Service data classification and grading method and device, equipment and storage medium

Abstract

The invention provides a business data classification and grading method and device, equipment and a storage medium, and relates to the technical field of data security. The method comprises the following steps: determining attribute information of target business data; constructing a knowledge database; the knowledge database comprises business data of multiple fields; and based on the attribute information of the target business data and the knowledge database, determining a classification and grading result of the target business data. Therefore, the business data in different fields can be classified and graded more accurately according to the attribute information of the business data in combination with the knowledge database.

Description

Technical Field

[0001] The present application relates to the field of data security technology, and in particular to a method, device, equipment and storage medium for classifying and grading business data. Background Art

[0002] Data classification refers to distinguishing and categorizing data according to certain principles and methods based on its attributes or characteristics, and establishing a certain classification system and arrangement order. Common methods of data classification include relationship-based classification, business (source)-based classification, content-based classification, etc.

[0003] At present, the classification and grading method for data mainly combines data with obvious data features based on global data recognition and a large amount of manual intervention. Manual intervention may cause data classification to take months or even longer, during which new data will continue to be generated and old data may also change, resulting in the inability to present classification results in a timely and accurate manner. In addition, the professional knowledge of staff is limited, making it difficult to accurately classify cross-domain business data. Summary of the invention

[0004] The present application provides a method, device, equipment and storage medium for classifying and grading business data, which can accurately classify data in different fields.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a method for classifying and grading business data, the method comprising: determining attribute information of target business data; constructing a knowledge database; the knowledge database includes relevant business data in multiple fields; based on the attribute information of the target business data and the knowledge database, determining the classification and grading results of the target business data.

[0007] According to the above technical means, the attribute information of the target business data can be determined, and a flow chart of the target business data can be constructed based on the attribute information of the target business data. The flow chart of the target business data includes the source of the target business data and the destination of the target business data. And based on the attribute information of the target business data and the flow chart of the target business data, the target business data is classified and graded. Therefore, based on the attribute information of the business data and combined with the knowledge database, the business data in different fields can be classified and graded more accurately.

[0008] In a possible implementation, the above-mentioned “determining the classification and grading results of the target business data based on the attribute information of the target business data and the knowledge database” includes: constructing a flow chart of the target business data based on the attribute information of the target business data; the flow chart of the target business data includes the source of the target business data and the destination of the target business data; determining the classification and grading results of the target business data based on the attribute information of the target business data, the flow chart of the target business data and the knowledge database.

[0009] According to the above technical means, a flow chart of the target business data can be constructed based on the attribute information of the target business data. The flow chart of the target business data includes the source of the target business data and the destination of the target business data. And based on the attribute information of the target business data, the flow chart of the target business data and the knowledge database, the classification and grading results of the target business data are determined. Therefore, based on the attribute information of the business data and the flow chart of the business data, combined with the knowledge database, the business data in different fields can be classified and graded more accurately.

[0010] In a possible implementation, the above-mentioned "determining the classification and grading results of the target business data based on the attribute information of the target business data, the flow chart of the target business data and the knowledge database" includes: inputting the attribute information of the target business data, the flow chart of the target business data and the business data in the knowledge database into a pre-trained business data classification model to obtain classification rules that match the target business data; based on the classification rules, determining the classification and grading results of the target business data.

[0011] According to the above technical means, the attribute information of the target business data, the flowchart of the target business data and the business data in the knowledge database can be input into the pre-trained business data classification model to obtain the classification rules matching the target business data; based on the classification rules, the classification and grading results of the target business data are determined. In this way, the classification and grading results of the business data can be quickly and accurately determined in combination with the business data classification model.

[0012] In a possible implementation, the above-mentioned "inputting the attribute information of the target business data, the flow chart of the target business data, and the business data in the knowledge database into a pre-trained business data classification model to obtain classification rules that match the target business data" includes: inputting the business data in the knowledge database into the business data classification model; training the business data classification model based on the business data in the knowledge database to determine the trained business data classification model; obtaining classification rules that match the target business data based on the trained business data classification model, the attribute information of the target business data, and the flow chart of the target business data.

[0013] According to the above technical means, the business data in the knowledge database can be input into the business data classification model; based on the business data in the knowledge database, the business data classification model is trained to determine the trained business data classification model; based on the trained business data classification model, the attribute information of the target business data and the flow chart of the target business data, the classification rules matching the target business data are obtained. In this way, different types of business data can be trained in combination with the business data in the knowledge database to more accurately match the classification rules of the business data.

[0014] In a possible implementation manner, before the attribute information of the target business data is determined, the steps include: acquiring the business data; and processing the business data to determine the target business data after the business data is processed.

[0015] According to the above technical means, business data can be obtained; the business data can be processed to determine the target business data after the business data processing. Thus, the business data can be preliminarily processed to facilitate the subsequent more accurate classification and grading of the business data.

[0016] In a possible implementation, the above-mentioned "determining the classification and grading results of the target business data based on the attribute information of the target business data, the flow chart of the target business data and the knowledge database" includes: deduplicating the business data, detecting and eliminating outliers; and determining the target business data after the business data is processed.

[0017] According to the above technical means, the business data can be deduplicated, outliers can be detected and removed, and the target business data after the business data processing can be determined. Thus, the business data can be preliminarily processed to improve the processing efficiency of the business data.

[0018] In one possible implementation, the classification and grading results of the above-mentioned target business data include level one data, level two data, level three data and level four data: level one data indicates that no harm will be caused to personal rights and interests, organizational rights and interests, etc.; level two data indicates that general harm will be caused to personal rights and interests, organizational rights and interests; level three data indicates that serious harm will be caused to personal rights and interests, organizational rights and interests; level four data indicates that particularly serious harm will be caused to personal rights and interests, organizational rights and interests, or general harm will be caused to economic operations, social order, and public interests.

[0019] According to the above technical means, the classification and grading results of the target business data can be divided into primary data, secondary data, tertiary data and quaternary data, and the degree of harm of the data can be accurately determined by the level of the data.

[0020] In a second aspect, the present application provides a classification and grading device for business data, the device comprising: a determination unit and a construction unit. The determination unit is used to determine the attribute information of the target business data; the construction unit is used to construct a knowledge database; the knowledge database includes relevant business data in multiple fields; the determination unit is also used to determine the classification and grading results of the target business data based on the attribute information of the target business data and the knowledge database.

[0021] In one possible implementation, the determination unit is specifically used to construct a flow chart of the target business data based on the attribute information of the target business data; the flow chart of the target business data includes the source of the target business data and the destination of the target business data; based on the attribute information of the target business data, the flow chart of the target business data and the knowledge database, the classification and grading results of the target business data are determined.

[0022] In a possible implementation, the determination unit is also specifically used to input the attribute information of the target business data, the flow chart of the target business data, and the business data in the knowledge database into a pre-trained business data classification model to obtain classification rules that match the target business data; based on the classification rules, determine the classification and grading results of the target business data.

[0023] In a possible implementation, the determination unit is also specifically used to input the business data in the knowledge database into the business data classification model; based on the business data in the knowledge database, the business data classification model is trained to determine the trained business data classification model; based on the trained business data classification model, the attribute information of the target business data and the flow chart of the target business data, a classification rule matching the target business data is obtained.

[0024] In a possible implementation manner, the determination unit is further configured to obtain business data; process the business data, and determine target business data after the business data is processed.

[0025] In a possible implementation, the determination unit is further configured to perform deduplication processing on the business data, detect and remove outliers, and determine target business data after the business data is processed.

[0026] In a possible implementation, the determination unit is further used to determine the classification and grading results of the target business data, and the classification and grading results of the target business data include primary data, secondary data, tertiary data, and quaternary data. Among them, primary data indicates that no harm will be caused to personal rights and interests, organizational rights and interests, etc.; secondary data indicates that general harm will be caused to personal rights and interests, organizational rights and interests; tertiary data indicates that serious harm will be caused to personal rights and interests, organizational rights and interests; and quaternary data indicates that particularly serious harm will be caused to personal rights and interests, organizational rights and interests, or general harm will be caused to economic operation, social order, and public interests.

[0027] In a third aspect, the present application provides an electronic device comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the above-mentioned first aspect and any possible implementation manner thereof.

[0028] In a fourth aspect, the present application provides a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute the method in the above-mentioned first aspect and any possible implementation method thereof.

[0029] In a fifth aspect, the present application provides a computer program product comprising instructions, which, when executed on a communication device, enables the communication device to execute the method in the first aspect and any possible implementation manner thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of a framework of a classification and grading system for business data provided in an embodiment of the present application;

[0031] Figure 2 A flow chart of a method for classifying and grading business data provided in an embodiment of the present application;

[0032] Figure 3 A flow chart of a method for classifying and grading telecommunication service data provided in an embodiment of the present application;

[0033] Figure 4 A schematic diagram of the structure of a device for classifying and grading business data provided in an embodiment of the present application;

[0034] Figure 5 A block diagram of an electronic device of a device for classifying and grading business data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] The following is a detailed description of a method, device, equipment and storage medium for classifying and grading business data provided by an embodiment of the present application in conjunction with the accompanying drawings.

[0036] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0037] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0038] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0039] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" 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 "exemplary" or "for example" is intended to present related concepts in a specific way.

[0040] Data classification is classified from the perspective of business or data management, including industry dimension, business field dimension, data source dimension, sharing dimension, data openness dimension, etc. At the same time, based on these dimensions, data with the same attributes or characteristics are classified according to certain principles and methods.

[0041] Although existing data classification methods can complete some tasks, they still have the following major shortcomings:

[0042] (1) Lack of data sensitivity check: Determining the sensitivity of data is a key step in data classification, but accurately assessing the sensitivity of data is a challenging task. Data may contain different levels of sensitive information such as personal identity information, financial data, and business secrets, which requires comprehensive consideration of the type of data, owner requirements, and applicable regulatory requirements.

[0043] (2) Strict implementation of classification standards: Formulating appropriate data classification standards is another difficulty. Classification standards should take into account the sensitivity, confidentiality, availability and other relevant factors of the data. At the same time, they need to be flexible and scalable to adapt to the needs of different organizations and changing environments, and be strictly implemented according to the formulated classification standards.

[0044] (3) Data consistency issues: Ensuring that data classification is consistent across the organization is a challenge. Different departments and teams may have different understandings and practices, leading to confusion and inconsistency in data classification. It is necessary to develop clear data classification policies and provide training and guidance to ensure that everyone adheres to the same classification method.

[0045] (4) Data adaptation and compatibility issues: Data classification needs to deal with management requirements from multiple perspectives and face multiple classification systems. It is not possible to achieve the goal of seamless connection simply by merging different classification systems. It is necessary to adapt and be compatible with various external requirements.

[0046] (5) Processing of massive data: Facing massive data, how to effectively classify and implement it is a huge challenge. The amount of data is growing exponentially, and how to efficiently classify and promote collaborative participation within the enterprise is a problem that needs to be solved.

[0047] In order to solve the above technical problems, the present application provides a method for classifying and grading business data. The method includes: determining the attribute information of the target business data; constructing a knowledge database; the knowledge database includes business data in multiple fields; and determining the classification and grading results of the target business data based on the attribute information of the target business data and the knowledge database. Thus, the business data in different fields can be classified and graded more accurately according to the attribute information of the business data and in combination with the knowledge database.

[0048] The execution entity in the embodiment of the present application may be a server control platform, and the embodiment is described in detail below.

[0049] For ease of understanding, a classification and grading method for business data provided by the present application is specifically introduced below with reference to the accompanying drawings.

[0050] Figure 1 A schematic diagram of a framework of a classification and grading system for business data provided in an embodiment of the present application, such as Figure 1 As shown, the framework diagram includes a server control platform 101 and multiple servers 102 .

[0051] It should be noted that the server control platform 101 in the present application may be connected to multiple servers 102. The server control platform 101 may manage multiple servers 102 according to the identifier corresponding to each server.

[0052] The server control platform 101 is used to receive business data reported by multiple servers 102, and determine the classification and grading results of the business data of each server according to the business data of each server in the multiple servers.

[0053] It should be noted that the server control platform 101 in the present application includes multiple databases. For example, the server control platform 101 may include a user database, a billing database, and a service database.

[0054] The server 102 is used to report business data to the server control platform 101. The server may be one or more of a cloud server, a resource server, and an application server.

[0055] Figure 2 A flow chart of a method for classifying and grading business data provided in an embodiment of the present application, the method comprising the following S201-S203.

[0056] S201: Determine attribute information of target business data.

[0057] Among them, the target business data may include data generated by the enterprise in its business activities. The target business data can be divided into structured data, semi-structured data and unstructured data. For example, the target business data can be the enterprise's operating data, transaction record data, etc., which are not limited in the embodiments of this application. The attribute information may include one or more of the business field, responsible department, description object, process link, data subject, content theme, data purpose and data source of the business data, which are not limited in the embodiments of this application.

[0058] In one possible implementation, the server control platform periodically receives business data reported by multiple servers, processes the business data, and determines processed target business data.

[0059] In an embodiment of the present application, the data processing of business data by the server control platform may include one or more of cleaning abnormal values, merging duplicate data, converting field names, splitting fields, and removing blank characters.

[0060] Among them, cleaning outliers is used to detect and remove outliers, such as orders with failed payments. Merging duplicate data means merging identical records into one record, such as merging identical orders into one order. Converting field names means converting field names into user-friendly names. Splitting fields means splitting data into multiple fields, such as storing date and time in different fields. Removing blank characters means deleting extra blank characters, such as deleting trailing spaces and carriage returns.

[0061] After receiving the target business data, the server control platform can identify and extract the attribute information in the target business data. The server control platform can extract the corresponding attribute information from the target business data through algorithms or models, such as regular expressions, natural language processing (NLP) algorithms or machine learning models.

[0062] In the embodiment of the present application, for structured data and semi-structured data (such as structured data, key-value pair data), the server control platform can directly read the field value as attribute information. For unstructured data (such as text, images, etc.), the server control platform can parse and extract attribute information through natural language processing algorithms.

[0063] For example, for unstructured data, natural language processing algorithms can be applied to images. The server control platform can identify and describe images through natural language processing algorithms and generate natural language text related to the image. For example, given an image of a user's phone bill, after processing it through a natural language processing algorithm, the attribute information can be generated that the business field in the telecom business is the phone bill field, and the content theme is the user package.

[0064] S202: Build a knowledge database.

[0065] Among them, the knowledge database includes relevant business data in multiple fields.

[0066] In one possible implementation, the server control platform can build a knowledge database through DB-GPT technology.

[0067] Among them, DB-GPT technology can uniformly vectorize and store massive amounts of structured and unstructured data, and achieve efficient semantic retrieval. DB-GPT technology supports connecting to a variety of data sources, including traditional databases (MySQL, PostgreSQL, etc.), data warehouses, and file formats such as Excel.

[0068] In an embodiment of the present application, the knowledge database includes multiple industry fields. For example, the multiple industry fields may include industry, telecommunications, finance, energy, transportation, natural resources, health, education, science and other fields.

[0069] For example, the following is a detailed description of the business data in the telecommunications field stored in the knowledge database. In the embodiment of the present application, the storage form of the business data in the telecommunications field stored in the knowledge database is not limited, and it can be stored in the form of text or in the form of a table. For example, Table 1 shows a method of storing in the form of a table.

[0070] Table 1 shows the business data in the telecommunication field stored in the knowledge database.

[0071] Table 1

[0072]

[0073]

[0074] S203: Determine the classification and grading result of the target business data based on the attribute information of the target business data and the knowledge database.

[0075] In a possible implementation, the server control platform can construct a flow chart of the target business data based on the attribute information of the target business data. The flow chart of the target business data includes the source of the target business data and the destination of the target business data. And based on the attribute information of the target business data, the flow chart of the target business data and the knowledge database, the classification and grading results of the target business data are determined.

[0076] In some embodiments, the server control platform can determine the flow of the target business data in the business process by analyzing the flowchart of the target business data, including the generation, processing, storage, use and destruction of data. It can also use the existing knowledge database to match the industry specifications, laws and regulations, safety standards and other information related to the target business data to provide a basis for data classification and grading.

[0077] In the embodiment of the present application, the server control platform can input the attribute information of the target business data, the flowchart of the target business data, and the business data in the knowledge database into the pre-trained business data classification model to obtain the classification rules matching the target business data. And based on the classification rules, the classification and grading results of the target business data are determined.

[0078] The training process of the business data classification model may include data collection, data preprocessing, feature selection, and model training. Data collection can collect a large amount of data in the knowledge database. Data preprocessing means cleaning and sorting the collected data to eliminate outliers and duplicate data and improve data quality. Feature selection means extracting attribute information of business data from raw data. Model training means selecting a matching machine learning algorithm (such as linear regression, decision tree, random forest, etc.) and training the model using historical data to determine the mapping relationship between business data and classification and grading rules.

[0079] For example, in the industrial field, the server control platform can classify industrial data into different levels according to the potential impact that different types of industrial data may have on industrial production, economic benefits, etc. after being tampered with, destroyed, leaked or illegally used. For example, the classified industrial data may include primary data, secondary data, tertiary data and quaternary data.

[0080] Among them, the first-level data indicates that there will be no harm to personal rights and interests, organizational rights, etc.; the second-level data indicates that it will cause general harm to personal rights and interests, organizational rights and interests; the third-level data indicates that it will cause serious harm to personal rights and interests, organizational rights and interests; the fourth-level data indicates that it will cause particularly serious harm to personal rights and interests, organizational rights and interests, or cause general harm to economic operations, social order, and public interests.

[0081] based on Figure 2According to the technical solution shown in the embodiment of the present application, a knowledge database can be constructed; the knowledge database includes business data in multiple fields; based on the attribute information of the target business data and the knowledge database, the classification and grading results of the target business data are determined. Therefore, the business data in different fields can be classified and graded more accurately according to the attribute information of the business data and in combination with the knowledge database.

[0082] Figure 3 A flow chart of a method for classifying and grading telecommunication business data provided in an embodiment of the present application, the method comprising the following S301-S306.

[0083] S301. Obtain telecommunication service data.

[0084] Exemplarily, the telecommunications service data may refer to the contents in Table 1 above, which will not be described in detail here.

[0085] S302: De-duplication the telecommunication service data.

[0086] The specific steps can refer to step S201, which will not be described in detail here.

[0087] S303: Build a knowledge database.

[0088] The specific steps may refer to step S202 and will not be described in detail here.

[0089] S304. Establish a telecommunications professional database.

[0090] In an embodiment of the present application, the server control platform can receive telecommunication business data reported by multiple servers and establish a telecommunication professional database based on the telecommunication business data.

[0091] Exemplarily, the telecommunications professional database may include a user database, a billing database, a device database, a network topology database, a service database, a customer relationship management database, a security management database, and a statistical analysis database.

[0092] Among them, the user database can store the user's personal information and account information, including the user's name, contact information, address, account balance, etc. This data is used to provide personalized services and bill management.

[0093] The billing database is used to record users' communications and network usage in order to accurately calculate users' charges and generate detailed bills.

[0094] The device database can store a variety of devices, including base stations, switches, routers, etc. The device database is used to record the model, location, status, and configuration information of the device.

[0095] The network topology database can store the network topology of the server, including network nodes, links, network addresses, etc. The network topology database is used to store and manage network topology information for network planning, troubleshooting, and performance optimization.

[0096] The business database can store various businesses and services, such as voice communication, text messages, data transmission, etc. The business database is used to manage and process the relevant data of these businesses, including call records, text message content, data usage, etc.

[0097] The customer relationship management database is used to store customer contact information, service records, complaint handling, etc. in order to provide better customer service.

[0098] The security management database is used to store and manage users' security policies, identity authentication information, security logs, etc.

[0099] The statistical analysis database is used to store and manage statistical data of business data to support operators' decision-making and planning.

[0100] S305. Establish classification and grading standards for operators.

[0101] In some embodiments, the server management platform may construct classification and grading standards for operators with reference to the GB / T 43697-2024 standard.

[0102] S306: Determine the classification and grading results of the telecommunication business data according to the knowledge database, the telecommunication professional database and the classification and grading standards of the operator.

[0103] The specific steps can refer to step S203, which will not be described in detail here.

[0104] based on Figure 3 The technical solution shown in the embodiment of the present application can more accurately determine the classification and grading results of telecommunications business data based on the knowledge database, telecommunications professional database and the classification and grading standards of the operator.

[0105] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to achieve the above functions, the data classification and grading device or electronic device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0106] The embodiment of the present application can exemplarily divide the functional modules of the classification and grading device or electronic device for business data according to the above method. For example, the classification and grading device or electronic device for business data may include various functional modules corresponding to the various functional divisions, or two or more functions may be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0107] Figure 4 A schematic diagram of a device for classifying and grading business data provided in an embodiment of the present application, the device comprising:

[0108] The determination unit 401 is used to determine the attribute information of the target business data.

[0109] The construction unit 402 is used to construct a knowledge database; the knowledge database includes relevant business data in multiple fields.

[0110] The determination unit 401 is further configured to determine the classification and grading result of the target business data based on the attribute information of the target business data and the knowledge database.

[0111] In a possible implementation, the determination unit 401 is specifically used to construct a flow chart of the target business data based on the attribute information of the target business data; the flow chart of the target business data includes the source of the target business data and the destination of the target business data; based on the attribute information of the target business data, the flow chart of the target business data and the knowledge database, determine the classification and grading results of the target business data.

[0112] In a possible implementation, the determination unit 401 is also specifically used to input the attribute information of the target business data, the flow chart of the target business data, and the business data in the knowledge database into a pre-trained business data classification model to obtain classification rules that match the target business data; based on the classification rules, determine the classification and grading results of the target business data.

[0113] In a possible implementation, the determination unit 401 is also specifically used to input the business data in the knowledge database into the business data classification model; based on the business data in the knowledge database, the business data classification model is trained to determine the trained business data classification model; based on the trained business data classification model, the attribute information of the target business data and the flow chart of the target business data, a classification rule matching the target business data is obtained.

[0114] In a possible implementation manner, the determination unit 401 is further specifically configured to obtain business data; process the business data, and determine target business data after the business data is processed.

[0115] In a possible implementation, the determination unit 401 is further configured to perform deduplication processing on the business data, detect and remove outliers, and determine target business data after the business data is processed.

[0116] In a possible implementation, the determination unit 401 is also specifically used to determine the classification and grading results of the target business data, and the classification and grading results of the target business data include primary data, secondary data, tertiary data and quaternary data: primary data indicates that no harm will be caused to personal rights and interests, organizational rights and interests, etc.; secondary data indicates that general harm will be caused to personal rights and interests, organizational rights and interests; tertiary data indicates that serious harm will be caused to personal rights and interests, organizational rights and interests; and quaternary data indicates that particularly serious harm will be caused to personal rights and interests, organizational rights and interests, or general harm will be caused to economic operations, social order, and public interests.

[0117] Figure 5 A block diagram of an electronic device of a business data classification and grading device involved in the above-mentioned embodiment is shown. The business data classification and grading device includes: a processor 501 and a communication interface 502. The processor 501 is used to determine the classification rules of the business data according to the business data classification model and classify and grade the business data based on the classification rules, and the communication interface 502 is used for communication between the server control platform and the server. The recommendation system may also include a memory 503 and a bus 504, and the memory 503 is used to store the business data of multiple servers.

[0118] Among them, the memory 503 can be a memory in a classification and grading device for business data, etc. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk or a solid-state drive; the memory may also include a combination of the above types of memory.

[0119] The processor 501 may be a processor that implements or executes various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the present application. The processor may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The processor may be a processor that implements or executes various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0120] The bus 504 may be an Extended Industry Standard Architecture (EISA) bus, etc. The bus 504 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0121] Through the description of the above implementation methods, technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, 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. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0122] An embodiment of the present application provides a computer program product including instructions. When the computer program product is run on a computer, the computer is enabled to execute the method for classifying and grading business data in the above method embodiment.

[0123] An embodiment of the present application also provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a computer, the computer executes the method for classifying and grading business data in the method flow shown in the above method embodiment.

[0124] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above, or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read products from the storage medium and can write products to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). In the embodiments of the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program, which may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0125] An embodiment of the present invention provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the method for classifying and grading business data of the embodiment of the present application.

[0126] Since the business data classification and grading device, computer-readable storage medium, and computer program product in the embodiments of the present invention can be applied to the above method, the technical effects that can be obtained can also refer to the above method embodiments, and the embodiments of the present invention will not be repeated here.

[0127] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and 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 interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0128] 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.

[0129] 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.

[0130] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for classifying and grading business data, characterized in that: The method comprises: Determine attribute information of target business data; Constructing a knowledge database; the knowledge database includes relevant business data in multiple fields; Based on the attribute information of the target business data and the knowledge database, a classification and grading result of the target business data is determined.

2. The method according to claim 1, characterized in that The determining the classification and grading result of the target business data based on the attribute information of the target business data and the knowledge database includes: Based on the attribute information of the target business data, construct a flow chart of the target business data; the flow chart of the target business data includes the source of the target business data and the destination of the target business data; Based on the attribute information of the target business data, the flow chart of the target business data and the knowledge database, a classification and grading result of the target business data is determined.

3. The method according to claim 2, characterized in that The determining the classification and grading result of the target business data based on the attribute information of the target business data, the flow chart of the target business data and the knowledge database includes: Inputting the attribute information of the target business data, the flow chart of the target business data and the business data in the knowledge database into a pre-trained business data classification model to obtain a classification rule matching the target business data; Based on the classification rule, a classification and grading result of the target business data is determined.

4. The method according to claim 3, characterized in that The step of inputting the attribute information of the target business data, the flow chart of the target business data, and the business data in the knowledge database into a pre-trained business data classification model to obtain a classification rule matching the target business data includes: Inputting the business data in the knowledge database into a business data classification model; Based on the business data in the knowledge database, the business data classification model is trained to determine the trained business data classification model; Based on the trained business data classification model, the attribute information of the target business data and the flow chart of the target business data, a classification rule matching the target business data is obtained.

5. The method according to claim 1, characterized in that Before determining the attribute information of the target service data, the method includes: Get business data; The business data is processed to determine the target business data after the business data is processed.

6. The method according to claim 5, characterized in that The processing of the business data to determine the target business data after the business data is processed includes: Deduplication processing is performed on the business data, and outlier processing is detected and eliminated; and the target business data after the business data processing is determined.

7. The method according to any one of claims 1 to 5, characterized in that The classification and grading results of the target business data include primary data, secondary data, tertiary data and quaternary data: The first-level data indicates that it will not cause harm to personal rights and interests, organizational rights and interests, etc.; The secondary data indicates general harm to the rights and interests of individuals and organizations; The third-level data indicates serious harm to the rights and interests of individuals and organizations; The four levels of data indicate particularly serious harm to individual rights and interests, organizational rights and interests, or general harm to economic operations, social order, and public interests.

8. A device for classifying and grading business data, characterized in that: The device comprises: A determination unit, used to determine attribute information of target business data; A construction unit, used to construct a knowledge database; the knowledge database includes relevant business data in multiple fields; The determining unit is further configured to determine a classification and grading result of the target business data based on the attribute information of the target business data and the knowledge database.

9. An electronic device, characterized in that: The electronic device comprises: processor; a memory configured to store instructions executable by the processor; The processor is configured to execute the instructions to implement the method for classifying and grading business data as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Instructions are stored on the computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by an electronic device, the electronic device is enabled to execute the method for classifying and grading business data as described in any one of claims 1 to 7.

11. A computer program product, characterized in that The computer program product includes computer instructions; when part or all of the computer instructions are run on a computer, the method for classifying and grading business data according to any one of claims 1 to 7 is executed.

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