CN117807177A - Data processing method, device, equipment and storage medium - Google Patents

Abstract

The embodiment of the application provides a data processing method, a device, equipment and a storage medium. The method comprises the following steps: acquiring a data set to be processed, wherein the data set comprises data acquired from a plurality of devices; performing feature extraction processing on the data set to obtain metadata features and semantic features of the data set; acquiring user characteristics corresponding to the data set and preset strategy characteristics, wherein the user characteristics are characteristics of a user using the data set; and determining a target resource catalog corresponding to the data set according to the metadata features, the semantic features, the user features and the strategy features. The efficiency of determining the resource catalog is improved.

Description

Data processing methods, devices, equipment and storage media

Technical field

The embodiments of the present application relate to the field of data processing technology, and in particular, to a data processing method, apparatus, device and storage medium.

Background technique

Data in multiple systems can form a data set, and data processing can be performed on the data set to form a resource catalog through which data in the multiple systems can be shared.

In the related art, after obtaining the data set, technical personnel can manually process the data set to determine the resource directory corresponding to the data set. However, when the amount of data in the data set is large, technicians cannot determine the corresponding resource directory based on the data set in a short period of time, resulting in low efficiency in determining the resource directory.

Contents of the invention

Embodiments of the present application provide a data processing method, device, equipment and storage medium to solve the problem of low efficiency in determining resource directories.

In the first aspect, embodiments of the present application provide a data processing method, including:

Obtaining a data set to be processed, the data set including data obtained from multiple devices;

Perform feature extraction processing on the data set to obtain metadata features and semantic features of the data set;

Obtain user characteristics and preset policy characteristics corresponding to the data set, where the user characteristics are characteristics of users who use the data set;

According to the metadata characteristics, the semantic characteristics, the user characteristics and the policy characteristics, the target resource directory corresponding to the data set is determined.

In a possible implementation, feature extraction processing is performed on the data set to obtain metadata features and semantic features of the data set, including:

Determine business metadata and technical metadata in said data set;

Determine the metadata characteristics according to the business metadata and the technical metadata;

determining structured data in said data set;

The semantic features are determined based on the structured data.

In a possible implementation, determining the metadata characteristics according to the business metadata and the technical metadata includes:

Extract data features from the business metadata to obtain business metadata features, where the business metadata features include regional themes;

Extract data features from the technical metadata to obtain technical metadata features;

Wherein, the metadata characteristics include the business metadata characteristics and the technical metadata characteristics.

In a possible implementation, determining the semantic features according to the structured data includes:

Perform word segmentation processing on the structured data to obtain multiple words;

Determine high-frequency words among the plurality of words;

The semantic features are determined based on the high-frequency words.

In a possible implementation, determining high-frequency words among the plurality of words includes:

Get the word frequency of each vocabulary;

Determine words with a word frequency greater than a preset threshold as the high-frequency words; or, sort the multiple words according to the word frequency, and determine the top N words among the sorted words as the high-frequency words. , the N is an integer greater than or equal to 1.

In a possible implementation, determining the semantic features based on the high-frequency words includes:

Obtain the word frequency inverse text frequency index TF-IDF value of the high-frequency vocabulary;

The semantic feature is determined according to the TF-IDF value of the high-frequency vocabulary.

In a possible implementation, determining the target resource directory corresponding to the data set according to the metadata characteristics, the semantic characteristics, the user characteristics and the policy characteristics includes:

Determine application scenarios;

Determine an initial feature model according to the application scenario, the metadata features and the semantic features;

The initial feature model is modified according to the user features and the policy features to obtain the target resource directory.

In a second aspect, embodiments of the present application provide a data processing device, including: a first acquisition module, a processing module, a second acquisition module and a determination module, wherein,

The first acquisition module is used to acquire a data set to be processed, wherein the data set includes data acquired from multiple devices;

The processing module is used to perform feature extraction processing on the data set to obtain metadata features and semantic features of the data set;

The second acquisition module is configured to acquire user characteristics and preset policy characteristics corresponding to the data set, where the user characteristics are characteristics of users who use the data set;

The determination module is configured to determine the target resource directory corresponding to the data set according to the metadata characteristics, the semantic characteristics, the user characteristics and the policy characteristics.

In a possible implementation, the processing module is used to:

Determine business metadata and technical metadata in said data set;

Determine the metadata characteristics according to the business metadata and the technical metadata;

determining structured data in said data set;

The semantic features are determined based on the structured data.

In a possible implementation, the processing module is used to:

Extract data features from the business metadata to obtain business metadata features, where the business metadata features include regional themes;

Extract data features from the technical metadata to obtain technical metadata features;

Wherein, the metadata characteristics include the business metadata characteristics and the technical metadata characteristics.

In a possible implementation, the processing module is used to:

Perform word segmentation processing on the structured data to obtain multiple words;

determining high frequency words among the plurality of words;

The semantic features are determined based on the high-frequency words.

In a possible implementation, the processing module is used to:

Get the word frequency of each vocabulary word;

Determine words with a word frequency greater than a preset threshold as the high-frequency words; or, sort the multiple words according to the word frequency, and determine the top N words among the sorted words as the high-frequency words. , the N is an integer greater than or equal to 1.

In a possible implementation, the processing module is used to:

Obtain the word frequency inverse text frequency index TF-IDF value of the high-frequency vocabulary;

The semantic features are determined according to the TF-IDF values of the high-frequency words.

In a possible implementation, the determining module is used to:

Determine application scenarios;

Determine an initial feature model according to the application scenario, the metadata features and the semantic features;

The initial feature model is modified according to the user features and the policy features to obtain the target resource directory.

In a third aspect, embodiments of the present application provide an electronic device, including: a memory and a processor;

The memory stores computer execution instructions;

The processor executes the computer-executable instructions stored in the memory, so that the processor executes the data processing method described in any one of the first aspects.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium. Computer-executable instructions are stored in the computer-readable storage medium. When the computer-executable instructions are executed by a processor, they are used to implement any one of the first aspects. The data processing methods described in the item.

The data processing method, device, equipment and storage medium provided by the embodiment of the present application can extract metadata features and semantic features in the data set after obtaining the data set to be processed, construct a resource directory of the data set based on the metadata features and semantic features, and then use user features and policy features to correct the resource directory to obtain the target resource directory. The process of obtaining the resource directory does not require the technician to perform tedious feature analysis in the data set, but extracts metadata features and semantic features, and then corrects the resource directory based on user features and policy features, thereby improving the efficiency of determining the resource directory.

Description of drawings

The drawings described here are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the attached picture:

Figure 1 is a schematic diagram of an application scenario provided by an embodiment of the present application;

Figure 2 is a schematic flow chart of a data processing method provided by an embodiment of the present application;

Figure 3 is a schematic flow chart of another data processing method provided by an embodiment of the present application;

Figure 4 is a schematic flow chart of data processing provided by the embodiment of the present application;

FIG5 is a schematic diagram of the structure of a data processing device provided in an embodiment of the present application;

FIG. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

FIG1 is a schematic diagram of an application scenario provided by an embodiment of the present application. Please refer to FIG1, which includes multiple data storage devices 101, a data processing device 102, and multiple user devices 103. The data storage device 101 can store data, and the multiple data storage devices 101 can be located in different geographical locations. For example, the data stored in the data storage device may include pension data, community data, etc. The data processing device 102 can obtain data from each data storage device 101 to obtain a data set to be processed, and the data processing device 102 can process the data set to obtain a target resource directory corresponding to the data set. The user device 103 can access the data set through the target resource directory.

In the related art, after obtaining the data set, technical personnel can manually process the data set to determine the resource directory corresponding to the data set. However, when the amount of data in the data set is large, technicians cannot determine the corresponding resource directory based on the data set in a short period of time, resulting in low efficiency in determining the resource directory.

In the embodiment of this application, after obtaining the data set to be processed, the metadata features and semantic features in the data set can be extracted, the resource directory of the data set can be constructed based on the metadata features and semantic features, and then the user features and policy features can be used Correct the resource directory to obtain the target resource directory. In the above process, there is no need for technicians to perform tedious feature analysis in the data set. Instead, equipment is used to extract metadata features and semantic features, and then the resource directory is corrected based on user characteristics and policy features, which improves the efficiency of determining the resource directory.

The method shown in the present application is described below by means of specific embodiments. It should be noted that the following embodiments can exist independently or in combination with each other, and the same or similar contents will not be described repeatedly in different embodiments.

Figure 2 is a data processing method provided by an embodiment of the present application. See Figure 2, the method can include:

S201. Obtain the data set to be processed.

The execution subject of the embodiment of the present application may be a data processing device, or a data processing device arranged in the data processing device. The data processing device may be implemented by software, or by a combination of software and hardware. The data processing device may be the data processing device 102 in the embodiment of FIG. 1 .

The data set includes data obtained from multiple devices.

The multiple devices may be data processing devices located at different geographical locations. For example, the multiple data processing devices may be the data storage device 101 in the embodiment of FIG. 1 .

For example, assuming that the plurality of data processing devices include device A, device B, and device C, the data set includes data obtained from device A, data obtained from device B, and data obtained from device C.

For example, the data in the data set can include elderly care data, community data, etc.

S202. Perform feature extraction processing on the data set to obtain metadata features and semantic features of the data set.

A dataset can include both business metadata and technical metadata.

Technical metadata is used to describe the data structure, data source information, value range and other characteristics of the data set.

Business metadata is used to describe the business domain to which the data set belongs, the relationship between business topics, the rules of business topics, and other characteristics.

The data set can include multiple business subjects, and a multi-level tree structure can be used to divide multiple business entities to obtain business metadata.

Feature extraction can be performed on business metadata and technical metadata to obtain metadata features.

The data set includes structured data, which can be data with regularity in storage and arrangement. For example, structured data can include enterprise resource planning system (Enterprise Resource Planning, ERP) data, financial system data, approval system data, etc.

Semantic features can be features of structured data in a dataset.

Semantic features can be determined in the following ways: segmenting structured data to obtain multiple words; determining high-frequency words among multiple words; and determining semantic features based on high-frequency words.

Feature extraction processing of data sets can reduce the amount of data that needs to be processed, thereby improving data processing efficiency.

S203: Obtain user features and preset policy features corresponding to the data set.

Among them, user characteristics are the characteristics of users who use the data set.

User characteristics can include industry background, call frequency, usage preferences, correlation between user units, etc.

The strategic features can be data set features described by industry experts based on current events.

S204. Determine the target resource directory corresponding to the data set based on metadata features, semantic features, user features and policy features.

The target resource directory can be determined in the following way: determine the resource directory based on the application scenarios, metadata features, and semantic features of the resource directory, and then modify the resource directory based on user characteristics and policy characteristics to obtain the target resource directory corresponding to the data set.

The data processing method provided by the embodiment of this application first obtains the data sets of multiple devices to be processed, performs feature extraction processing on the data sets, and obtains the metadata features and semantic features of the data set; and then obtains the user features and semantic features corresponding to the data set. Preset policy features; finally, determine the target resource directory corresponding to the data set based on metadata features, semantic features, user features and policy features. In the above process, the initial feature model can be determined based on metadata features and semantic features, and then modified through user features and policy features to determine the target resource directory. Extracting metadata features and semantic features of the data set to construct a resource catalog, and then modifying the resource catalog through user features and policy features improves the efficiency of determining the target resource catalog.

Next, with reference to Figure 3, the data processing method shown in the embodiment of the present application will be described in further detail.

Figure 3 is a schematic flowchart of a data processing method provided by an embodiment of the present application. See Figure 3, the method can include:

S301. Obtain the data set to be processed.

For the execution process of S301, please refer to the execution process of S201, which will not be described again here.

S302: Determine business metadata and technical metadata in the data set.

A multi-level tree structure can be used to divide the business themes of the data set to determine the business metadata of the data set, where the business themes can include regional themes.

The multi-level tree structure is a data structure that can be used to represent the relationship between data.

Technical metadata can include data used in the central development and day-to-day management of the dataset.

S303. Determine metadata characteristics based on business metadata and technical metadata.

Metadata characteristics can be determined in the following ways: Extract data characteristics from business metadata to obtain business metadata characteristics, in which business metadata characteristics can include regional themes; Extract data characteristics from technical metadata to obtain technical metadata characteristics ; Metadata characteristics can include business metadata characteristics and technical metadata characteristics.

Metadata features have both technical and business characteristics.

S304. Determine structured data in the data set.

Structured data can be determined based on regular data in the data set.

S305. Perform word segmentation processing on the structured data to obtain multiple words.

The Chinese Lexical Analysis System (Institute of Computing Technology, Chinese Lexical Analysis System, ICTCLAS) can be used to segment structured data to obtain multiple words.

S306. Determine high-frequency words among multiple words.

High-frequency words can be determined from a plurality of words in the following manner: obtaining the word frequency of each word, and determining words whose word frequency is greater than a preset threshold as high-frequency words; or, sorting a plurality of words according to the word frequency, and determining the first N words from the sorted plurality of words as high-frequency words, where N is an integer greater than or equal to 1.

Word frequency can refer to the frequency of occurrence of words in the data set.

S307. Determine semantic features based on high-frequency words.

Obtain the TF-IDF value of the term frequency–inverse document frequency index (TF-IDF) of the high-frequency vocabulary.

Semantic features are determined based on the TF-IDF values of high-frequency words.

S308: Determine the application scenario.

Application scenarios can refer to scenarios using resource directories. For example, application scenarios may include scenarios for building recommendation systems, scenarios for knowledge push, scenarios for using indicator predictions, etc.

S309. Determine the initial feature model based on the application scenario, metadata features and semantic features.

The initial feature model refers to a set of features that conforms to the application scenario. The initial feature model can include a feature library and a label set.

Metadata features and semantic features can be normalized to determine the feature library.

Dictionary matching can be performed on high-frequency words in semantic analysis to determine the tag set. The dictionary may be a dictionary library preset by the application scenario.

The initial feature model can be determined based on the feature library and label library.

S310. Determine user characteristics and policy characteristics.

Industry experts can analyze the user's industry background, call frequency, usage preferences, etc. to determine user characteristics. Among them, the analysis methods may include clustering methods, classification methods, etc.

Industry experts can determine the characteristics of the strategy based on current events.

S311. Modify the initial feature model according to user features and policy features to obtain a target resource directory.

The corresponding features in the initial feature model can be modified according to user features and policy features. A weighted approach can be used to strengthen the user features and policy features in the initial feature model to obtain a revised target resource directory.

The embodiments of this application provide another data processing method, which first obtains the data sets of multiple devices to be processed; and then performs feature extraction processing based on the technical metadata and business metadata in the data set to obtain the technical metadata of the data set. Data characteristics and business metadata characteristics; determine metadata characteristics based on technical metadata characteristics and business metadata characteristics; determine structured data in the data set; perform word segmentation processing on structured data to obtain multiple words; among multiple words Determine high-frequency words and determine semantic features based on high-frequency words; after determining the application scenario, determine the initial feature model based on the application scenario, metadata features, and semantic features. After determining the user characteristics and policy characteristics, the initial feature model is modified according to the user characteristics and policy characteristics to obtain the target resource directory. In the above process, the technical metadata and business metadata of the data set are first extracted to obtain the metadata features, and then the semantic features are obtained based on the high-frequency vocabulary of the data set. Finally, the target resource directory is obtained through correction of user features and policy features, which improves Determine the efficiency of the target resource directory.

Below, with reference to Figure 4, the data processing method shown in the embodiment of the present application will be described in detail through specific examples.

Fig. 4 is a schematic diagram of the data processing process provided by the embodiment of the present application. Referring to Fig. 4, in the actual application process, after obtaining the data set to be processed, business metadata, technical metadata and structured data can be determined in the data set to be processed.

By performing feature extraction processing on business metadata and technical metadata, metadata features can be obtained. By processing structured data, semantic features can be obtained. And the initial feature model is determined based on metadata features and semantic features.

User characteristics (for example, including user refinement and user industry background, etc.) and policy characteristics (for example, including regional characteristics and industry characteristics, etc.) can also be obtained to update the initial feature model to obtain the target resource directory.

FIG. 5 is a schematic structural diagram of a data processing device provided by an embodiment of the present disclosure. Referring to Figure 5, the data processing device may include a first acquisition module 11, a processing module 12, a second acquisition module 13 and a determination module 14, where,

The first acquisition module 11 is used to acquire a data set to be processed, where the data set includes data acquired from multiple devices;

The processing module 12 is used to perform feature extraction processing on the data set to obtain metadata features and semantic features of the data set;

The second acquisition module 13 is used to acquire user characteristics and preset policy characteristics corresponding to the data set, where the user characteristics are characteristics of the user who uses the data set;

The determination module 14 is used to determine the target resource directory corresponding to the data set based on metadata features, semantic features, user features and policy features.

The data processing device provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment, and its implementation principle and beneficial effects are similar, which will not be repeated here.

In a possible implementation, the processing module 12 is specifically used to:

Determine business metadata and technical metadata in said data set;

Determine the metadata characteristics according to the business metadata and the technical metadata;

determining structured data in the dataset;

The semantic features are determined based on the structured data.

In a possible implementation manner, the processing module 12 is specifically configured to:

Extract data features from the business metadata to obtain business metadata features, where the business metadata features include regional themes;

Extract data features from the technical metadata to obtain technical metadata features;

The metadata features include the business metadata features and the technical metadata features.

In a possible implementation, the processing module 12 is specifically used to:

Perform word segmentation processing on the structured data to obtain multiple words;

Determine high-frequency words among the plurality of words;

The semantic feature is determined according to the high-frequency words.

In a possible implementation, the processing module 12 is specifically used to:

Get the word frequency of each vocabulary word;

Determine words with a word frequency greater than a preset threshold as the high-frequency words; or, sort the multiple words according to the word frequency, and determine the top N words among the sorted words as the high-frequency words. , the N is an integer greater than or equal to 1.

In a possible implementation, the processing module 12 is specifically used to:

Obtain the word frequency inverse text frequency index TF-IDF value of the high-frequency vocabulary;

The semantic features are determined according to the TF-IDF values of the high-frequency words.

In a possible implementation, the determining module 14 is specifically used to:

Determine application scenarios;

Determine an initial feature model according to the application scenario, the metadata features and the semantic features;

The initial feature model is modified according to the user features and the policy features to obtain the target resource directory.

The data processing device provided by the embodiments of the present application can execute the technical solutions shown in the above method embodiments. The implementation principles and beneficial effects are similar and will not be described again here.

This embodiment of the present application provides a schematic structural diagram of an electronic device. See FIG. 6 . The electronic device 20 may include a processor 21 and a memory 22 . For example, the processor 21 and the memory 22 are connected to each other through a bus 23 .

The memory 22 stores computer-executable instructions;

The processor 21 executes the computer execution instructions stored in the memory 22, so that the processor 21 executes the data processing method shown in the above method embodiment.

Correspondingly, embodiments of the present application provide a computer-readable storage medium in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the above method embodiments. data processing methods.

Accordingly, an embodiment of the present application may also provide a computer program product, including a computer program, which, when executed by a processor, can implement the data processing method shown in the above method embodiment.

Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

The invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a use A device for implementing the functions specified in one process or processes of the flowchart and/or one block or blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory that causes a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction means, the instructions The device implements the functions specified in a process or processes of the flowchart and/or a block or blocks of the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device. Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

Memory may include non-permanent storage in computer-readable media, random access memory (RAM), and/or non-volatile memory in the form of read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer-readable media includes both persistent and non-volatile, removable and non-removable media that can be implemented by any method or technology for storage of information. Information may be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), and read-only memory. (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, Magnetic tape cassettes, tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium can be used to store information that can be accessed by a computing device. As defined in this article, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

The above descriptions are only examples of the present application and are not intended to limit the present application. To those skilled in the art, various modifications and variations may be made to this application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application shall be included in the scope of the claims of this application.

Claims (10)

1. A method of data processing, comprising:

acquiring a data set to be processed, wherein the data set comprises data acquired from a plurality of devices;

performing feature extraction processing on the data set to obtain metadata features and semantic features of the data set;

acquiring user characteristics and preset strategy characteristics corresponding to the data set, wherein the user characteristics are characteristics of a user using the data set;

and determining a target resource catalog corresponding to the data set according to the metadata features, the semantic features, the user features and the strategy features.

2. The method of claim 1, wherein performing feature extraction processing on the dataset to obtain metadata features and semantic features of the dataset comprises:

determining business metadata and technology metadata in the dataset;

determining the metadata characteristics according to the business metadata and the technical metadata;

determining structured data in the dataset;

and determining the semantic features according to the structured data.

3. The method of claim 2, wherein determining the metadata characteristics from the business metadata and the technology metadata comprises:

extracting data characteristics of the service metadata to obtain service metadata characteristics, wherein the service metadata characteristics comprise regional subjects;

extracting data characteristics of the technical metadata to obtain technical metadata characteristics;

wherein the metadata features include the business metadata features and the technology metadata features.

4. The method of claim 2, wherein determining the semantic features from the structured data comprises:

word segmentation processing is carried out on the structured data to obtain a plurality of words;

determining a high frequency vocabulary among the plurality of vocabularies;

and determining the semantic features according to the high-frequency vocabulary.

5. The method of claim 4, wherein determining a high frequency vocabulary among the plurality of vocabularies comprises:

acquiring word frequency of each word;

determining the vocabulary with the word frequency larger than a preset threshold value as the high-frequency vocabulary; or sorting the plurality of words according to the word frequency, and determining the first N words in the sorted plurality of words as the high-frequency words, wherein N is an integer greater than or equal to 1.

6. The method of claim 4, wherein determining the semantic features from the high frequency vocabulary comprises:

acquiring word frequency inverse text frequency index TF-IDF values of the high-frequency words;

and determining the semantic features according to the TF-IDF values of the high-frequency vocabulary.

7. The method of any of claims 1-6, wherein determining a target resource catalog corresponding to the dataset based on the metadata features, the semantic features, the user features, and the policy features comprises:

determining an application scene;

determining an initial feature model according to the application scene, the metadata features and the semantic features;

and carrying out correction processing on the initial feature model according to the user features and the strategy features to obtain the target resource catalog.

8. A data processing apparatus, comprising: the device comprises a first acquisition module, a processing module, a second acquisition module and a determination module, wherein,

the first acquisition module is used for acquiring a data set to be processed, wherein the data set comprises data acquired from a plurality of devices;

the processing module is used for carrying out feature extraction processing on the data set to obtain metadata features and semantic features of the data set;

the second acquisition module is used for acquiring user characteristics corresponding to the data set and preset strategy characteristics, wherein the user characteristics are characteristics of a user using the data set;

the determining module is used for determining a target resource catalog corresponding to the data set according to the metadata features, the semantic features, the user features and the policy features.

9. An electronic device, comprising: a memory and a processor;

the memory stores computer-executable instructions;

the processor executing computer-executable instructions stored in the memory, causing the processor to perform the data processing method of any one of claims 1 to 7.

10. A computer-readable storage medium, in which computer-executable instructions are stored, which when executed by a processor are adapted to carry out the data processing method of any one of claims 1 to 7.