Data processing method and device, equipment and storage medium

Abstract

The invention discloses a data processing method and device, equipment and a storage medium, and relates to the technical field of computers, and the data processing method comprises the steps: obtaining a use number demand of a user, and carrying out the loss completion of the use number demand, and obtaining a target use number demand; determining target data information according to the user information of the user and the target usage demand; a data template is constructed based on the target data information, the data demand is responded according to the data template, and the data template comprises a data field and a query statement corresponding to the data demand. As the data template is directly determined according to the use number demand of the user, and the use number demand of the user is responded according to the data template, compared with an existing mode that the user manually queries the data one by one according to the query condition, the mode can feed back the corresponding data according to the use number demand of the user, the query operation of the user is reduced, and the user experience is improved. And user query time is saved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to data processing methods, devices, equipment and storage media. Background Art

[0002] Currently, many banks are already carrying out digital transformation, improving service quality, optimizing operational efficiency, enhancing risk management capabilities, and assisting management decision-making through data-driven development. The demand for data by business personnel is becoming more and more vigorous and personalized. The conventional simple data needs can be met through business system query functions, the use of BI tools, and the development of reporting systems. However, there are still a large number of flexible data usage scenarios in the business process. Such scenarios are often implemented in the banking industry by manual data acquisition, which has the following problems: high requirements for IT skills and complex business knowledge, resulting in low data query efficiency. Summary of the invention

[0003] The main purpose of this application is to provide a data processing method, device, equipment and storage medium, aiming to solve the technical problem of low efficiency of existing data query.

[0004] To achieve the above purpose, the present application proposes a data processing method, which includes:

[0005] Obtaining the user's usage demand, performing loss compensation on the usage demand, and obtaining a target usage demand;

[0006] Determine target data information according to the user information of the user and the target data requirement;

[0007] A data template is constructed based on the target data information, and the data demand is responded to according to the data template, wherein the data template includes data fields and query statements corresponding to the data demand.

[0008] Optionally, the step of determining target data information according to the user information of the user and the target data requirement includes:

[0009] Obtaining user information of the user, wherein the user information includes basic user information and user behavior information;

[0010] Segmenting the target number requirement to obtain a segmentation result;

[0011] Target data information is determined according to the word segmentation result and the user information, wherein the target data information includes data source information, data relationship information, data processing information and data result information.

[0012] Optionally, the step of determining target data information according to the word segmentation result and the user information includes:

[0013] Determine a data source word segmentation set according to the word segmentation result;

[0014] Determine relevant user information related to the user according to the user information;

[0015] Determine candidate asset information based on the relevant user information and the data source segmentation set;

[0016] Data source information is determined based on the candidate asset information.

[0017] Optionally, the step of determining target data information according to the word segmentation result and the user information includes:

[0018] Determine initial data information according to the word segmentation result and the user information;

[0019] Constructing candidate data field information based on the initial data information;

[0020] Displaying the candidate data field information, wherein the candidate data field information includes the candidate data field and the business description information corresponding to the candidate data field;

[0021] Obtaining a usage suggestion from the user based on the displayed candidate data field information feedback;

[0022] The target data information is determined according to the usage suggestion and the initial data information.

[0023] Optionally, the step of constructing a data template based on the target data information includes:

[0024] Determining a data field based on the target data information;

[0025] Inputting the target data information into a preset data template generation model to obtain a query statement corresponding to the target data information;

[0026] A data template is constructed according to the query statement and the data field.

[0027] Optionally, after the step of inputting the target data information into a preset data template generation model to obtain a query statement corresponding to the target data information, the step further includes:

[0028] Get metadata information of data assets;

[0029] Optimizing the performance of the query statement according to the metadata information to obtain a target query statement;

[0030] The data template is updated based on the target query statement.

[0031] Optionally, after the step of constructing a data template based on the target data information, the step further includes:

[0032] publishing the data template;

[0033] Obtaining subscription information submitted by a target user based on the published data template;

[0034] The task is run according to the subscription information and the data template configuration, and relevant data is fed back to the target user according to the run task.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a data processing device, the data processing device comprising:

[0036] An acquisition module is used to acquire the user's usage demand, and to perform loss compensation on the usage demand to obtain a target usage demand;

[0037] A determination module, configured to determine target data information according to the user information of the user and the target data requirement;

[0038] A data template construction module is used to construct a data template based on the target data information and respond to the data demand according to the data template. The data template includes data fields and query statements corresponding to the data demand.

[0039] In addition, to achieve the above objectives, the present application also proposes a data processing device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data processing method described above.

[0040] In addition, to achieve the above objectives, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the data processing method described above are implemented.

[0041] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the data processing method described above are implemented.

[0042] The present application obtains the user's usage demand, performs loss-filling on the usage demand, and obtains the target usage demand; determines the target data information according to the user information of the user and the target usage demand; constructs a data template based on the target data information, and responds to the usage demand according to the data template, wherein the data template includes data fields and query statements corresponding to the usage demand. Since the present application directly determines the data template according to the user's usage demand, and responds to the user's usage demand according to the data template, compared to the existing method in which the user manually queries data one by one according to the query conditions, the above method of the present application can feedback the corresponding data according to the user's usage demand, reduce the user's query operations, and save the user's query time. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the embodiments of the present application 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.

[0045] Figure 1 A flowchart of the first embodiment of the data processing method of the present application is provided;

[0046] Figure 2 A flowchart of the second embodiment of the data processing method of this application is provided;

[0047] Figure 3 A schematic diagram of a candidate asset information determination process provided in Example 2 of the data processing method of this application;

[0048] Figure 4 This is a schematic diagram of the module structure of the data processing device according to an embodiment of the present application;

[0049] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the data processing method in the embodiment of the present application.

[0050] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0052] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0053] The main solution of the embodiment of the present application is: obtain the user's usage demand, complete the loss of the usage demand, and obtain the target usage demand; determine the target data information according to the user information of the user and the target usage demand; construct a data template based on the target data information, and respond to the usage demand according to the data template, the data template includes data fields and query statements corresponding to the usage demand. Since the present application directly determines the data template according to the user's usage demand, and responds to the user's usage demand according to the data template, compared with the existing method in which users manually query data one by one according to query conditions, the above method of the present application can feedback the corresponding data according to the user's usage demand, reduce the user's query operations, and save the user's query time.

[0054] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a data processing device, etc. The following takes a data processing device as an example to illustrate this embodiment and the following embodiments.

[0055] Based on this, the present application embodiment provides a data processing method, referring to Figure 1 , Figure 1 A flowchart of the first embodiment of the data processing method of the present application is provided.

[0056] In this embodiment, the data processing method includes the following steps:

[0057] Step S10, obtaining the user's usage demand, performing loss compensation on the usage demand, and obtaining a target usage demand;

[0058] It should be noted that the usage demand may be a user's data query demand, for example, querying the transaction information of customer A in the past year. The loss-completing of the usage demand to obtain the target usage demand may be an attempt to understand and correct the error and improve the usage demand when the usage demand input by the user is incorrect or incomplete. Specifically, the loss-completing model may be used to perform loss-completing on the usage demand to obtain the target usage demand.

[0059] Step S20, determining target data information according to the user information of the user and the target usage requirement;

[0060] It should be noted that the determination of the target data information according to the user information of the user and the target usage demand may be to determine the identity information of the user according to the user information of the user, determine the data table information that may be queried according to the identity information, and then perform natural language processing on the usage demand to obtain the semantic information corresponding to the usage demand, and determine the data source, data relationship, data processing information and output data result information corresponding to the target usage demand according to the semantic information and the data table information. Among them, the data source may be the data table information that needs to be queried corresponding to the target usage demand; the data relationship may be the data processing information that needs to be performed on the data corresponding to the target usage demand, for example, if the target usage demand is to query the total transaction amount of the user, then the data relationship is: the total transaction amount is the accumulation of the transaction amounts of each channel. The data processing information is to obtain the transaction amount of each channel, and then accumulate the transaction amount of each channel to obtain the total transaction amount; the data result may include information such as the output format and output conditions of the data result, for example, only output the data details with a total transaction amount greater than 100, and output in the form of a table.

[0061] Step S30, constructing a data template based on the target data information, and responding to the data demand according to the data template, wherein the data template includes data fields and query statements corresponding to the data demand.

[0062] It should be noted that the data template constructed based on the target data information may be a static data template determined according to the data field in the target data information, the data field may be a field that needs to be queried corresponding to the target user demand, and the static data template may be a data table constructed according to the data field. Then, the query statement for obtaining the data corresponding to the data field is determined according to the data source, data relationship, data processing information and output data result information in the target data information. That is, an SQL statement for querying the data in the static data template is generated. A data template is constructed according to a static data template containing a data field and its corresponding data query statement. When the user calls the data template, the corresponding data query statement can be called to query the relevant data, and the data query result is output according to the static data template and the queried data.

[0063] Further, in order to improve the query efficiency of data, the step S30 may include: determining a data field based on the target data information;

[0064] Inputting the target data information into a preset data template generation model to obtain a query statement corresponding to the target data information;

[0065] A data template is constructed according to the query statement and the data field.

[0066] It should be noted that the preset data template generation model can be a pre-trained model for generating corresponding query semantics according to the target data information. The preset data template can be a sequence-to-sequence model (Seq2Seq), which can input four types of information of the target data information, namely, data source, data relationship, data processing, and data result, into the encoder and convert them into a context vector, and the decoder generates the corresponding SQL query statement according to this vector. In the training phase of the Seq2Seq model, the real SQL statements of the full production operation of the big data platform are collected as training corpus, and the generated SQL statements and the real SQL statements are compared by using the cross entropy loss function. The model parameters are optimized by back propagation to minimize the loss and provide the accuracy of the predicted SQL statement. The data template constructed according to the query statement and the data field can be a static data template generated according to the data field, and the static data template can be a data table showing the target user demand. The data template is constructed according to the static data template and the corresponding query statement.

[0067] Furthermore, after the step of inputting the target data information into a preset data template generation model to obtain a query statement corresponding to the target data information, the step further includes:

[0068] Get metadata information of data assets;

[0069] Optimizing the performance of the query statement according to the metadata information to obtain a target query statement;

[0070] The data template is updated based on the target query statement.

[0071] It should be noted that the metadata information of the data asset may include the PI, PK, PPI (partition index), table form, etc. of the data asset. The query statement is optimized according to the metadata information to obtain the target query statement, which can be optimized according to the metadata information of the data asset with reference to the real SQL produced, to solve the performance bottleneck problem of complex SQL under massive data query.

[0072] After the data template of this embodiment generates the SQL statement, the user can complete the data query and extraction by calling the data template, view the data through the online watermark preview, or submit an application to download the data results. At the same time, this embodiment introduces the DLP security scanning capability to scan the result data. If sensitive data is included, it is only allowed to be viewed and downloaded in the business network segment, and is not allowed to be viewed and downloaded in the office network, to ensure that sensitive data is not leaked.

[0073] Furthermore, in order to make the data template reusable and improve the data efficiency, after the step S30, it further includes: publishing the data template;

[0074] Obtaining subscription information submitted by a target user based on the published data template;

[0075] The task is run according to the subscription information and the data template configuration, and relevant data is fed back to the target user according to the run task.

[0076] It should be noted that the complete workflow of data templates can be summarized as "publish-certify-subscribe", which are: Publish: The publisher develops / maintains a data template, and after verification through trial operation, configures the corresponding business person in charge and publishes it quickly; Authentication: The business person in charge conducts data security review and data caliber review on the publisher's data template, and controls the scope of authority of the data template based on business security after the review is passed; Subscribe: Subscribers subscribe to the corresponding data template according to their respective business needs, and can configure running tasks for the data template. After the task is completed, data can be obtained. The publisher of the data template can also configure template parameters to achieve the flexibility of the template so that the template can be fully reused later; Template subscribers can view and subscribe to the published data templates. For a high-quality data template, when the publisher publishes the data template, the template description should clearly state the adapted scenario and brief data caliber, and after being certified by the business service person, the subscriber can use it with confidence. By customizing the template parameters, the data template task will automatically calculate the corresponding data after running, and the subscriber can quickly preview and obtain data, which greatly improves the responsiveness of business needs.

[0077] In order to ensure data security, this embodiment meets the requirements of data isolation and business isolation by controlling the scope of authority; the data processing equipment integrates DLP data security scanning tools to scan the operation result data, strictly control sensitive data from leaving the business network and landing on the office network, and users use watermark technology to manage during the preview process, and implement the feasibility of not taking photos or screenshots and post-event investigation. For necessary data download work, a channel is provided, and the approval is implemented once the download is completed, and the approval process is differentiated according to the data security level.

[0078] This embodiment obtains the user's usage demand, completes the loss of the usage demand, and obtains the target usage demand; determines the target data information according to the user information of the user and the target usage demand; constructs a data template based on the target data information, and responds to the usage demand according to the data template, wherein the data template includes data fields and query statements corresponding to the usage demand. Since this embodiment directly determines the data template according to the user's usage demand, and responds to the user's usage demand according to the data template, compared with the existing method in which the user manually queries data one by one according to the query conditions, the above method of this embodiment can feedback the corresponding data according to the user's usage demand, reduce the user's query operations, and save the user's query time.

[0079] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , Figure 2 The flowchart provided in the second embodiment of the data processing method of the present application, the step S20 further includes the following steps:

[0080] Step S201: Acquire user information of the user, wherein the user information includes basic user information and user behavior information;

[0081] It should be noted that the basic user information may include the user's position, authority, department, relevant colleagues in the department, and behavior information of relevant colleagues, etc. The user behavior information may include the user's previous historical query information, including accessed data table information, etc.

[0082] Step S202: segmenting the target number demand to obtain a segmentation result;

[0083] It should be noted that the word segmentation of the target number demand to obtain the word segmentation result may be performed by using a general text word segmentation method to segment the target number demand to obtain the word segmentation result. When the target number demand is voice, the target number demand needs to be mechanically converted into text first.

[0084] Step S203: determining target data information according to the word segmentation result and the user information, wherein the target data information includes data source information, data relationship information, data processing information and data result information.

[0085] It should be noted that the determination of the target data information according to the word segmentation result and the user information can be to determine the data field information to be queried according to the word segmentation result, determine the data asset information corresponding to the data field information according to the data field information to be queried and the identity, position, authority and previous query records of the user in the user information, including the data table information to be queried, and obtain the data source. Then, the data relationship information is determined according to the data field information to be queried in the word segmentation result and the field information in the data table. For example, if the data field information to be queried is the total transaction amount and the field information in the data table is generally the transaction amount of a single channel, then the data relationship is the total transaction amount-the transaction amount of a single channel. Determine the data processing information based on the data relationship information, that is, determine what kind of data processing operation needs to be performed on the queried data according to the data relationship information. Then, determine the data result information according to the data query conditions and output format and other information in the word segmentation result. For example, the data query condition is to output data with a transaction amount greater than 100 and the year being this year.

[0086] In a specific implementation, a data asset recommendation model can be used to determine the target data information based on the word segmentation results and the user information. The training corpus of the data asset recommendation model includes conventional content such as user behavior data, data asset information, and user information. It also introduces metadata information of data assets and behavior data of department users of the department to which the user belongs. It can be closer to the actual needs of users and solve the scenario needs of some users with weak business knowledge to query data. The data asset recommendation model is divided into two parts: recall and sorting. The success rate of recall is improved through four-way recall, and weighted calculation and sorting are performed according to the matching situation to improve the accuracy of recommendations. Please refer to the above steps for details.

[0087] Further, in order to improve the query efficiency of data, the step S203 may include: determining a data source word segmentation set according to the word segmentation result;

[0088] Determine relevant user information related to the user according to the user information;

[0089] Determine candidate asset information based on the relevant user information and the data source segmentation set;

[0090] Data source information is determined based on the candidate asset information.

[0091] It should be noted that the data source segmentation set determined according to the segmentation result may be extracting segmentations related to the data source from the segmentation result, for example, XX table, XX data. The relevant user information may be user information of the user's department colleagues and other users who have intersections with the user. The candidate asset information determined based on the relevant user information and the data source segmentation set may be determining the data asset information corresponding to the target user demand according to the relevant user information and the data source segmentation set, including information such as the data table where the data to be queried is located.

[0092] In the specific implementation, please refer to Figure 3 , Figure 3 A schematic diagram of a candidate asset information determination process provided in Embodiment 2 of the data processing method of this application, Figure 3 The data source-segmentation set in is the data source segmentation set. Figure 3 The recommended data assets in are the candidate asset information. The determination of the candidate asset information based on the relevant user information and the data source segmentation set includes two stages: data recall and sorting. The data recall stage is to determine the data assets matching the text, the data assets matching the semantic similarity, the historical browsing data assets of the relevant user information, and the assets having a blood relationship with the historical browsing assets of the user according to the relevant user information, the user information, and the data source segmentation set. Then, in the sorting stage, the above-recalled assets are sorted by relevance according to the relevance to the user's target usage demand, and the popularity of the assets is also included. Specifically, the score can be used to represent the similarity and asset popularity information, and the total score of each asset is obtained. The data assets are sorted according to the total score to obtain the candidate asset information. The determination of the data source information according to the candidate asset information can be to determine the data table that can retrieve the data field corresponding to the user's target usage demand from the candidate asset information as the data source information. When multiple tables store the data field corresponding to the target usage demand, the data source information can be determined according to the total score of the data asset.

[0093] Furthermore, in order to make the generated data template more in line with the needs of the user, the step S203 may include: determining initial data information according to the word segmentation result and the user information;

[0094] Constructing candidate data field information based on the initial data information;

[0095] Displaying the candidate data field information, wherein the candidate data field information includes the candidate data field and the business description information corresponding to the candidate data field;

[0096] Obtaining a usage suggestion from the user based on the displayed candidate data field information feedback;

[0097] The target data information is determined according to the usage suggestion and the initial data information.

[0098] It should be noted that the initial data information may include data fields corresponding to the user's target data needs and source information of the data fields, as well as information such as data processing and data relationships. The construction of candidate data field information based on the initial data information may be to generate business description information corresponding to the data field (i.e., the candidate data field) and determine the data type of the data field based on the data fields and source information of the data fields in the initial data information. Displaying the candidate data field information may be to display the candidate data fields, business description information, data types, and other information in the candidate data field information in the form of a table, and refer to the following Table 1-Candidate Data Field Information Table:

[0099] Table 1-Candidate data field information table

[0100]

[0101] It should be noted that the user can modify and expand the displayed candidate data field information according to actual needs, that is, feedback usage suggestions. The determination of the target data information according to the usage suggestions and the initial data information can be to supplement and optimize the initial data information according to the user's usage suggestions, and can also supplement, modify and optimize data processing and data relationships. In order to obtain more accurate target data information.

[0102] This embodiment obtains the user information of the user, the user information includes the user's basic information and user behavior information; performs word segmentation on the target data demand to obtain a word segmentation result; determines the target data information according to the word segmentation result and the user information, the target data information includes data source information, data relationship information, data processing information and data result information. This embodiment determines the target data information according to the user information and the word segmentation result of the user, facilitates the subsequent generation of data templates, and improves the user's data query efficiency.

[0103] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the data processing method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0104] This application also provides a data processing device, please refer to Figure 4 , the data processing device comprises:

[0105] An acquisition module 10 is used to acquire the user's usage demand, and to perform loss compensation on the usage demand to obtain a target usage demand;

[0106] A determination module 20, configured to determine target data information according to the user information of the user and the target usage requirement;

[0107] The data template construction module 30 is used to construct a data template based on the target data information and respond to the data demand according to the data template. The data template includes data fields and query statements corresponding to the data demand.

[0108] This embodiment obtains the user's usage demand, completes the loss of the usage demand, and obtains the target usage demand; determines the target data information according to the user information of the user and the target usage demand; constructs a data template based on the target data information, and responds to the usage demand according to the data template, wherein the data template includes data fields and query statements corresponding to the usage demand. Since this embodiment directly determines the data template according to the user's usage demand, and responds to the user's usage demand according to the data template, compared with the existing method in which the user manually queries data one by one according to the query conditions, the above method of this embodiment can feedback the corresponding data according to the user's usage demand, reduce the user's query operations, and save the user's query time.

[0109] The data processing device provided by the present application adopts the data processing method in the above embodiment, which can solve the technical problem of low efficiency of existing data query. Compared with the prior art, the beneficial effects of the data processing device provided by the present application are the same as the beneficial effects of the data processing method provided by the above embodiment, and other technical features in the data processing device are the same as the features disclosed in the above embodiment method, which will not be described in detail here.

[0110] The present application provides a data processing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the data processing method in the above-mentioned embodiment 1.

[0111] Reference below Figure 5 , which shows a schematic diagram of the structure of a data processing device suitable for implementing an embodiment of the present application. The data processing device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The data processing 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 application.

[0112] like Figure 5 As shown, the data processing device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the data processing device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 may allow the data processing device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a data processing device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0113] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a 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 a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0114] The data processing device provided by the present application adopts the data processing method in the above embodiment, which can solve the technical problem of low efficiency of existing data query. Compared with the prior art, the beneficial effects of the data processing device provided by the present application are the same as the beneficial effects of the data processing method provided by the above embodiment, and other technical features in the data processing device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0115] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0116] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0117] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the data processing method in the above-mentioned embodiment.

[0118] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0119] The computer-readable storage medium may be included in a data processing device, or may exist independently without being incorporated into a data processing device.

[0120] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code 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 the case of 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).

[0121] 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 application. 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 a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0122] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0123] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned data processing method, and can solve the technical problem of low efficiency of existing data query. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the data processing method provided in the above-mentioned embodiment, and will not be described in detail here.

[0124] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned data processing method when executed by a processor.

[0125] The computer program product provided by this application can solve the technical problem of low efficiency of existing data query. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the data processing method provided by the above embodiment, which will not be repeated here.

[0126] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A data processing method, characterized in that: The data processing method comprises the following steps: Obtaining the user's usage demand, performing loss compensation on the usage demand, and obtaining a target usage demand; Determine target data information according to the user information of the user and the target data requirement; A data template is constructed based on the target data information, and the data demand is responded to according to the data template, wherein the data template includes data fields and query statements corresponding to the data demand.

2. The data processing method according to claim 1, characterized in that: The step of determining target data information according to the user information of the user and the target data requirement includes: Obtaining user information of the user, wherein the user information includes basic user information and user behavior information; Segmenting the target number requirement to obtain a segmentation result; Target data information is determined according to the word segmentation result and the user information, wherein the target data information includes data source information, data relationship information, data processing information and data result information.

3. The data processing method according to claim 2, characterized in that: The step of determining target data information according to the word segmentation result and the user information comprises: Determine a data source word segmentation set according to the word segmentation result; Determine relevant user information related to the user according to the user information; Determine candidate asset information based on the relevant user information and the data source segmentation set; Data source information is determined based on the candidate asset information.

4. The data processing method according to claim 3, characterized in that: The step of determining target data information according to the word segmentation result and the user information comprises: Determine initial data information according to the word segmentation result and the user information; Constructing candidate data field information based on the initial data information; Displaying the candidate data field information, wherein the candidate data field information includes the candidate data field and the business description information corresponding to the candidate data field; Obtaining a usage suggestion from the user based on the displayed candidate data field information feedback; The target data information is determined according to the usage suggestion and the initial data information.

5. The data processing method according to any one of claims 1 to 4, characterized in that: The step of constructing a data template based on the target data information includes: Determining a data field based on the target data information; Inputting the target data information into a preset data template generation model to obtain a query statement corresponding to the target data information; A data template is constructed according to the query statement and the data field.

6. The data processing method according to claim 5, characterized in that: After the step of inputting the target data information into a preset data template generation model to obtain a query statement corresponding to the target data information, the method further includes: Get metadata information of data assets; Optimizing the performance of the query statement according to the metadata information to obtain a target query statement; The data template is updated based on the target query statement.

7. The data processing method according to any one of claims 1 to 4, characterized in that: After the step of constructing a data template based on the target data information, the method further includes: publishing the data template; Obtaining subscription information submitted by a target user based on the published data template; The task is run according to the subscription information and the data template configuration, and relevant data is fed back to the target user according to the run task.

8. A data processing device, characterized in that: The data processing device comprises: An acquisition module is used to acquire the user's usage demand, and to perform loss compensation on the usage demand to obtain a target usage demand; A determination module, configured to determine target data information according to the user information of the user and the target data requirement; A data template construction module is used to construct a data template based on the target data information and respond to the data demand according to the data template. The data template includes data fields and query statements corresponding to the data demand.

9. A data processing device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data processing method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the data processing method according to any one of claims 1 to 7 are implemented.

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