CN118332407B - Method and system for automatically carrying out data identification, classification and classification - Google Patents
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Abstract
The embodiment of the invention discloses a method and a system for automatically carrying out data identification, classification and classification, wherein the method for automatically carrying out data identification, classification and classification comprises the steps of responding to an input request of a data source, inputting the data source; the method comprises the steps of preprocessing a data source to determine data resources, identifying the data resources based on an identification model to obtain identification data, classifying the identification data based on a classification model to obtain classification data, classifying the classification data based on a classification model to obtain classification data, establishing a label for each classification data to obtain label data, and managing a catalog of the label data. The automatic data identification, classification and classification method solves the problem that automatic identification, classification and classification of data cannot be accurately performed in the prior art.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method, a system, electronic equipment and a storage medium for automatically carrying out data identification, classification and classification.
Background
Data is one of important production materials in social activities, and comprehensive mastering of organization data assets becomes a primary task, wherein development of data identification, data classification and data grading are basic preconditions for comprehensive mastering of organization data assets.
However, the method of manually combing the asset management ledger is low in efficiency and difficult to deal with personnel post adjustment and change, the method of interfacing the information system is rough in asset acquisition and easy to cause incomplete data resources or complex data use process, and the method of manually combing the asset management ledger is combined with the method of interfacing the information system, so that the effort and cost for data identification, classification and classification are extremely easy to be excessive.
The identification of trade secret data is more required to integrate industry characteristics, such as comprehensive consideration of multi-industry attribute data, such as technical patents, development results, product design, business plans, sales strategies, market research reports, customer lists, supply chain information, and the like.
The business secret data shows more complex data attributes than personal information, which always causes the inefficiency of data identification classification, and can cause the management of data assets of organizations to become air pavilions, and further can cause the lack of the situation of the data assets of organizations as a whole, and even the value-keeping and value-increasing of the data assets are in trouble.
Therefore, a method capable of accurately performing automatic data identification, classification and classification is needed.
Disclosure of Invention
The embodiment of the invention aims to provide a method, a system, electronic equipment and a storage medium for automatically carrying out data identification, classification and classification, which are used for solving the problem that the automatic identification, classification and classification of data cannot be accurately carried out in the prior art.
In order to achieve the above object, an embodiment of the present invention provides a method for automatically classifying and classifying data, where the method specifically includes:
Inputting a data source in response to an input request of the data source;
Preprocessing the data source to determine a data resource;
identifying the data resource based on an identification model to obtain identification data;
Classifying the identification data based on a classification model to obtain classification data;
classifying the classified data based on a classification model to obtain classified data;
Establishing a label for each hierarchical data to obtain label data;
and performing catalog management on the tag data.
Based on the technical scheme, the invention can also be improved as follows:
Further, the preprocessing of the data source to determine data resources includes;
Extracting one or more data sources from the real-time service buried point data, the offline imported data and the batch converged data;
Converting voice data into text data, and converting video data into image data;
one or more data sources of the original file and the index value are stored.
Further, the recognition model is a deep learning model integrated into a convolutional neural network and a cyclic neural network;
extracting integral features and local features of the data resources through the identification model;
identifying the data resources based on automatic data identification rules, automatic data identification policies, and samples;
the automatic data recognition rules comprise keyword recognition, regular expressions, digital fingerprint technology and natural language processing;
the automatic data identification strategy comprises accurate data identification, fingerprint document identification and vector machine classification identification;
The sample includes structured data, unstructured data, and microfeature data.
Further, the classifying the identification data based on the classification model to obtain classification data includes:
dividing the identification data into static data and dynamic data based on data attributes;
Injecting the static data into the tree structure area to judge constraint conditions, when the constraint conditions are not met, discarding the static data, and when the constraint conditions are met, flowing into a grading flow;
and injecting the dynamic data into the graph structure area to judge the attribute conditions, when the attribute conditions are not met, discarding the dynamic data, and when the attribute conditions are met, flowing into the grading flow.
Further, the classifying the classified data based on the classification model to obtain classified data includes:
Judging whether the classified data meets the strategy judgment conditions, when the classified data does not meet the strategy judgment conditions, discarding, and when the classified data meets the strategy judgment conditions, executing one or more classification strategies to complete deviation analysis, wherein the classification strategies comprise a comparison method, a label matching method and an calculation method.
Further, the classifying data is classified based on the classification model to obtain classification data, and the method further includes:
K is calculated by equation (1):
Where i is the fractional element duty ratio, j is the fractional score, and K is the data level.
Further, the step of creating a tag for each hierarchical data to obtain tag data includes:
extracting data attributes of the data resources through a classification model and the data grading model;
Carrying out data tag prediction on the data attributes, wherein the data tags comprise common tags, attribute tags and business secret tags;
and establishing a label for each hierarchical data at least through a common label and an attribute label, and combining a service scene selection mark field or a mapping table to obtain label data.
A system for automating data identification classification, comprising:
the data source acquisition module is used for responding to the input request of the data source and inputting the data source;
the preprocessing module is used for preprocessing the data source to determine the data resource;
the data identification module is used for identifying the data resources based on the identification model to obtain identification data;
The data classification module is used for classifying the identification data based on a classification model to obtain classification data;
the data grading module is used for grading the classified data based on the grading model to obtain graded data;
the label building module is used for building labels for each hierarchical data to obtain label data;
And the catalog management module is used for carrying out catalog management on the tag data.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method when the computer program is executed.
A non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method.
The embodiment of the invention has the following advantages:
The method for automatically carrying out data identification, classification and classification in the invention responds to an input request of a data source, inputs the data source, carries out pretreatment on the data source to determine the data resource, identifies the data resource based on an identification model to obtain identification data, classifies the identification data based on a classification model to obtain classification data, classifies the classification data based on a classification model to obtain classification data, establishes a label for each classification data to obtain label data, carries out catalog management on the label data, and solves the problem that automatic identification, classification and classification cannot be accurately carried out on data in the prior art.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those skilled in the art from this disclosure that the drawings described below are merely exemplary and that other embodiments may be derived from the drawings provided without undue effort.
The structures, proportions, sizes, etc. shown in the present specification are shown only for the purposes of illustration and description, and are not intended to limit the scope of the invention, which is defined by the claims, so that any structural modifications, changes in proportions, or adjustments of sizes, which do not affect the efficacy or the achievement of the present invention, should fall within the scope of the invention.
FIG. 1 is a flow chart of a method for automating data identification classification according to the present invention;
FIG. 2 is a block diagram of a system for automating data identification classification in accordance with the present invention;
FIG. 3 is a block diagram of a preprocessing module according to the present invention;
FIG. 4 is a diagram of a feature extraction process for identifying a model in accordance with the present invention;
FIG. 5 is a flow chart of the data classification of the present invention;
FIG. 6 is a flow chart of the data staging of the present invention;
FIG. 7 is a diagram of a hierarchical model implementation of the present invention;
FIG. 8 is a diagram of a classification model and hierarchical model data tag generation worksheet of the present invention;
fig. 9 is a schematic diagram of an entity structure of an electronic device according to the present invention.
Wherein the reference numerals are as follows:
the system comprises a data source acquisition module 10, a preprocessing module 20, an extraction module 201, a conversion module 202, a storage module 203, a data identification module 30, a data classification module 40, a data classification module 50, a build tag module 60, a catalog management module 70, an electronic device 80, a processor 801, a memory 802 and a bus 803.
Detailed Description
Other advantages and advantages of the present invention will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flowchart of an embodiment of a method for automatically classifying and classifying data according to the present invention, as shown in fig. 1, the method for automatically classifying and classifying data according to the embodiment of the present invention includes the following steps:
s101, responding to an input request of a data source, and inputting the data source;
s102, preprocessing a data source to determine a data resource;
specifically, one or more data sources of real-time service buried point data, offline imported data and batch converged data are extracted;
The batch aggregation data comprises one or more modes of flow analysis, port scanning and interface transmission;
The business embedded points comprise page embedded points, functional embedded points and resident embedded points, and the data resource acquisition management of application behaviors or user behaviors is realized by customizing the starting conditions through a business system;
the offline importing comprises processing importing by utilizing a big data component, importing a database table, automatically importing a script and importing a data file, so as to realize the acquisition and management of data resources of application behaviors or user behaviors;
The batch aggregation comprises the steps of utilizing a database or a big data component to realize heterogeneous data aggregation, finishing and analyzing and importing, and realizing the acquisition and management of data resources of application behaviors or user behaviors;
The method comprises the steps of converting voice data into text data by utilizing a PAI model compression and MNN reasoning engine voice recognition method, and converting video data into image data by utilizing a video stripping method and a video frame extraction method;
one or more data sources of the original file and the index value are stored.
S103, recognizing the data resource based on the recognition model to obtain recognition data;
Specifically, automatic data identification is carried out through an identification model, wherein the identification model at least comprises one or more external parameters, internal training and special identifications, and the automatic data identification flow at least comprises rules, strategies and samples;
As shown in fig. 4. The recognition model is a deep learning model (transducer) integrated into a Convolutional Neural Network (CNN) and a cyclic neural network (RNN (), the bit filling and filling are realized by adopting the transducer, and the transducer is integrated into the CNN (database table) or the RNN (text file), wherein I is training data, Y is a predefined label, X is a weighting parameter, and the whole feature and the local feature are extracted.
Extracting integral features and local features of the data resources through the identification model;
identifying the data resources based on automatic data identification rules, automatic data identification policies, and samples;
the automatic data recognition rules comprise keyword recognition, regular expressions, digital fingerprint technology and natural language processing;
the automatic data identification strategy comprises accurate data identification, fingerprint document identification and vector machine classification identification;
The sample includes structured data, unstructured data, and microfeature data.
S104, classifying the identification data based on the classification model to obtain classification data;
Specifically, the classification model at least comprises sample data, a learning algorithm and data abstraction, wherein the data abstraction at least comprises one or more of attribute cluster analysis and similarity analysis;
the learning algorithm is a preset regression algorithm and a decision tree algorithm;
Performing target data characteristic measurement according to a preset Euclidean distance and cosine similarity to realize attribute cluster analysis;
As shown in fig. 5, the identification data is divided into static data and dynamic data based on data attributes;
Injecting the static data into the tree structure area to judge constraint conditions, when the constraint conditions are not met, discarding the static data, and when the constraint conditions are met, flowing into a grading flow;
and injecting the dynamic data into the graph structure area to judge the attribute conditions, when the attribute conditions are not met, discarding the dynamic data, and when the attribute conditions are met, flowing into the grading flow.
S105, grading the grading data based on the grading model to obtain grading data;
Specifically, as shown in fig. 6 and fig. 7, whether the classification data meets the policy judgment conditions is judged, when the policy judgment conditions are not met, discarding is performed, and when the policy judgment conditions are met, one or more classification policies are executed to complete deviation analysis, wherein the classification policies comprise a comparison method, a calibration method and a calculation method.
The method comprises the steps of combining business scenes, carrying out grading and comparison method according to a preset check list, carrying out data level classification according to a preset discriminant method according to industry specifications or supervision requirements, carrying out grading by utilizing a key parameter method in the process of data identification and classification, and calculating K through a formula (1):
In the formula, i is the fraction of the element of the sub-term, j is the sub-term score, K is the grading level of the data, and in practical application, K can be reduced positively.
S106, establishing a label for each hierarchical data to obtain label data;
specifically, as shown in fig. 8, extracting data attributes of the data resources through a classification model and the classification model;
Carrying out data tag prediction on the data attributes, wherein the data tags comprise common tags, attribute tags and business secret tags;
and establishing a label for each hierarchical data at least through a common label and an attribute label, and combining a service scene selection mark field or a mapping table to obtain label data.
The common tag comprises a data owner, a data size and a data type, the attribute tag comprises data access authority, a data sensitive level, a data physical position, a data logical position, data meta information, a data state and data security protection requirement information, the business secret tag comprises a data type, a security class, a security period and a decryption identifier, and the mark field comprises a file attribute mark and a database expansion field mark.
S107, managing the label data in a catalog;
Specifically, the tag data catalog management is realized through catalog management, data input is provided for an organization data asset management platform, a data security management platform, a data risk monitoring system and a data security audit system, and external data service is realized through an external service interface, presentation layer integration and data integration.
The automatic data identification, classification and classification method includes the steps of responding to an input request of a data source, inputting the data source, preprocessing the data source to determine data resources, identifying the data resources based on an identification model to obtain identification data, classifying the identification data based on a classification model to obtain classification data, classifying the classification data based on a classification model to obtain classification data, establishing a label for each classification data to obtain label data, and managing a catalog of the label data. The automatic data identification classification method solves the problem that automatic identification classification cannot be accurately performed on data in the prior art, can provide data input for other important asset management systems, improves data identification precision and classification intelligent degree, and achieves scientific and reasonable data identification classification management.
Fig. 2 is a diagram of an embodiment of a system for automatically classifying and classifying data according to the present invention, and as shown in fig. 2, the system for automatically classifying and classifying data according to the embodiment of the present invention includes the following steps:
A data source acquisition module 10 for inputting a data source in response to an input request of the data source;
a preprocessing module 20, configured to preprocess the data source to determine a data resource;
As shown in fig. 3, the preprocessing module 20 includes an extraction module 201, a conversion module 202, and a storage module 203;
The extraction module 201 is configured to extract one or more data sources of real-time service buried point data, offline import data, and batch aggregation data;
the conversion module 202 is configured to convert voice data into text data and convert video data into image data;
the storage module 203 is configured to store one or more data sources of an original file and an index value;
the data identification module 30 is configured to identify the data resource based on an identification model, so as to obtain identification data;
the recognition model is a deep learning model integrated with a convolutional neural network and a cyclic neural network;
extracting integral features and local features of the data resources through the identification model;
identifying the data resources based on automatic data identification rules, automatic data identification policies, and samples;
the automatic data recognition rules comprise keyword recognition, regular expressions, digital fingerprint technology and natural language processing;
the automatic data identification strategy comprises accurate data identification, fingerprint document identification and vector machine classification identification;
The sample includes structured data, unstructured data, and microfeature data.
A data classification module 40, configured to classify the identification data based on a classification model, so as to obtain classification data;
the data classification module 40 is further configured to:
dividing the identification data into static data and dynamic data based on data attributes;
Injecting the static data into the tree structure area to judge constraint conditions, when the constraint conditions are not met, discarding the static data, and when the constraint conditions are met, flowing into a grading flow;
and injecting the dynamic data into the graph structure area to judge the attribute conditions, when the attribute conditions are not met, discarding the dynamic data, and when the attribute conditions are met, flowing into the grading flow.
A data grading module 50, configured to grade the classified data based on a grading model, so as to obtain graded data;
The data ranking module 50 is also configured to:
Judging whether the classified data meets the strategy judgment conditions, when the classified data does not meet the strategy judgment conditions, discarding, and when the classified data meets the strategy judgment conditions, executing one or more classification strategies to complete deviation analysis, wherein the classification strategies comprise a comparison method, a label matching method and an calculation method.
K is calculated by equation (1):
where i is the fraction factor ratio, j is the fraction score, K is the data level, and rounding or other reduction is performed according to the predefined level number in actual application.
A label creating module 60, configured to create a label for each of the hierarchical data, so as to obtain label data;
The setup tag module 60 is further configured to:
extracting data attributes of the data resources through a classification model and the data grading model;
Carrying out data tag prediction on the data attributes, wherein the data tags comprise common tags, attribute tags and business secret tags;
and establishing a label for each hierarchical data at least through a common label and an attribute label, and combining a service scene selection mark field or a mapping table to obtain label data.
The catalog management module 70 is configured to manage the catalog of the tag data.
The automatic data identification, classification and classification system is used for responding to the input request of the data source through the data source acquisition module 10 and inputting the data source; preprocessing the data source by a preprocessing module 20 to determine a data resource; identifying the data resources based on the identification model by a data identification module 30 to obtain identification data; the system for automatically classifying the data by the identification data comprises a data classification module 40, a data classification module 50, a label establishing module 60, a catalog management module 70 and an automatic data identification classification system.
FIG. 9 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present invention, where, as shown in FIG. 9, the electronic device 80 includes a processor 801 (processor), a memory 802 (memory), and a bus 803;
The processor 801 and the memory 802 complete communication with each other through the bus 803;
The processor 801 is configured to invoke program instructions in the memory 802 to perform the methods provided in the above embodiments of the method, and for example, the method includes inputting a data source in response to an input request of the data source, preprocessing the data source to determine a data resource, identifying the data resource based on an identification model to obtain identification data, classifying the identification data based on a classification model to obtain classification data, classifying the classification data based on a classification model to obtain classification data, creating a tag for each of the classification data to obtain tag data, and performing catalog management on the tag data.
The embodiment provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the methods provided by the above-described method embodiments, for example, including inputting a data source in response to an input request of the data source, preprocessing the data source to determine a data resource, identifying the data resource based on an identification model to obtain identification data, classifying the identification data based on a classification model to obtain classification data, classifying the classification data based on a classification model to obtain classification data, creating a tag for each of the classification data to obtain tag data, and performing catalog management on the tag data.
It will be appreciated by those of ordinary skill in the art that implementing all or part of the steps of the above method embodiments may be accomplished by hardware associated with program instructions, and that the above program may be stored in a computer readable storage medium which, when executed, performs the steps comprising the above method embodiments, where the above storage medium includes various storage media such as ROM, RAM, magnetic or optical disks, etc. that can store program code.
The apparatus embodiments described above are merely illustrative, wherein elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the embodiments or the methods of some parts of the embodiments.
While the invention has been described in detail in the foregoing general description and specific examples, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
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