Information extraction method and device, electronic equipment and storage medium

Abstract

The invention discloses an information extraction method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a to-be-extracted data field in an information extraction task; sending an examination and approval instruction to a first examination and approval terminal corresponding to the data field according to the data field; under the condition that a first examination and approval instruction sent by a first examination and approval terminal is received, a sensitive data column with a preset sensitive identifier in the data field is obtained, and the first examination and approval instruction indicates to agree to extract the data field to be extracted; performing desensitization processing on the sensitive data column, and combining a data field obtained by the desensitization processing with other data fields except the sensitive data column to obtain target data; and scanning the target data, and if the first data does not exist in the target data, sending the target data to the client, the first data being sensitive data not including the sensitive identifier. The method can be applied to the fields of financial science and technology, medical health and the like, and information extraction efficiency can be improved.

Description

Information extraction methods, devices, electronic equipment and storage media Technical Field

[0001] This application relates to the fields of artificial intelligence technology and data processing technology, and in particular to an information extraction method, apparatus, electronic device and storage medium. Background Technology

[0002] In today's data-driven business environment, business users often need to extract relevant data from business systems to their local environment in order to conduct effective data analysis to support decision-making. However, in practice, this data to be extracted may often contain sensitive information related to the company's core competitiveness, such as customer privacy data, trade secrets, or undisclosed financial data. With enterprises placing increasing emphasis on data security management and regulatory requirements becoming more stringent, stricter security policies have been implemented for the access and transmission of sensitive data, making traditional information extraction processes exceptionally complex and time-consuming.

[0003] Currently, when business users need to extract business data, they must first initiate an information extraction request through the internal approval system. This request requires approval from multiple levels of relevant personnel, including the data owner, information security officer, and department head. After the entire approval process is completed, the user must contact a dedicated data developer to extract, clean, transform, and process the required data from the source system according to the user's needs. After the data processing is completed, the resulting data must undergo a compliance check using a preset security scanning tool to confirm that it does not contain sensitive information or meets data anonymization requirements. Only after the result data scan is completed can the user finally obtain the required data. Therefore, the efficiency of information extraction is affected by many factors, making the entire information extraction cycle uncontrollable and potentially significantly extending the time required, greatly impacting work efficiency. Summary of the Invention

[0004] The main objective of this application is to provide an information extraction method, apparatus, electronic device, and storage medium, aiming to solve the technical problem that the information extraction process is complex and thus affects work efficiency.

[0005] To achieve the above objectives, a first aspect of this application proposes an information extraction method applied to an information extraction system, the method comprising:

[0006] The process involves: acquiring the data field to be extracted in the information extraction task; sending an approval instruction to the first review terminal corresponding to the data field, the approval instruction instructing the first review terminal to initiate the approval process; upon receiving the first approval instruction from the first review terminal, acquiring the sensitive data column with a preset sensitive identifier in the data field, the first approval instruction indicating agreement to extract the data field to be extracted; performing desensitization processing on the sensitive data column, merging the data field obtained from the desensitization processing with other data fields excluding the sensitive data column to obtain the target data; scanning the target data, and if the target data does not contain the first data, sending the target data to the client, the first data being sensitive data that does not include the sensitive identifier.

[0007] In some embodiments, the information extraction task includes a data query statement input by the user and a business database corresponding to the data field to be extracted; obtaining the data field to be extracted from the information extraction task includes: obtaining the information extraction task created by the user; and obtaining the data field to be extracted from the business database according to the data query statement.

[0008] In some embodiments, sending an approval instruction to a first review terminal corresponding to the data field based on the data field includes: a first review terminal obtaining permission to manage the business database based on the business database corresponding to the data field; and sending an approval instruction to the first review terminal.

[0009] In some embodiments, the information extraction task further includes a scheduling period and a scheduling start time for the information extraction task; upon receiving a first approval instruction sent by the first review terminal, obtaining a sensitive data column with a preset sensitive identifier in the data field includes: upon receiving a first approval instruction sent by the first review terminal, sending the approval result corresponding to the first approval instruction to the client, and periodically extracting the data field according to the scheduling period and the scheduling start time; obtaining the sensitive data column in the data field according to the sensitive identifier.

[0010] In some embodiments, after sending an approval instruction to the first review terminal corresponding to the data field based on the data field, the method further includes: upon receiving a second approval instruction sent by the first review terminal, sending to the client the approval result corresponding to the second approval instruction and a prompt message for prompting the user to modify the information extraction task, wherein the second approval instruction indicates that the extraction of the data field to be extracted is not approved.

[0011] In some embodiments, the process of desensitizing the sensitive data column includes: desensitizing the sensitive data column according to a preset desensitization algorithm.

[0012] In some embodiments, after de-identifying the sensitive data column and merging the data fields obtained from the de-identification process with other data fields besides the sensitive data column to obtain the target data, the method further includes: scanning the target data; if the target data contains the first data, then sending the first data to a second auditing end for security audit, and receiving the security audit result sent by the second auditing end; if the security audit result indicates that the first data contains sensitive data, then sending the security audit result and a prompt message to the client to prompt the user to modify the information extraction task; if the security audit result indicates that the first data does not contain sensitive data, then sending the target data to the client.

[0013] To achieve the above objectives, a second aspect of this application provides an information extraction device applied to an information extraction system. The device includes: a first acquisition module for acquiring a data field to be extracted in an information extraction task; a sending module for sending an approval instruction to a first review terminal corresponding to the data field, the approval instruction instructing the first review terminal to initiate an approval process; a second acquisition module for acquiring a sensitive data column with a preset sensitive identifier in the data field upon receiving a first approval instruction sent by the first review terminal, the first approval instruction indicating agreement to extract the data field to be extracted; a desensitization module for desensitizing the sensitive data column and merging the data field obtained from the desensitization process with other data fields besides the sensitive data column to obtain target data; and a scanning module for scanning the target data, and if the target data does not contain first data, sending the target data to a client, the first data being sensitive data excluding the sensitive identifier.

[0014] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0015] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0016] The information extraction method, apparatus, electronic device, and storage medium proposed in this application acquire data fields to be extracted in an information extraction task through an information extraction system; send an approval instruction to the first review end corresponding to the data field to be extracted; after receiving the approval instruction, the first review end conducts a review and evaluation; if the review is passed, the first review end sends a first approval instruction to the information extraction system to agree to extract the data field to be extracted; identify sensitive data columns in the data field to be extracted according to preset sensitive identifiers; perform desensitization processing on the sensitive data columns, and merge the desensitized data fields with other data fields except for the sensitive data columns to obtain target data; scan the target data to check whether there is any un-desensitized sensitive data (first data); if the scan result shows that the first data does not exist in the target data, the system sends the target data to the client. In this invention, by combining an automated approval process with sensitive data desensitization processing, data extraction, desensitization, and security verification are automatically completed after receiving the approval instruction, effectively solving the problems of low efficiency, high risk of manual operation, and insufficient control of sensitive data in traditional multi-level approvals. It has the effects of improving information extraction efficiency, reducing manual intervention, strengthening data security control mechanisms, and reducing the risk of data leakage. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 is a schematic diagram of an application environment for the information extraction method provided in an embodiment of this application; Figure 2 is a schematic flowchart of the information extraction method provided in an embodiment of this application; Figure 3 is a schematic flowchart of a specific implementation of step S10 in Figure 1; Figure 4 is a schematic flowchart of a specific implementation of step S20 in Figure 1; Figure 5 is a schematic flowchart of a specific implementation of step S30 in Figure 1; Figure 6 is a schematic flowchart of the data extraction workflow provided in an embodiment of this application; Figure 7 is a schematic structural diagram of the information extraction device provided in an embodiment of this application; Figure 8 is a schematic hardware structure diagram of the electronic device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] In today's data-driven business environment, business users often need to extract relevant data from business systems to their local environment in order to conduct effective data analysis to support decision-making. However, in practice, this data to be extracted may often contain sensitive information related to the company's core competitiveness, such as customer privacy data, trade secrets, or undisclosed financial data. With enterprises placing increasing emphasis on data security management and regulatory requirements becoming more stringent, stricter security policies have been implemented for the access and transmission of sensitive data, making traditional information extraction processes exceptionally complex and time-consuming.

[0023] Currently, when business users need to extract business data, they must first initiate an information extraction request through the internal approval system. This request requires approval from multiple levels of relevant personnel, including the data owner, information security officer, and department head. After the entire approval process is completed, the user must contact a dedicated data developer to extract, clean, transform, and process the required data from the source system according to the user's needs. After the data processing is completed, the resulting data must undergo a compliance check using a preset security scanning tool to confirm that it does not contain sensitive information or meets data anonymization requirements. Only after the result data scan is completed can the user finally obtain the required data. Therefore, the efficiency of information extraction is affected by many factors, making the entire information extraction cycle uncontrollable and potentially significantly extending the time required, greatly impacting work efficiency.

[0024] Based on this, embodiments of this application provide an information extraction method, apparatus, electronic device, and storage medium, aiming to improve information extraction efficiency, reduce manual intervention, strengthen data security management mechanisms, and reduce the risk of data leakage.

[0025] The information extraction method, apparatus, electronic device, and storage medium provided in this application are specifically described through the following embodiments. First, the information extraction method in this application is described.

[0026] The information extraction method provided in this application relates to the field of data processing technology and can be applied to business areas such as fintech and healthcare. The information extraction method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the information extraction method, but is not limited to the above forms.

[0027] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0028] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0029] The information extraction method provided in this application embodiment can be applied in the application environment shown in Figure 1, where the client, the first review end, and the second review end all communicate with the server via a network. The server can obtain the data fields to be extracted in the information extraction task; the server determines the business direction or relevant department to which the data fields belong based on the data fields to be extracted, and sends an approval instruction to the first review end corresponding to the business direction or relevant department to which the data fields belong; after receiving the approval instruction, the first review end performs a review and evaluation. If the review is passed, the first review end sends a first approval instruction to the server, indicating that it agrees to extract the data fields to be extracted, and sends the approval result of agreeing to extract the data fields to be extracted to the client through the server; if the review is passed, the first review end sends a second approval instruction to the server, indicating that it disagrees to extract the data fields to be extracted, and sends an instruction of approval failure and a prompt message to the user to modify the information extraction task to the client through the server; upon receiving the first approval instruction sent by the first review end, the server identifies the sensitive data columns in the data fields to be extracted based on sensitive identifiers, and processes the sensitive data... The data is anonymized. The server merges the anonymized data fields with all other data fields except the sensitive data columns to obtain the target data. The server scans the target data to check for any unanonymized suspected sensitive data. If the scan shows no unanonymized suspected sensitive data, the server sends the target data to the client. If the scan shows unanonymized suspected sensitive data, the server sends the unanonymized suspected sensitive data to a second reviewer. The second reviewer performs a security review on the suspected sensitive data. If the security review result indicates no sensitive data, the second reviewer sends a successful review result to the server, and the server sends the target data to the client. If the security review result indicates sensitive data is contained, the second reviewer sends a failed review result to the server, and the server sends a failed review notification to the client, along with a prompt to the user to modify the information extraction task. In this invention, the security protection of sensitive data is crucial in the financial and medical fields. For example, when banks assess customer risk, they need to extract customer financial data for analysis. In the process of extracting customer financial data, sensitive information such as customer ID numbers often needs to be anonymized. Similarly, when medical institutions conduct epidemiological analysis, they need to extract patient medical records for research. When extracting patient medical records, sensitive information such as patient names and ID numbers often needs to be anonymized. This process protects sensitive data while meeting the needs of data analysis and research, providing strong support for the business operations of financial institutions and medical institutions.The information extraction system can be applied to the client side and can be an insurance system, banking system, transaction system, order system, medical system, etc. The data can be financial data, such as income, liabilities, and transaction records, or medical data, such as personal health records, prescriptions, and examination reports. The client side, the first review terminal, and the second review terminal can be, but are not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server side can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will be described in detail below through specific embodiments.

[0030] Please refer to Figure 2, which is a flowchart of an information extraction method provided in an embodiment of this application. The method in Figure 2 may include, but is not limited to, steps S100 to S500.

[0031] Step S100: Obtain the data fields to be extracted in the information extraction task.

[0032] An information extraction system refers to a computer system used to perform data extraction, review, and de-identification processing. It can be implemented using a distributed architecture or a cloud computing platform. Its function is to centrally manage the data extraction process and coordinate the operations of each stage. A data field refers to a specific data unit to be extracted from the business database. It can be obtained through structured query statements or API interfaces, and its function is to clarify the specific range of data that the user needs to extract.

[0033] First, the user selects the business database that has been pre-maintained in the data extraction tool system in the user interface module and fills in the data query statement (e.g., SQL statement). The information extraction system then retrieves the data fields to be extracted from the information extraction task based on the user's query statement.

[0034] For example, if a user needs to extract data fields such as customer name, ID card number, and mobile phone number, the user fills in the relevant SQL statement in the user interface module, and the information extraction system will automatically parse the data fields to be extracted from the SQL statement as customer name (name), ID card number (id_card), mobile phone number (phone_number), etc.

[0035] It is important to understand that the information extraction task includes the data query statement input by the user and the business database corresponding to the data fields to be extracted. Specifically, as shown in Figure 3, step S100, which involves obtaining the data fields to be extracted from the information extraction task, includes the following steps S110-S120: Step S110, obtaining the information extraction task created by the user; Step S120, obtaining the data fields to be extracted from the business database according to the data query statement.

[0036] When a user submits a data extraction request containing specific query conditions through the client, the system automatically parses the request and generates a standardized data query statement. This statement is transmitted to the query interface of the business database, triggering the database engine to execute the query operation. The query results are returned to the information extraction system in the form of a structured dataset, and the system converts the raw data into the target format according to field mapping rules.

[0037] For example, when a user needs to retrieve sales records for a specific time period, their query includes a date range filter. The system parses the user's query, identifying the table name, field names, and other information. Then, the system connects to the corresponding business database, executes the query, and filters out matching rows from the sales business table based on the specified conditions, extracting the designated fields.

[0038] By simplifying the data extraction process, users only need to create a data extraction task, and the system will automatically initiate the other steps, eliminating the need for complex operations and improving efficiency. Users no longer need to communicate with personnel from multiple departments, reducing communication and time costs. Since the data extraction process is completed internally, the risk of sensitive data leakage is also reduced, enhancing data security.

[0039] Step S200: Send an approval instruction to the first review terminal corresponding to the data field according to the data field. The approval instruction is used to instruct the first review terminal to start the approval process.

[0040] The first review end refers to the terminal device or system module with data access permission approval function. Specifically, it can be implemented using a role-based access control system or an approval workflow engine. Its role is to ensure that data extraction behavior complies with the preset security policy.

[0041] In some embodiments of the invention, as shown in 4, step S200, that is, sending an approval instruction to the first review terminal corresponding to the data field according to the data field, specifically includes the following steps S210-S220: Step S210, obtaining the first review terminal with the authority to manage the business database according to the business database corresponding to the data field; Step S220, sending an approval instruction to the first review terminal.

[0042] The permission relationship between the business database and the first review end is implemented through a preset permission mapping table, which stores administrator information corresponding to different business databases. The permission mapping table can be associated with review end accounts through database identifiers, such as binding a database ID to the system account of a department head. When a data field originates from a specific business database, the system automatically queries the permission mapping table to match a review end account with management permissions. Specifically, in some implementations, the identification information of the business database can be included in the metadata of the data field, for example, identified by database name, storage path, or category tag.

[0043] After obtaining the data fields to be extracted, the system parses the business database identifier to which the data fields belong. By calling the permission mapping interface, the system uses the business database identifier as an input parameter to retrieve the corresponding first-level approval account information. For example, when the data field comes from the financial database, the permission mapping interface returns the system account of the finance department head as the first-level approval account. The system generates an approval instruction based on the obtained account information and sends it to the corresponding approval interface via an internal message channel. This automatically directs the approval process to the responsible person with data management authority, avoiding errors or delays that may occur due to manual selection of the approval end. For example, by setting the sales director account for the sales database and the supply chain manager account for the procurement database in the permission mapping table, the system automatically triggers the corresponding approval process based on the data source, ensuring that approval permissions strictly match data management responsibilities.

[0044] This embodiment ensures the compliance and security of data extraction by sending approval requests to administrators with appropriate permissions; the automated approval process reduces manual intervention and improves the efficiency of information extraction.

[0045] In some embodiments of the invention, after step S200, that is, after sending an approval instruction to the first review terminal corresponding to the data field according to the data field, the following step S201 may be included: Step S201, upon receiving a second approval instruction sent by the first review terminal, sending the approval result corresponding to the second approval instruction and a prompt message for prompting the user to modify the information extraction task to the client, wherein the second approval instruction indicates that the data field to be extracted is not approved.

[0046] The second approval instruction is triggered when the first reviewer rejects the data extraction request during the approval process. The approval result is sent asynchronously via a message queue or synchronously via a pre-defined client interface. The logic for generating the notification message includes attaching a pre-defined text template to the approval result. This template contains a task identifier, a rejection reason code, and a modification suggestion field. The content of the modification suggestion field is dynamically generated based on the business attributes of the data field. For example, when a data field involves customer privacy, the notification message suggests adjusting the query scope or adding de-identification rules.

[0047] Step S300: Upon receiving a first approval instruction from the first review terminal, obtain the sensitive data column with a preset sensitive identifier in the data field. The first approval instruction indicates that it is agreed to extract the data field to be extracted.

[0048] Sensitive data markers are metadata tags predefined in data fields. They can be stored as field attribute labels or database comment fields, and their purpose is to quickly identify sensitive data types that require de-identification. Utilizing pre-configured sensitive data marker rules to automatically identify sensitive data columns avoids the delays and potential inaccuracies of manual judgment, improving automation and efficiency. After approval, the system automatically identifies sensitive data columns without manual intervention, significantly shortening data preparation time and improving overall data extraction efficiency.

[0049] It should be noted that the information extraction task also includes the scheduling period and scheduling start time of the information extraction task. In some embodiments of this invention, as shown in Figure 5, step S300, that is, upon receiving the first approval instruction sent by the first review terminal, obtaining the sensitive data column with a preset sensitive identifier in the data field, specifically includes the following steps S310-S320: Step S310, upon receiving the first approval instruction sent by the first review terminal, sending the approval result corresponding to the first approval instruction to the client, and periodically extracting the data field according to the scheduling period and the scheduling start time; Step S320, obtaining the sensitive data column in the data field according to the sensitive identifier.

[0050] The scheduling cycle and scheduling start time are preset by the user when creating the information extraction task, which are used to define the time rules for data extraction; the timed extraction of data fields is automatically executed by the system's built-in task scheduling module without manual intervention; sensitive identifiers are stored in the business database in the form of metadata for quick identification of sensitive data columns.

[0051] On the data extraction task configuration page, users can customize the scheduling cycle and scheduling time. Once the first reviewer returns an approval instruction to proceed with the extraction, the system automatically sends the approval result back to the client and extracts data fields periodically according to the scheduling cycle and start time. For example, if the scheduling cycle is set to daily and the start time is set to 8:00 AM daily, the system automatically triggers the data extraction task at 8:00 AM every day, pulling the latest data fields from the business database. Then, by scanning the metadata attributes of the data fields, it filters out columns with sensitive flags. Sensitive flags can be specific database column names, data formats, or keywords. This process reduces the frequency of manual operations through an automated scheduling mechanism, avoiding data update delays caused by human error or operational delays. Simultaneously, scheduled extraction ensures that the data content remains synchronized with the business database, preventing extraction results from becoming outdated due to changes in the data source. The preset sensitive flags allow the system to accurately locate columns requiring de-identification, preventing sensitive data from being overlooked in subsequent processing stages.

[0052] It should be noted that after the data extraction request submitted by the user is reviewed and approved by the relevant approvers, the user will receive an approval completion notification. At this time, the user can choose to manually trigger the data extraction task immediately according to their needs, or they can choose to let the system automatically schedule and trigger the data extraction task according to the preset time.

[0053] This embodiment reduces manual intervention and improves the efficiency and accuracy of data extraction by automating the scheduled execution of information extraction tasks. By predefining and automatically identifying sensitive identifiers, it ensures the timely discovery and subsequent processing of sensitive data, thereby enhancing data security. The scheduled extraction mechanism also avoids frequent manual operations, reduces the risk of human error, and ensures data consistency and timeliness.

[0054] Step S400: De-identify the sensitive data column, and merge the data fields obtained from the de-identification process with other data fields besides the sensitive data column to obtain the target data.

[0055] Anonymization refers to the technical means of irreversibly transforming sensitive data. Specifically, it can be achieved using data masking, hash encryption, or data generalization algorithms. Its purpose is to eliminate the risk of sensitive information leakage while preserving data usability. Target data refers to the complete dataset after anonymization, which can be generated through data merging tools or database connection operations. Its purpose is to ensure that the output data simultaneously meets user needs and security compliance requirements.

[0056] The system applies predefined de-identification strategies to each identified sensitive data column. These strategies can be pre-configured within the system and are typically based on unified enterprise or industry standards, such as Ping An's unified de-identification strategy, differentiated according to field type and sensitivity level. The system then merges the de-identified sensitive data columns with other data columns deemed non-sensitive and requiring no further processing during the identification phase. This "merging" usually refers to combining them into a new, structurally complete dataset (e.g., a table or dataset object), ensuring all fields specified in the original query are present in the final result, only replacing the sensitive fields with their de-identified values. The resulting dataset after merging is the "target data." This data contains all the field information required for user business analysis while ensuring sensitive information is properly handled, reducing the risk of leakage. The target data can then be sent to the user or used for further processing or analysis.

[0057] In some embodiments of the invention, step S400, that is, the desensitization processing of the sensitive data column, specifically includes the following step S410: Step S410, the sensitive data column is desensitized according to a preset desensitization algorithm.

[0058] The preset data masking algorithm includes various configurable data transformation rules, such as partial field replacement for ID card numbers, masking of phone numbers, and generalization of address information. The algorithm achieves standardized operations through a predefined rule base, which stores processing logic for different sensitive data types; for example, hash encryption is used for numeric fields, and regular expression matching and replacement are used for text fields. During execution, the algorithm calls a rule matching engine to automatically select the appropriate processing method based on the type of sensitive data column.

[0059] In this embodiment of the invention, a unified desensitization strategy can be used to desensitize sensitive data columns. For example, for sensitive data columns containing user names or bank card numbers, a partial character replacement method can be used to replace some characters in the name or card number with asterisks; for sensitive fields containing dates, the algorithm converts the precise date into a quarterly or year format; for sensitive data columns containing mobile phone numbers, the mobile phone number can be replaced with a specific placeholder or a randomly generated non-real number; for some specific character types, hashing or masking methods can also be used to convert sensitive data into a fixed-length hash value or mask value.

[0060] This embodiment protects user privacy and sensitive corporate information by de-identifying sensitive data. By using a preset de-identification algorithm, it can achieve automated de-identification of different types of sensitive data, reducing manual intervention and improving the accuracy and consistency of data processing.

[0061] In some embodiments of this invention, after step S400, i.e., after the sensitive data column is de-identified, the data fields obtained by the de-identification process are merged with other data fields besides the sensitive data column to obtain the target data. Specifically, the process includes the following steps S401-S403: Step S401: Scan the target data. If the first data exists in the target data, send the first data to the second auditing end for security auditing and receive the security audit result sent by the second auditing end; Step S402: If the security audit result indicates that the first data contains sensitive data, send the security audit result and a prompt message to the client to prompt the user to modify the information extraction task; Step S403: If the security audit result indicates that the first data does not contain sensitive data, send the target data to the client.

[0062] After the sensitive data scan is complete, if the un-anonymized field results do not involve sensitive data, the system will directly send a data retrieval email to the user. If suspected sensitive data is found, the system will automatically initiate a security review and notify security personnel for manual confirmation. If sensitive data is found, security personnel can reject the data retrieval task, and the user needs to modify the data retrieval logic and resubmit. If no sensitive data is found, and the system's judgment is incorrect, security personnel can approve the retrieval, and the user will receive a data retrieval email.

[0063] Specifically, after the target data has been anonymized, the system automatically performs a full check on the merged dataset. If data items without sensitive identifiers but meeting preset sensitive characteristics are found during the scan, this type of data is marked as the first data. After the first data is transmitted to the second review end, the reviewer can determine the data sensitivity based on the business context, such as whether a set of numbers belongs to internal coding or real sensitive information. If the review confirms the existence of sensitive data, the system blocks data transmission and generates a modification prompt; if it confirms that there is no sensitive data, the target data is allowed to be output to the client. By adding a secondary review mechanism on top of automated anonymization, the potential for sensitive data leakage due to missing identifiers or rule defects is eliminated.

[0064] This embodiment effectively identifies and prevents unmarked but substantially sensitive data leakage risks through secondary scanning, providing more comprehensive protection for data security; and improves the accuracy and efficiency of the review process by combining automated and manual review methods.

[0065] Step S500: Scan the target data. If the first data is not present in the target data, send the target data to the client. The first data is sensitive data that does not include the sensitive identifier.

[0066] After the data is anonymized, the system will perform a full scan of the result data again according to the group's sensitive data policy to identify sensitive data that has not been anonymized. This is to prevent fields in sensitive datasets from being mistakenly marked as non-sensitive data, which could lead to the leakage of sensitive data.

[0067] In one specific implementation of this embodiment, for a financial institution's data analysis system, when an analyst needs to extract customer transaction data, the system first obtains a data extraction request containing fields such as account balance, transaction amount, and transaction time. The system automatically sends an approval instruction to the data management department's review end, initiating the approval process. Upon receiving the request, the reviewer reviews it; if the extraction is approved, the system sends a first approval instruction. Upon receiving this instruction, the system immediately identifies and extracts data columns marked with "sensitive" tags from the database, such as account balance and transaction amount. Subsequently, the system performs anonymization processing on these sensitive data columns. For example, account balances are range-based, converting specific amounts into range values; transaction amounts are masked, retaining only partial numbers. The anonymized data is merged with other non-sensitive data (such as transaction time) to form the target dataset. The system performs a comprehensive scan of the target dataset to check for any unmarked but potentially sensitive data. If no such data is found, the system securely transmits the target dataset to the analyst's client.

[0068] As can be seen, the above solution reduces manual intervention and time delays by tightly integrating the approval process with data processing; real-time identification and processing of sensitive data ensures data security while maintaining data availability; the secondary scanning mechanism further reduces the risk of data leakage; thus, it achieves automation and security improvement in the information extraction process, significantly improves the efficiency of data extraction, shortens the time from request to data acquisition, and ensures the security of sensitive information, meeting the balance between data analysis needs and compliance requirements.

[0069] The data extraction workflow is shown in Figure 6. Users create a data extraction task, select a business database already maintained in the information extraction system, fill in the data extraction SQL, and configure the task scheduling cycle. After creating the task, the user clicks "Complete." The information extraction system automatically parses the data fields in the SQL and initiates a corresponding approval report according to the business direction, sending the data extraction information to the relevant personnel for approval. If the approval fails, the data extraction task is rejected, and the user is prompted to modify the task and resubmit the approval report. If the approval is successful, the user receives an approval completion notification. At this point, the user can manually trigger the data extraction task or wait for the task to be automatically scheduled. After the task is triggered, the information extraction system executes the user's data extraction SQL. During the data query process, the system uses a unified data anonymization strategy configured by the user to anonymize sensitive data. Data is anonymized according to the data list. Whether or not data fields are anonymized depends on whether the fields in the user-submitted data retrieval report are marked as sensitive. After data anonymization, the information extraction system will perform a full scan of the result data again according to the group's sensitive data policy to identify any unanonymized sensitive data. After the sensitive data scan is completed, if the unanonymized field results do not involve sensitive data, the tool system will directly send a data retrieval email to the user. If suspected sensitive data is found, the tool system will automatically initiate a security review and notify security personnel for manual confirmation. If it is indeed sensitive data, security personnel can reject the data retrieval task, and the user needs to modify the data extraction logic and resubmit. If it is not sensitive data and it is a system misjudgment, security personnel can approve it, and the user will receive a data retrieval email. In the entire data extraction process, the user only needs to create the relevant data extraction task as required. All other parts are automatically initiated by the system, eliminating the need for additional work. This greatly improves user usability and the data retrieval experience, significantly increasing user data retrieval efficiency while ensuring data security.

[0070] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0071] In one embodiment, an information extraction device 600 is provided, which corresponds one-to-one with the information extraction methods in the above embodiments. Referring to Figure 7, the information extraction device includes a first acquisition module 10, a sending module 20, a second acquisition module 30, a desensitization module 40, and a scanning module 50. The functional modules are described in detail below: The first acquisition module 10 is used to acquire the data field to be extracted in the information extraction task; the sending module 20 is used to send an approval instruction to the first review terminal corresponding to the data field according to the data field, the approval instruction being used to instruct the first review terminal to start the approval process; the second acquisition module 30 is used to acquire the sensitive data column with a preset sensitive identifier in the data field upon receiving the first approval instruction sent by the first review terminal, the first approval instruction indicating agreement to extract the data field to be extracted; the desensitization module 40 is used to desensitize the sensitive data column, merging the data field obtained from the desensitization process with other data fields besides the sensitive data column to obtain the target data; the scanning module 50 is used to scan the target data, and if the target data does not contain the first data, the target data is sent to the client, the first data being sensitive data that does not include the sensitive identifier.

[0072] In some implementations, the first acquisition module 10 may include: a first acquisition submodule, used to acquire a user-created information extraction task, the information extraction task including a data query statement input by the user and a business database corresponding to the data field to be extracted; and a second acquisition submodule, used to acquire the data field to be extracted from the business database according to the data query statement.

[0073] In some implementations, the sending module 20 may include: a business submodule, used to obtain a first review terminal with the authority to manage the business database based on the business database corresponding to the data field; and a sending submodule, used to send an approval instruction to the first review terminal.

[0074] In some implementations, the second acquisition module 30 may include: a sending submodule, configured to send the approval result corresponding to the first approval instruction to the client upon receiving the first approval instruction sent by the first review terminal; the information extraction task further includes the scheduling period and scheduling start time of the information extraction task, and extracts the data field periodically according to the scheduling period and scheduling start time; and a third acquisition submodule, configured to acquire the sensitive data column in the data field according to the sensitive identifier.

[0075] In some embodiments, the information extraction device may further include: a first prompting module, used to send to the client, upon receiving a second approval instruction sent by the first review terminal, the approval result corresponding to the second approval instruction and a prompting message for prompting the user to modify the information extraction task, wherein the second approval instruction indicates that the data field to be extracted is not approved.

[0076] In some implementations, the desensitization module 40 may include a desensitization submodule, used to desensitize the sensitive data column according to a preset desensitization algorithm.

[0077] In some embodiments, the information extraction device may further include: an auditing module, configured to scan the target data; if the target data contains the first data, send the first data to a second auditing terminal for security auditing, and receive the security audit result sent by the second auditing terminal; a second prompting module, configured to send the security audit result and a prompt message to the client to prompt the user to modify the information extraction task if the security audit result indicates that the first data contains sensitive data; and a third sending module, configured to send the target data to the client if the security audit result indicates that the first data does not contain sensitive data.

[0078] The specific implementation of this information extraction device is basically the same as the specific implementation of the information extraction method described above, and will not be repeated here.

[0079] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described information extraction method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0080] Please refer to Figure 8, which illustrates the hardware structure of an electronic device according to another embodiment. The electronic device includes: a processor 801, which can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, for executing related programs to implement the technical solutions provided in the embodiments of this application; and a memory 802, which can be implemented using a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM), etc. The memory 802 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801 to implement the information extraction method of the embodiments of this application. The input / output interface 803 is used to implement information input and output. The communication interface 804 is used to realize communication interaction between this device and other devices. Communication can be realized by wired means (such as USB, network cable, etc.) or by wireless means (such as mobile network, WIFI, Bluetooth, etc.). The bus 805 transmits information between the various components of the device (such as processor 801, memory 802, input / output interface 803 and communication interface 804). The processor 801, memory 802, input / output interface 803 and communication interface 804 realize communication connection between each other within the device through the bus 805.

[0081] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described information extraction method.

[0082] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0083] The information extraction method, information extraction device, electronic device, and storage medium provided in this application embodiment obtain the data field to be extracted in the information extraction task through an information extraction system; send an approval instruction to the first review terminal corresponding to the data field to be extracted; after receiving the approval instruction, the first review terminal will conduct a review and evaluation. If the review is passed, the first review terminal will send a first approval instruction to the information extraction system to agree to extract the data field to be extracted; identify the sensitive data column in the data field to be extracted according to the preset sensitive identifier; perform desensitization processing on the sensitive data column, and merge the desensitized data field with other data fields except for the sensitive data column to obtain the target data; scan the target data to check whether there is any undesensitized sensitive data (first data). If the scan result shows that the first data does not exist in the target data, the system sends the target data to the client. In this invention, by combining an automated approval process with sensitive data desensitization, data extraction, desensitization, and security verification are automatically completed after receiving the approval instruction. This effectively solves the problems of low efficiency, high risk of manual operation, and insufficient control of sensitive data in traditional multi-level approval processes. It has the effects of improving information extraction efficiency, reducing manual intervention, strengthening data security control mechanisms, and reducing the risk of data leakage.

[0084] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0085] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0086] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0087] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0088] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0089] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0090] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0091] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0092] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0093] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. An information extraction method, characterized in that, The method, applied to an information extraction system, includes: acquiring a data field to be extracted from an information extraction task; sending an approval instruction to a first review terminal corresponding to the data field, the approval instruction instructing the first review terminal to initiate an approval process; upon receiving a first approval instruction from the first review terminal, acquiring a sensitive data column with a preset sensitive identifier in the data field, the first approval instruction indicating agreement to extract the data field to be extracted; performing desensitization processing on the sensitive data column, merging the data field obtained from the desensitization processing with other data fields besides the sensitive data column to obtain target data; scanning the target data, and if the target data does not contain first data, sending the target data to a client, the first data being sensitive data excluding the sensitive identifier.

2. The information extraction method as described in claim 1, characterized in that, The information extraction task includes a data query statement input by the user and a business database corresponding to the data field to be extracted; obtaining the data field to be extracted from the information extraction task includes: obtaining the information extraction task created by the user; and obtaining the data field to be extracted from the business database according to the data query statement.

3. The information extraction method as described in claim 2, characterized in that, Sending an approval instruction to the first review terminal corresponding to the data field based on the data field includes: obtaining the authority to manage the business database based on the business database corresponding to the data field from the first review terminal; and sending an approval instruction to the first review terminal.

4. The information extraction method as described in claim 1, characterized in that, The information extraction task also includes a scheduling period and a scheduling start time for the information extraction task; upon receiving a first approval instruction sent by the first review terminal, obtaining sensitive data columns with preset sensitive identifiers in the data field includes: upon receiving a first approval instruction sent by the first review terminal, sending the approval result corresponding to the first approval instruction to the client, and periodically extracting the data field according to the scheduling period and the scheduling start time; obtaining sensitive data columns in the data field according to the sensitive identifiers.

5. The information extraction method as described in claim 1, characterized in that, After sending an approval instruction to the first review terminal corresponding to the data field based on the data field, the method further includes: upon receiving a second approval instruction sent by the first review terminal, sending the approval result corresponding to the second approval instruction and a prompt message for prompting the user to modify the information extraction task to the client, wherein the second approval instruction indicates that the extraction of the data field to be extracted is not approved.

6. The information extraction method as described in claim 1, characterized in that, The process of desensitizing the sensitive data column includes: desensitizing the sensitive data column according to a preset desensitization algorithm.

7. The information extraction method as described in claim 1, characterized in that, After performing anonymization on the sensitive data column and merging the data fields obtained from the anonymization process with other data fields besides the sensitive data column to obtain the target data, the method further includes: scanning the target data; if the target data contains the first data, then sending the first data to a second auditing end for security audit, and receiving the security audit result sent by the second auditing end; if the security audit result indicates that the first data contains sensitive data, then sending the security audit result and a prompt message to the client to prompt the user to modify the information extraction task; if the security audit result indicates that the first data does not contain sensitive data, then sending the target data to the client.

8. An information extraction device, characterized in that, An information extraction system includes: a first acquisition module for acquiring data fields to be extracted in an information extraction task; a sending module for sending an approval instruction to a first review terminal corresponding to the data field, the approval instruction instructing the first review terminal to initiate an approval process; a second acquisition module for acquiring sensitive data columns with preset sensitive identifiers in the data fields upon receiving a first approval instruction from the first review terminal, the first approval instruction indicating agreement to extract the data fields to be extracted; a desensitization module for desensitizing the sensitive data columns, merging the data fields obtained from the desensitization process with other data fields besides the sensitive data columns to obtain target data; and a scanning module for scanning the target data, and if the target data does not contain first data, sending the target data to a client, the first data being sensitive data excluding the sensitive identifier.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the information extraction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the information extraction method as described in any one of claims 1 to 7.

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