CN112506483A - Data augmentation method, data augmentation device, electronic device, and storage medium - Google Patents
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Abstract
The invention discloses a data augmentation method, a data augmentation device, electronic equipment and a storage medium, which can design respective corresponding general augmentation methods for each product requirement of service data, and then when the service data with more complex data types is processed, the method can be executed by not only executing the target augmentation method corresponding to the data types of the service data, but also acquiring the general augmentation method corresponding to the product requirements, so that the data augmentation logic of the whole is not required to be designed for the combination type of the data types and the product requirements. When a plurality of service data have the same product requirements, a universal augmentation method is adopted for processing the data, so that writing of a plurality of repeated logic codes can be reduced, the universal augmentation method can be suitable for processing service data of different data types, and the universality is strong.
Description
Technical Field
The present invention relates to the field of deep learning technologies, and in particular, to a data augmentation method and apparatus, an electronic device, and a storage medium.
Background
Data augmentation is one of the common skills in deep learning, and is mainly used for increasing a training data set, so that the data set is diversified as much as possible, and a trained model has stronger generalization capability. In the current data augmentation method, different data types have different type IDs (Identity documents) or type names, and each type ID or type name is respectively bound to its corresponding data augmentation logic, so that when data is obtained, correct data augmentation logic can be obtained according to the type ID of the data or the type name to which the data belongs, and then the data is processed in the next step according to the data augmentation logic.
The data augmentation method may be suitable for simple-type data, however, in the current deep learning, the type of one data is usually complicated, and in the case of complicated type, the data augmentation method may cause a problem of logic code redundancy. For example, if the data has data types but the data types also have product requirements to which the data belongs, the data can be regarded as complex hybrid data formed by combining the data types and the requirements, and if the data is too many, the combination modes of the data types and the requirements are too many, technicians need to design data augmentation logics corresponding to the data augmentation logics in different combination modes, so that the data augmentation logics obviously have lots of redundant data augmentation logic codes, and each data augmentation logic can only be realized by aiming at one type or one combination mode, and the universality is poor.
Disclosure of Invention
The invention provides a data augmentation method, a data augmentation device, electronic equipment and a storage medium, and aims to solve the problems that code redundancy and augmentation logic are not universal easily caused by the existing data augmentation mode under the condition of relatively complex data types.
In a first aspect, the present invention provides a data augmentation method, including:
acquiring the data type and product requirements of service data; the product requirement is used for expressing the functions or functions which can be realized by the service data;
respectively determining a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product demand by using the data type and the product demand; the target augmentation method is used for representing specific data augmentation logic designed for the data category; the general augmentation method is used for expressing data augmentation logic which is designed aiming at the product requirements and can be universally used for various data types;
after the target augmentation method is executed, the general augmentation method is executed.
With reference to the first aspect, in an implementation manner of the first aspect, the step of obtaining the data type and the product requirement of the service data includes:
acquiring a service name and a service scene to which service data belongs;
and determining the product requirements corresponding to the service data by using the service name and the service scene.
With reference to the first aspect, in an implementation manner of the first aspect, before the obtaining the data type and the product requirement of the business data, the method further includes:
acquiring all product requirements;
generating respective corresponding general augmentation methods for different product requirements;
and storing the general augmentation methods corresponding to different product requirements in a configuration file.
With reference to the first aspect, in one implementation manner of the first aspect, the step of executing the general augmentation method after executing the target augmentation method includes:
executing the target augmentation method;
and under the condition that the general augmentation method corresponding to the product requirement exists in the configuration file, continuously executing the general augmentation method.
With reference to the first aspect, in an implementation manner of the first aspect, before the obtaining the data type and the product requirement of the business data, the method further includes:
acquiring all data types in all service data;
specific data augmentation logic is generated for each data category.
In a second aspect, the present invention provides a data augmentation apparatus, comprising:
the information acquisition module is used for acquiring the data type and the product requirement of the service data; the product requirement is used for expressing the functions or functions which can be realized by the service data;
the method determination module is used for respectively determining a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product demand by using the data type and the product demand; the target augmentation method is used for representing specific data augmentation logic designed for the data category; the general augmentation method is used for expressing data augmentation logic which is designed aiming at the product requirements and can be universally used for various data types;
a data augmentation module to execute the generic augmentation method after executing the target augmentation method.
With reference to the second aspect, in an implementable manner of the second aspect, the information acquisition module includes:
the information acquisition unit is used for acquiring the service name and the service scene to which the service data belongs;
and the requirement determining unit is used for determining the product requirement corresponding to the service data by using the service name and the service scene.
With reference to the second aspect, in an implementation manner of the second aspect, the apparatus further includes:
the demand type acquisition module is used for acquiring all product demands;
the first generation module is used for generating corresponding general augmentation methods for different product requirements;
and the method configuration module is used for storing the general augmentation methods corresponding to different product requirements in a configuration file.
With reference to the second aspect, in one implementation manner of the second aspect, the data amplification module includes:
a first amplification unit configured to execute the target amplification method;
and the second augmentation unit is used for continuously executing the general augmentation method under the condition that the general augmentation method corresponding to the product requirement exists in the configuration file.
With reference to the second aspect, in an implementation manner of the second aspect, the apparatus further includes:
the data type acquisition module is used for acquiring all data types in all service data;
and the second generation module is used for generating specific data augmentation logic corresponding to each data type.
In a third aspect, the present invention provides an electronic device, comprising:
a memory for storing program instructions;
a processor for calling and executing the program instructions in the memory to implement the data augmentation method of the first aspect.
In a fourth aspect, the present invention further provides a storage medium, in which a computer program is stored, and when at least one processor of the data amplification apparatus executes the computer program, the data amplification apparatus executes the data amplification method according to the first aspect.
According to the technical scheme, the data augmentation method, the data augmentation device, the electronic equipment and the storage medium provided by the invention can design the corresponding general augmentation method for each product requirement of the service data, and then when the service data with more complex data types is processed, the target augmentation method corresponding to the data types of the service data is executed, and the general augmentation method corresponding to the product requirements is acquired for execution, so that the data augmentation logic of the whole is not required to be designed for the combination type of the data types and the product requirements. When a plurality of service data have the same product requirements, a universal augmentation method is adopted for processing the data, so that writing of a plurality of repeated logic codes can be reduced, the universal augmentation method can be suitable for processing service data of different data types, and the universality is strong.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any inventive exercise.
Fig. 1 is a flowchart illustrating a data augmentation method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a data augmentation process according to an embodiment of the present invention;
FIG. 3 is a block diagram of a data augmentation logic according to an embodiment of the present invention;
FIG. 4 is a block diagram of a second data augmentation logic according to an embodiment of the present invention;
FIG. 5 is a flow chart illustrating a method for determining product requirements in accordance with an embodiment of the present invention;
FIG. 6 is a flow chart illustrating a method for generating a generic augmentation system according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating a second data augmentation process according to an embodiment of the present invention;
fig. 8 is a block diagram illustrating a data expansion apparatus according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects and embodiments of the present invention clearer, the following description of exemplary embodiments of the present invention will clearly and completely describe the exemplary embodiments of the present invention with reference to the accompanying drawings in the exemplary embodiments of the present invention, and it is obvious that the described exemplary embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
It should be noted that the brief descriptions of the terms in the present invention are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of the present invention. These terms should be understood in their ordinary and customary meaning unless otherwise indicated.
Data augmentation is one of the common skills in deep learning, and is mainly used for increasing a training data set, so that the data set is diversified as much as possible, and a trained model has stronger generalization capability. In the current data augmentation method, different data types have different type IDs (Identity documents) or type names, and each type ID or type name is respectively bound to its corresponding data augmentation logic, so that when data is obtained, correct data augmentation logic can be obtained according to the type ID of the data or the type name to which the data belongs, and then the data is processed in the next step according to the data augmentation logic.
The data augmentation method may be suitable for simple-type data, however, in the current deep learning, the type of one data is usually complicated, and in the case of complicated type, the data augmentation method may cause a problem of logic code redundancy. For example, if the data has data types but the data types also have product requirements to which the data belongs, the data can be regarded as complex hybrid data formed by combining the data types and the requirements, and if the data is too many, the combination modes of the data types and the requirements are too many, technicians need to design data augmentation logics corresponding to the data augmentation logics in different combination modes, so that the data augmentation logics obviously have lots of redundant data augmentation logic codes, and each data augmentation logic can only be realized by aiming at one type or one combination mode, and the universality is poor.
Based on the above, embodiments of the present invention provide a data augmentation method, an apparatus, an electronic device, and a storage medium, which can design respective corresponding general augmentation methods for each product requirement of service data, so that writing of many repeated logic codes can be reduced, and the designed general augmentation methods can be applied to processing of service data of different data types, and have relatively strong universality.
Fig. 1 is a flowchart illustrating a data augmentation method according to an embodiment of the present invention. As shown in fig. 1, the data augmentation method provided by the embodiment of the present invention includes:
and step S101, acquiring the data type and product requirement of the service data.
Generally, service data is data transmitted from a service upstream, the service itself is determined, and a scene to which the service belongs is also determined, so that the data itself has a well-determined service name, a service scene to which the service belongs, and the like, and the service data also has data types, such as image data, character data, and the like.
And the product requirements corresponding to the service data can be specifically analyzed through the service name and the service scene. In the embodiment of the invention, the product requirements represent the functions or functions and the like which can be realized by the service data. Only when the product requirement is determined, specific operation can be performed on the service data according to the product requirement, so that the corresponding function or function can be realized.
And S102, respectively determining a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product requirement by using the data type and the product requirement.
In the embodiment of the present invention, the target augmentation method is used to represent specific data augmentation logics designed for data types, that is, each data type has its own specific data augmentation logic, for example, image data has a specific set of data augmentation logic a, text data has a specific set of data augmentation logic B, and the like. The general augmentation method is used for expressing data augmentation logics which are designed aiming at product requirements and can be generally used for various data types, namely, one product requirement corresponds to one general augmentation logic, but various service data can correspond to one product requirement, for example, the data types between image data and character data are different, but the image data and the character data belong to the same product requirement, and then the same general augmentation method, namely the data augmentation logic C, can be used for the image data and the character data.
Step S103, after executing the target augmentation method, executes the general augmentation method.
In the embodiment of the present invention, if the service data has a complex category structure, that is, the data category also has specific product requirements, etc., then, usually, the target augmentation method corresponding to the data category needs to be executed first, and then, on the basis, the general augmentation method corresponding to the product requirements needs to be executed. Still taking the content of the above embodiment as an example, when the service data is image data and text data and both belong to the same product requirement, the data amplification logic a and the data amplification logic C may be executed first for processing the image data, and the data amplification logic B and the data amplification logic C may be executed first for processing the text data.
Fig. 2 is a schematic diagram illustrating a data augmentation process according to an embodiment of the present invention.
The content of the above embodiment of the present invention can be described in a more specific manner, and as shown in fig. 2, the data augmentation method in the embodiment of the present invention may first obtain the service data from the upstream of the service, then obtain the data type and the product requirement of the service data, then respectively obtain the target augmentation method corresponding to the data type and obtain the general augmentation method corresponding to the product requirement, and first execute the target augmentation method and then execute the general augmentation method.
Fig. 3 is a schematic diagram illustrating a data augmentation logic according to an embodiment of the present invention.
As shown in fig. 3, the service data 1 is image data and has a product requirement c, and the service data 2 is text data and has a product requirement c, then in the current data augmentation method, a specific data augmentation logic E needs to be written for the service data 1 of the combination of image + requirement c, and a specific data augmentation logic F needs to be written for the service data 2 of the combination of text + requirement c. If the service data 3 is image data and has a product requirement d, and the service data 4 is text data and has a product requirement d, then in the current data augmentation method, a specific data augmentation logic G needs to be written for the service data 3 of the combination of image + requirement d, and a specific data augmentation logic H needs to be written for the service data 4 of the combination of text + requirement d. If any combination is a new combination form and does not appear before, new data augmentation logic needs to be written for each new type of combined service data when data augmentation is performed, and the new type of combination needs to be written once every time the new type of combination appears. In an actual application scenario, the scheme of the embodiment of the present invention may relate to a large amount of service data, and as the types of the service data are more, the new types of combinations are more, and then the new data augmentation logic written for the service data is more and more, obviously, some unnecessary code redundancy situations may occur, and one data augmentation logic is not suitable for multiple types of combinations, and the universality is also poor.
Fig. 4 is a schematic diagram illustrating a second data augmentation logic according to an embodiment of the present invention.
As shown in fig. 4, by using the data augmentation method in the embodiment of the present invention, a data augmentation logic a may be compiled for the service data whose type is image, a data augmentation logic B may be compiled for the service data whose type is text, and a corresponding general augmentation method may be designed for each product requirement of the service data, such as compiling a data augmentation logic C for product requirement C, and compiling a data augmentation logic D for product requirement D. When processing the service data, the data type and the product requirement are divided, so that when the service data have the same product requirement, a general data augmentation can be adopted, as shown in fig. 4, the service data 1 is divided into image data and a product requirement c, the service data 2 is divided into text data and a product requirement c, the service data 3 is divided into image data and a product requirement d, and the service data 4 is divided into text data and a product requirement d. Then, for both the service data 1 and the service data 2, the data augmentation logic C corresponding to the product requirement C may be used, and for both the service data 3 and the service data 4, the data augmentation logic D corresponding to the product requirement D may be used. Therefore, even if the types of the combination are increased, the data types and the product requirements only need to write corresponding data augmentation logic in advance, and when the data types and the product requirements appear again, new data augmentation logic does not need to be written, and the writing can be directly called. Therefore, the embodiment of the invention does not need to design specific data augmentation logic aiming at each data type and combination of requirements, so that writing of a plurality of repeated logic codes can be reduced, the designed general augmentation method can be suitable for processing service data of different data types, and the universality is strong.
FIG. 5 is a flow chart illustrating a method for determining product requirements, according to an embodiment of the present invention.
As described in the foregoing embodiments, the product requirement corresponding to the service data can be specifically analyzed through the service name and the service scenario. Furthermore, in some embodiments, as shown in fig. 5, the method in the embodiments of the present invention may further include the following steps:
step S201, acquiring a service name and a service scene to which service data belongs; step S202, determining the product requirement corresponding to the service data by using the service name and the service scene.
Fig. 6 is a flowchart illustrating a method for generating a general augmentation according to an embodiment of the present invention.
In order to quickly perform data augmentation on service data, a general augmentation method is usually required to be designed in advance, and when the service data needs to be processed, the general augmentation method stored in advance can be directly acquired only according to product requirements. Furthermore, in some embodiments, as shown in fig. 6, the method in the embodiments of the present invention may further include the following steps:
step S301, acquiring all product requirements on each service line; step S302, generating corresponding general augmentation methods for different product requirements; or step S302 may also write their respective corresponding general augmentation logics for different product requirements; step S303, storing each general augmentation method in a configuration file so as to be called when processing the service data.
In addition, in the configuration file, different identifiers can be allocated to different product requirements, and each identifier is respectively associated with a universal augmentation method corresponding to each product requirement, so that when the service data is processed, the product requirements of the service data are determined, and the corresponding universal augmentation method can be obtained from the configuration file.
Fig. 7 is a diagram illustrating a second data augmentation process according to an embodiment of the present invention.
In practical situations, not all the service data need to be subjected to the universal augmentation processing, that is, the service data belonging to some product requirements may not be subjected to the universal augmentation processing. As described in the foregoing embodiment, the configuration file has a general augmentation method corresponding to all product requirements on a service line, where the service line refers to all services related in an actual application scenario of the embodiment of the present invention, and when the product requirements of service data do not belong to the services applied in the embodiment of the present invention, the general augmentation method corresponding to the product requirements of the service data may not be performed again, and the configuration file may not have the general augmentation method corresponding to the product requirements.
Furthermore, in some embodiments, as shown in fig. 7, after the product requirement of the service data is obtained, it may also be detected whether a general augmentation method corresponding to the product requirement exists in the configuration file, and if the general augmentation method does not exist, it is indicated that the general augmentation method does not need to be executed, and only a specific data augmentation method corresponding to the data type of the service data, that is, the target augmentation method in the embodiment of the present invention, is executed. When the configuration file has the general augmentation method corresponding to the product requirement, which indicates that the product requirement belongs to the service related in the embodiment of the present invention, the general augmentation method corresponding to the product requirement needs to be continuously executed after the target augmentation method is executed.
In the solution of the embodiment of the present invention, corresponding specific data augmentation logic may also be pre-designed for different data types, for example, the image data and the text data described in the foregoing embodiment may be pre-designed with corresponding data augmentation logic a and data augmentation logic B, respectively. In some embodiments, all data types of all service data that can be referred to in the embodiments of the present invention need to be obtained as much as possible, and then a specific data augmentation logic corresponding to each data type is generated for each data type, so that when the data augmentation method in the embodiments of the present invention is subsequently implemented, a target augmentation method corresponding to the data type can be directly and quickly obtained.
As can be seen from the above, the embodiments of the present invention provide a data augmentation method, which can design respective corresponding general augmentation methods for each product requirement of service data, and then, when processing service data with a relatively complex data type, except executing a target augmentation method corresponding to the data type of the service data itself, obtain a general augmentation method corresponding to the product requirement to execute, so that it is not necessary to design an overall data augmentation logic for a combination type of the data type and the product requirement. When a plurality of service data have the same product requirements, a general augmentation method is adopted when the data are processed. Therefore, the data augmentation method in the embodiment of the invention can reduce writing of many repeated logic codes related to the product requirement aspect. And the general augmentation method corresponding to the product requirement can be suitable for processing the service data of different data types as long as the service data has the corresponding product requirement, so that the data augmentation method in the embodiment of the invention has stronger universality.
In addition, the data augmentation method in the embodiment of the invention reduces the writing time of technical personnel for writing logic codes while reducing the writing of a plurality of repeated logic codes, improves the code development efficiency of the technical personnel in the aspect of deep learning, and can also reduce the investment of excessive development cost.
Fig. 8 is a block diagram of a data expansion apparatus according to an embodiment of the present invention. As shown in fig. 8, the apparatus may include:
an information obtaining module 801, configured to obtain data types and product requirements of service data; the product requirement is used for expressing the functions or functions which can be realized by the service data; a method determining module 802, configured to determine, by using the data type and the product requirement, a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product requirement, respectively; the target augmentation method is used for representing specific data augmentation logic designed for the data category; the general augmentation method is used for expressing data augmentation logic which is designed aiming at the product requirements and can be universally used for various data types; a data augmentation module 803 for executing the generic augmentation method after executing the target augmentation method.
In some embodiments, the information acquisition module 801 comprises: the information acquisition unit is used for acquiring the service name and the service scene to which the service data belongs; and the requirement determining unit is used for determining the product requirement corresponding to the service data by using the service name and the service scene.
In some embodiments, the apparatus further comprises: the demand type acquisition module is used for acquiring all product demands; the first generation module is used for generating corresponding general augmentation methods for different product requirements; and the method configuration module is used for storing the general augmentation methods corresponding to different product requirements in a configuration file.
In some embodiments, the data augmentation module 803 comprises: a first amplification unit configured to execute the target amplification method; and the second augmentation unit is used for continuously executing the general augmentation method under the condition that the general augmentation method corresponding to the product requirement exists in the configuration file.
In some embodiments, the apparatus further comprises: the data type acquisition module is used for acquiring all data types in all service data; and the second generation module is used for generating specific data augmentation logic corresponding to each data type.
Fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention. As shown in fig. 9, the present invention also provides an electronic device, including: a memory 901 for storing program instructions; the processor 902 is used for calling and executing the program instructions in the memory to implement the data augmentation method described in the above embodiments. Reference may be made in particular to the description in relation to the preceding embodiments.
In an embodiment of the present invention, the processor 902 and the memory 901 may be connected by a bus or other means. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application specific integrated circuit, or one or more integrated circuits configured to implement embodiments of the present invention. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, a hard disk, or a solid state disk.
An embodiment of the present invention further provides a storage medium, where a computer program is stored in the storage medium, and when at least one processor of the data amplification apparatus executes the computer program, the data amplification apparatus executes the data amplification method described in the foregoing embodiment.
The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The same and similar parts in the various embodiments in this specification may be referred to each other. In particular, for the embodiments of the service construction apparatus and the service loading apparatus, since they are substantially similar to the embodiments of the method, the description is simple, and the relevant points can be referred to the description in the embodiments of the method.
The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention.
Claims (12)
1. A method of data augmentation, comprising:
acquiring the data type and product requirements of service data; the product requirement is used for expressing the functions or functions which can be realized by the service data;
respectively determining a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product demand by using the data type and the product demand; the target augmentation method is used for representing specific data augmentation logic designed for the data category; the general augmentation method is used for expressing data augmentation logic which is designed aiming at the product requirements and can be universally used for various data types;
after the target augmentation method is executed, the general augmentation method is executed.
2. The method of claim 1, wherein the step of obtaining the data type and product requirement of the service data comprises:
acquiring a service name and a service scene to which service data belongs;
and determining the product requirements corresponding to the service data by using the service name and the service scene.
3. The method of claim 1, wherein before the obtaining the data category and the product requirement of the service data, further comprising:
acquiring all product requirements;
generating respective corresponding general augmentation methods for different product requirements;
and storing the general augmentation methods corresponding to different product requirements in a configuration file.
4. The method of claim 3, wherein the step of performing the generic augmentation method after performing the target augmentation method comprises:
executing the target augmentation method;
and under the condition that the general augmentation method corresponding to the product requirement exists in the configuration file, continuously executing the general augmentation method.
5. The method of claim 1, wherein before the obtaining the data category and the product requirement of the service data, further comprising:
acquiring all data types in all service data;
specific data augmentation logic is generated for each data category.
6. A data augmentation apparatus, comprising:
the information acquisition module is used for acquiring the data type and the product requirement of the service data; the product requirement is used for expressing the functions or functions which can be realized by the service data;
the method determination module is used for respectively determining a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product demand by using the data type and the product demand; the target augmentation method is used for representing specific data augmentation logic designed for the data category; the general augmentation method is used for expressing data augmentation logic which is designed aiming at the product requirements and can be universally used for various data types;
a data augmentation module to execute the generic augmentation method after executing the target augmentation method.
7. The apparatus of claim 6, wherein the information obtaining module comprises:
the information acquisition unit is used for acquiring the service name and the service scene to which the service data belongs;
and the requirement determining unit is used for determining the product requirement corresponding to the service data by using the service name and the service scene.
8. The apparatus of claim 6, further comprising:
the demand type acquisition module is used for acquiring all product demands;
the first generation module is used for generating corresponding general augmentation methods for different product requirements;
and the method configuration module is used for storing the general augmentation methods corresponding to different product requirements in a configuration file.
9. The apparatus of claim 8, wherein the data augmentation module comprises:
a first amplification unit configured to execute the target amplification method;
and the second augmentation unit is used for continuously executing the general augmentation method under the condition that the general augmentation method corresponding to the product requirement exists in the configuration file.
10. The apparatus of claim 6, further comprising:
the data type acquisition module is used for acquiring all data types in all service data;
and the second generation module is used for generating specific data augmentation logic corresponding to each data type.
11. An electronic device, comprising:
a memory for storing program instructions;
a processor for calling and executing program instructions in the memory to implement the data augmentation method of any one of claims 1-5.
12. A storage medium having a computer program stored therein, wherein when the computer program is executed by at least one processor of a data augmentation apparatus, the data augmentation apparatus performs the data augmentation method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202011414781.0A CN112506483B (en) | 2020-12-04 | 2020-12-04 | Data augmentation method, device, electronic equipment and storage medium |
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