Data processing method, warehousing control system, equipment and storage medium

Abstract

The invention provides a data processing method, a storage control system, equipment and a storage medium, and relates to the technical field of computers, the technical field of intelligent logistics and the technical field of big data. The method comprises the steps that root node data is determined from a to-be-processed data set, the root node data is suitable for representing a to-be-migrated business object, and the root node data has root node data attributes; according to business data rule topological information related to the attribute of the root node data, leaf node data related to the attribute of the root node data is determined from the to-be-processed data set, and the business data rule topological information comprises root nodes related to the root node data and leaf nodes related to the leaf node data; and based on the leaf node data, target business data suitable for being migrated to the target storage area is determined, and the target business data is related to the business object.

Description

Technical Field

[0001] The present disclosure relates to the fields of computer technology, smart logistics technology and big data technology, and more specifically, to a data processing method, a warehouse control system, equipment, a storage medium and a computer program product. Background Art

[0002] With the rapid development of computer technology, more and more companies are building business service systems to improve the efficiency of business execution. For example, the transaction efficiency of goods can be improved based on the constructed Internet e-commerce platform to meet the shopping needs of users. As the business scale continues to expand, the data scale related to business transactions has also exploded. Therefore, it is necessary to properly store data related to business transactions so that the data analysis system can analyze the business transaction data and improve business functions based on the data analysis results.

[0003] In the process of realizing the concept disclosed herein, the inventors discovered that there are at least the following problems in the related art: when the data scale of business transaction-related data is growing rapidly, it is difficult to efficiently and completely query the business transaction data in the related art, resulting in low efficiency of data query. At the same time, there is a negative situation in which the operating efficiency of business service resources is affected by querying business transaction data. Summary of the invention

[0004] In view of this, the present disclosure provides a data processing method, a warehouse control system, a device, a storage medium and a computer program product.

[0005] One aspect of the present disclosure provides a data processing method, comprising:

[0006] Determine root node data from the data set to be processed, wherein the root node data is suitable for representing the business object to be migrated, and the root node data has root node data attributes;

[0007] Determining leaf node data related to the root node data attribute from the data set to be processed according to the business data rule topology information related to the root node data attribute, wherein the business data rule topology information includes a root node related to the root node data and a leaf node related to the leaf node data; and

[0008] Based on the leaf node data, target business data suitable for migration to the target storage area is determined, wherein the target business data is related to the business object.

[0009] According to an embodiment of the present disclosure, the data processing method further includes:

[0010] In response to the nth level topology construction operation for the leaf node component in the interactive interface, based on the node component information of the leaf node component, generate an nth level leaf node object related to the nth level leaf node in the interactive interface;

[0011] Based on the nth-level leaf node data attributes associated with the nth-level leaf node objects and the n-1th-level leaf node data attributes associated with the n-1th-level leaf node objects, constructing the nth-level edge relationship between the n-1th-level leaf nodes and the nth-level leaf nodes; and

[0012] In the case of n=N, the business data rule topology information is constructed based on the N-level edge relationships and the N-level leaf nodes, N≥n>1, and N and n are both integers.

[0013] According to an embodiment of the present disclosure, the data processing method further includes:

[0014] In response to an initial topology construction operation for a root node component in the interactive interface, a root node object related to the root node is generated in the interactive interface based on node component information of the root node component; and

[0015] Based on the first-level leaf node data attributes related to the first-level leaf node objects and the root node data attributes, the first-level edge relationship between the root node and the first-level leaf nodes is constructed.

[0016] According to an embodiment of the present disclosure, the data processing method further includes:

[0017] A business data rule topology structure related to the business data rule topology information is generated in the above-mentioned interactive interface.

[0018] According to an embodiment of the present disclosure, the above-mentioned data set to be processed includes at least one data to be processed;

[0019] The step of determining the root node data from the data set to be processed includes:

[0020] In the case where it is detected that the task execution state of the task related to the data to be processed is the target task execution state, determining the task as a candidate task;

[0021] According to the task level attributes of the candidate tasks, the candidate tasks are determined as target tasks; and

[0022] Among the above-mentioned data sets to be processed, at least one of the above-mentioned data sets to be processed that is related to the target task identifier of the above-mentioned target task is determined as the above-mentioned root node data.

[0023] According to an embodiment of the present disclosure, the above-mentioned target task execution status includes at least one of the following:

[0024] Task execution completion status, task execution abort status, and task execution exception status.

[0025] According to an embodiment of the present disclosure, the leaf node data has leaf node data attributes, and the leaf node data associated with the root node data attributes includes N levels;

[0026] Wherein, the leaf node data related to the above-mentioned root node data attribute is determined from the node data set to be processed according to the business data rule topology information related to the above-mentioned root node data attribute, including:

[0027] According to the root node data attribute, determine the leaf node of level 1 related to the root node from the business data rule topology information;

[0028] Determine, from the above-mentioned node data set to be processed, the above-mentioned leaf node data of the first level that is related to the first-level leaf node data attribute of the above-mentioned leaf node of the first level;

[0029] According to the data attributes of the leaf nodes at level n-1, determining the leaf nodes at level n related to the leaf nodes at level n-1 from the business data rule topology information;

[0030] Determining, from the node data set to be processed, the leaf node data of the nth level related to the leaf node data attribute of the nth level; and

[0031] When n=N, the leaf node data related to the attributes of the root node data are determined based on the N-level leaf node data.

[0032] According to an embodiment of the present disclosure, the target storage area includes a target storage sub-area suitable for storing target type data, the target type data has a target data structure, and the leaf node data includes a plurality of;

[0033] The above data processing method further includes:

[0034] From the plurality of target business data, each target type leaf node data having the target data structure is determined; and

[0035] Migrate the target type leaf node data to the target storage sub-area.

[0036] Another aspect of the present disclosure provides a warehouse control system, comprising:

[0037] A root node data determination module, used to determine root node data from the data set to be processed, wherein the root node data is suitable for representing the business object to be migrated, and the root node data has root node data attributes;

[0038] a leaf node data determination module, configured to determine leaf node data related to the root node data attribute from the data set to be processed according to the business data rule topology information related to the root node data attribute, wherein the business data rule topology information includes a root node related to the root node data and a leaf node related to the leaf node data; and

[0039] The migration module is used to determine target business data suitable for migration to the target storage area based on the leaf node data, wherein the target business data is related to the business object.

[0040] Another aspect of the present disclosure provides an electronic device, comprising:

[0041] one or more processors;

[0042] a memory for storing one or more programs,

[0043] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0044] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0045] Another aspect of the present disclosure provides a computer program product, which includes computer executable instructions, and when the instructions are executed, are used to implement the method as described above.

[0046] According to the embodiments of the present disclosure, leaf node data related to root node data is queried through business data rule topology information, and target business data suitable for migration to the target storage area is determined based on the leaf node data. Based on the association relationship between leaf nodes in the business data rule topology information, relatively complete target business data matching the business rules can be obtained. It is also possible to make some data in the target business data contain business rule attribute relationships such as rule hierarchical relationships and rule association relationships corresponding to the business data rules, thereby achieving the technical effect of improving the subsequent query accuracy for the target business data and improving the query efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0048] Figure 1 An exemplary system architecture to which a data processing method and a warehouse control system according to an embodiment of the present disclosure can be applied is schematically shown.

[0049] Figure 2 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0050] Figure 3 The application scenario diagram of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0051] Figure 4 A schematic diagram of a migration record according to an embodiment of the present disclosure is schematically shown.

[0052] Figure 5 The flowchart schematically shows a data processing method according to another embodiment of the present disclosure.

[0053] Figure 6 A block diagram of a warehouse control system according to an embodiment of the present disclosure is schematically shown.

[0054] Figure 7 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0055] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0056] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0057] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0058] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0059] In the embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security, and national security.

[0060] In the embodiments of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0061] In the process of realizing the inventive concept of the present disclosure, the inventors found that in the automated logistics business scenario, historical data related to the logistics business is usually stored in a database associated with the logistics business system. As the scale of the logistics business continues to expand and the logistics business continues to operate, the business data related to the logistics business has grown on a large scale, thereby affecting the operating efficiency of the logistics business system. The business data generated in the historical stage is usually carried forward (migrated to the historical database) or deleted. However, it is usually migrated or deleted according to the temporal relationship of historical time, and the integrity of the business data cannot be guaranteed, resulting in low query efficiency of related business data.

[0062] An embodiment of the present disclosure provides a data processing method, comprising: determining root node data from a data set to be processed, wherein the root node data is suitable for characterizing a business object to be migrated, and the root node data has root node data attributes; determining leaf node data related to the root node data attributes from the data set to be processed based on business data rule topology information related to the root node data attributes, wherein the business data rule topology information includes a root node related to the root node data, and a leaf node related to the leaf node data; and determining target business data suitable for migration to a target storage area based on the leaf node data, wherein the target business data is related to the business object.

[0063] According to the embodiments of the present disclosure, leaf node data related to root node data is queried through business data rule topology information, and target business data suitable for migration to the target storage area is determined based on the leaf node data. Based on the association relationship between leaf nodes in the business data rule topology information, relatively complete target business data matching the business rules can be obtained. It is also possible to make some data in the target business data contain business rule attribute relationships such as rule hierarchical relationships and rule association relationships corresponding to the business data rules, thereby achieving the technical effect of improving the subsequent query accuracy for the target business data and improving the query efficiency.

[0064] Figure 1 The exemplary system architecture 100 to which the data processing method and warehouse control system according to the embodiment of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0065] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0066] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (only as examples).

[0067] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0068] The server 105 may be a server that provides various services, such as a background management server (only an example) that provides support for websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0069] It should be noted that the data processing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the warehouse control system provided in the embodiment of the present disclosure can generally be set in the server 105. The data processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the warehouse control system provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Alternatively, the data processing method provided in the embodiment of the present disclosure can also be executed by the terminal devices 101, 102, or 103, or can also be executed by other terminal devices different from the terminal devices 101, 102, or 103. Accordingly, the warehouse control system provided in the embodiment of the present disclosure can also be set in the terminal devices 101, 102, or 103, or can be set in other terminal devices different from the terminal devices 101, 102, or 103.

[0070] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0071] Figure 2 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0072] like Figure 2 As shown, the data processing method includes operations S210 to S230.

[0073] In operation S210 , root node data is determined from the data set to be processed, wherein the root node data is suitable for representing the business object to be migrated, and the root node data has root node data attributes.

[0074] According to the embodiments of the present disclosure, the data to be processed in the data set to be processed may include data related to business transactions, or may also include data related to task execution. For example, it may be task execution data generated in a historical time period and in a task execution process. The format of the data to be processed may be in any form, such as data in a data table format, or data in XML format, data in Json format, etc. The embodiments of the present disclosure do not limit the format of the data to be processed, and those skilled in the art may select it according to actual needs.

[0075] According to the embodiments of the present disclosure, the root node data may be data with a high degree of relevance to the business object, such as the object identifier, object name, etc. of the business object. The business object may be an object related to a business transaction, such as a buyer customer object, a seller customer object, etc. Or it may be an object related to business execution, such as logistics equipment objects such as unmanned guided vehicles and intelligent robots in logistics business. Alternatively, the business object may also be a task object related to logistics tasks such as handling tasks and mailing tasks in related logistics business scenarios. The embodiments of the present disclosure do not limit the specific object type of the business object.

[0076] According to an embodiment of the present disclosure, the root node data may be data associated with a business object, such as object account information of the business object, etc. The root node data attributes may be used to characterize data of object attributes such as the name attribute, task type attribute, and business transaction type attribute of the business object.

[0077] In operation S220, leaf node data related to the root node data attributes are determined from the data set to be processed based on the business data rule topology information related to the root node data attributes, wherein the business data rule topology information includes a root node related to the root node data, and a leaf node related to the leaf node data.

[0078] According to an embodiment of the present disclosure, when the root node data is a data table, the root node data attribute may also be a data table index field such as a data table identifier, so as to query the leaf node data associated with the root node data through the root node data attribute.

[0079] According to an embodiment of the present disclosure, business data rule topology information can be constructed based on business rules related to business objects. Business data rule topology information can include edge relationships between root nodes and leaf nodes, as well as edge relationships between different leaf nodes. Edge relationships can be characterized by index fields corresponding to leaf node data attributes and root node data attributes, as well as index fields between different leaf nodes. Therefore, based on the business data rule topology information related to the root node data attributes, leaf node data that has a business rule relationship with the business object can be queried, so that the complete data related to the business object can be queried according to the business rules based on the determined leaf node data, thereby improving the integrity of the queried data.

[0080] In operation S230, target business data suitable for migration to the target storage area is determined based on the leaf node data, wherein the target business data is related to the business object.

[0081] According to the embodiments of the present disclosure, the target business data may include leaf node data and business rule relationships between leaf node data, so the target business data obtained can be more completely and clearly characterized with the business object. In this way, migrating the target business data to the target storage area can achieve full decoupling of the business service execution of the business object from the business data storage, avoid affecting the business service efficiency due to querying data related to the business object, and at the same time retain the integrity and relevance of the data related to the business object in the business service process, so as to improve the efficiency of subsequent data queries.

[0082] According to an embodiment of the present disclosure, the data processing method may further include the following operations.

[0083] In response to an initial topology construction operation for a root node component in an interactive interface, a root node object related to the root node is generated in the interactive interface based on the node component information of the root node component; and based on the first-level leaf node data attributes related to the first-level leaf node object and the root node data attributes, a first-level edge relationship between the root node and the first-level leaf node is constructed.

[0084] According to an embodiment of the present disclosure, the root node component can be pre-built based on preset business rules. The root node component can be stored based on a Json format file so that the root node data and the index of the root node component are stored in association. Accordingly, the root node component can be displayed in the interactive interface so that relevant personnel can build business data rule topology information corresponding to the root node data without editing the code.

[0085] According to the embodiments of the present disclosure, the initial topology construction operation may be a drag operation, but is not limited thereto, and may also be other types of operations, such as a box selection operation, a double-click operation, etc. The embodiments of the present disclosure do not limit the specific operation type of the initial topology construction operation, and those skilled in the art may select according to actual needs.

[0086] According to the embodiments of the present disclosure, the first-level edge relationship between the root node and the first-level leaf node can represent the association relationship between the root node data and the first-level leaf node data, and the first-level edge relationship can be represented by an index field composed of the root node data attributes and the first-level leaf node data attributes. In this way, the first-level leaf node data associated with the root node data can be queried through the first-level edge relationship, thereby conveniently constructing a data storage structure related to the business object, which is convenient for subsequent accurate and complete queries to the entire data related to the root node data.

[0087] Figure 3 The application scenario diagram of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0088] like Figure 3 As shown, the application scenario may include an interactive interface 300, which may include a root node component 301'. By dragging the root node component 301' to the topology structure construction area Z310 in the interactive interface 300, the user may obtain a root node object 301 rendered based on the root node component 301'.

[0089] According to an embodiment of the present disclosure, the data processing method may further include the following operations.

[0090] In response to an n-th level topology construction operation for a leaf node component in an interactive interface, an n-th level leaf node object related to the n-th level leaf node is generated in the interactive interface based on the node component information of the leaf node component; based on the n-th level leaf node data attributes related to the n-th level leaf node object and the n-1-th level leaf node data attributes related to the n-1-th level leaf node object, an n-th level edge relationship between the n-1-th level leaf node and the n-th level leaf node is constructed; and in the case of n=N, based on the N-level edge relationship and the N-level leaf nodes, business data rule topology information is constructed, N≥n>1, and N and n are both integers.

[0091] like Figure 3 As shown, in the case of N=n=2, the user can drag the first-level leaf node component 311' in the interactive interface 300 to the topology structure construction area Z310, so as to generate the first-level leaf node object 311 related to the first-level leaf node in the interactive interface 300. According to the first-level leaf node data attributes related to the first-level leaf node object 311 (such as the data table identifier of the first-level leaf node data), and the root node data attributes (such as the root node data identifier), the first-level index field between the root node data and the first-level leaf node data is constructed, and the first-level edge relationship between the first-level leaf node and the root node is constructed according to the first-level index field. At the same time, the first-level edge relationship object B311 representing the first-level edge relationship can be generated in the interactive interface 300.

[0092] For another example, the user can drag the second-level leaf node component 321' in the interactive interface 300 to the topology structure construction area Z310, thereby generating a second-level leaf node object 321 related to the second-level leaf node in the interactive interface 300. According to the second-level leaf node data attributes related to the second-level leaf node object 321 (such as the data table identifier of the second-level leaf node data), and the first-level leaf node data attributes, a second-level index field between the first-level leaf node data and the second-level leaf node data is constructed, and a second-level edge relationship between the first-level leaf node and the second-level leaf node is constructed based on the second-level index field. At the same time, a second-level edge relationship object B321 representing the second-level edge relationship can be generated in the interactive interface 300.

[0093] Based on the data processing method provided by the embodiment of the present disclosure, users can obtain N-level leaf nodes and the n-th level edge relationship between the n-th level leaf node and the n-1-th level leaf node based on the topology construction operation for the leaf node component. According to the N-level edge relationship and the N-level leaf nodes and the root node, the business data rule topology information can be constructed.

[0094] According to an embodiment of the present disclosure, the data processing method may further include the following operations.

[0095] A business data rule topology structure related to the business data rule topology information is generated in the interactive interface.

[0096] like Figure 3 As shown, in the case of N=n=2, the business data rule topology information may include a root node, a first-level leaf node, and a second-level leaf node. According to the business data rule topology information, a business data rule topology structure including a root node 301, first-level leaf node objects 311 and 312, and second-level leaf node objects 321, 322, and 323 may be generated in the topology structure construction area Z310 in the interactive interface 300. Through the business data rule topology structure, relevant users can construct business rule relationships between leaf node data according to business data rules related to business objects, and intuitively construct target business data related to business objects without editing codes, thereby improving the construction efficiency of business data integrity construction.

[0097] According to an embodiment of the present disclosure, the data set to be processed includes at least one data to be processed.

[0098] According to an embodiment of the present disclosure, determining root node data from a data set to be processed may include the following operations.

[0099] When it is detected that the task execution status of the task related to the data to be processed is the target task execution status, the task is determined as a candidate task; the candidate task is determined as the target task according to the task level attribute of the candidate task; and at least one to-be-processed data related to the target task identifier of the target task in the to-be-processed data set is determined as the root node data.

[0100] According to an embodiment of the present disclosure, the target task execution state may be an execution state indicating that data to be processed needs to be migrated, for example, it may be a task execution completion state, a task pending transfer state, etc. The embodiment of the present disclosure does not limit the specific state type of the target task execution state, and those skilled in the art may select it according to actual needs.

[0101] According to an embodiment of the present disclosure, since task execution can be based on the sequential execution of process nodes corresponding to the process engine, the task level attribute of the candidate task can be determined by determining the process node level attribute related to the candidate execution task. In the case where the task level attribute is a root level attribute, for example, when the process node code does not have a parent level process node code, the candidate task can be determined as the target task.

[0102] In another embodiment of the present disclosure, it is also possible to query whether the task level code of the candidate task has a parent level task code. If there is no parent level task code, the task level attribute can be determined as the root level attribute, and then the candidate task can be determined as the target task, that is, the target task can be determined as the root task.

[0103] According to an embodiment of the present disclosure, through the target task identification, the root node data that needs to be migrated can be queried from the data set to be processed, for example, the object identity information related to the business object can be queried, and then the source of the target business data that needs to be migrated can be queried from the data set to be processed.

[0104] According to the embodiments of the present disclosure, the root node data that needs to be migrated can be queried in time by detecting the task execution status in real time, and the target business data can be generated in time to avoid the target business data occupying the storage space of the business service resource system and affecting the execution efficiency of the business service resources. At the same time, the target business data that needs to be migrated can be decoupled from the running data of the business service resources in real time to avoid the negative impact of querying the target business data on the execution efficiency of the business service resources.

[0105] According to an embodiment of the present disclosure, the target task execution status includes at least one of the following: task execution completion status, task execution suspension status, and task execution abnormal status.

[0106] According to the embodiments of the present disclosure, the root node data related to the aborted tasks or the abnormally executed tasks can also be migrated to avoid the aborted tasks or the abnormally executed tasks occupying the storage space of the business service resources for a long time, avoid direct query to improve the execution efficiency of the business service resources, and facilitate quick query of the reasons for the abnormal task execution, or the business service impact caused by the task abortion, thereby improving the business service efficiency.

[0107] According to the embodiments of the present disclosure, when it is detected that the task execution state of the task related to the data to be processed is the target task execution state, and the detected task level attribute of the task related to the data to be processed is relatively the root task level attribute, the task can be determined as the target task. In this way, the root node data that needs to be migrated can be queried asynchronously, thereby realizing the determination and migration of the target business data under the conditions of high idle rate of business service resources and low computing overhead consumed by business service resources, thereby improving the migration efficiency of the target business data.

[0108] According to an embodiment of the present disclosure, the leaf node data has leaf node data attributes, and the leaf node data related to the root node data attributes includes N levels.

[0109] According to an embodiment of the present disclosure, based on the business data rule topology information related to the root node data attribute, determining the leaf node data related to the root node data attribute from the node data set to be processed may include the following operations.

[0110] According to the root node data attributes, determine the first-level leaf node related to the root node from the business data rule topology information; determine the first-level leaf node data related to the first-level leaf node data attributes of the first-level leaf node from the node data set to be processed; according to the n-1-level leaf node data attributes, determine the n-level leaf node related to the n-1-level leaf node from the business data rule topology information; determine the n-level leaf node data related to the n-level leaf node data attributes of the n-level leaf node from the node data set to be processed; and when n=N, determine the leaf node data related to the root node data attributes based on the N-level leaf node data.

[0111] According to an embodiment of the present disclosure, the service data rule topology information may be set based on the form of Table 1.

[0112] Table 1

[0113]

[0114] According to an embodiment of the present disclosure, by querying the first-level edge relationship related to the root node data attribute, the first-level leaf node related to the root node can be obtained. Furthermore, according to the first-level leaf node data attribute of the first-level leaf node, the first-level leaf node data can be queried from the data set to be processed.

[0115] According to an embodiment of the present disclosure, when N=n=2, the leaf node data corresponding to each of the N-level leaf nodes can be queried, that is, the leaf node data related to the root node data attributes can be obtained, so that the leaf node data corresponding to each of the N-level leaf nodes can be stored according to the business data rule topology information, and then the target business data can be obtained.

[0116] According to an embodiment of the present disclosure, the migration record of the target business data may also be recorded based on the following Table 2.

[0117] Table 2

[0118]

[0119] According to the embodiments of the present disclosure, a migration record chart can also be generated in the interactive interface based on the migration record information in Table 2, so that relevant personnel can query the migration status of the target business data in real time, and then timely query the target business data with abnormal migration status, and take necessary measures in time to ensure the integrity of the target business data. At the same time, according to the data processing method provided by the embodiments of the present disclosure, it is also possible to timely avoid repeated data migration and reduce the data storage volume in the storage area based on the status information related to the root node data in the migration record.

[0120] Figure 4 A schematic diagram of a migration record according to an embodiment of the present disclosure is schematically shown.

[0121] like Figure 4 As shown, a migration record chart 400 of target business data related to the root task can be generated in the interactive interface. By recording the real-time target data migration status and root node data attributes (task number, task type, business type, etc.), the data migration status of the target business data can be clearly displayed.

[0122] According to an embodiment of the present disclosure, the target storage area includes a target storage sub-area suitable for storing target type data, the target type data has a target data structure, and the leaf node data includes multiple.

[0123] Figure 5 The flowchart schematically shows a data processing method according to another embodiment of the present disclosure.

[0124] like Figure 5 As shown, the data processing method may further include operations S510 to S520.

[0125] In operation S510, target type leaf node data having a target data structure is determined from each of a plurality of target business data.

[0126] In operation S520, target type leaf node data is migrated to the target storage sub-area.

[0127] According to the embodiments of the present disclosure, the target data structure may include a picture data structure, a character data structure, etc. The embodiments of the present disclosure do not limit the specific type of the target data structure, and those skilled in the art may select it according to actual needs.

[0128] According to an embodiment of the present disclosure, by migrating the target type leaf node data with the target data structure to the target storage sub-area, classified storage of the target business data can be achieved to improve the query efficiency of the target business data and facilitate relevant personnel to obtain historical data information related to the business objects in a timely manner.

[0129] Figure 6 A block diagram of a warehouse control system according to an embodiment of the present disclosure is schematically shown.

[0130] like Figure 6 As shown, the warehouse control system 600 includes a root node data determination module 610 , a leaf node data determination module 620 and a target business data determination module 630 .

[0131] The root node data determination module 610 is used to determine the root node data from the data set to be processed, wherein the root node data is suitable for representing the business object to be migrated, and the root node data has root node data attributes.

[0132] The leaf node data determination module 620 is used to determine the leaf node data related to the root node data attributes from the data set to be processed based on the business data rule topology information related to the root node data attributes, wherein the business data rule topology information includes the root node related to the root node data, and the leaf node related to the leaf node data.

[0133] The target business data determination module 630 is used to determine target business data suitable for migration to the target storage area based on the leaf node data, wherein the target business data is related to the business object.

[0134] According to an embodiment of the present disclosure, the warehouse control system further includes: a leaf node object acquisition module, an edge relationship construction module and a business data rule topology information construction module.

[0135] The leaf node object generation module is used to respond to the nth level topology construction operation for the leaf node component in the interactive interface, and generate the nth level leaf node object related to the nth level leaf node in the interactive interface based on the node component information of the leaf node component.

[0136] The first edge relationship construction module is used to construct the nth level edge relationship between the n-1th level leaf node and the nth level leaf node based on the nth level leaf node data attributes related to the nth level leaf node object and the n-1th level leaf node data attributes related to the n-1th level leaf node object.

[0137] The business data rule topology information construction module is used to construct business data rule topology information based on N-level edge relationships and N-level leaf nodes when n=N, N≥n>1, and N and n are both integers.

[0138] According to an embodiment of the present disclosure, the warehouse control system further includes: a root node object generation module and a second edge relationship construction module.

[0139] The root node object generation module is used to generate a root node object related to the root node in the interaction interface based on the node component information of the root node component in response to the initial topology construction operation for the root node component in the interaction interface.

[0140] The second edge relationship building module is used to build the first-level edge relationship between the root node and the first-level leaf node based on the first-level leaf node data attributes related to the first-level leaf node object and the root node data attributes.

[0141] According to an embodiment of the present disclosure, the warehouse control system further includes a business data rule topology structure building module.

[0142] The business data rule topology structure building module is used for the business data rule topology structure generation module to generate a business data rule topology structure related to the business data rule topology information in the interactive interface.

[0143] According to an embodiment of the present disclosure, the data set to be processed includes at least one data to be processed.

[0144] The root node data determination module includes: a candidate task determination unit, a target task determination unit and a root node data determination unit.

[0145] The candidate task determining unit is used to determine the task as a candidate task when it is detected that the task execution status of the task related to the to-be-processed data is the target task execution status.

[0146] The target task determination unit is used to determine the candidate task as the target task according to the task level attributes of the candidate task.

[0147] The root node data determination unit is used to determine at least one to-be-processed data related to the target task identifier of the target task in the to-be-processed data set as the root node data.

[0148] According to an embodiment of the present disclosure, the target task execution status includes at least one of the following: task execution completion status, task execution suspension status, and task execution abnormal status.

[0149] According to an embodiment of the present disclosure, the leaf node data has leaf node data attributes, and the leaf node data related to the root node data attributes includes N levels.

[0150] Among them, the leaf node data determination module includes:

[0151] The first leaf node determination unit is used to determine the first level leaf node related to the root node from the business data rule topology information according to the root node data attribute;

[0152] The first leaf node data determination unit is used to determine the first-level leaf node data related to the first-level leaf node data attribute of the first-level leaf node from the node data set to be processed.

[0153] The second leaf node determination unit is used to determine the nth level leaf node related to the n-1th level leaf node from the business data rule topology information according to the data attributes of the n-1th level leaf node.

[0154] The second leaf node data determination unit is used to determine the n-th level leaf node data related to the n-th level leaf node data attribute of the n-th level leaf node from the node data set to be processed.

[0155] When n=N, the third leaf node data determining unit determines leaf node data related to the root node data attribute according to the N-level leaf node data.

[0156] According to an embodiment of the present disclosure, the target storage area includes a target storage sub-area suitable for storing target type data, the target type data has a target data structure, and the leaf node data includes multiple.

[0157] Among them, the warehouse control system also includes: a target type leaf node data determination module and a migration module.

[0158] The target type leaf node data determination module is used to determine target type leaf node data having a target data structure from multiple target business data.

[0159] The migration module is used to migrate the target type leaf node data to the target storage sub-area.

[0160] According to the embodiments of the present invention, any one or more of the modules, units, and sub-units, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, units, and sub-units can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, units, and sub-units can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, units, and sub-units can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0161] For example, any multiple of the root node data determination module 610, the leaf node data determination module 620, and the target business data determination module 630 can be combined in one module / unit / subunit for implementation, or any one of the modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functions of one or more modules / units / subunits in these modules / units / subunits can be combined with at least part of the functions of other modules / units / subunits and implemented in one module / unit / subunit. According to an embodiment of the present disclosure, at least one of the root node data determination module 610, the leaf node data determination module 620, and the target business data determination module 630 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware or in any appropriate combination of any of them. Alternatively, at least one of the root node data determination module 610, the leaf node data determination module 620 and the target business data determination module 630 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0162] It should be noted that the warehouse control system part in the embodiment of the present disclosure corresponds to the data processing method part in the embodiment of the present disclosure. The description of the warehouse control system part specifically refers to the data processing method part, which will not be repeated here.

[0163] Figure 7A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0164] like Figure 7 As shown, the electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 to a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include an onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0165] In RAM 703, various programs and data required for the operation of electronic device 700 are stored. Processor 701, ROM 702 and RAM 703 are connected to each other via bus 704. Processor 701 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 702 and / or RAM 703. It should be noted that the program can also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0166] According to an embodiment of the present disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to the bus 704. The system 700 may further include one or more of the following components connected to the input / output (I / O) interface 705: an input portion 706 including a keyboard, a mouse, etc.; an output portion 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 708 including a hard disk, etc.; and a communication portion 709 including a network interface card such as a LAN card, a modem, etc. The communication portion 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage portion 708 as needed.

[0167] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure 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 computer-readable storage medium, and the computer program contains 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 part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0168] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0169] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0170] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 702 and / or the RAM 703 described above and / or one or more memories other than the ROM 702 and the RAM 703 .

[0171] The embodiments of the present disclosure also include a computer program product, which includes a computer program, and the computer program contains program code for executing the method provided by the embodiments of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the method provided by the embodiments of the present disclosure.

[0172] When the computer program is executed by the processor 701, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0173] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 709, and / or installed from the removable medium 711. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0174] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0175] The flowcharts and block diagrams in the accompanying drawings illustrate the possible 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 may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments and / or claims of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0176] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data processing method, comprising: Determine root node data from the data set to be processed, wherein the root node data is suitable for representing the business object to be migrated, and the root node data has root node data attributes; Determining leaf node data related to the root node data attribute from the data set to be processed according to the business data rule topology information related to the root node data attribute, wherein the business data rule topology information includes a root node related to the root node data, and a leaf node related to the leaf node data; and Based on the leaf node data, target business data suitable for migration to the target storage area is determined, wherein the target business data is related to the business object.

2. The method according to claim 1, further comprising: In response to an n-th level topology construction operation for a leaf node component in an interactive interface, generating an n-th level leaf node object related to the n-th level leaf node in the interactive interface based on node component information of the leaf node component; Based on the nth-level leaf node data attributes associated with the nth-level leaf node object and the n-1th-level leaf node data attributes associated with the n-1th-level leaf node object, construct an nth-level edge relationship between the n-1th-level leaf node and the nth-level leaf node; as well as In the case of n=N, the business data rule topology information is constructed based on the N-level edge relationships and the N-level leaf nodes, N≥n>1, and N and n are both integers.

3. The method according to claim 2, further comprising: In response to an initial topology construction operation for a root node component in the interactive interface, generating a root node object related to the root node in the interactive interface based on node component information of the root node component; and Based on the first-level leaf node data attributes related to the first-level leaf node objects and the root node data attributes, the first-level edge relationship between the root node and the first-level leaf nodes is constructed.

4. The method according to claim 2, further comprising: A business data rule topology structure related to the business data rule topology information is generated in the interactive interface.

5. The method according to claim 1, wherein: The data set to be processed includes at least one data to be processed; Wherein, determining the root node data from the data set to be processed includes: In the case where it is detected that the task execution state of the task related to the to-be-processed data is the target task execution state, determining the task as a candidate task; Determining the candidate task as a target task according to the task level attribute of the candidate task; and At least one of the to-be-processed data sets that is related to the target task identifier of the target task is determined as the root node data.

6. The method according to claim 5, wherein: The target task execution status includes at least one of the following: Task execution completion status, task execution abort status, and task execution exception status.

7. The method according to claim 1, wherein: The leaf node data has leaf node data attributes, and the leaf node data related to the root node data attributes includes N levels; Wherein, determining the leaf node data related to the root node data attribute from the node data set to be processed according to the business data rule topology information related to the root node data attribute includes: According to the root node data attribute, determine the leaf node of the first level related to the root node from the business data rule topology information; Determining, from the node data set to be processed, the leaf node data of the first level that is related to the leaf node data attribute of the leaf node of the first level; According to the data attributes of the leaf nodes at the n-1th level, determining the leaf nodes at the nth level related to the leaf nodes at the n-1th level from the business data rule topology information; Determining, from the node data set to be processed, the leaf node data of the nth level that is related to the leaf node data attribute of the nth level of the leaf node; and When n=N, the leaf node data related to the root node data attribute is determined based on the N-level leaf node data.

8. The method according to any one of claims 1 to 7, wherein: The target storage area includes a target storage sub-area suitable for storing target type data, the target type data has a target data structure, and the leaf node data includes a plurality of leaf nodes; Wherein, the data processing method further includes: Determine, from the plurality of target business data, target type leaf node data having the target data structure; and Migrate the target type leaf node data to the target storage sub-area.

9. A warehouse control system, comprising: A root node data determination module, used to determine root node data from the data set to be processed, wherein the root node data is suitable for representing the business object to be migrated, and the root node data has root node data attributes; a leaf node data determination module, configured to determine, from a data set to be processed, leaf node data related to the root node data attribute according to business data rule topology information related to the root node data attribute, wherein the business data rule topology information includes a root node related to the root node data, and a leaf node related to the leaf node data; and A migration module is used to determine target business data suitable for migration to a target storage area based on the leaf node data, wherein the target business data is related to the business object.

10. An electronic device comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 8.

12. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 8.

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