Data processing method and device, computer equipment and storage medium

Abstract

The invention relates to a data processing method and device, computer equipment and a storage medium. The method comprises the following steps: determining a data set structure of each to-be-processed data set; the data set structure comprises a nested structure and a non-nested structure; for each to-be-processed data set, simplifying the to-be-processed data set according to the data set structure of the to-be-processed data set to obtain a single-layer data set; and generating a target data set according to each single-layer data set. By adopting the method, the query efficiency of data query can be improved.

Description

Technical Field

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

[0002] At present, with the development of big data and artificial intelligence technology, higher requirements are placed on data storage and retrieval technology. Users not only need to access data quickly, but also need to be able to process and analyze data flexibly. In the internal purchase mall and business middle-end system within the enterprise, some deeply nested and complex data structures are often used. In the development and coding process, this complex data structure not only increases the cost of understanding, but also increases the cost of joint debugging before and after. Often when the front end retrieves data, it also needs to retrieve it layer by layer, which is not only complicated, but also increases the risk of null pointers.

[0003] As the amount of data grows and the data structure becomes more complex, traditional data storage and retrieval methods face performance bottlenecks. Especially when dealing with nested data structures, traditional indexing and querying methods may lead to inefficient queries. Summary of the invention

[0004] Based on this, it is necessary to provide a data processing method, device, computer equipment and storage medium that can improve data query efficiency in response to the above technical problems.

[0005] In a first aspect, the present application provides a data processing method, comprising:

[0006] Determine the data set structure of each data set to be processed; the data set structure includes a nested structure and a non-nested structure;

[0007] For each data set to be processed, according to the data set structure of the data set to be processed, the data set to be processed is simplified to obtain a single-layer data set;

[0008] Generate a target dataset based on each single-layer dataset.

[0009] In one embodiment, the data set to be processed is simplified according to the data set structure of the data set to be processed to obtain a simplified data set, including:

[0010] When the data set structure is a nested structure, the nested structure of the data set to be processed is disassembled to obtain a single-layer data set;

[0011] When the dataset structure is a non-nested structure, the dataset to be processed is directly used as a single-layer dataset.

[0012] In one embodiment, the nested structure of the data set to be processed is disassembled to obtain a single-layer data set, including:

[0013] Recursively process the data set to be processed, and identify the data to be processed at each nested level in the data set to be processed;

[0014] The data to be processed identified in each nested layer are reflected to the first nested layer to obtain a single-layer data set.

[0015] In one embodiment, generating a target data set according to each single-layer data set includes:

[0016] For each single-layer data set, redundancy removal is performed on each single-layer data set according to the data set type of the single-layer data set to obtain a simplified data set;

[0017] A target data set is generated based on each simplified data set.

[0018] In one embodiment, redundancy removal is performed on the single-layer data set according to the data type of the single-layer data set to obtain a simplified data set, including:

[0019] When the data type of the single-layer data set is retrieval data, the single-layer data having the same data content as the preset data content is deleted from the single-layer data set to obtain a simplified data set.

[0020] In one embodiment, the method further comprises:

[0021] When the data type of the single-layer data set is the preset data type, data cleaning is performed on the single-layer data in the single-layer data set to obtain a simplified data set.

[0022] In a second aspect, the present application further provides a data processing device, comprising:

[0023] A structure determination module, used to determine the data set structure of each data set to be processed; the data set structure includes a nested structure and a non-nested structure;

[0024] A data simplification module is used to simplify each data set to be processed according to the data set structure of the data set to be processed to obtain a single-layer data set;

[0025] The target generation module is used to generate a target data set according to each single-layer data set.

[0026] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0027] Determine the data set structure of each data set to be processed; the data set structure includes a nested structure and a non-nested structure;

[0028] For each data set to be processed, according to the data set structure of the data set to be processed, the data set to be processed is simplified to obtain a single-layer data set;

[0029] Generate a target dataset based on each single-layer dataset.

[0030] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0031] Determine the data set structure of each data set to be processed; the data set structure includes a nested structure and a non-nested structure;

[0032] For each data set to be processed, according to the data set structure of the data set to be processed, the data set to be processed is simplified to obtain a single-layer data set;

[0033] Generate a target dataset based on each single-layer dataset.

[0034] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0035] Determine the data set structure of each data set to be processed; the data set structure includes a nested structure and a non-nested structure;

[0036] For each data set to be processed, according to the data set structure of the data set to be processed, the data set to be processed is simplified to obtain a single-layer data set;

[0037] Generate a target dataset based on each single-layer dataset.

[0038] The above-mentioned data processing method, device, computer equipment and storage medium determine the data set structure of each data set to be processed; the data set structure includes a nested structure and a non-nested structure; for each data set to be processed, according to the data set structure of the data set to be processed, the data set to be processed is simplified to obtain a single-layer data set; according to each single-layer data set, a target data set is generated. The present application simplifies the data solution structure of the data set to be processed so that the data set structure of the data set to be processed becomes a flat structure, thereby improving the query efficiency of subsequent query data. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] FIG1 is an application environment diagram of a data processing method provided by this embodiment;

[0041] FIG2 is a schematic flow chart of a first data processing method provided in this embodiment;

[0042] FIG3 is a schematic flow chart of a second data processing method provided in this embodiment;

[0043] FIG4 is a structural block diagram of a data processing device provided in this embodiment;

[0044] FIG5 is an internal structure diagram of a first computer device provided in this embodiment;

[0045] FIG6 is a diagram showing the internal structure of a second type of computer device provided in this embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] The data processing method provided in the embodiment of the present application can be applied in the application environment shown in Figure 1. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The computer device determines the data set structure of each data set to be processed; the data set structure includes a nested structure and a non-nested structure; for each data set to be processed, according to the data set structure of the data set to be processed, the data set to be processed is simplified and processed to obtain a single-layer data set; according to each single-layer data set, a target data set is generated. Among them, the computer device can be a terminal or a server. The terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0048] In an exemplary embodiment, as shown in FIG2 , a data processing method is provided, which is described by taking the method applied to the computer device in FIG1 as an example, and includes the following steps 201 to 203. Among them:

[0049] Step 201: determine the data set structure of each data set to be processed.

[0050] The data set to be processed may be a data set of the data to be processed that is queried. The data set structure includes a nested structure and a non-nested structure; the nested structure may be a structure including at least two levels; the non-nested structure may be a structure including only one level; for example, if the data set to be processed is a folder named "department information", the "department information" folder includes a folder named "rule maintenance" and a document file named "activity", and the "rule maintenance" folder includes a document file named "rule 1" and a document file named "rule 2", then it is determined that the data set structure of the "department information" folder includes a nested structure with two nested levels.

[0051] Specifically, for each data set to be processed, the number of nested levels in the data set to be processed is determined; the data set structure of the data set to be processed whose number of nested levels is greater than 1 is determined to be a nested structure; and the data set structure of the data set to be processed whose number of nested levels is equal to 1 is determined to be a non-nested structure.

[0052] Exemplarily, in this embodiment, the data set to be processed may also be obtained by pre-querying. For example, when there is a need to determine whether a user is eligible to participate in an activity, the user name set corresponding to the activity may be queried locally first, and the set may be a nested structure of data.

[0053] Step 202 , for each data set to be processed, simplify the data set according to the data set structure of the data set to be processed to obtain a single-layer data set.

[0054] The single-layer data set may be a data set that includes only one nested level. For example, the data set to be processed is a folder named "department information", and the "department information" folder includes a folder named "rule maintenance" and a document file named "activity", and the "rule maintenance" folder is empty.

[0055] Specifically, for each data set to be processed, the data set structure of the data set to be processed is determined, and the data to be processed in each nested level in the data set to be processed is transferred to the first nested level, and the simplified processing of the data set to be processed is completed to obtain a single-layer data set.

[0056] Exemplarily, if the data set to be processed is a User ID set, and the User ID set includes at least one User ID object, this embodiment can transfer the required User ID object class in the User ID set into the required set to obtain a single-layer data set.

[0057] Step 203: Generate a target data set based on each single-layer data set.

[0058] Specifically, the single-layer data sets are combined into a target data set. It should be noted that the data set types of the single-layer data sets can be the same or different.

[0059] The above data processing method determines the data set structure of each data set to be processed; the data set structure includes a nested structure and a non-nested structure; for each data set to be processed, according to the data set structure of the data set to be processed, the data set to be processed is simplified to obtain a single-layer data set; according to each single-layer data set, a target data set is generated. The present application simplifies the data solution structure of the data set to be processed so that the data set structure of the data set to be processed becomes a flat structure, thereby improving the query efficiency of subsequent query data.

[0060] In some embodiments, the data set to be processed is simplified according to its data set structure to obtain a simplified data set, including: when the data set structure is a nested structure, the nested structure of the data set to be processed is disassembled to obtain a single-layer data set; when the data set structure is a non-nested structure, the data set to be processed is directly treated as a single-layer data set.

[0061] In some embodiments, when the data set structure is a non-nested structure, the data to be processed in the test data set has no other nested structure, so the data set to be processed can be directly used as a single-layer data set.

[0062] In some embodiments, the nested structure of the data set to be processed is disassembled to obtain a single-layer data set, including: recursively processing the data set to be processed to identify the data to be processed in each nested layer in the data set to be processed; reflecting the data to be processed identified in each nested layer to the first nested layer to obtain a single-layer data set.

[0063] Specifically, the data set to be processed is recursively processed to identify the data to be processed in the first nested layer of the data set to be processed. If the first nested layer includes other data sets in addition to the data to be processed, it proves that the data set to be processed also includes a second nested layer. The data to be processed in other data sets are identified again until the data to be processed in each nested layer of the data set to be processed is identified. The data to be processed identified in each nested layer is moved to the first nested layer, and the reflection processing of the data to be processed is completed to obtain a single-layer data set including only one nested layer.

[0064] This embodiment uses recursion and reflection processing to more flexibly assign values ​​to the incoming objects, facilitate the extraction of custom fields, and convert them into flat objects. It also supports the input of different objects, thereby improving the accuracy of data processing.

[0065] In some embodiments, a target data set is generated based on each single-layer data set, including: for each single-layer data set, according to the data set type of the single-layer data set, redundancy removal processing is performed on each single-layer data set to obtain a simplified data set; and a target data set is generated based on each simplified data set.

[0066] Specifically, for each single-layer data set, the data set type of the single-layer data set is determined, and each single-layer data in the single-layer data set is subjected to redundancy removal processing to obtain a simplified data set; and each simplified data set with redundancy removed is combined to generate a target data set.

[0067] In some embodiments, according to the data type of the single-layer data set, redundancy removal processing is performed on the single-layer data set to obtain a simplified data set, including: when the data type of the single-layer data set is retrieval data, deleting the single-layer data whose data content is the same as the preset data content from the single-layer data set to obtain the simplified data set. When the data type of the single-layer data set is the preset data type, data cleaning is performed on the single-layer data in the single-layer data set to obtain the simplified data set.

[0068] The single-layer data in the single-layer data set whose data type is the search data is obtained by searching. For example, the single-layer data may be data obtained by searching through a search engine (Elasticsearch, ES), and ES supports complex nested data structures. The preset data type may be file data, for example, text file data, image file data, and table file data.

[0069] Specifically, when the data type of the single-layer data set is the search data, the data content of each single-layer data in each single-layer data set is determined, the single-layer data with the same data content as the preset data content is deleted from the single-layer data set, and the deleted single-layer data set is used as the simplified data set. When the data type of the single-layer data set is the preset data type, the data content of each single-layer data in each single-layer data set is determined, and the single-layer data is cleaned, and the cleaned single-layer data set is used as the simplified data set.

[0070] This embodiment can simplify and flatten the data more accurately.

[0071] In one embodiment, this embodiment provides an optional method for data processing, and takes the method applied to a server as an example for explanation. As shown in FIG3 , the method includes the following steps:

[0072] Step 301, start.

[0073] Step 302 , determining whether the data set structure of the data set to be processed is a nested structure, if so, executing step 303 , if not, executing step 305 .

[0074] Step 303: recursively process the data set to be processed to identify the data to be processed at each nested level in the data set to be processed.

[0075] Step 304 , the data to be processed identified in each nested layer is reflected to the first nested layer to obtain a single-layer data set.

[0076] Step 305 , directly use the data set to be processed as a single-layer data set.

[0077] Step 306 , for each single-layer data set, redundancy removal processing is performed on each single-layer data set according to the data set type of the single-layer data set to obtain a simplified data set.

[0078] Step 307: Generate a target data set based on each simplified data set.

[0079] Exemplarily, this embodiment can simplify the nested structured data set to be processed corresponding to the following example 1 into the target data set corresponding to the following example 2. While simplifying multiple nested levels in example 1 into a single nested level, the data content in example 1 is simplified, and finally example 2 is obtained.

[0080] Example 1:

[0081]

[0082] Example 2:

[0083]

[0084] It should be noted that the difference in query efficiency between the traditional method and the flattening conversion method is compared. For example, it can be mentioned that when processing large-scale data sets, the flattened structure can significantly reduce the query time and meet the needs of real-time data processing. It is explained how the flattened structure reduces resource consumption and improves the overall performance of the system in search and aggregation operations. The flattened data structure makes the development coding process simpler and more intuitive. When the front end takes data, there is no need to take it layer by layer, which reduces the cost of understanding and joint debugging, and reduces development time and manpower investment. Reduce the risk of null pointers and improve the stability and reliability of the system. The custom flattening conversion method of this embodiment can be flexibly adjusted according to the characteristics of different data sources and adapt to various complex data structures. It shows that the method is easy to expand and can be adjusted and optimized as data requirements change and new data sources are added. The flattened structure makes data easier to access, which provides greater convenience for developers to analyze and process data or for users to query and use data. This embodiment can effectively integrate data from different data sources (ES, files, DB data, etc.) and convert them into a flat structure for comprehensive analysis and processing.

[0085] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0086] Based on the same inventive concept, the embodiment of the present application also provides a data processing device for implementing the data processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more data processing device embodiments provided below can refer to the limitations on the data processing method above, and will not be repeated here.

[0087] In an exemplary embodiment, as shown in FIG4 , a data processing device is provided, comprising: a structure determination module 10, a data reduction module 11 and a target generation module 12, wherein:

[0088] A structure determination module 10 is used to determine the data set structure of each data set to be processed; the data set structure includes a nested structure and a non-nested structure;

[0089] The data simplification module 11 is used for simplifying each data set to be processed according to the data set structure of the data set to be processed to obtain a single-layer data set;

[0090] The target generation module 12 is used to generate a target data set according to each single-layer data set.

[0091] In one embodiment, the data simplification module 11 in Figure 4 is also used to disassemble the nested structure of the data set to be processed to obtain a single-layer data set when the data set structure is a nested structure; when the data set structure is a non-nested structure, directly treat the data set to be processed as a single-layer data set.

[0092] In one embodiment, the data simplification module 11 in FIG. 4 is also used to recursively process the data set to be processed, identify the data to be processed in each nested layer in the data set to be processed, and reflect the data to be processed identified in each nested layer to the first nested layer to obtain a single-layer data set.

[0093] In one embodiment, the target generation module 12 in FIG. 4 is further used to perform redundancy removal processing on each single-layer data set according to the data set type of the single-layer data set to obtain a simplified data set; and generate a target data set based on each simplified data set.

[0094] In one embodiment, the target generation module 12 in FIG. 4 is further used to delete single-layer data having the same data content as the preset data content from the single-layer data set to obtain a simplified data set when the data type of the single-layer data set is retrieval data.

[0095] In one embodiment, the target generation module 12 in FIG. 4 is further used to perform data cleaning on the single-layer data in the single-layer data set to obtain a simplified data set when the data type of the single-layer data set is a preset data type.

[0096] Each module in the above data processing device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0097] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG5. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a data processing method is implemented.

[0098] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a data processing method is implemented. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0099] Those skilled in the art will understand that the structures shown in FIGS. 5 and 6 are merely block diagrams of partial structures related to the scheme of the present application, and do not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device may include more or fewer components than those shown in the figures, or combine certain components, or have a different arrangement of components.

[0100] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0101] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0102] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0103] It should be noted that the data involved in this application (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0104] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A data processing method, characterized in that: The method comprises: Determine the data set structure of each data set to be processed; the data set structure includes a nested structure and a non-nested structure; For each data set to be processed, simplifying the data set to be processed according to the data set structure of the data set to be processed to obtain a single-layer data set; A target data set is generated according to each of the single-layer data sets.

2. The method according to claim 1, characterized in that The step of simplifying the data set to be processed according to the data set structure of the data set to be processed to obtain a simplified data set includes: In the case where the data set structure is a nested structure, the nested structure of the data set to be processed is disassembled to obtain a single-layer data set; When the data set structure is a non-nested structure, the data set to be processed is directly used as the single-layer data set.

3. The method according to claim 2, characterized in that The step of disassembling the nested structure of the data set to be processed to obtain a single-layer data set includes: Recursively processing the data set to be processed to identify the data to be processed at each nested layer in the data set to be processed; The data to be processed identified in each nested layer are reflected to the first nested layer to obtain a single-layer data set.

4. The method according to claim 1, characterized in that The step of generating a target data set according to each of the single-layer data sets comprises: For each single-layer data set, according to the data set type of the single-layer data set, redundancy removal processing is performed on each single-layer data set to obtain a simplified data set; A target data set is generated according to each of the simplified data sets.

5. The method according to claim 4, characterized in that The step of performing redundancy removal processing on the single-layer data set according to the data type of the single-layer data set to obtain a simplified data set includes: In the case that the data type of the single-layer data set is retrieval data, single-layer data having the same data content as the preset data content is deleted from the single-layer data set to obtain a simplified data set.

6. The method according to claim 5, characterized in that The method further comprises: When the data type of the single-layer data set is a preset data type, data cleaning is performed on the single-layer data in the single-layer data set to obtain a simplified data set.

7. A data processing device, characterized in that: The device comprises: A structure determination module, used to determine the data set structure of each data set to be processed; the data set structure includes a nested structure and a non-nested structure; A data simplification module, used for simplifying each data set to be processed according to the data set structure of the data set to be processed to obtain a single-layer data set; The target generation module is used to generate a target data set according to each of the single-layer data sets.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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