution data of the first business node executed by the first target incremental business before time T according to the first upstream data table and the sixth data table corresponding to the first upstream data table if the corresponding first upstream data table indicates that the first target incremental business is a business with a changed state, wherein the sixth data table stores the execution data of the first business node executed by the third target incremental business before time T-1 within the first specified time period, and the third target incremental business includes all or part of the incremental business in the first incremental business; a fourth determination module, which is used to use the execution data of the first business node executed by the first target incremental business within the first time period in the first upstream data table as the execution data of the first business node executed by the first target incremental business before time T if the corresponding first upstream data table indicates that the first target incremental business is a newly created business.

[0171] Optionally, the third determination module includes: a second acquisition sub-module, used to obtain the execution data of the first business node that has been executed by the first target incremental business within the first time period from the first upstream data table; a third acquisition sub-module, used to obtain the execution data of the first business node that has been executed by the first target incremental business within the first specified time period from the sixth data table.

[0172] Optionally, the first determination module 21 includes: a third determination sub-module, used to determine the first data table based on a second upstream data table, wherein the fields in the second upstream data table include fields corresponding to all business nodes related to the business, and the second upstream data table stores the execution data of the business nodes that have been executed by the target incremental business within the first time period.

[0173] Optionally, the third determination submodule includes: a fourth determination submodule, which is used to determine the execution data of the business node that has been executed before time T of the target incremental business based on the second upstream data table and the second data table if the second upstream data table indicates that the target incremental business is a business whose status has changed; and a fifth determination submodule, which is used to use the execution data of the business node that has been executed by the target incremental business in the second upstream data table within the first time period as the execution data of the business node that has been executed before time T of the target incremental business if the second upstream data table indicates that the target incremental business is a newly created business.

[0174] Optionally, the fourth determination submodule includes: a fourth acquisition submodule, used to obtain the execution data of the business nodes that have been executed by the target incremental business within the first time period from the second upstream data table; and a fifth acquisition submodule, used to obtain the execution data of the business nodes that have been executed by the target incremental business within the first specified time period from the second data table.

[0175] Optionally, the second data table includes a first incremental data table corresponding to the T-1 moment, the first specified time period is N consecutive preset time periods with the T-1 moment as the end time, the second specified time period is N consecutive preset time periods with the T moment as the end time, and the third data table includes a second incremental data table corresponding to the T moment; the second determination module 22 includes: a sixth determination submodule, used to determine the second incremental data table based on the first data table, the first incremental data table and the seventh data table corresponding to the TN moment, wherein the seventh data table stores the execution data of the service nodes that have been executed before the TN moment for the third incremental service within the second time period, and the second time period is a preset time period with the TN moment as the end time.

[0176] Optionally, the sixth determination submodule includes: a second merging submodule, used to perform a data merging operation on the first data table and the first incremental data table according to the business identifier; a deleting submodule, used to delete the record corresponding to the first business identifier if there is a record corresponding to the first business identifier in the result obtained by the data merging operation, so as to obtain the second incremental data table, where the first business identifier is the business identifier existing in the seventh data table.

[0177] Optionally, the second data table includes a first full data table corresponding to time T-1, the first specified time period is the time period from the preset business start time to time T-1, the second specified time period is the time period from the preset business start time to time T, and the third data table includes a second full data table corresponding to time T; the second determination module 22 includes: a third merging sub-module, which is used to perform a data merging operation on the first data table and the first full data table according to the business identifier to obtain the second full data table.

[0178] Optionally, the duration of the second specified time period is greater than the duration of the preset time period, time T-1 is before time T, and the time interval between time T-1 and time T is the preset time period.

[0179] Reference below Figure 3, which shows a schematic structural diagram of an electronic device 600 suitable for implementing the embodiments of the present disclosure. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 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.

[0180] like Figure 3 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the electronic device 600 are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0181] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0182] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0183] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A 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 of the above. More specific examples of computer-readable storage media may include, but are not limited to: 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 of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, 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 a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. 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. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0184] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0185] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0186] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: determines a first data table corresponding to time T, where the first data table is used to store execution data of service nodes that have been executed before time T for target incremental services within a first time period, where the first time period is a preset time period ending at time T, and the incremental services include services with changed status and / or newly created services;

[0187] Based on the first data table and the second data table corresponding to time T-1, the third data table corresponding to time T is determined, wherein the second data table stores the execution data of the business nodes that have been executed before time T-1 for the first incremental business in the first specified time period, and the first specified time period ends at time T-1. The third data table is used to store the execution data of the business nodes that have been executed before time T for the second incremental business in the second specified time period, and the second specified time period ends at time T.

[0188] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may 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 may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0189] 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 module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than 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 and / or flowchart, and the combination of the boxes in the block diagram and / 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.

[0190] The modules described in the embodiments of the present disclosure may be implemented in software or hardware. In some cases, the name of a module does not necessarily limit the module itself. For example, the first determination module may also be described as a "module for determining a first data table."

[0191] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0192] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, 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 of the foregoing.

[0193] According to one or more embodiments of the present disclosure, Example 1 provides a data processing method, which includes: determining a first data table corresponding to time T, the first data table being used to store execution data of business nodes that have been executed before time T for target incremental business within a first time period, the first time period being a preset time period with time T as the end time, and the incremental business including business with changed status and / or newly created business; determining a third data table corresponding to time T based on the first data table and the second data table corresponding to time T-1, wherein the second data table stores execution data of business nodes that have been executed before time T-1 for the first incremental business within a first specified time period, the first specified time period ending with time T-1, and the third data table being used to store execution data of business nodes that have been executed before time T for the second incremental business within a second specified time period, and the second specified time period ending with time T.

[0194] According to one or more embodiments of the present disclosure, Example 2 provides the method of Example 1, wherein determining the first data table corresponding to time T includes: determining the first data table based on the fourth data table corresponding to multiple first upstream data tables respectively; wherein the fields in each of the first upstream data tables include fields corresponding to some business nodes related to the business, and the fields in the multiple first upstream data tables include fields corresponding to all business nodes related to the business; the first upstream data table stores the execution data of the first business node that has been executed by the first target incremental business within the first time period, the first target incremental business includes all or part of the incremental business in the target incremental business, and the first business node includes at least one of the some business nodes corresponding to the fields in the first upstream data table; the fourth data table stores the execution data of the first business node that has been executed by the first target incremental business before time T.

[0195] According to one or more embodiments of the present disclosure, Example 3 provides the method of Example 2, which determines the first data table based on the fourth data tables corresponding to multiple first upstream data tables, including: performing a data merging operation on the multiple fourth data tables according to the business identifier to obtain a fifth data table corresponding to time T, wherein the fields in the fifth data table include fields corresponding to all the business nodes; and determining the first data table based on the fifth data table.

[0196] According to one or more embodiments of the present disclosure, Example 4 provides the method of Example 3, wherein determining the first data table based on the fifth data table includes: if there is missing data in the fifth data table, obtaining the missing data in the fifth data table from the sixth data table corresponding to the multiple first upstream data tables respectively; completing the data of the fifth data table based on the obtained data to obtain the first data table; wherein the missing data is the execution data of the second business node of the second target incremental business in the first time period that has been executed before time T-1, and there is a first target upstream data table in the multiple first upstream data tables that does not indicate that the second target incremental business is a business with a changed status and / or a newly created business, and the second business node includes at least one of the partial business nodes corresponding to the fields in the first target upstream data table; the sixth data table stores the execution data of the first business node of the third target incremental business that has been executed before time T-1, and the third target incremental business includes all or part of the incremental business in the first incremental business.

[0197] According to one or more embodiments of the present disclosure, Example 5 provides a method of any one of Examples 2 to 4, and the fourth data table is obtained in the following manner: if the corresponding first upstream data table indicates that the first target incremental business is a business whose status has changed, then according to the first upstream data table and the sixth data table corresponding to the first upstream data table, the execution data of the first business node executed by the first target incremental business before time T is determined, wherein the sixth data table stores the execution data of the first business node executed by the third target incremental business before time T-1 within the first specified time period, and the third target incremental business includes all or part of the incremental business in the first incremental business; if the corresponding first upstream data table indicates that the first target incremental business is a newly created business, then the execution data of the first business node executed by the first target incremental business within the first time period in the first upstream data table is used as the execution data of the first business node executed by the first target incremental business before time T.

[0198] According to one or more embodiments of the present disclosure, Example 6 provides the method of Example 5, which determines the execution data of the first business node that has been executed by the first target incremental business before time T based on the first upstream data table and the sixth data table corresponding to the first upstream data table, including: obtaining the execution data of the first business node that has been executed by the first target incremental business within the first time period from the first upstream data table; obtaining the execution data of the first business node that has been executed by the first target incremental business within the first specified time period from the sixth data table.

[0199] According to one or more embodiments of the present disclosure, Example 7 provides the method of Example 1, wherein determining the first data table corresponding to time T includes: determining the first data table based on a second upstream data table, wherein the fields in the second upstream data table include fields corresponding to all business nodes related to the business, and the second upstream data table stores the execution data of the business nodes that have been executed by the target incremental business within the first time period.

[0200] According to one or more embodiments of the present disclosure, Example 8 provides the method of Example 7, which determines the first data table based on the second upstream data table, including: if the second upstream data table indicates that the target incremental business is a business whose status has changed, then determining the execution data of the business node that has been executed before time T for the target incremental business based on the second upstream data table and the second data table; if the second upstream data table indicates that the target incremental business is a newly created business, then using the execution data of the business node that has been executed by the target incremental business in the second upstream data table within the first time period as the execution data of the business node that has been executed before time T for the target incremental business.

[0201] According to one or more embodiments of the present disclosure, Example 9 provides the method of Example 8, which determines the execution data of the business nodes that have been executed by the target incremental business before time T based on the second upstream data table and the second data table, including: obtaining the execution data of the business nodes that have been executed by the target incremental business within the first time period from the second upstream data table; obtaining the execution data of the business nodes that have been executed by the target incremental business within the first specified time period from the second data table.

[0202] According to one or more embodiments of the present disclosure, Example 10 provides the method of Example 1, wherein the second data table includes a first incremental data table corresponding to moment T-1, the first specified time period is N consecutive preset time periods with moment T-1 as the end time, the second specified time period is N consecutive preset time periods with moment T as the end time, and the third data table includes a second incremental data table corresponding to moment T; determining the third data table corresponding to moment T based on the first data table and the second data table corresponding to moment T-1 includes: determining the second incremental data table based on the first data table, the first incremental data table and the seventh data table corresponding to moment TN, wherein the seventh data table stores execution data of service nodes that have been executed before moment TN for the third incremental service within the second time period, and the second time period is a preset time period with moment TN as the end time.

[0203] According to one or more embodiments of the present disclosure, Example 11 provides the method of Example 10, which determines the second incremental data table based on the first data table, the first incremental data table and the seventh data table corresponding to the TN moment, including: performing a data merging operation on the first data table and the first incremental data table based on the business identifier; if there is a record corresponding to the first business identifier in the result obtained by the data merging operation, deleting the record corresponding to the first business identifier to obtain the second incremental data table, and the first business identifier is the business identifier existing in the seventh data table.

[0204] According to one or more embodiments of the present disclosure, Example 12 provides the method of Example 1, wherein the second data table includes a first full data table corresponding to time T-1, the first specified time period is a period from the preset business start time to time T-1, the second specified time period is a period from the preset business start time to time T, and the third data table includes a second full data table corresponding to time T; determining the third data table corresponding to time T based on the first data table and the second data table corresponding to time T-1 includes: performing a data merging operation on the first data table and the first full data table according to the business identifier to obtain the second full data table.

[0205] According to one or more embodiments of the present disclosure, Example 13 provides the method of Example 1, wherein the duration of the second specified time period is greater than the duration of the preset time period, moment T-1 is before moment T, and the time interval between moment T-1 and moment T is the preset time period.

[0206] According to one or more embodiments of the present disclosure, Example 14 provides a data processing device, which includes: a first determination module, used to determine a first data table corresponding to time T, the first data table being used to store execution data of business nodes that have been executed before time T for target incremental business within a first time period, the first time period being a preset time period with time T as the end time, and the incremental business including business with changed status and / or newly created business; a second determination module, used to determine a third data table corresponding to time T based on the first data table and the second data table corresponding to time T-1, wherein the second data table stores execution data of business nodes that have been executed before time T-1 for the first incremental business within a first specified time period, the first specified time period ending with time T-1, and the third data table being used to store execution data of business nodes that have been executed before time T for the second incremental business within a second specified time period, and the second specified time period ending with time T.

[0207] According to one or more embodiments of the present disclosure, Example 15 provides a computer-readable medium having a computer program stored thereon, which implements the steps of the method described in any one of Examples 1 to 13 when executed by a processing device.

[0208] According to one or more embodiments of the present disclosure, Example 16 provides an electronic device, comprising: a storage device on which a computer program is stored; and a processing device for executing the computer program in the storage device to implement the steps of any one of the methods described in Examples 1 to 13.

[0209] According to one or more embodiments of the present disclosure, Example 17 provides a computer program product, including a computer program, which implements the steps of any one of the methods of Examples 1 to 13 when executed by a processor.

[0210] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0211] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0212] Although the subject matter has been described using language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims. Regarding the apparatus in the above-described embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments related to the method and will not be elaborated upon here.

Claims

1. A data processing method, characterized in that: The method comprises: Determine a first data table corresponding to time T, where the first data table is used to store execution data of service nodes that have been executed before time T for target incremental services within a first time period, where the first time period is a preset time period ending at time T, and the incremental services include services with changed status and / or newly created services; Based on the first data table and the second data table corresponding to time T-1, the third data table corresponding to time T is determined, wherein the second data table stores the execution data of the business nodes that have been executed before time T-1 for the first incremental business in the first specified time period, and the first specified time period ends at time T-1. The third data table is used to store the execution data of the business nodes that have been executed before time T for the second incremental business in the second specified time period, and the second specified time period ends at time T.

2. The method according to claim 1, characterized in that The determining of the first data table corresponding to time T includes: determining the first data table according to fourth data tables respectively corresponding to the plurality of first upstream data tables; The fields in each of the first upstream data tables include fields corresponding to some business nodes related to the business, and the fields in the multiple first upstream data tables include fields corresponding to all business nodes related to the business; The first upstream data table stores execution data of a first service node of a first target incremental service that has been executed within the first time period, the first target incremental service including all or part of the incremental services in the target incremental service, and the first service node including at least one of the part of the service nodes corresponding to a field in the first upstream data table; The fourth data table stores the execution data of the first service node that has been executed before time T for the first target incremental service.

3. The method according to claim 2, characterized in that The determining the first data table according to the fourth data tables respectively corresponding to the plurality of first upstream data tables includes: performing a data merging operation on the plurality of fourth data tables according to the service identifier to obtain a fifth data table corresponding to time T, wherein the fields in the fifth data table include fields corresponding to all the service nodes; The first data table is determined according to the fifth data table.

4. The method according to claim 3, characterized in that The determining the first data table according to the fifth data table includes: If there is missing data in the fifth data table, obtaining the missing data in the fifth data table from the sixth data tables respectively corresponding to the plurality of first upstream data tables; Completing the fifth data table according to the acquired data to obtain the first data table; The missing data is execution data of a second service node of a second target incremental service executed before time T-1 within the first time period; a first target upstream data table in the multiple first upstream data tables does not indicate that the second target incremental service is a service with a changed status and / or a newly created service; and the second service node includes at least one of the partial service nodes corresponding to a field in the first target upstream data table; The sixth data table stores execution data of the first service node for a third target incremental service that has been executed before time T-1, and the third target incremental service includes all or part of the incremental services in the first incremental service.

5. The method according to any one of claims 2 to 4, characterized in that The fourth data table is obtained in the following manner: If the corresponding first upstream data table indicates that the first target incremental service is a service with a changed state, determining, based on the first upstream data table and a sixth data table corresponding to the first upstream data table, execution data of the first service node that has been executed before time T for the first target incremental service, wherein the sixth data table stores execution data of the first service node that has been executed before time T-1 for a third target incremental service within the first specified time period, the third target incremental service including all or part of the incremental services in the first incremental service; If the corresponding first upstream data table indicates that the first target incremental business is a newly created business, the execution data of the first business node executed by the first target incremental business within the first time period in the first upstream data table will be used as the execution data of the first business node executed by the first target incremental business before time T.

6. The method according to claim 5, characterized in that The determining, based on the first upstream data table and a sixth data table corresponding to the first upstream data table, execution data of the first service node that has been executed before time T for the first target incremental service includes: Obtaining, from the first upstream data table, execution data of the first service node that has been executed for the first target incremental service within the first time period; The execution data of the first service node executed by the first target incremental service within the first specified time period is obtained from the sixth data table.

7. The method according to claim 1, characterized in that The determining of the first data table corresponding to time T includes: The first data table is determined based on the second upstream data table, wherein the fields in the second upstream data table include fields corresponding to all business nodes related to the business, and the second upstream data table stores the execution data of the business nodes that have been executed by the target incremental business within the first time period.

8. The method according to claim 7, characterized in that The determining the first data table according to the second upstream data table includes: If the second upstream data table indicates that the target incremental service is a service whose state has changed, determining execution data of a service node that has been executed before time T for the target incremental service based on the second upstream data table and the second data table; If the second upstream data table indicates that the target incremental business is a newly created business, the execution data of the business nodes executed by the target incremental business in the second upstream data table within the first time period will be used as the execution data of the business nodes executed by the target incremental business before time T.

9. The method according to claim 8, characterized in that The determining, based on the second upstream data table and the second data table, execution data of the service node that has been executed before time T for the target incremental service includes: Obtaining, from the second upstream data table, execution data of the service nodes of the target incremental service that have been executed within the first time period; The execution data of the service nodes of the target incremental service that have been executed within the first specified time period is obtained from the second data table.

10. The method according to claim 1, characterized in that The second data table includes a first incremental data table corresponding to time T-1, the first specified time period is N consecutive preset time periods starting at time T-1, the second specified time period is N consecutive preset time periods starting at time T, and the third data table includes a second incremental data table corresponding to time T; The determining, based on the first data table and the second data table corresponding to time T-1, a third data table corresponding to time T, includes: The second incremental data table is determined based on the first data table, the first incremental data table and the seventh data table corresponding to the TN moment, wherein the seventh data table stores the execution data of the service nodes that have been executed before the TN moment of the third incremental service within the second time period, and the second time period is a preset time period with the TN moment as the end moment.

11. The method according to claim 10, characterized in that The determining the second incremental data table according to the first data table, the first incremental data table, and the seventh data table corresponding to the TN time includes: Performing a data merging operation on the first data table and the first incremental data table according to the service identifier; If a record corresponding to the first business identifier exists in the result obtained by the data merging operation, the record corresponding to the first business identifier is deleted to obtain the second incremental data table, where the first business identifier is the business identifier existing in the seventh data table.

12. The method according to claim 1, characterized in that The second data table includes a first full data table corresponding to time T-1, the first designated period is a period from the preset service start time to time T-1, the second designated period is a period from the preset service start time to time T, and the third data table includes a second full data table corresponding to time T; The determining, based on the first data table and the second data table corresponding to time T-1, a third data table corresponding to time T, includes: According to the service identifier, a data merging operation is performed on the first data table and the first full data table to obtain the second full data table.

13. The method according to claim 1, wherein The duration of the second designated time period is longer than the duration of the preset time period, time T-1 is before time T, and the time interval between time T-1 and time T is the preset time period.

14. A data processing device, characterized in that: The device comprises: a first determining module, configured to determine a first data table corresponding to time T, wherein the first data table is configured to store execution data of service nodes executed before time T for target incremental services within a first time period, wherein the first time period is a preset time period ending at time T, and the incremental services include services with changed status and / or newly created services; The second determination module is used to determine the third data table corresponding to time T based on the first data table and the second data table corresponding to time T-1, wherein the second data table stores the execution data of the business nodes that have been executed before time T-1 for the first incremental business in the first specified time period, and the first specified time period ends at time T-1; the third data table is used to store the execution data of the business nodes that have been executed before time T for the second incremental business in the second specified time period, and the second specified time period ends at time T.

15. A computer-readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processing device, the steps of the method according to any one of claims 1 to 13 are implemented.

16. An electronic device, characterized in that: include: a storage device having a computer program stored thereon; A processing device, configured to execute the computer program in the storage device to implement the steps of the method according to any one of claims 1 to 13.

17. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 13 are implemented.

Images