Data classification and grading method, system and equipment based on big data and medium

Abstract

The invention discloses a data classification and grading method, system and device based on big data and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring to-be-graded data of a plurality of data nodes and a reference index table of a central management node; extracting content features and business attributes of the to-be-graded data, and generating corresponding data feature identifiers according to the content features and the business attributes; scanning each data feature identifier by adopting a preset sensitive word bank to generate a corresponding data sensitivity score, and evaluating each data sensitivity score through a preset data evaluation matrix to obtain corresponding data grade information; and associating the reference index table of the central management node based on the data feature identifier and the data level information of each data node to obtain a hierarchical index table, and allocating a corresponding access path to each data node in the hierarchical index table. By implementing the technical scheme provided by the invention, the data classification and grading efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically to a data classification and grading method, system, device and medium based on big data. Background Art

[0002] In the digital age, data has become an important asset. As companies become increasingly dependent on the collection, processing and storage of large amounts of data, data security and compliance issues have also increased. All types of data, whether personal information, financial records or trade secrets, need to be properly managed and protected to prevent data leakage, abuse or other forms of security threats.

[0003] At present, the existing data classification and grading methods mainly rely on collecting the enterprise's operating data through documents, databases, etc., and having the relevant data governance staff conduct manual analysis of these data to achieve the purpose of data classification and grading. However, in actual applications, due to the large amount of enterprise operating data, data classification and grading only through manual methods often requires a lot of processing time, thereby reducing the efficiency of data classification and grading. Summary of the invention

[0004] The present application provides a data classification and grading method, system, device and medium based on big data, which can improve the efficiency of data classification and grading.

[0005] In a first aspect, the present application provides a data classification and grading method based on big data, comprising: Obtaining the data to be classified of multiple data nodes and the benchmark index table of the central management node; Extracting content features and business attributes of each of the data to be classified, and generating corresponding data feature identifiers according to each of the content features and each of the business attributes; Scanning and processing each of the data feature identifiers using a preset sensitive word library to generate a corresponding data sensitivity score, and evaluating each of the data sensitivity scores using a preset data evaluation matrix to obtain corresponding data level information; The reference index table of the central management node is associated based on the data feature identifier and data level information of each data node to obtain a hierarchical index table, and a corresponding access path is allocated to each data node in the hierarchical index table.

[0006] In a second aspect of the present application, a data classification and grading system based on big data is provided, the system comprising: A data acquisition module, used to acquire the data to be classified of multiple data nodes and the benchmark index table of the central management node; A feature identification determination module, used to extract the content features and business attributes of each of the to-be-classified data, and generate a corresponding data feature identification according to each of the content features and each of the business attributes; A level information determination module, used to scan and process each of the data feature identifiers using a preset sensitive word library to generate a corresponding data sensitivity score, and evaluate each of the data sensitivity scores using a preset data evaluation matrix to obtain corresponding data level information; The data classification module is used to associate the reference index table of the central management node based on the data feature identifier and data level information of each data node to obtain a hierarchical index table, and allocate a corresponding access path to each data node in the hierarchical index table.

[0007] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a data classification and grading method based on big data when loaded and executed by the processor.

[0008] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a data classification and grading method based on big data.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical solution, firstly, the data to be classified of multiple data nodes and the benchmark index table of the central management node are obtained to establish a unified data management foundation; then, data feature identifiers are generated by extracting the content characteristics and business attributes of the data to be classified, and the standardized expression of data features is realized; then, the preset sensitive word library is used for automatic scanning and combined with the data evaluation matrix for evaluation to obtain objective data level information, ensuring the accuracy and consistency of the classification results. The method finally forms a hierarchical index table by associating the data feature identifier and data level information with the benchmark index table, and assigns corresponding access paths to each data node, thus constructing a complete data classification management system. Compared with the existing manual data classification and grading method, this classification method based on automated processing can quickly process large-scale data sets, reduce the process of manual judgment, improve the accuracy of classification, and avoid the problem of inefficiency in the traditional data classification and grading process. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flow chart of a data classification and grading method based on big data provided in an embodiment of the present application; Figure 2It is a structural diagram of a data classification and grading system based on big data provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0011] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0012] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0013] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0014] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0015] The present application embodiment provides a data classification and grading method based on big data. In one embodiment, please refer to Figure 1 , Figure 1 This is a flow chart of a data classification and grading method based on big data provided by an embodiment of the present application. The method can be implemented by a computer program, which can be integrated into an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run on a data classification and grading system based on big data based on the von Neumann system. Specifically, the method can include the following steps: Step 101: Obtain the data to be classified of multiple data nodes and the benchmark index table of the central management node.

[0016] Data nodes refer to data storage and processing units distributed in various business systems of an enterprise, which can be different types of data sources, such as business database servers, file storage servers, application system servers, etc. Each data node independently manages and stores data resources in a specific business field, and together they constitute the distributed data storage architecture of the enterprise.

[0017] Data to be classified refers to the original data stored in various data nodes that need to be classified and managed, including but not limited to: business data generated during the operation of the enterprise (such as customer information, transaction records), management data (such as personnel files, financial statements), technical data (such as R&D documents, design drawings), etc. These data may exist in different formats, including both structured data and unstructured data.

[0018] The central management node refers to the core control unit of the enterprise data classification and grading system, which is responsible for unified management and coordination of the entire classification and grading process. It maintains the enterprise-level data classification and grading strategy, security rules and access control strategy, and is responsible for communication and data exchange with each data node. The central management node undertakes core functions such as the formulation of data classification standards, storage of classification results, and management of access rights.

[0019] The benchmark index table is a basic data structure stored in the central management node, which defines the basic framework and rule system for data classification and grading. The benchmark index table contains information such as the hierarchical structure of data classification, basic attribute definitions of various types of data, grading standards, and sensitivity assessment rules. It serves as a reference standard for data classification and grading, guiding the system to extract features and grade the data to be graded.

[0020] Specifically, it is necessary to first obtain the data to be classified from multiple data nodes and the benchmark index table of the central management node. Since enterprise data is usually distributed in different business systems and storage locations, a centralized data collection mechanism needs to be established to achieve unified data classification and grading management. The system connects the data nodes of each business system by configuring the data collection interface. These data nodes can be file servers, database servers or other data storage devices. The system adopts a distributed collection method to obtain the data to be classified from multiple data nodes at the same time. These data may include structured data (such as database records) and unstructured data (such as documents, pictures, etc.). While obtaining the data to be classified, the system also needs to obtain the benchmark index table from the central management node. The benchmark index table is an important reference for data classification and grading, which contains the basic rules, index structure and key attribute definitions of data classification. The benchmark index table is usually pre-prepared by the data governance team and stored in the central management node. The system reads the benchmark index table from the central management node through a secure data transmission channel to ensure the integrity and consistency of the index information. This centralized data acquisition method can ensure that all data to be classified can be uniformly managed and evaluated. By establishing a connection between data nodes and central management nodes, the system can obtain the latest data content and indexing rules in real time, providing a reliable data foundation for subsequent data feature extraction and hierarchical evaluation.

[0021] Step 102: extracting content features and business attributes of each data to be classified, and generating corresponding data feature identifiers according to each content feature and each business attribute.

[0022] Among them, content features refer to a preset number of high-frequency keywords selected from the keyword set obtained after processing the data to be classified through text segmentation. The system will count the frequency of occurrence of keywords and regard keywords with a frequency greater than the frequency threshold as high-frequency keywords. These high-frequency keywords directly reflect the core content and theme information of the data to be classified, and can accurately characterize the specific content characteristics of the data.

[0023] Business attributes refer to business characteristics determined by matching the preset business type rule template with the basic information such as the creation time, business scope, and user department of the data to be classified. The preset business type rule template contains a variety of combinations of different creation times, different business scopes, and different user departments and their corresponding business attribute definitions. These business attributes reflect the positioning and importance of data in the enterprise business process.

[0024] Data feature identification refers to the standardized feature vector generated by quantizing content features and business attributes. This feature vector combines the content features reflected by high-frequency keywords and the business attributes determined by business type rules to form a unified numerical feature representation. This feature vector representation method enables the system to perform quantitative analysis and comparison of different types of data with a unified standard.

[0025] Specifically, after obtaining the data to be classified, it is necessary to extract features of the data to identify and evaluate its importance and sensitivity. The system first extracts content features of the data to be classified through text segmentation processing, and splits the text content into a set of keywords. For these keywords, the system will count the frequency of occurrence of each keyword, and select keywords whose frequency of occurrence exceeds the preset frequency threshold as high-frequency keywords. These high-frequency keywords can effectively reflect the core content and theme characteristics of the data, and the system will select a preset number of high-frequency keywords as content features of the data to be classified. At the same time, the system also needs to extract business attribute information of the data to be classified. The system will obtain basic information such as the creation time, business scope, and use department of the data to be classified, and match this information with the preset business type rule template. The business type rule template predefines the business attributes corresponding to the combination of different creation times, different business scopes, and different use departments. Through this matching, the system can accurately identify the business attributes of the data to be classified, thereby understanding the importance and usage scenarios of the data in the business process.

[0026] After obtaining content features and business attributes, the system needs to convert this information into standardized data feature identifiers. The system uses feature vectorization to convert text features and business attributes into numerical feature vectors. The feature vectorization process takes into account factors such as the importance of high-frequency keywords and the weight of business attributes, and integrates these features into a unified data feature identifier through a mathematical model. This feature vectorization process allows different types of data to be compared and evaluated under the same standard.

[0027] Based on the above embodiment, as an optional embodiment, in step 102: extracting the content features and business attributes of each data to be classified, and generating corresponding data feature identifiers according to each content feature and each business attribute, this step may also include the following steps: Step 201: For each data to be classified, perform text segmentation processing on the data to be classified to obtain a keyword set.

[0028] Specifically, the system first determines the format type of the data to be classified. For structured data, the system directly extracts the content of its text fields; for unstructured data such as documents and emails, the system needs to perform format conversion first and then extract the text content. After obtaining the text content, the system uses a preset word segmentation dictionary to segment the text, splitting the continuous text content into a sequence of words. During the word segmentation process, the system filters out stop words (such as function words like "de", "le", etc.) and special characters, only retaining words with actual semantic meaning. At the same time, the system also performs part-of-speech tagging on the word segmentation results to identify different types of words such as nouns and verbs, and this information helps to judge the importance of words in subsequent processes. After text word segmentation, the system obtains a keyword set composed of words with independent semantics. Each word in this keyword set carries its position information and part-of-speech information in the original text, providing basic data for subsequent extraction of high-frequency keywords. Compared with the original text content, the keyword set is more structured, facilitating statistical analysis and feature extraction by the system.

[0029] Step 202: Count the occurrence frequencies of each keyword in the keyword set, and select a preset number of high-frequency keywords as content features. The high-frequency keywords are those with occurrence frequencies greater than the frequency threshold.

[0030] Specifically, after obtaining the keyword set, the system needs to identify the keywords that best represent the core content of the data to be classified as content features. Since the keyword set may contain a large number of words, and the importance of these words to the expression of text content is not the same, it is necessary to find the most representative high-frequency keywords through statistical analysis. The system first performs frequency statistics on each keyword in the keyword set and calculates the number of times each keyword appears in the text. In order to eliminate the influence of text length on frequency statistics, the system divides the original number of occurrences by the total number of words in the text to obtain a standardized frequency of occurrence. During the statistical process, the system will consider the word part information of the word, give a higher weight to nouns, proper nouns and other content words, and give a lower weight to general verbs, adjectives, etc., so as to highlight the importance of keywords expressing the core content. After the statistics are completed, the system sorts all keywords from high to low according to the frequency of occurrence. The system pre-sets a frequency threshold, and only keywords with a frequency exceeding this threshold will be regarded as high-frequency keywords. The setting of this frequency threshold needs to take into account the length of the text and the characteristics of the field, and is usually determined through data analysis and practical experience. From the high-frequency keywords, the system further selects a preset number of keywords as content features. Determining the preset number requires balancing the representativeness of features and computational efficiency, ensuring that the selected keywords can fully express the core content of the text, while avoiding excessive feature dimensions that reduce subsequent processing efficiency. The content features selected in this way have good representativeness and discrimination, and can accurately reflect the core content of the data to be classified. These features provide the content basis for subsequent data classification, helping the system to accurately assess the sensitivity and importance of the data.

[0031] Step 203: Obtain the creation time, business scope and user department of the data to be classified, and determine the business attributes based on the preset business type rule template. The preset business type rule template includes business attributes corresponding to a combination of multiple different creation times, different business scopes and different user departments; perform feature vectorization on content features and business attributes to generate data feature identifiers.

[0032] Specifically, the system first extracts basic information such as creation time, business scope, and user department from the metadata information of the data to be classified. The creation time reflects the timeliness of the data, and the system converts it into a standard time format; the business scope indicates the specific business field to which the data belongs, such as finance, personnel, research and development, etc.; the user department indicates the main users and managers of the data. The system matches this information with the preset business type rule template, which predefines the corresponding business attribute definitions for various combinations of different time periods, different business scopes, and different user departments. For example, the budget data of the finance department created in the last month may be defined as "highly sensitive real-time financial information", while the daily notifications of the general office department two years ago may be defined as "ordinary historical information". After completing the determination of business attributes, the system needs to uniformly convert content features and business attributes into numerical feature vectors. For high-frequency keywords in content features, the system uses a word vector model to convert each keyword into a numerical vector of fixed dimension, and calculates the weighted average in combination with word frequency information to obtain a vector representing content features. For business attributes, the system converts them into vector representations in a preset format of encoding. After normalization, the two vectors are concatenated according to the preset dimensions and weights to form the final data feature identifier. In this way, the system can more accurately assess the sensitivity of the data and provide a basis for formulating corresponding protection strategies.

[0033] Step 103: Scan and process each data feature identifier using a preset sensitive word library to generate a corresponding data sensitivity score, and evaluate each data sensitivity score using a preset data evaluation matrix to obtain corresponding data level information.

[0034] The preset sensitive word library refers to a systematically organized collection of sensitive word data, which contains sensitive word entries classified and stored according to different business fields and information types. Each sensitive word entry consists of three key elements: sensitive word text (specific sensitive words or phrases), sensitive word weight (a numerical value reflecting the sensitivity of the word), and the category to which the sensitive word belongs (such as commercial secrets, personal information, financial data, etc.). This sensitive word library supports dynamic updates and maintenance, and can add new sensitive words or adjust the properties of existing sensitive words according to enterprise needs.

[0035] Data sensitivity score refers to the numerical index obtained by scanning and calculating sensitive words in text content. It consists of two levels of calculation: first, the sub-sensitivity score, which is the product of the sensitive word weight of a single sensitive word and its number of occurrences in the text; and then the comprehensive sensitivity score, which is the final score obtained by weighting and summing the sub-sensitivity scores according to the category coefficients of the categories to which they belong. This score reflects the overall sensitivity of the data.

[0036] The preset data evaluation matrix is ​​a mapping table that defines the correspondence between sensitivity scores and data levels. It contains the definitions of multiple scoring intervals (such as [0-30], [31-70], [71-100], etc.) and the data level information corresponding to each interval. This matrix is ​​used as an evaluation standard to convert numerical sensitivity scores into specific data levels. The design of the evaluation matrix takes into account the actual data distribution characteristics, and the interval division and level definition can be adjusted as needed.

[0037] Data level information refers to the data classification results obtained by matching the data evaluation matrix according to the data sensitivity score. It usually includes the level name (such as "highly sensitive", "relatively sensitive", "generally sensitive", etc.), level description, specific protection requirements (such as access control requirements, encrypted storage requirements, etc.). This level information provides specific guidance for data security management and protection measures.

[0038] Specifically, the system first uses the preset sensitive word library to scan and process the content feature part in the data feature identifier. The preset sensitive word library contains keywords and phrases with different sensitivity levels. These sensitive words are divided into different levels according to their sensitivity, and each level corresponds to a different basic score. During the scanning process, the system will identify the sensitive words appearing in the content features and calculate the initial sensitivity score according to the level and frequency of occurrence of the sensitive words. At the same time, the system will also consider the context in which the sensitive words appear, and adjust the initial score through semantic analysis to avoid misjudgment caused by word ambiguity. After obtaining the initial sensitivity score, the system uses the preset data evaluation matrix to perform a multi-dimensional evaluation of the data. The data evaluation matrix is ​​a multi-dimensional evaluation model that comprehensively considers the business attribute information in the data feature identifier, including factors such as the timeliness of the data, the importance of the business, and the scope of use. The evaluation matrix sets different weight coefficients for each dimension, and obtains the final data sensitivity score through weighted calculation. For example, data involving core business will receive a higher weight, while general daily data will receive a lower weight. Based on the final data sensitivity score, the system determines the data level information with reference to the preset classification standard. This classification standard defines the data levels corresponding to different score ranges, usually including multiple levels, such as highly sensitive, relatively sensitive, and generally sensitive. The system will map the data sensitivity score to the corresponding level range to obtain the final data level information.

[0039] Based on the above embodiment, as an optional embodiment, in step 103: using a preset sensitive word library to scan and process each data feature identifier to generate a corresponding data sensitivity score, this step may also include the following steps: Step 301: Determine the corresponding text scanning range according to each data feature identifier; for each data feature identifier, retrieve multiple sensitive word entries corresponding to the text scanning range from a preset sensitive word library, the sensitive word entries including sensitive word text, sensitive word weight and category to which the sensitive word belongs.

[0040] Specifically, the system first analyzes the content features and business attribute information in the data feature identifier, and determines the text scanning scope based on this information. The high-frequency keywords in the content features can reflect the subject area to which the data belongs, while the business attributes indicate the business type and usage scenario of the data. For example, when the data feature identifier shows that the data belongs to the financial field and is used by the financial department, the system will determine the scanning scope as the sensitive information category related to finance; when the data involves personnel information, the scanning scope will include the sensitive information category related to personal privacy. After determining the scanning scope, the system retrieves the corresponding sensitive word entries from the preset sensitive word library. The preset sensitive word library adopts a hierarchical classification structure, and classifies and stores sensitive words according to different business fields and information types. Each sensitive word entry contains three key elements: sensitive word text, sensitive word weight, and sensitive word category. Sensitive word text is a specific word or phrase; sensitive word weight reflects the sensitivity of the word, usually expressed in numerical values; the category to which the sensitive word belongs indicates the information category to which the word belongs, such as commercial secrets, personal information, financial data, etc. In practical applications, this processing method shows good flexibility and scalability. The system can dynamically adjust the structure and content of the sensitive word library according to the specific needs of the enterprise, add new sensitive word categories or update the weights of existing sensitive words. At the same time, the sensitive word library supports multiple matching modes, including exact matching, fuzzy matching and regular expression matching, which can adapt to the sensitive information identification needs in different scenarios. The text scanning range and sensitive word entries determined in this way provide a data basis for subsequent sensitivity scoring.

[0041] Step 302: Match the text within the text scanning range with the sensitive word text in the sensitive word entry to obtain a sensitive word hit record; multiply the sensitive word weight and the number of sensitive word hits of each target sensitive word entry in the sensitive word hit record to obtain a sub-sensitivity score.

[0042] Specifically, the system first performs sensitive word matching on the content within the text scanning range. The matching process uses a multi-mode string matching algorithm, such as the AC automaton algorithm, which can match multiple sensitive words in parallel at the same time to improve processing efficiency. During the matching process, the system considers word variants and combinations, such as homophones, synonyms, and different permutations and combinations of sensitive words. When a sensitive word is found in the text, the system records the specific location of the sensitive word, the sensitive word entry information to which it belongs, and the number of occurrences to form a sensitive word hit record. In order to accurately assess the sensitivity of the data, the system needs to comprehensively consider the importance and frequency of occurrence of sensitive words. For each target sensitive word entry in the sensitive word hit record, the system multiplies its sensitive word weight by the number of hits to obtain the sub-sensitivity score of the sensitive word. The sensitive word weight reflects the sensitivity of the word. Generally, more sensitive words are assigned higher weight values; while the number of hits reflects the frequency of use of the sensitive word in the text. Sensitive words that appear multiple times usually indicate that the data is highly associated with the sensitive information. In practical applications, this processing method has shown good performance and accuracy. The system uses an efficient matching algorithm that can quickly process large amounts of text data. At the same time, by recording detailed hit information, the system can provide managers with a visual display of the distribution of sensitive information to help them understand the sensitive characteristics of the data. The system also regularly analyzes the scoring results to optimize the configuration of sensitive word weights so that the scoring results are more in line with the actual situation. The sub-sensitivity scores calculated in this way can more accurately reflect the distribution of various types of sensitive information in the text. Especially when dealing with complex data containing multiple types of sensitive information, this scoring method based on weight and frequency can effectively distinguish the importance of different sensitive words and provide a basis for subsequent comprehensive evaluation.

[0043] Step 303: Based on the category to which the sensitive word of each target sensitive word entry belongs, a corresponding category coefficient is determined, and each sub-sensitivity score is weighted and summed using each category coefficient as a weight to obtain a sensitivity score corresponding to the data feature identifier.

[0044] Specifically, the system first determines the corresponding category coefficient according to the category to which the sensitive word of the target sensitive word entry belongs. Different categories of sensitive information have different impacts on data sensitivity. For example, information involving corporate secrets may have a higher sensitivity than general commercial information. The setting of category coefficients is usually based on the company's data security policies and compliance requirements, reflecting the relative importance of different categories of sensitive information. The system maintains a category coefficient configuration table to define corresponding weight values ​​for each sensitive information category. More important categories are assigned higher category coefficients. After determining the category coefficient, the system performs a weighted summation calculation on each sub-sensitivity score. For each target sensitive word entry, the system multiplies its sub-sensitivity score by the corresponding category coefficient, and then adds all weighted scores to obtain the final sensitivity score. This weighted summation method ensures that the scoring result can reflect the comprehensive impact of different categories of sensitive information, while also maintaining the dominant role of higher sensitivity categories in the final score. In practical applications, this processing method shows good adaptability and maintainability. The system can adjust the category coefficient in a timely manner according to new security requirements or business changes to ensure that the scoring standard always meets current management needs. At the same time, the system will record the detailed scoring process, including the contribution value of each category. This information can help managers understand the sensitive characteristics of the data and provide a basis for formulating targeted protection measures. The sensitivity score calculated in this way has a high degree of accuracy. The scoring result not only reflects the overall level of sensitive information in the data, but also reflects the relative importance of different categories of sensitive information.

[0045] Based on the above embodiment, as an optional embodiment, in step 103: evaluating each data sensitivity score by using a preset data evaluation matrix to obtain corresponding data level information, this step may also include the following steps: Step 304: Obtain a preset data evaluation matrix, where the preset data evaluation matrix includes multiple scoring intervals and data level information corresponding to each scoring interval.

[0046] Specifically, the system first determines the corresponding category coefficient according to the category to which the sensitive word of the target sensitive word entry belongs. Different categories of sensitive information have different impacts on data sensitivity. For example, information involving state secrets may have a higher category coefficient, while general commercial information has a relatively lower category coefficient. The setting of the category coefficient is usually based on the enterprise's security policy and compliance requirements, and can be dynamically adjusted according to actual needs. The system multiplies each sub-sensitivity score with the corresponding category coefficient, and then performs a weighted sum to obtain the overall sensitivity score corresponding to the data feature identifier. This weighted calculation method ensures that the scoring result can accurately reflect the comprehensive impact of different categories of sensitive information. After obtaining the sensitivity score, the system determines the data level through a preset data evaluation matrix. The data evaluation matrix is ​​a mapping relationship table that defines the data level information corresponding to different scoring intervals. The division of the scoring interval needs to take into account the characteristics of the sensitivity distribution of the data, and usually adopts a non-uniform division method to better distinguish data of different sensitivity levels. Each scoring interval corresponds to specific data level information, such as "highly sensitive", "relatively sensitive", "generally sensitive", etc., and also includes processing suggestions and protection requirements for the data of that level. This evaluation method has multiple advantages. First, by introducing the category coefficient, the system can reflect the differences in the importance of different categories of sensitive information, making the evaluation results more in line with actual security needs. Secondly, the preset data evaluation matrix provides a standardized basis for grading, ensuring the consistency and comparability of the evaluation results. In addition, the configurability of the evaluation matrix enables the system to flexibly adjust the rating standards according to business needs.

[0047] Step 305: Match each data sensitivity score with each scoring interval in the data evaluation matrix to obtain data level information corresponding to the scoring interval in which each data sensitivity score is located.

[0048] Specifically, the system first compares the data sensitivity score with the score interval defined in the data assessment matrix. These score intervals are usually defined in the form of numerical ranges, such as [0-30] for the low sensitivity interval, [31-70] for the medium sensitivity interval, and [71-100] for the high sensitivity interval. The system uses an interval detection algorithm to determine the specific interval to which the score belongs. This process needs to consider the processing principle of the interval boundary, such as using the interval definition method of left closed and right open or left open and right closed to ensure that the score can uniquely correspond to a certain interval. After determining the score interval, the system directly obtains the data level information corresponding to the interval. Data level information usually includes level name, level description, protection requirements, etc. For example, when the score falls into the high sensitivity interval, the system will extract the "highly sensitive" level information corresponding to the interval, which may include specific protection requirements such as "strict access control required" and "encrypted storage must be required". This level information provides clear guidelines for the subsequent processing and protection of data. This matching method has multiple advantages. First, it provides a standardized data classification method to ensure that data with the same score can receive consistent level judgments. Secondly, through the preset evaluation matrix, the system can flexibly adjust the division of scoring intervals and the corresponding level definition to meet the grading requirements of different scenarios. In addition, this method is also convenient for statistics and analysis of evaluation results, which helps to understand the overall sensitivity distribution of enterprise data. In practical applications, this processing method shows good practicality and maintainability. The system can optimize the division of scoring intervals according to the actual data distribution, making the level division more reasonable. At the same time, by recording detailed information of the matching process, including the score value, the interval to which it belongs, and the final level, the system can provide managers with complete traceability of the evaluation process. These records can also be used for subsequent audits and optimization of evaluation standards.

[0049] Step 104: The reference index table of the central management node is associated based on the data feature identifier and data level information of each data node to obtain a hierarchical index table, and a corresponding access path is allocated to each data node in the hierarchical index table.

[0050] Among them, the hierarchical index table is an enhanced index structure formed by associating the data feature identification and data level information of the data nodes on the basis of the benchmark index table of the central management node. This index structure supports hierarchical management and access control based on data sensitivity, enabling the system to implement differentiated management strategies based on the sensitivity level of the data.

[0051] The access path refers to the data access method and routing information assigned by the system to data nodes of different levels. It contains the specific technical path required to access the data node, such as network access protocol, authentication requirements, encrypted transmission channel and other information. For highly sensitive data, the access path may include security mechanisms such as multiple authentication requirements, mandatory encrypted transmission, access log records, etc.; for low-sensitivity data, a more simplified access method may be used. The design of the access path takes into account multiple factors such as the sensitivity level of the data, the security level of the storage location, and the network topology structure to ensure that data access is both safe and controllable and easy to use.

[0052] Specifically, the system first associates the data feature identifier and data level information of the data node with the records in the benchmark index table. The benchmark index table contains basic information of the data node, such as node identifier, storage location, data type, etc. The system appends the data feature identifier and level information to the corresponding index record through the unique identifier of the data node to form a more complete data description. This association operation not only retains the original index information, but also adds the sensitivity attribute of the data, so that the index table can support sensitivity-based data management. After completing the association, the system assigns a corresponding access path to each data node based on the level information and physical storage location of the data. The design of the access path needs to consider multiple factors, including the sensitivity level of the data, the security level of the storage location, and the network topology. For highly sensitive data, the system may assign a multi-layer authentication access path or perform access control through a security gateway; for low-sensitivity data, a relatively simplified access method can be used. This differentiated access path allocation ensures that data of different levels can be protected at the corresponding level. This index management method has multiple advantages. First, by integrating data features and level information into the index table, the system can achieve unified data management and access control. Secondly, the allocation of access paths based on hierarchical information enables the system to provide corresponding protection mechanisms for data of different sensitivities. In addition, the structural design of the hierarchical index table supports flexible query and management operations, making it easy to implement various data management functions. In practical applications, this processing method shows good scalability and maintainability. The system can dynamically update the structure of the hierarchical index table, add new attribute fields, or optimize the configuration of the access path as needed. At the same time, by recording detailed index information and access paths, the system can provide managers with a complete view of data assets to help understand the distribution and usage of data. The system also regularly checks the validity of the access path to ensure the security and availability of data access.

[0053] Based on the above embodiment, as an optional embodiment, in step 104: associating the reference index table of the central management node based on the data feature identifier and data level information of each data node to obtain a hierarchical index table, this step may also include the following steps: Step 401: Divide the reference index table into a plurality of initial sub-index tables corresponding to data level information; for each data node, determine the corresponding sub-index table in each initial sub-index table according to the data level information.

[0054] Specifically, the system first divides the base index table into multiple initial sub-index tables according to the preset data level information. Each initial sub-index table corresponds to a specific data level, such as highly sensitive, relatively sensitive, and generally sensitive. This division retains the basic field structure in the base index table, but establishes an independent data storage space, so that data of different levels are logically completely separated. The creation process of the initial sub-index table includes operations such as table structure replication and index reconstruction to ensure that each sub-index table has complete management functions. After the creation of the initial sub-index table is completed, the system allocates it to the corresponding sub-index table according to the data level information of each data node. This process is completed by comparing the level information of the data node with the level attribute of the sub-index table. When the sub-index table to which the data node should belong is determined, the system will migrate or copy the complete index information of the node to the corresponding sub-index table. This allocation mechanism ensures that each data node can be correctly classified into the index table of the corresponding level. The hierarchical index structure formed in this way can effectively support the hierarchical management of data. Especially when processing a large amount of heterogeneous data, this hierarchical indexing method can provide more efficient data organization and query capabilities. The system will regularly check the data distribution of the sub-index table and perform structural optimization or load balancing when necessary to ensure management efficiency.

[0055] Step 402: Establish an association relationship between the data feature identifier of each data node and the corresponding sub-index table, and assign corresponding access rights to each sub-index table to obtain the target sub-index table of each data node; integrate each target sub-index table to obtain a hierarchical index table.

[0056] Specifically, the system first establishes an association between the data feature identifier of the data node and its corresponding sub-index table. This association is achieved by adding a feature association field in the sub-index table, which records the feature information of the data node, such as data type, business attributes, sensitive features, etc. The system converts the data feature identifier into a standardized index attribute through a feature mapping algorithm, and writes these attribute information into the corresponding sub-index table. This association mechanism enables the system to quickly locate and retrieve relevant data nodes based on data features. While establishing the feature association relationship, the system assigns corresponding access rights to each sub-index table. The setting of access rights is based on data level information, covering the permission control rules of different operation types such as reading, modifying, and deleting. For the sub-index table corresponding to highly sensitive data, the system will set stricter access restrictions, such as requiring multiple authentications and operation audits; for the sub-index table of low-sensitivity data, a relatively loose access control strategy is adopted. This differentiated permission setting ensures that data of different levels can be protected at the corresponding level. After completing the feature association and permission allocation, each sub-index table becomes a target sub-index table containing complete management information. These target sub-index tables contain complete management metadata such as basic information, feature attributes, and access rights of data nodes. The system uses a unified index integration mechanism to merge all target sub-index tables into a unified hierarchical index table. This integration process retains the independence of each sub-index table while establishing a unified retrieval and management interface. The hierarchical index table constructed in this way can effectively support the data governance needs of enterprises. Especially when dealing with complex data access scenarios, this multi-level index structure can provide precise access control and efficient data retrieval services.

[0057] Based on the above embodiment, as an optional embodiment, in step 104: allocating a corresponding access path to each data node in the hierarchical index table, this step may also include the following steps: Step 403: Obtain access rights for each data node; generate access path identifiers for each data node based on a preset access path template and each access right.

[0058] Specifically, the system first obtains the access rights information of each data node from the hierarchical index table. This access rights information includes security control parameters such as the access level, operation restrictions, and authentication requirements of the data node. The system will parse this access rights information and extract key parameters that affect the generation of the access path, such as whether encrypted transmission is required, whether multiple authentications are required, and whether access audits are required. These parameters will directly affect the structure and characteristics of the access path that is finally generated. After obtaining the access rights information, the system uses the preset access path template as the basic framework to generate the access path identifier. The access path template is a standardized path description structure that includes multiple configurable components such as protocol type, authentication method, transmission encryption, and access control. The system selects a suitable component combination from the access path template based on the access rights of the data node and fills in specific parameter values. For example, for highly sensitive data nodes, the system will select a path component that includes mandatory encrypted transmission and multiple authentications; while for low-sensitivity data nodes, it may select a simpler access path component.

[0059] Based on the above embodiment, as an optional embodiment, in step 403: based on the preset access path template and each access right, the access path identifier of each data node is generated. This step may also include the following steps: Step 413: Determine the access protocol type corresponding to the security level of each access permission according to each access permission; bind each access protocol type with a preset access path template to obtain an access path identifier of each data node.

[0060] Specifically, in the process of data access management, in order to ensure the security and standardization of data access, the system needs to establish a corresponding access protocol mechanism based on the access rights and effectively bind it to the access path template. This process is of great significance for achieving differentiated security access control. The system first analyzes the access rights information of each data node, extracts the security level requirements from it, and determines the corresponding access protocol type according to the preset security policy. This protocol type determination process takes into account multiple security factors, including data sensitivity, access authentication requirements, transmission security requirements, etc. For high-security access rights, the system will select access protocols with strong authentication mechanisms and high security, such as encrypted transmission protocols, multi-factor authentication protocols, etc.; for low-security access rights, relatively simplified access protocols may be selected, such as basic identity authentication protocols. After determining the access protocol type, the system binds these protocol types to the preset access path template. As a standardized access structure framework, the access path template includes multiple components such as protocol parameter configuration, authentication method settings, and transmission control. Through the protocol mapping mechanism, the system associates the determined access protocol type with the corresponding components in the template and configures specific protocol parameters according to security requirements. This binding process ensures that each data node can obtain the access control mechanism that matches its security level. This processing method realizes the standardized management of access protocols and ensures that all data accesses follow the predetermined security specifications. Secondly, by binding the protocol type with the template, the system can flexibly adjust the access control parameters and quickly adapt to different security requirements. In addition, the standardized access path structure facilitates the system to perform unified access management and security audits.

[0061] Step 404: Establish a mapping relationship between the physical storage address of each data node and the access path identifier, and encrypt each access path identifier according to a preset encryption rule to obtain an access path corresponding to each data node.

[0062] Specifically, the system first establishes a mapping relationship between the physical storage address and the access path identifier for each data node. This mapping is achieved by establishing an association table, which contains information such as the unique identifier of the data node, the physical storage address, and the access path identifier. The system uses a unified mapping rule to bind the storage address information with the access path identifier to form a complete access link description. This mapping mechanism ensures that the system can accurately direct access requests to the correct data storage location while maintaining the integrity of access control. After the mapping relationship is established, the system encrypts the access path identifier according to the preset encryption rules. The encryption rules include multiple security parameters such as encryption algorithm selection, key management, and encryption strength setting. The system first normalizes the access path identifier to ensure that its format meets the encryption requirements. Then, the corresponding encryption algorithm and key strength are selected according to the sensitivity level of the data node, and the access path identifier is encrypted and converted. For highly sensitive data, the system will use a stronger encryption algorithm and a more complex key system; for low-sensitivity data, a relatively simplified encryption scheme may be used. In practical applications, this access path processing mechanism shows good security and maintainability. The system can regularly update the encryption key to achieve dynamic changes in the access path and increase security. At the same time, by recording detailed mapping and encryption operation logs, the system can accurately track the generation and use of access paths, facilitating security audits and troubleshooting. The access paths generated in this way can effectively support secure data access requirements. Especially when dealing with sensitive data access, this multi-layer protection mechanism can provide reliable access control and security. The system will regularly evaluate the security of access paths, and update encryption parameters or adjust mapping rules when necessary to ensure the effectiveness of access control measures.

[0063] Reference Figure 2 , is a data classification and grading system based on big data provided by an embodiment of the present application, the system comprises: a data acquisition module, a feature identification determination module, a level information determination module, and a data grading module, wherein: A data acquisition module, used to acquire the data to be classified of multiple data nodes and the benchmark index table of the central management node; A feature identification determination module is used to extract the content features and business attributes of each data to be classified, and generate corresponding data feature identification according to each content feature and each business attribute; A level information determination module is used to scan and process each data feature identifier using a preset sensitive word library to generate a corresponding data sensitivity score, and evaluate each data sensitivity score using a preset data evaluation matrix to obtain corresponding data level information; The data classification module is used to associate the benchmark index table of the central management node based on the data feature identifier and data level information of each data node to obtain a hierarchical index table, and allocate a corresponding access path to each data node in the hierarchical index table.

[0064] On the basis of the above embodiments, the feature identification determination module is also used to perform text segmentation processing on each data to be classified to obtain a keyword set; count the occurrence frequency of each keyword in the keyword set, and select a preset number of high-frequency keywords as content features, and the high-frequency keywords are keywords whose occurrence frequency is greater than the frequency threshold; obtain the creation time, business scope and use department of the data to be classified, and determine the business attributes based on the preset business type rule template, the preset business type rule template includes business attributes corresponding to a combination of multiple different creation times, different business scopes and different use departments; perform feature vectorization on the content features and business attributes to generate data feature identification.

[0065] On the basis of the above embodiment, the level information determination module is also used to determine the corresponding text scanning range according to each data feature identifier; for each data feature identifier, multiple sensitive word entries corresponding to the text scanning range are retrieved from a preset sensitive word library, and the sensitive word entry includes sensitive word text, sensitive word weight and sensitive word category; the text within the text scanning range is matched with the sensitive word text in the sensitive word entry to obtain a sensitive word hit record; the sensitive word weight and the number of sensitive word hits of each target sensitive word entry in the sensitive word hit record are multiplied to obtain a sub-sensitivity score; based on the sensitive word category of each target sensitive word entry, the corresponding category coefficient is determined, and the sub-sensitivity scores are weighted and summed with each category coefficient as the weight to obtain the sensitivity score corresponding to the data feature identifier.

[0066] Based on the above embodiment, the level information determination module is also used to obtain a preset data evaluation matrix, which includes multiple scoring intervals and data level information corresponding to each scoring interval; each data sensitivity score is matched with each scoring interval in the data evaluation matrix to obtain the data level information corresponding to the scoring interval in which each data sensitivity score is located.

[0067] On the basis of the above embodiment, the data classification module is also used to divide the baseline index table into multiple initial sub-index tables corresponding to the data level information; for each data node, according to the data level information, the corresponding sub-index table is determined in each initial sub-index table; an association relationship is established between the data feature identifier of each data node and the corresponding sub-index table, and corresponding access rights are assigned to each sub-index table to obtain the target sub-index table of each data node; each target sub-index table is integrated to obtain a hierarchical index table.

[0068] Based on the above embodiments, the data classification module is also used to obtain access rights for each data node; based on a preset access path template and each access right, an access path identifier for each data node is generated; a mapping relationship is established between the physical storage address of each data node and the access path identifier, and each access path identifier is encrypted according to preset encryption rules to obtain the access path corresponding to each data node.

[0069] Based on the above embodiment, the data classification module is also used to determine the access protocol type corresponding to the security level of each access right according to each access right; bind each access protocol type with a preset access path template to obtain the access path identifier of each data node.

[0070] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0071] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0072] The communication bus 302 is used to realize the connection and communication between these components.

[0073] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0074] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0075] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0076] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program based on a data classification and grading method of big data.

[0077] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the memory 305 to store an application program for a data classification and grading method based on big data. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0078] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0080] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0081] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0082] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0083] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0084] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A data classification and grading method based on big data, characterized in that: include: Obtaining the data to be classified of multiple data nodes and the benchmark index table of the central management node; Extracting content features and business attributes of each of the data to be classified, and generating corresponding data feature identifiers according to each of the content features and each of the business attributes; Scanning and processing each of the data feature identifiers using a preset sensitive word library to generate a corresponding data sensitivity score, and evaluating each of the data sensitivity scores using a preset data evaluation matrix to obtain corresponding data level information; The reference index table of the central management node is associated based on the data feature identifier and data level information of each data node to obtain a hierarchical index table, and a corresponding access path is allocated to each data node in the hierarchical index table.

2. The data classification and grading method based on big data according to claim 1 is characterized in that: The extracting of content features and service attributes of each of the to-be-classified data, and generating corresponding data feature identifiers according to each of the content features and each of the service attributes, includes: For each of the to-be-classified data, performing text segmentation processing on the to-be-classified data to obtain a keyword set; Counting the frequency of occurrence of each keyword in the keyword set, and selecting a preset number of high-frequency keywords as content features, wherein the high-frequency keywords are keywords whose frequency of occurrence is greater than a frequency threshold; Acquire the creation time, business scope and user department of the data to be classified, and determine the business attributes based on a preset business type rule template, wherein the preset business type rule template includes business attributes corresponding to a combination of multiple different creation times, different business scopes and different user departments; The content features and service attributes are subjected to feature vectorization processing to generate data feature identifiers.

3. The data classification and grading method based on big data according to claim 1 is characterized in that: The method of using a preset sensitive word library to scan and process each of the data feature identifiers to generate a corresponding data sensitivity score includes: Determine the corresponding text scanning range according to each of the data feature identifiers; For each of the data feature identifiers, a plurality of sensitive word entries corresponding to the text scanning range are retrieved from the preset sensitive word library, wherein the sensitive word entries include sensitive word text, sensitive word weight, and category to which the sensitive word belongs; Matching the text within the text scanning range with the sensitive word text in the sensitive word entry to obtain a sensitive word hit record; Multiplying the sensitive word weight and the number of sensitive word hits of each target sensitive word entry in the sensitive word hit record to obtain a sub-sensitivity score; Based on the category to which the sensitive word of each target sensitive word entry belongs, a corresponding category coefficient is determined, and a weighted sum is performed on each sub-sensitivity score using each category coefficient as a weight to obtain a sensitivity score corresponding to the data feature identifier.

4. The data classification and grading method based on big data according to claim 1 is characterized in that: The data sensitivity scores are evaluated by a preset data evaluation matrix to obtain corresponding data level information, including: Obtaining a preset data evaluation matrix, wherein the preset data evaluation matrix includes a plurality of scoring intervals and data level information corresponding to each of the scoring intervals; Each of the data sensitivity scores is matched with each scoring interval in the data evaluation matrix to obtain data level information corresponding to the scoring interval in which each of the data sensitivity scores is located.

5. The data classification and grading method based on big data according to claim 1 is characterized in that: The step of associating the reference index table of the central management node based on the data feature identifier and the data level information of each data node to obtain a hierarchical index table includes: Dividing the reference index table into a plurality of initial sub-index tables corresponding to data level information; For each of the data nodes, determining a corresponding sub-index table in each of the initial sub-index tables according to the data level information; Establishing an association relationship between the data feature identifier of each data node and the corresponding sub-index table, and assigning corresponding access rights to each sub-index table, to obtain a target sub-index table of each data node; The target sub-index tables are integrated to obtain a hierarchical index table.

6. The data classification and grading method based on big data according to claim 1 is characterized in that: The allocating a corresponding access path to each of the data nodes in the hierarchical index table includes: Obtaining access rights to each of the data nodes; Based on a preset access path template and each of the access permissions, generating an access path identifier for each of the data nodes; A mapping relationship is established between the physical storage address of each data node and the access path identifier, and each access path identifier is encrypted according to a preset encryption rule to obtain an access path corresponding to each data node.

7. The data classification and grading method based on big data according to claim 6 is characterized in that: The step of generating an access path identifier for each data node based on a preset access path template and each access permission includes: Determine, based on each of the access rights, the type of access protocol corresponding to the security level of each of the access rights; Each of the access protocol types is respectively bound to the preset access path template to obtain an access path identifier of each of the data nodes.

8. A data classification and grading system based on big data, characterized in that: The system comprises: A data acquisition module, used to acquire the data to be classified of multiple data nodes and the benchmark index table of the central management node; A feature identification determination module, used to extract the content features and business attributes of each of the to-be-classified data, and generate a corresponding data feature identification according to each of the content features and each of the business attributes; A level information determination module, used to scan and process each of the data feature identifiers using a preset sensitive word library to generate a corresponding data sensitivity score, and evaluate each of the data sensitivity scores using a preset data evaluation matrix to obtain corresponding data level information; The data classification module is used to associate the reference index table of the central management node based on the data feature identifier and data level information of each data node to obtain a hierarchical index table, and allocate a corresponding access path to each data node in the hierarchical index table.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the data classification and grading method based on big data as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the data classification and grading method based on big data as described in any one of claims 1 to 7 is executed.

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