Classification and grading method and system for incremental data, electronic equipment and product

Abstract

The invention belongs to the technical field of data processing, and aims to provide a classification and grading method and system for incremental data, electronic equipment and a product. In the implementation process, the incremental data can be extracted by setting the data baseline table and regularly scanning the target data source, and the incremental data can be classified and graded by setting and updating the classification and grading database. According to the embodiment, acquisition of the incremental data and classification and grading processing of the incremental data can be achieved in a single classification and grading system, the manual workload is reduced, the automation degree and the intelligence degree of classification and grading of the incremental data are higher, and safety management of the data can be achieved easily.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and specifically relates to a classification and grading method, system, electronic equipment and product for incremental data. Background Art

[0002] In order to better manage and protect data assets and ensure the security, compliance and effective use of data, it is usually necessary to classify and grade data. Data classification and grading is an important part of the data governance system, which helps to improve the standardization and efficiency of data management. By classifying and grading data, the sensitivity, importance and value of the data can be clarified, so as to formulate corresponding management strategies and security measures to prevent data leakage, tampering and abuse, and protect personal privacy and national security. At present, with the introduction of laws such as the Personal Information Protection Law, the Cybersecurity Law and the Data Security Law, data classification and grading methods have been widely used.

[0003] In the process of data management, the types of data may change at any time. Data classification and grading usually need to be carried out at different stages. Since the amount of data to be processed is usually large, it is difficult to achieve real-time classification. Therefore, it is usually only necessary to classify and grade the incremental data (that is, when the data is updated, the new or modified data recorded since the last export or synchronization) according to the specific situation. However, the existing data classification and grading schemes often require the incremental data to be reclassified and graded as a whole. The operation is cumbersome and the workload of classification and grading is large.

[0004] In order to realize the automatic classification and grading of incremental data, the prior art has already appeared in the Chinese patent with the publication number CN118228316A, which discloses a classification and grading method, system, terminal device and storage medium, wherein the classification and grading method is as follows: generating key log information according to data usage activities; obtaining incremental data according to the analysis of the key log information; synchronizing the incremental data to the data classification and grading system, so that the data classification and grading system can classify and grade the incremental data. This prior art solves the problem that the existing data classification and grading cannot classify and grade incremental data, and improves the security protection effect of data.

[0005] Although the above-mentioned prior art can realize the classification and grading monitoring of the newly added data, the inventors found that there are at least the following problems in the prior art during the use of the prior art: When using existing technologies to classify and grade incremental data, it is necessary to cooperate with the database audit system for application. At the same time, because there is a lack of unified standards and protocols between classification and grading and audit systems in the current market, the audit system usually does not provide output data to the classification and grading systems of other manufacturers. Therefore, it is necessary to ensure that the data classification and grading system and the database audit system are products of the same manufacturer, which makes the use of the existing technology for data classification and grading more restrictive and less versatile. In addition, due to the wide variety of audit data in the database audit system, if the newly added data is deleted, the system may mistakenly report that there is incremental data, resulting in false alarms or interference, requiring manual intervention, making the intelligence level of classification and grading using existing technologies low. Summary of the invention

[0006] The present invention aims to solve the above-mentioned technical problems at least to a certain extent. The present invention provides a classification and grading method, system, electronic device and product for incremental data.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for classifying and grading incremental data, comprising: Acquire a target data source according to preset basic configuration information, scan and process the target data source to obtain various data fields in the target data source, and then store various data fields in a preset data baseline table; Perform preliminary classification and grading processing on each data field to obtain the classification and grading results of each data field; Storing each data field and the classification and grading results of each data field in a preset classification and grading database; According to the preset timing configuration information, the target data source is scanned regularly to obtain scanned data, so as to extract the incremental data of the target data source from the scanned data based on the data baseline table; Based on the classification and grading database, the incremental data is classified and graded to obtain a classification and grading result of the incremental data; The classification and grading results of the incremental data are stored in the classification and grading database to obtain an updated classification and grading database, so as to classify and grade the new incremental data based on the updated classification and grading database.

[0008] In a possible design, when each data field is initially classified and graded, the classification and grading are performed from multiple dimensions of each data field.

[0009] In a possible design, after obtaining the incremental data of the target data source, the method further includes: Generate classification and grading prompt information, and visualize the classification and grading prompt information and the incremental data, so as to receive the classification and grading confirmation instruction sent by the user, and classify and grade the incremental data according to the classification and grading confirmation instruction.

[0010] In a possible design, based on the classification and grading database, the incremental data is classified and graded to obtain the classification and grading result of the incremental data, including: Randomly selecting a specified number of new data fields from the incremental data, and constructing a sample data set based on the specified number of new data fields; Extracting a data field set under each classification and grading result from the classification and grading database, and extracting a data field set with the greatest similarity to the sample data set from the data field set under each classification and grading result, and recording the similarity between the data field set with the greatest similarity and the sample data set as the maximum similarity; Determine whether the maximum similarity is greater than a preset similarity threshold; if so, use the classification and grading result of the data field set with the maximum similarity as the classification and grading result of the incremental data; if not, proceed to the next step; Using a pre-trained mixed Gaussian model, the category of each newly added data field in the incremental data is obtained, and the level of each newly added data field is obtained according to the category of each newly added data field. The category and level combination of each newly added data field constitutes the classification and grading result of the incremental data.

[0011] In a possible design, a specified number of new data fields are randomly selected from the incremental data by random sampling.

[0012] In a possible design, in the data field sets under each classification and grading result, the similarity between any data field set and the sample data set is: ; In the formula, A represents the sample data set, A i is the first i word vectors, n is the total number of word vectors in the sample data set; B represents any set of data fields, B i is the first i word vectors.

[0013] In a possible design, the Gaussian mixture model is trained based on the classification and grading database; correspondingly, the category of any newly added data field in the incremental data is obtained by using the pre-trained Gaussian mixture model, including: The pre-trained mixed Gaussian model is used to calculate the probability that any new data field in the incremental data belongs to each field category in the classification and grading database; wherein the probability that any new data field belongs to any field category in the classification and grading database is: ; In the formula, x represents any newly added data field; k represents any of the field categories; π k Indicates any of the field categories k The weight of all field categories in the classification and grading database; Indicates the probability density that the category of any newly added data field x belongs to any field category k; The maximum probability among the probabilities that any newly added data field belongs to each field category in the classification and grading database is obtained, and the field category corresponding to the maximum probability is used as the category of any newly added data field.

[0014] In a second aspect, the present invention provides a classification and grading system for incremental data, which is used to implement an incremental data classification and grading method as described in any one of the above; the incremental data classification and grading system comprises: A baseline construction module is used to obtain a target data source according to preset basic configuration information, and to scan and process the target data source to obtain each data field in the target data source, and then store each data field in a preset data baseline table; to perform preliminary classification and grading processing on each data field to obtain classification and grading results of each data field; and to store each data field and the classification and grading results of each data field in a preset classification and grading database; An incremental data acquisition module, which is in communication with the baseline construction module and is used to scan the target data source at a fixed time according to preset timing configuration information to obtain scanned data, so as to extract incremental data of the target data source from the scanned data based on the data baseline table; An incremental data classification and grading module is in communication with the incremental data acquisition module and is used to classify and grade the incremental data based on the classification and grading database to obtain a classification and grading result of the incremental data; The baseline update module is in communication with the incremental data classification and grading module and is used to store the classification and grading results of the incremental data into the classification and grading database to obtain an updated classification and grading database, so as to classify and grade the new incremental data based on the updated classification and grading database.

[0015] In a third aspect, the present invention provides an electronic device, comprising: a memory for storing computer program instructions; and, A processor is used to execute the computer program instructions to complete the operation of a classification and grading method for incremental data as described in any one of the above.

[0016] In a fourth aspect, the present invention provides a computer program product, including a computer program or instructions, which, when executed by a computer, implements a classification and grading method for incremental data as described in any one of the above.

[0017] The beneficial effects of the present invention are: The present invention does not require the additional deployment of other data security systems such as audit systems, has a wider range of applications, and can improve the intelligence level of incremental data classification and grading. Specifically, during the implementation of the present invention, the incremental data can be extracted by setting a data baseline table and scanning the target data source regularly, and the classification and grading of the incremental data can be achieved by setting and updating the classification and grading database. Based on this, the present invention can achieve the acquisition of incremental data and the classification and grading of incremental data in a single classification and grading system, and the manual workload can be reduced. The automation and intelligence level of incremental data classification and grading is higher, which is conducive to the realization of data security management.

[0018] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of a classification and grading method for incremental data in Example 1; Figure 2 is a module block diagram of a classification and grading system for incremental data in Example 2; Figure 3 This is a module block diagram of an electronic device in Example 3. DETAILED DESCRIPTION

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0021] Embodiment 1: This embodiment discloses a classification and grading method for incremental data, which can be executed by, but is not limited to, a computer device or a virtual machine with certain computing resources, such as a personal computer, a smart phone, a personal digital assistant, or a wearable device, or by a virtual machine.

[0022] like Figure 1 As shown, a classification and grading method for incremental data may include, but is not limited to, the following steps: S1. Obtain the target data source according to the preset basic configuration information, and scan and process the target data source to obtain each data field in the target data source, and then store each data field in the preset data baseline table; specifically, in this embodiment, the data to be classified and graded is the structural data of the target data source, that is, the structural information of each data field in the target data source, and the structural information of any data field, such as the field attributes of any data field, is not limited here. The basic configuration information includes a data source identifier, which is used to identify the target data source from one or more databases to be scanned. Of course, the basic configuration information may also include information such as the scan start time, the scan end time, and the scan timing information, as the configuration reference data for subsequent scanning.

[0023] In addition, in this embodiment, after obtaining each data field in the target data source, the basic configuration information can also be stored in a preset scan configuration baseline table, so that the incremental data can be subsequently identified and extracted based on the scan configuration baseline table and the data baseline table. Specifically, during the implementation process, if the timing configuration information also includes other data source identifiers in addition to the data source identifier of the target data source, all data fields in other data sources corresponding to the other data source identifiers also constitute incremental data.

[0024] S2. Perform preliminary classification and grading processing on each data field to obtain classification and grading results of each data field; specifically, in this embodiment, preliminary classification and grading processing can be performed on each data field in multiple dimensions from dimensions such as data resource attributes, organizational attributes and open attributes to improve the accuracy of the initial classification and grading results of the data fields in the target data source.

[0025] S3. Store each data field and the classification and grading results of each data field into a preset classification and grading database; it should be noted that, in this embodiment, the data baseline table and the classification and grading database constitute the processing benchmark for subsequent incremental data classification and grading processing.

[0026] S4. According to the preset timing configuration information, the target data source is scanned regularly to obtain the scanned data, so as to extract the incremental data of the target data source from the scanned data based on the data baseline table; it should be noted that, in this embodiment, the incremental data can be identified by comparing the scanned data with the data baseline table. If there is no incremental data, the target data source is scanned again after a specified period of time according to the timing configuration information until the incremental data is identified and extracted.

[0027] In step S4, after obtaining the incremental data of the target data source, the method further includes: Generate classification and grading prompt information, and visualize the classification and grading prompt information and the incremental data, so as to receive the classification and grading confirmation instructions sent by the user, and classify and grade the incremental data according to the classification and grading confirmation instructions. It should be noted that during the implementation process, the newly added data fields therein can be directly classified and graded after the incremental data is obtained, or the classification and grading can be performed after manual confirmation. In this embodiment, adding a manual confirmation step can help users to keep abreast of data changes in a timely manner. Users can dynamically confirm whether to perform subsequent classification and grading processing based on business changes or security requirements, and can further specify classification and grading methods to ensure the flexibility of data security management.

[0028] S5. Based on the classification and grading database, the incremental data is classified and graded to obtain the classification and grading results of the incremental data.

[0029] Specifically, in step S5, based on the classification and grading database, the incremental data is classified and graded to obtain the classification and grading results of the incremental data, including: S501. Randomly select a specified number of new data fields from the incremental data, and construct a sample data set based on the specified number of new data fields; it should be noted that, in this embodiment, randomly selecting a specified number of new data fields from the incremental data can avoid data bias, ensure that the data in the sample data set is representative, and make subsequent classification and grading results more accurate.

[0030] In step S501 of this embodiment, a specified number of newly added data fields are randomly selected from the incremental data by random sampling. By this random sampling method, part of the data is obtained from the newly added data fields for subsequent classification and grading, thereby improving the efficiency of subsequent classification and grading without traversing all the newly added data fields.

[0031] Specifically, during the implementation process, the newly added data field to be selected is represented as "k_name", and the incremental data is represented as "p_table". If the specified number of extractions is set to 10, the following SQL (Structured Query Language) query statement can be used to randomly select 10 newly added data fields from the incremental data: "select k_name from p_table limit row1, 10", where row1 is a random number used to specify the query starting position, indicating that data is selected from a random starting point, and "10" represents the specified number of extractions, that is, 10 newly added data fields are continuously extracted starting from the randomly specified starting position.

[0032] S502. Extract the data field set under each classification and grading result from the classification and grading database, and extract the data field set with the greatest similarity to the sample data set from the data field set under each classification and grading result, and record the similarity between the data field set with the greatest similarity and the sample data set as the maximum similarity.

[0033] Specifically, in step S502, in the data field sets under each classification and grading result, the similarity between any data field set and the sample data set is: ; In the formula, A represents the sample data set, A i is the first i word vectors, n is the total number of word vectors in the sample data set; B represents any set of data fields, Bi is the first i It should be understood that, in this embodiment, the data in the target data source is stored and processed in the form of vectors.

[0034] S503. Determine whether the maximum similarity is greater than a preset similarity threshold. If so, use the classification and grading result of the data field set with the greatest similarity as the classification and grading result of the incremental data. If not, proceed to the next step. Specifically, in this embodiment, the similarity threshold is set to 60%, which is not limited here.

[0035] S504. Using a pre-trained Gaussian Mixture Model (GMM), obtain the category of each newly added data field in the incremental data, and obtain the level of each newly added data field according to the category of each newly added data field. The category and level combination of each newly added data field constitute the classification and grading result of the incremental data. It should be noted that in this embodiment, the Gaussian Mixture Model can match the classification of any newly added data field input from different dimensions (such as data resource attribute dimension, organizational attribute dimension or open attribute dimension), and then obtain the category of any newly added data field. Subsequently, the level of any newly added data field can be obtained according to the category of any newly added data field and the preset grading rules.

[0036] Specifically, in this embodiment, the mixed Gaussian model is trained based on the classification and grading database. Specifically, in this embodiment, the EM (Expectation Maximization Algorithm) algorithm is used to implement the training of the initial mixed Gaussian model, so as to obtain a trained mixed Gaussian model, and the training steps are as follows: A1. Dividing the data fields in the classification and grading database and the field categories corresponding to the data fields into a training data set and a validation data set; A2. Pre-calculate the probability that each data field in the training data set belongs to each field category (expectation step, also called E step), where any data field x i Belongs to any field category c The probability is: ; In the formula, π c Represents any field category c The weight of all field categories in the classification and grading database; Represents any data field x iThe category belongs to any field category c The probability density of , specifically, μ c represents the mean of any field category c, Represents any field category c The covariance matrix of π k Represents any field category in each field category k The weight of all field categories in the classification and grading database; Represents any data field x i The category belongs to any field category k The probability density of μ k Represents any field category k The mean of Represents any field category k The covariance matrix of .

[0037] A3. Use the EM algorithm to update the parameters in the initial Gaussian mixture model (maximization step, also called M step), and repeat the E step and M step, that is, return to step A2, until the Gaussian mixture model converges (that is, the parameter changes are very small or the predetermined number of iterations is reached). At this time, the Gaussian mixture model is the trained Gaussian mixture model.

[0038] It should be noted that the GMM model can handle complex distributions of different categories. It uses soft classification, which is suitable for processing data fields with unclear categories, helps reduce the risk of misclassification, and has better prediction effects on newly added data field categories.

[0039] Correspondingly, in step S504, a pre-trained Gaussian Mixture Model (GMM) is used to obtain the category of any newly added data field in the incremental data, including: S5041. Using the pre-trained mixed Gaussian model, calculate the probability that any new data field in the incremental data belongs to each field category in the classification and grading database; wherein the probability that any new data field belongs to any field category in the classification and grading database is: ; In the formula, x represents any newly added data field; k represents any of the field categories; π k Indicates any of the field categories k The weight of all field categories in the classification and grading database; Indicates any of the newly added data fields x The category belongs to any of the fields k The probability density of , specifically, μ k Indicates any of the field categories k The mean of Indicates any of the field categories k The covariance matrix of .

[0040] S5042. Obtain the maximum probability among the probabilities that any of the newly added data fields belongs to various field categories in the classification and grading database, and use the field category corresponding to the maximum probability as the category of any of the newly added data fields.

[0041] S6. The classification and grading results of the incremental data are stored in the classification and grading database to obtain an updated classification and grading database, thereby establishing a new data classification and grading baseline so as to classify and grade the new incremental data based on the updated classification and grading database.

[0042] It should be noted that in step S1 of this embodiment, after obtaining each data field in the target data source, each data field in the target data source can be visualized for manual confirmation, and each data field can be stored in a preset data baseline table after manual confirmation; in step S2, after obtaining the classification and grading results of each data field, the classification and grading results can also be manually confirmed, and each data field and the classification and grading results of each data field can be stored in a preset classification and grading database, thereby obtaining a baseline for subsequent incremental data classification and grading processing. The manual confirmation step can further ensure the reliability of subsequent data classification and grading.

[0043] This embodiment does not require the additional deployment of other data security systems such as audit systems, has a wider range of applications, and can improve the intelligence level of incremental data classification and grading. Specifically, during the implementation of this embodiment, the incremental data can be extracted through the setting of the data baseline table and the scheduled scanning of the target data source, and the classification and grading of the incremental data can be achieved through the setting and updating of the classification and grading database. Based on this, this embodiment can achieve the acquisition of incremental data and the classification and grading of incremental data in a single classification and grading system, and the manual workload can be reduced. The automation and intelligence level of incremental data classification and grading is higher, which is conducive to the realization of data security management.

[0044] Embodiment 2: This embodiment discloses a classification and grading system for incremental data, which is used to implement the classification and grading method for incremental data in Embodiment 1; Figure 2 As shown, the incremental data classification and grading system includes: A baseline construction module is used to obtain a target data source according to preset basic configuration information, and to scan and process the target data source to obtain each data field in the target data source, and then store each data field in a preset data baseline table; to perform preliminary classification and grading processing on each data field to obtain classification and grading results of each data field; and to store each data field and the classification and grading results of each data field in a preset classification and grading database; An incremental data acquisition module, which is in communication with the baseline construction module and is used to scan the target data source at a fixed time according to preset timing configuration information to obtain scanned data, so as to extract incremental data of the target data source from the scanned data based on the data baseline table; An incremental data classification and grading module is in communication with the incremental data acquisition module and is used to classify and grade the incremental data based on the classification and grading database to obtain a classification and grading result of the incremental data; The baseline update module is in communication with the incremental data classification and grading module and is used to store the classification and grading results of the incremental data into the classification and grading database to obtain an updated classification and grading database, so as to classify and grade the new incremental data based on the updated classification and grading database.

[0045] It should be noted that the working process, working details and technical effects of the incremental data classification and grading system provided in this embodiment 2 can be found in embodiment 1 and will not be repeated here.

[0046] Embodiment 3: Based on Embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smart phone, a tablet computer, a laptop computer, or a desktop computer. The electronic device may be referred to as a user terminal, a portable terminal, a desktop terminal, etc. Figure 3 As shown, the electronic equipment includes: a memory for storing computer program instructions; and, A processor is used to execute the computer program instructions to complete the operation of a classification and grading method for incremental data as described in any one of Example 1.

[0047] Specifically, the processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen.

[0048] The memory 302 may include one or more computer-readable storage media, which may be non-transitory. The memory 302 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 302 is used to store at least one instruction, which is used to be executed by the processor 301 to implement the incremental data classification and grading method provided in Example 1 of the present application.

[0049] In some embodiments, the terminal may further optionally include: a communication interface 303 and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected via a bus or a signal line. Each peripheral device may be connected to the communication interface 303 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 304, a display screen 305 and a power supply 306.

[0050] The communication interface 303 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the communication interface 303 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0051] The radio frequency circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with a communication network and other communication devices through electromagnetic signals.

[0052] The display screen 305 is used to display a UI (User Interface). The UI may include any combination of graphics, text, icons, and videos.

[0053] The power supply 306 is used to supply power to various components in the electronic device.

[0054] Embodiment 4: Based on any one of Embodiments 1 to 3, this embodiment discloses a computer program product, including a computer program or instructions, which, when executed by a computer, implements a classification and grading method for incremental data as described in any one of Embodiments 1. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0055] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A classification and grading method for incremental data, characterized in that: include: Acquire a target data source according to preset basic configuration information, scan and process the target data source to obtain various data fields in the target data source, and then store various data fields in a preset data baseline table; Perform preliminary classification and grading processing on each data field to obtain the classification and grading results of each data field; Storing each data field and the classification and grading results of each data field in a preset classification and grading database; According to the preset timing configuration information, the target data source is scanned regularly to obtain scanned data, so as to extract the incremental data of the target data source from the scanned data based on the data baseline table; Based on the classification and grading database, the incremental data is classified and graded to obtain a classification and grading result of the incremental data; The classification and grading results of the incremental data are stored in the classification and grading database to obtain an updated classification and grading database, so as to classify and grade the new incremental data based on the updated classification and grading database.

2. A classification and grading method for incremental data according to claim 1, characterized in that: When each data field is initially classified and graded, classification and grading are performed from multiple dimensions of each data field.

3. A classification and grading method for incremental data according to claim 1, characterized in that: After obtaining the incremental data of the target data source, the method further includes: Generate classification and grading prompt information, and visualize the classification and grading prompt information and the incremental data, so as to receive the classification and grading confirmation instruction sent by the user, and classify and grade the incremental data according to the classification and grading confirmation instruction.

4. The incremental data classification and grading method according to claim 1, characterized in that: Based on the classification and grading database, the incremental data is classified and graded to obtain the classification and grading results of the incremental data, including: Randomly selecting a specified number of new data fields from the incremental data, and constructing a sample data set based on the specified number of new data fields; Extracting a data field set under each classification and grading result from the classification and grading database, and extracting a data field set with the greatest similarity to the sample data set from the data field set under each classification and grading result, and recording the similarity between the data field set with the greatest similarity and the sample data set as the maximum similarity; Determine whether the maximum similarity is greater than a preset similarity threshold; if so, use the classification and grading result of the data field set with the maximum similarity as the classification and grading result of the incremental data; if not, proceed to the next step; Using a pre-trained mixed Gaussian model, the category of each newly added data field in the incremental data is obtained, and the level of each newly added data field is obtained according to the category of each newly added data field. The category and level combination of each newly added data field constitutes the classification and grading result of the incremental data.

5. A classification and grading method for incremental data according to claim 4, characterized in that: A specified number of newly added data fields are randomly selected from the incremental data by random sampling.

6. A method for classifying and grading incremental data according to claim 4, characterized in that: In the data field sets under each classification and grading result, the similarity between any data field set and the sample data set is: ； In the formula, A represents the sample data set, A i is the first i word vectors, n is the total number of word vectors in the sample data set; B represents any set of data fields, B i is the first i word vectors.

7. A method for classifying and grading incremental data according to claim 4, characterized in that: The mixed Gaussian model is trained based on the classification and grading database; correspondingly, the category of any newly added data field in the incremental data is obtained by using the pre-trained mixed Gaussian model, including: The pre-trained mixed Gaussian model is used to calculate the probability that any new data field in the incremental data belongs to each field category in the classification and grading database; wherein the probability that any new data field belongs to any field category in the classification and grading database is: ； In the formula, x represents any newly added data field; k represents any of the field categories; π k Indicates any of the field categories k The weight of all field categories in the classification and grading database; Indicates the probability density that the category of any newly added data field x belongs to any field category k; The maximum probability among the probabilities that any newly added data field belongs to each field category in the classification and grading database is obtained, and the field category corresponding to the maximum probability is used as the category of any newly added data field.

8. A classification and grading system for incremental data, characterized in that: Used to implement a classification and grading method for incremental data as described in any one of claims 1 to 7; the classification and grading system for incremental data comprises: A baseline construction module is used to obtain a target data source according to preset basic configuration information, and to scan and process the target data source to obtain each data field in the target data source, and then store each data field in a preset data baseline table; to perform preliminary classification and grading processing on each data field to obtain classification and grading results of each data field; and to store each data field and the classification and grading results of each data field in a preset classification and grading database; An incremental data acquisition module, which is in communication with the baseline construction module and is used to scan the target data source at a fixed time according to preset timing configuration information to obtain scanned data, so as to extract incremental data of the target data source from the scanned data based on the data baseline table; An incremental data classification and grading module is in communication with the incremental data acquisition module and is used to classify and grade the incremental data based on the classification and grading database to obtain a classification and grading result of the incremental data; The baseline update module is in communication with the incremental data classification and grading module and is used to store the classification and grading results of the incremental data into the classification and grading database to obtain an updated classification and grading database, so as to classify and grade the new incremental data based on the updated classification and grading database.

9. An electronic device, characterized in that: include: a memory for storing computer program instructions; as well as, A processor is used to execute the computer program instructions to complete the operation of a classification and grading method for incremental data as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that When executed by a computer, the computer program or the instructions implement a classification and grading method for incremental data as described in any one of claims 1 to 7.

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