Data processing method, device, electronic device and storage medium

Abstract

The present invention relates to a data processing method, device, electronic device and storage medium, the method comprising: collecting business source data associated with a preset data analysis scenario in a set manner to obtain target business source data; constructing a data identifier based on the target business source data and the preset data analysis scenario; associating and storing the data identifier with the target business source data, wherein the data identifier is used by a user to obtain the target business source data. Through the technical solution of the embodiment of the present invention, the purpose of statistically storing business source data according to set dimensions is achieved, thereby facilitating users to search and use, and reducing the difficulty of users using data.

Description

Technical Field

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

[0002] As power grid companies further deepen their comprehensive digital transformation, there is an increasing demand for unified data service capabilities for data applications such as large-screen monitoring, thematic analysis, ad hoc reporting, detailed lists, data mining, and self-service queries.

[0003] However, power grid data itself has the characteristics of large data volume, diversified business systems, diversified data types, and rich application scenarios. As business departments' demand for mining the value of data assets increases urgently, quickly and accurately acquiring target data assets has become a technical difficulty that restricts data analysts from various business departments from conducting self-service analysis and data mining. It is also a barrier that restricts data-driven business, process and operational decisions, and drives changes in corporate production, operations, and service formats.

[0004] Therefore, how to accurately and clearly provide the data assets of each business system to data analysts is the key to the efficient use of data assets. Summary of the invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the present invention provides a data processing method, device, electronic device and storage medium, which achieves the purpose of statistical storage of business source data according to set dimensions, thereby facilitating users to search and use and reducing the difficulty of users using data.

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

[0007] The business source data associated with the preset data analysis scenario is collected in a set manner to obtain the target business source data;

[0008] Constructing a data identifier based on the target business source data and the preset data analysis scenario;

[0009] The data identifier is associated with the target service source data and stored, wherein the data identifier is used by a user to obtain the target service source data.

[0010] In a second aspect, an embodiment of the present invention further provides a data processing device, the device comprising:

[0011] A collection module is used to collect business source data associated with a preset data analysis scenario in a set manner to obtain target business source data;

[0012] A construction module, used to construct a data identifier based on the target business source data and the preset data analysis scenario;

[0013] A storage module is used to associate and store the data identifier with the target business source data, wherein the data identifier is used by a user to obtain the target business source data.

[0014] In a third aspect, an embodiment of the present invention further provides a device, the device comprising:

[0015] one or more processors;

[0016] a storage device for storing one or more programs,

[0017] When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method as described in any one of the embodiments of the present invention.

[0018] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute a data processing method as described in any one of the embodiments of the present invention.

[0019] The technical solution provided by the embodiment of the present invention has the following advantages compared with the prior art:

[0020] The target business source data is obtained by collecting the business source data associated with the preset data analysis scenario in a set manner; a data identifier is constructed based on the target business source data and the preset data analysis scenario; the data identifier is associated with the target business source data and stored, wherein the data identifier is used by the user to obtain the target business source data, thereby achieving the purpose of statistically storing the business source data according to the set dimension (such as the data analysis scenario), thereby facilitating the user to search and use, and reducing the difficulty of users using the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] Figure 1 A schematic diagram of a data processing method provided by an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of a blood relationship diagram provided by an embodiment of the present invention;

[0025] Figure 3 A schematic diagram of the structure of a data processing device provided by an embodiment of the present invention;

[0026] Figure 4 The present invention is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the scheme of the present invention will be further described below. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present invention, rather than all of the embodiments.

[0029] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0030] It should be understood that the various steps described in the method embodiments of the present invention 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 invention is not limited in this respect.

[0031] The term "including" and its variations used herein are open inclusions, 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"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0032] It should be noted that the concepts such as "first" and "second" mentioned in the present invention 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.

[0033] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0034] Figure 1 A flow chart of a data processing method provided by an embodiment of the present invention. The method can be applied to processing big data assets to visualize the content and meaning of each data asset, allowing users to easily and intuitively understand the content of the data asset. When users need to use data, they can easily select the data they want from the visualized data assets, reducing the complexity of users using data. The data processing method can be executed by a data processing device, which can be implemented in the form of software and / or hardware.

[0035] like Figure 1 As shown, the data processing method provided in this embodiment includes the following steps:

[0036] Step 110: collect the business source data associated with the preset data analysis scenario in a set manner to obtain target business source data.

[0037] The preset data analysis scenario may be, for example, a power outage analysis scenario or a user satisfaction analysis scenario, or other specific scenarios. In one embodiment, the business source data associated with the power outage analysis scenario includes, for example, the power outage time, the power outage area, or the data of each power line in the power outage area (such as voltage, current, etc.). In another embodiment, the business source data associated with the user satisfaction analysis scenario includes, for example, the average power outage time within a period of time or the average number of power outages within a period of time.

[0038] In one embodiment, the business source data associated with the preset data analysis scenario is collected in a set manner to obtain the target business source data, including: determining the data field according to the preset data analysis scenario; obtaining the target business source data through a database SQL search or a data cube search based on the data field. Specifically, one or more data sources are associated based on the database SQL or data cube, multiple fields that may be used by users in the preset data analysis scenario are selected, and operations or indicator derivation are performed (for example, the average value is calculated, and the derived field is the source data corresponding to the "average value"), and a data set that will be used by users when performing data analysis in the preset data analysis scenario is generated or a data table is generated and stored in the database. The generated data set or data table can be called a data asset card, and the essence of the data in the data asset card is the target business source data that users will use when performing data analysis in the preset data analysis scenario.

[0039] Step 120: construct a data identifier based on the target business source data and the preset data analysis scenario.

[0040] Among them, data identification can be information used to represent data content or data meaning. Data identification can enable users to easily and intuitively understand the content and meaning of data, thereby reducing the difficulty for users to use data.

[0041] In one embodiment, a data identifier is constructed based on target business source data and a preset data analysis scenario, including: constructing primary metadata and / or secondary metadata based on the target business source data and the preset data analysis scenario, respectively; wherein the data identifier includes primary metadata and / or secondary metadata.

[0042] In one implementation, the first-level metadata is constructed based on the target business source data and the preset data analysis scenario, including at least one of the following:

[0043] Construct a business domain identifier based on the business domain to which the preset data analysis scenario belongs;

[0044] Construct a business object identifier based on the business object targeted by the preset data analysis scenario;

[0045] Construct a time identifier based on the acquisition time of the target business source data;

[0046] Correspondingly, the primary metadata includes at least one of a business domain identifier, a business object identifier, and a time identifier. Exemplarily, the business domain identifier includes at least one of the following: marketing domain, material domain, or human resources domain; the business object identifier includes at least one of the following: power outage analysis or user satisfaction analysis. The primary metadata may also include the creation time, release time, update time, description information of the data asset card (i.e., data set or data table) (e.g., "the average power outage time of a certain area summarized by month and the average power outage time after manual review can be used as the main reference data for customer complaints, power outage analysis, etc."), creator information, manager information, data standard responsible department, data quality responsible department, primary data subject and secondary data subject (e.g., divided according to department and business item classification), key indicators (business-related key analysis indicators, such as in the automatic meter reading analysis business, the key analysis indicator is the automatic meter reading rate) and other data asset cards related to it (which may be other data asset cards recommended by association rule algorithms and viewed by users at the same time).

[0047] In one embodiment, the data processing method further includes: storing the data identifier in the form of a data asset card. Exemplarily, refer to the schematic diagram of a data asset card shown in Table 1 below, whose first-level metadata includes the data asset card name "automatic meter reading situation", the business domain identifier "marketing domain", the business object "meter reading management", the creation time "2020-1-1", the release time "2020-2-3", the update time "2020-2-20", the card description information "automatic meter reading situation summarizes the automatic meter reading rate, and compares the differences between automatic meter reading and manual meter reading in important indicators", the creator "AAA", the manager "BBB", the data standard responsible department "Marketing Department", the data quality responsible department "Information Center", the first-level data theme "Marketing Department", the second-level data theme "Business Management", the key indicator "automatic meter reading rate", the related data asset card 1 "meter reading rate statistics", the related data asset card 2 "issuance rate statistics", the related data asset card 3 "meter reading section maintenance", the number of fields "10", the number of records "1000" and the size "15K".

[0048] In one implementation, constructing secondary metadata based on the target business source data and a preset data analysis scenario includes at least one of the following:

[0049] Determine the metadata of each field of the target business source data; determine the metadata of each field as secondary metadata. Exemplarily, the metadata of each field includes at least one of the following: business metadata, first technical metadata (data divided from the database perspective), and second technical metadata (data divided from the data analysis perspective). Business metadata includes at least one of the following: field name, business rules, and encoding; first technical metadata includes at least one of the following: data type, measurement type, data unit, value range, data length, data precision, whether it is a primary key, whether it is nullable, and whether it is a foreign key; second technical metadata includes at least one of the following: valid records, nominal value, number of nominal values, maximum value, minimum value, average value, and standard deviation.

[0050] Table 1: Schematic diagram of a data asset card

[0051]

[0052] Step 130: associate the data identifier with the target business source data and store them, wherein the data identifier is used by the user to obtain the target business source data.

[0053] In one embodiment, the data identifier is associated with the target business source data for storage, including: taking the table storing the target business source data as the main table, and respectively associating the first data table for storing the primary metadata and the second data table for storing the secondary metadata. For example, taking the table storing the target business source data as the main table, left-linking the first data table, and then left-linking the second data table, the comprehensive semanticization and visualization of the data asset card can be achieved.

[0054] The technical solution of this embodiment, by targeting a specific data analysis scenario, organizes the data related to the specific data analysis scenario together to form a data asset card, and semanticizes the data asset card and the target business source data contained therein, such as displaying the name of the data asset card, the responsible department, the data description, the sharing scope, the business items involved, the number of fields of the target business source data included in the data asset card, the field name, the business meaning of each field, the type, length, precision of the data, etc., so that the content of the data asset is clear at a glance, and the user can simply, clearly and comprehensively understand the overall picture of the data asset card, thereby reducing the complexity of users' use of data and improving the efficiency of users' use of data.

[0055] In one embodiment, the data processing method further includes:

[0056] A blood relationship diagram is established between the data tables where the target business source data is located based on the common fields, and the blood relationship diagram is displayed. By displaying the association relationship between all related tables under the asset card in the form of a diagram, users can clearly understand the content of the data asset card and the relationship between the data tables.

[0057] For example, reference may be made to Figure 2The schematic diagram of a blood relationship diagram is shown, wherein the data tables where the target business source data is located include: supplier data table (the data table includes fields such as supplier number, supplier company name, supplier address, etc.), customer data table (the data table includes fields such as customer number, company name, etc.), product catalog data table (the data table includes fields such as product catalog number, product catalog name, product catalog description, etc.), product data table (the data table includes fields such as product number, product name, supplier number, product catalog number, etc.), order data table (the data table includes fields such as order number, customer number, employee number, etc.), order details data table (the data table includes fields such as order number, product number, etc.) and employee data table (the data table includes fields such as age, employee number, etc.). Among them, because the supplier data table and the product data table both include the field "supplier number", that is, "supplier number" is a common field of the supplier data table and the product data table, there is an association between the supplier data table and the product data table, and a blood relationship diagram between the supplier data table and the product data table is established. Similarly, because both the product catalog data table and the product data table include the field "product catalog number", that is, "product catalog number" is a common field of the product catalog data table and the product data table, there is an association between the product catalog data table and the product data table, and a blood relationship diagram between the product catalog data table and the product data table is established. Because both the order detail data table and the product data table include the field "product number", that is, "product number" is a common field of the order detail data table and the product data table, there is an association between the order detail data table and the product data table, and a blood relationship diagram between the order detail data table and the product data table is established. Because both the order detail data table and the order data table include the field "order number", that is, "order number" is a common field of the order detail data table and the order data table, there is an association between the order detail data table and the order data table, and a blood relationship diagram between the order detail data table and the order data table is established. Because both the order data table and the customer data table include the field "Customer Number", that is, "Customer Number" is a common field of the order data table and the customer data table, there is an association between the order data table and the customer data table, and a blood relationship diagram between the order data table and the customer data table is established. Because both the order data table and the employee data table include the field "Employee Number", that is, "Employee Number" is a common field of the order data table and the employee data table, there is an association between the order data table and the employee data table, and a blood relationship diagram between the order data table and the employee data table is established.

[0058] By creating the first-level metadata, second-level metadata and blood relationship diagram of the data asset card, the content, purpose, management characteristics, business meaning and business meaning of each field and data characteristics of the data asset card are fully displayed; by associating the data asset card with the first-level metadata, second-level metadata and field metadata and displaying them to the user, the full picture of the data asset card is fully displayed, allowing the user to clearly understand the meaning and content of the source data in the data asset card.

[0059] In one embodiment, the data processing method further includes:

[0060] In response to a user's search instruction, a matching first data table or a second data table is determined based on the received search keyword; a matching main table is determined based on the matching first data table or the matching second data table; and target business source data stored in the main table is provided to the user.

[0061] In one embodiment, the data processing method further includes: recommending business source data to the user based on the user's historical browsing records and / or current browsing information.

[0062] The data asset cards generated by the technical solution implemented in this paper are more business-specific and can better meet the needs of users. By fully displaying the business meaning and technical characteristics of the data, it is helpful to lower the threshold for users to use data.

[0063] Figure 3 A schematic diagram of the structure of a data processing device provided in an embodiment of the present invention, the device comprises: a collection module 310, a construction module 320 and a storage module 330.

[0064] Among them, the collection module 310 is used to collect the business source data associated with the preset data analysis scenario in a set manner to obtain the target business source data; the construction module 320 is used to construct the data identifier based on the target business source data and the preset data analysis scenario; the storage module 330 is used to associate the data identifier with the target business source data and store it, wherein the data identifier is used by the user to obtain the target business source data.

[0065] Based on the above technical solution, the collection module 310 includes:

[0066] The first determination unit is used to determine the data field according to the preset data analysis scenario; the search unit is used to obtain the target business source data through database SQL search or data cube search based on the data field.

[0067] Based on the above technical solutions, the construction module 320 includes:

[0068] A construction unit is used to construct primary metadata and / or secondary metadata based on the target business source data and the preset data analysis scenario; wherein the data identifier includes the primary metadata and / or the secondary metadata.

[0069] On the basis of the above technical solutions, the construction unit is specifically used for at least one of the following:

[0070] Constructing a business domain identifier according to the business domain to which the preset data analysis scenario belongs;

[0071] Constructing a business object identifier according to the business object targeted by the preset data analysis scenario;

[0072] Constructing a time identifier according to the acquisition time of the target business source data;

[0073] Correspondingly, the first-level metadata includes at least one of the business domain identifier, the business object identifier and the time identifier.

[0074] On the basis of the above technical solutions, the business domain identifier includes at least one of the following: marketing domain, material domain or human resources domain;

[0075] The business object identification includes at least one of the following: power outage analysis or user satisfaction analysis.

[0076] On the basis of the above technical solutions, the construction unit is specifically used for:

[0077] Determine metadata of each field of the target business source data;

[0078] The metadata of each field is determined as the secondary metadata.

[0079] Based on the above technical solutions, the metadata of each field includes at least one of the following: business metadata, first technical metadata and second technical metadata.

[0080] Based on the above technical solutions, the business metadata includes at least one of the following: field name, business rules and code;

[0081] The first technical metadata includes at least one of the following: data type, measurement type, data unit, value range, data length, data precision, whether it is a primary key, whether it is nullable, and whether it is a foreign key;

[0082] The second technical metadata includes at least one of the following: valid records, nominal values, number of nominal values, maximum value, minimum value, average value and standard deviation.

[0083] On the basis of the above-mentioned technical solutions, the device further includes: an establishment module for establishing a blood relationship diagram between the data tables where the target business source data is located, and displaying the blood relationship diagram.

[0084] On the basis of the above technical solutions, the storage module 330 is further used to store the data identifier in the form of a data asset card.

[0085] Based on the above technical solutions, the storage module 330 is also specifically used to: use the table storing the target business source data as the main table, and associate the first data table for storing the primary metadata and the second data table for storing the secondary metadata.

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

[0087] A first determination module, configured to determine the first data table or the second data table that matches based on the received search keyword in response to a user's search instruction;

[0088] A second determining module, configured to determine the matching main table based on the matching first data table or the matching second data table;

[0089] The return module is used to provide the target business source data stored in the main table to the user.

[0090] On the basis of the above technical solutions, the device further includes:

[0091] The recommendation module is used to recommend business source data to users based on the user's historical browsing records and / or current browsing information.

[0092] The technical solution of the embodiment of the present invention, by targeting a specific data analysis scenario, organizes the data related to the specific data analysis scenario together to form a data asset card, and semanticizes the data asset card and the target business source data contained therein, such as displaying the name of the data asset card, the responsible department, the data description, the sharing scope, the business matters involved, the number of fields of the target business source data included in the data asset card, the field name, the business meaning of each field, the type, length, precision of the data, etc., so that the content of the data asset is clear at a glance, and the user can simply, clearly and comprehensively understand the overall picture of the data asset card, thereby reducing the complexity of users' use of data and improving the efficiency of users' use of data.

[0093] The data processing device provided in the embodiment of the present invention can execute the data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0094] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present invention.

[0095] Reference below Figure 4 , which shows an electronic device (eg Figure 4 The terminal device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 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 invention.

[0096] like Figure 4 As shown, the electronic device 400 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0097] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0098] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention 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 409, or installed from the storage device 408, or installed from the ROM 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of the embodiment of the present invention are executed.

[0099] The terminal provided in the embodiment of the present invention and the data processing method provided in the above embodiment belong to the same inventive concept. The technical details not fully described in the embodiment of the present invention can be referred to the above embodiment, and the embodiment of the present invention has the same beneficial effects as the above embodiment.

[0100] An embodiment of the present invention further provides a computer storage medium on which a computer program is stored. When the program is executed by a processor, the data processing method provided by the above embodiment is implemented.

[0101] It should be noted that the computer-readable medium of the present invention can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can 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 invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit 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 of the above.

[0102] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may 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.

[0103] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0104] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0105] The business source data associated with the preset data analysis scenario is collected in a set manner to obtain the target business source data;

[0106] Constructing a data identifier based on the target business source data and the preset data analysis scenario;

[0107] The data identifier is associated with the target service source data and stored, wherein the data identifier is used by a user to obtain the target service source data.

[0108] Computer program code for performing the operations of the present invention 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 separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer 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., via the Internet using an Internet service provider).

[0109] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the 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 square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0110] The units involved in the embodiments of the present invention may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, an editable content display unit may also be described as an "editing unit".

[0111] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, 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 chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0112] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may 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.

[0113] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) by each other.

[0114] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0115] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments described herein, but should conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data processing method, characterized in that: include: The business source data associated with the preset data analysis scenario is collected in a set manner to obtain the target business source data; The preset data analysis scenario includes a power outage analysis scenario or a user satisfaction analysis scenario; Constructing a data identifier based on the target business source data and the preset data analysis scenario; The data identifier is associated with the target business source data and stored, wherein the data identifier is semantic information used to represent data content or data meaning, and the data identifier is used by the user to obtain the target business source data; The business source data associated with the preset data analysis scenario is collected in a set manner to obtain the target business source data, including: determining the data field according to the preset data analysis scenario; obtaining the target business source data through a database SQL search or a data cube search based on the data field; The constructing of the data identifier based on the target business source data and the preset data analysis scenario includes: constructing primary metadata and / or secondary metadata based on the target business source data and the preset data analysis scenario respectively; Wherein, the data identifier includes the primary metadata and / or the secondary metadata; The primary metadata includes at least one of a business domain identifier, a business object identifier and a time identifier; the business domain identifier includes at least one of the following: a marketing domain, a material domain or a human resources domain; the business object identifier includes at least one of the following: a power outage analysis or a user satisfaction analysis; The secondary metadata is metadata of each field of the target business source data; the metadata of each field includes at least one of the following: business metadata, first technical metadata and second technical metadata; the first technical metadata is data divided from the perspective of database, and the second technical metadata is data divided from the perspective of data analysis; The constructing of the primary metadata based on the target business source data and the preset data analysis scenario includes at least one of the following: constructing a business domain identifier according to the business domain to which the preset data analysis scenario belongs; constructing a business object identifier according to the business object targeted by the preset data analysis scenario; constructing a time identifier according to the acquisition time of the target business source data; The method further includes: establishing a blood relationship diagram between data tables where the target business source data is located based on common fields, and displaying the blood relationship diagram.

2. The method according to claim 1, characterized in that The business metadata includes at least one of the following: field name, business rules and coding; The first technical metadata includes at least one of the following: data type, measurement type, data unit, value range, data length, data precision, whether it is a primary key, whether it is nullable, and whether it is a foreign key; The second technical metadata includes at least one of the following: valid records, nominal values, number of nominal values, maximum value, minimum value, average value and standard deviation.

3. The method according to any one of claims 1 to 2, characterized in that: The method further comprises: The data identification is stored in the form of a data asset card.

4. The method according to any one of claims 1 to 2, characterized in that: The storing the data identifier in association with the target service source data includes: The table storing the target business source data is used as the main table, and a first data table for storing the primary metadata and a second data table for storing the secondary metadata are respectively associated.

5. The method according to claim 4, characterized in that Also includes: In response to a user's search instruction, determining a matching first data table or second data table based on the received search keyword; Determine the matching main table based on the matching first data table or the matching second data table; The target business source data stored in the master table is provided to the user.

6. The method according to claim 5, characterized in that Also includes: Recommend business source data to users based on their historical browsing records and / or current browsing information.

7. A data processing device, characterized in that: include: A collection module is used to collect business source data associated with a preset data analysis scenario in a set manner to obtain target business source data; The preset data analysis scenario includes a power outage analysis scenario or a user satisfaction analysis scenario; A construction module, used to construct a data identifier based on the target business source data and the preset data analysis scenario; A storage module, used for associating and storing the data identifier with the target business source data, wherein the data identifier is semantic information used to represent data content or data meaning, and the data identifier is used by a user to obtain the target business source data; The collection module includes: a first determination unit, used to determine the data field according to the preset data analysis scenario; a search unit, used to obtain the target business source data through database SQL search or data cube search based on the data field; The construction module includes: a construction unit, which is used to construct primary metadata and / or secondary metadata based on the target business source data and the preset data analysis scenario; Wherein, the data identifier includes the primary metadata and / or the secondary metadata; The primary metadata includes at least one of a business domain identifier, a business object identifier and a time identifier; the business domain identifier includes at least one of the following: a marketing domain, a material domain or a human resources domain; the business object identifier includes at least one of the following: a power outage analysis or a user satisfaction analysis; The secondary metadata is metadata of each field of the target business source data; the metadata of each field includes at least one of the following: business metadata, first technical metadata and second technical metadata; the first technical metadata is data divided from the perspective of database, and the second technical metadata is data divided from the perspective of data analysis; The construction unit is specifically used for at least one of the following: constructing a business domain identifier according to the business domain to which the preset data analysis scenario belongs; constructing a business object identifier according to the business object targeted by the preset data analysis scenario; constructing a time identifier according to the acquisition time of the target business source data; The device further comprises: a recommendation module, which is used to recommend business source data to the user based on the user's historical browsing records and / or current browsing information.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method according to any one of claims 1 to 6.

9. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the data processing method according to any one of claims 1 to 6.

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