Data analysis process construction method and device, computer equipment and storage medium

Abstract

The embodiment of the invention provides a data analysis process construction method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining an original data analysis process; the original data analysis process comprises at least one data analysis node and a node category, data attribute information and first execution object information of the data analysis node; screening out a selected verification component from the candidate verification components according to the node category; performing verification rule setting on the selected verification component according to the data attribute information and the first execution object information to obtain a target verification component; and inserting a target verification component into each data analysis node of the original data analysis process according to the node category to obtain a target data analysis process. The method can be applied to business systems needing a large amount of data, such as financial science and technology, manpower for constructing the data analysis process can be saved, and the safety of the data analysis process is improved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and is applied to the field of financial technology, particularly to a data analysis process construction method and apparatus, computer equipment and storage medium. Background Technology

[0002] Building data analysis workflows typically requires specialized developers, making it not only highly technical but also labor-intensive. To overcome these limitations, low-code / no-code data analysis tools have been developed, allowing workflows to be built via drag-and-drop. However, these tools only support basic workflows. For scenarios with high data security requirements, manual configuration of security rules at each analysis node is still necessary, further increasing the labor costs associated with workflow construction. For example, in fintech scenarios, insurance and banking data analysis involves large amounts of sensitive fields such as user information and financial data, necessitating stringent security measures. Therefore, how to reduce labor costs in building data analysis workflows while simultaneously improving data security has become a pressing technical challenge. Summary of the Invention

[0003] The main objective of this application is to provide a data analysis workflow construction method and apparatus, computer equipment and storage medium, which aims to save manpower in the construction of data analysis workflow and improve the data security of the data analysis process.

[0004] To achieve the above objectives, a first aspect of this application proposes a data analysis workflow construction method, the method comprising:

[0005] Obtain the original data analysis process; wherein the original data analysis process includes at least one data analysis node and node attribute parameters of the data analysis node; wherein the node attribute parameters include: node category, data attribute information and first execution object information; Selected verification components are selected from the preset candidate verification components based on the node category; Based on the data attribute information and the first execution object information, the selected verification component is configured with verification rules to obtain the target verification component; According to the node category, the target verification component is inserted into each of the data analysis nodes in the original data analysis process to obtain the target data analysis process.

[0006] In some embodiments, selecting a verification component from a preset pool of candidate verification components based on the node category includes: The data analysis node is analyzed and risk assessment is performed according to the node category to obtain analysis risk assessment information; wherein, the analysis risk assessment information indicates whether the data analysis node needs or does not need risk assessment; Determine the security requirements of the data analysis node based on the node category; The selected verification component is selected from the candidate verification components based on the risk assessment information and the security requirement characteristics.

[0007] In some embodiments, the data attribute information includes: data field labels and data output scenarios; the step of setting verification rules for the selected verification component based on the data attribute information and the first execution object information to obtain the target verification component includes: Selected verification rules are selected from preset candidate verification rules based on the data field labels, the data output scenario, and the first execution object information; The selected verification rule is compiled into the selected verification component to obtain the target verification component.

[0008] In some embodiments, after inserting the target validation component into each data analysis node of the original data analysis flow according to the node category to obtain the target data analysis flow, the process includes: Obtain the input and output data during the execution of the target data analysis process; Based on the input data and the output data, an execution risk assessment is performed on the target data analysis process to obtain execution risk assessment data. Based on the execution risk assessment data, risk analysis nodes are extracted from the target data analysis process; The target data analysis process is risk-marked based on the risk analysis nodes.

[0009] In some embodiments, the input data includes data metadata, second execution object information, and operation category; the step of performing an execution risk assessment on the target data analysis process based on the input data and the output data to obtain execution risk assessment data includes: Field sensitivity assessments are performed on the data metadata and the operation categories to obtain sensitivity assessment data; Perform an object risk assessment on the second execution object information to obtain object risk assessment data; A risk assessment is performed on the sensitive information in the output data to obtain output risk assessment data; The sensitivity assessment data, the object risk assessment data, and the output risk assessment data are concatenated to obtain the execution risk assessment data.

[0010] In some embodiments, after inserting the target validation component into each data analysis node of the original data analysis flow according to the node category to obtain the target data analysis flow, the method further includes: Obtain the release information and execution logs of the target data analysis process; Based on the published information, an identification code is set for the target data analysis process to obtain an analysis identification code; The execution log is stored in a preset database according to the analysis identifier.

[0011] In some embodiments, after inserting the target validation component into each data analysis node of the original data analysis flow according to the node category to obtain the target data analysis flow, the method further includes: The execution log is parsed to obtain log parsing data; wherein, the log parsing data includes: execution risk assessment data, execution verification rules, and rule description information of the execution verification rules; A risk assessment summary is generated based on the execution risk assessment data and the execution verification rules. Based on the execution risk assessment data and the rule description information, the preset candidate rule adjustment information is filtered to obtain the selected rule adjustment information; Input the execution risk assessment data, the execution verification rules, the rule description information, the risk assessment summary, and the selected rule adjustment information into a preset visualization template to obtain a risk analysis view.

[0012] To achieve the above objectives, a second aspect of this application provides a data analysis workflow construction apparatus, the apparatus comprising: An acquisition module is used to acquire the original data analysis process; wherein the original data analysis process includes at least one data analysis node and node attribute parameters of the data analysis node; wherein the node attribute parameters include: node category, data attribute information, and first execution object information; The component filtering module is used to filter out selected verification components from preset candidate verification components according to the node category; The rule setting module is used to set verification rules for the selected verification component based on the data attribute information and the first execution object information, so as to obtain the target verification component; The component insertion module is used to insert the target verification component into each data analysis node of the original data analysis process according to the node category, so as to obtain the target data analysis process.

[0013] To achieve the above objectives, a third aspect of the present application provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method described in the first aspect.

[0014] 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.

[0015] This application proposes a data analysis workflow construction method, apparatus, computer equipment, and storage medium. It adds a target verification component for security checks to the existing data analysis workflow. The target verification component is configured according to selection rules based on the node category, data attribute information, and first execution node information of each data analysis node in the original data analysis workflow, thus constructing a target verification component that meets the security requirements of each data analysis node. Finally, the target verification component is inserted before each data analysis node according to the node category to construct the target data analysis workflow. Therefore, by automatically injecting a security verification component into each data analysis node, security checks are ensured at every stage, improving the security of data analysis. Furthermore, the entire workflow is automated, saving manpower. Attached Figure Description

[0016] Figure 1 This is a flowchart of the data analysis process construction method provided in the embodiments of this application; Figure 2 yes Figure 1 The flowchart of step S102 in the document; Figure 3A This is a mapping diagram of node categories and risk assessment components in the data analysis process construction method provided in this application embodiment; Figure 3B This is a mapping diagram of data analysis nodes and candidate verification components in the data analysis process construction method provided in this application embodiment; Figure 4 yes Figure 1 The flowchart of step S103 in the process; Figure 5 This is a flowchart of a data analysis workflow construction method provided in another embodiment of this application; Figure 6 yes Figure 5 The flowchart of step S502 in the document; Figure 7 This is a schematic diagram of the target data analysis process call in the data analysis process construction method provided in the embodiments of this application; Figure 8This is a flowchart of a data analysis workflow construction method provided in another embodiment of this application; Figure 9 This is a flowchart of a data analysis workflow construction method provided in another embodiment of this application; Figure 10 This is a schematic diagram of the risk analysis view in the data analysis process construction method provided in the embodiments of this application; Figure 11 This is an overall flowchart of the data analysis process construction method provided in the embodiments of this application; Figure 12 This is a schematic diagram of the data analysis process construction device provided in the embodiments of this application; Figure 13 This is a schematic diagram of the hardware structure of the computer device provided in the embodiments of this application. Detailed Implementation

[0017] 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.

[0018] 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.

[0019] 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.

[0020] First, let's analyze some of the terms used in this application: Artificial intelligence (AI) is a new branch of computer science that studies, develops, and applies theories, methods, technologies, and systems to simulate, extend, and expand human intelligence. It aims to understand the essence of intelligence and produce intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thought. Furthermore, AI utilizes digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results.

[0021] Data orchestration refers to the process of integrating, processing, and managing distributed and heterogeneous data sources through a series of automated processes to achieve efficient data utilization. Data orchestration involves not only the physical integration of data but also its logical organization and optimization.

[0022] Compliance as a component: It is the process of abstracting and encapsulating complex compliance requirements into standardized, reusable, and pluggable software components.

[0023] Hash algorithm: It maps input data (usually of arbitrary length) to a fixed-length output through a hash function. This output value is called hash value or hash code.

[0024] A Domain Specific Language (DSL) is a computer language focused on a specific application domain. Unlike general-purpose programming languages ​​(such as Java and C), DSLs have limited expressive power and are typically used to solve problems within a specific domain.

[0025] Data analysis workflows in the financial sector are tailored to the needs of different business teams, and these workflows vary across teams, typically involving actuarial science, risk control, and marketing. Traditional data analysis workflows are built manually, incurring significant manpower costs. To reduce these costs, several low-code / no-code data analysis tools have emerged (e.g., Alteryx, Power BI Dataflow, Databricks Pipeline Designer). These tools allow users to drag and drop elements on a webpage to build data analysis workflows, enabling data access, transformation, analysis, and visualization. However, financial data analysis workflows involve large amounts of sensitive data, resulting in high security requirements. Workflows built solely with data analysis tools cannot meet these security requirements. For example, in banking scenarios, analysis nodes involve user authorization, privacy protection, and risk assessment. Ordinary data analysis tools cannot meet compliance requirements, necessitating manual data review at each analysis node, increasing manpower costs. Therefore, how to save manpower in the data analysis process while ensuring data security has become a pressing technical challenge.

[0026] Based on this, embodiments of this application provide a data analysis workflow construction method and apparatus, computer equipment, and storage medium. The aim is to add a target verification component to an existing original data analysis workflow. The addition of the target verification component is based on the node category, data attribute information, and first execution object information of each data analysis node, thereby constructing a target data analysis workflow that meets the security requirements of each data analysis node. It should be noted that the entire target data analysis workflow construction process is fully automated, saving manpower and improving the security of data analysis.

[0027] The data analysis workflow construction method, apparatus, computer equipment, and storage medium provided in this application are specifically described through the following embodiments. First, the data analysis workflow construction method in this application is described.

[0028] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0029] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0030] The data analysis process construction method provided in this application relates to the field of artificial intelligence technology and is applied in the field of fintech. The data analysis process construction 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 data analysis process construction method, but is not limited to the above forms.

[0031] 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 computer devices, 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.

[0032] 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.

[0033] Figure 1 This is an optional flowchart of the data analysis process construction method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0034] Step S101: Obtain the original data analysis process; wherein the original data analysis process includes at least one data analysis node and node attribute parameters of the data analysis node; wherein the node attribute parameters include: node category, data attribute information and first execution object information; Step S102: Select the verification component from the preset candidate verification components according to the node category; Step S103: Set verification rules for the selected verification component based on the data attribute information and the first execution object information to obtain the target verification component; Step S104: Insert target validation components into each data analysis node of the original data analysis process according to the node category to obtain the target data analysis process.

[0035] Steps S101 to S104 of this embodiment determine the data analysis nodes and their node attribute parameters in the original data analysis process. These node attribute parameters include node category, data attribute information, and first execution object information. A selected verification component is chosen from candidate verification components based on the node category. Verification rules are then compiled within the selected verification component according to the data attribute information and the first execution object information to obtain the target verification component. Therefore, setting a target verification component for each data analysis node based on its data analysis security requirements improves data analysis security. Finally, the target verification component is inserted between the data analysis nodes in the original data analysis process according to their node categories, achieving security verification at each stage and enhancing data analysis security. Simultaneously, the entire target data analysis process is constructed automatically, saving manpower during the data analysis process.

[0036] In step S101 of some embodiments, the original data analysis process can be a pre-built data analysis process or it can be retrieved from a publicly available analysis process database. This embodiment does not impose specific restrictions on the method of obtaining the original data analysis process. If the original data analysis process is built in real time, a low-code or no-code process orchestrator can be set up. Users can drag and drop data analysis nodes on the drag-and-drop interface of the process encoder to form the original data analysis process, without requiring developers to automatically program the original data analysis process. This lowers the threshold for building the original data analysis process and reduces the difficulty of building the original data analysis process.

[0037] Specifically, the node attribute parameters of a data analysis node include node category, data attribute information, and first execution object information. The node category can be data reading, cleaning, transformation, modeling and training, aggregation and statistics, or report output. It's important to note that the node type determines the potential compliance risks of the data analysis node and which type of verification component to choose. Data attribute information refers to the input and output data attributes of the data analysis node. Input data attributes are data field labels, including sensitive personal labels, financial labels, and health labels, while output data attributes are output scenarios, including internal analysis scenarios and external reporting scenarios. If the data analysis node is applied in the financial field, the first execution object information represents the user role operating the data analysis node, including actuarial roles, risk control roles, and outsourcing roles. Verification rules are configured based on the user roles in the first execution node information.

[0038] In step S102 of some embodiments, the node category determines the compliance verification requirements of the data analysis node. Therefore, a selected verification component that matches the node category is selected from the candidate verification components based on the node category. It should be noted that at least one selected verification component can be selected for each node category.

[0039] Please see Figure 2 In some embodiments, step S102 may include, but is not limited to, steps S201 to S203: Step S201: Perform analysis risk assessment on data analysis nodes according to node category to obtain analysis risk assessment information; Step S202: Determine the security requirements of the data analysis nodes based on the node category; Step S203: Select the verification component from the candidate verification components based on the analysis of risk assessment information and security requirement characteristics.

[0040] In step S201 of some embodiments, the risk assessment of the data analysis node mainly assesses whether the data analysis node will trigger a risk assessment and determines whether the data analysis node needs to have a risk assessment component set. Specifically, this embodiment searches for analysis risk assessment information matching the node category in the risk trigger assessment table according to the node category. The analysis risk assessment is mainly determined based on whether the data analysis node involves data access, aggregation, or other risk exposure behaviors. Therefore, not all data analysis nodes need to undergo risk assessment; only when a data analysis node is determined to require risk assessment should a risk assessment category component be set.

[0041] like Figure 3A As shown, if the node category is "data reading," the risk assessment is triggered because the data reading node involves access to sensitive data. If the node category is "aggregation and statistics," the risk assessment is triggered because the aggregation and statistics node may lead to sensitive inferences. If the node category is "intermediate transformation," the risk assessment is not triggered because the intermediate transformation node does not expose any new risks and therefore does not require a risk assessment. It should be noted that if the risk assessment triggers a risk assessment, the selected validation component is set to the risk assessment category, i.e., a risk assessment component is set.

[0042] In step S202 of some embodiments, security requirement features of data analysis nodes are selected from preset candidate requirement features according to node category, and the security requirement features include at least one of the following: de-identification requirement features, permission verification requirement features, and output supervision requirement features. Based on the security requirement features, it is determined whether to set components for field de-identification category, permission verification category, and output supervision category.

[0043] In step S203 of some embodiments, if the risk assessment information analysis determines that a risk assessment analysis has been triggered, a risk assessment component is selected, and at least one of a field anonymization component, a permission verification component, and an output monitoring component is determined based on security requirement characteristics. At least one verification component is selected. It should be noted that the risk assessment component is used to perform dynamic risk assessment by combining historical access data and data sensitivity of the data analysis node; the field anonymization component is used to automatically encrypt or replace fields based on the sensitivity level of the data involved in the data analysis node; the permission verification component is used to determine access permissions based on the user role and task context executed by the data analysis node; and the output monitoring component is used to detect whether the output of the data analysis node needs to be monitored. For example, if the data analysis node is a health status assessment node, the output customer health status needs to be monitored.

[0044] In steps S201 to S203 of this embodiment, before screening candidate verification components, it is first determined whether to trigger analysis risk assessment based on node category, then the security requirement characteristics are determined, and finally, at least one selected verification component is selected from the candidate verification components by combining analysis risk assessment information and security requirement characteristics. This makes the selection of selected verification components more in line with the verification requirements of data analysis nodes, so that a more matching verification component can be inserted in subsequent component insertion.

[0045] Please refer to Figure 3B , Figure 3B This shows the candidate verification components corresponding to different node categories, through... Figure 3B It can be seen that if the data analysis node is a data reading node, the automatically matched selected verification components are the permission verification component and the risk assessment component; if the data analysis node is a cleaning / transformation node, the automatically matched selected verification component is the field anonymization component; if the data analysis node is a modeling and training node, the automatically matched selected verification component is the output supervision component; if the data analysis node is an aggregation and statistics node, the automatically matched selected verification component is the risk assessment component; and if the data analysis node is a report output node, the automatically matched selected verification components are the output supervision component and the field anonymization component. Therefore, for different node categories, the selected verification components are automatically matched, improving the selection efficiency of the selected verification components.

[0046] In step S103 of some embodiments, after determining the selected verification component, it is important to note that the selected verification component is only a general-purpose verification component. Further verification rules adapted to the data risk nodes need to be set. Therefore, verification rules need to be loaded into the selected verification component. It should be noted that the verification rules are dynamically compiled and injected to construct a target verification component perfectly suited for data analysis node verification.

[0047] In some embodiments, the data attribute information includes data field labels and data output scenarios. Data field labels represent the field labels involved in the data analysis node, and the data for compliance verification can be determined through the data field labels. Data output scenarios represent the scenarios in which the data output by the data analysis node is applicable.

[0048] Please see Figure 4 In some embodiments, step S103 may include, but is not limited to, steps S401 to S402: Step S401: Select the selected verification rule from the preset candidate verification rules based on the data field label, data output scenario and first execution object information; Step S402: Compile the selected verification rules into the selected verification component to obtain the target verification component.

[0049] In step S401 of some embodiments, rule description information of candidate verification rules is obtained, and the rule description information represents the adapted data field label, data output scenario and first execution object information. Therefore, target description information is filtered from the rule description information according to the data field label, data output scenario and first execution object information, and selected verification rule is filtered from the candidate verification rules according to the target description information.

[0050] It should be noted that each candidate validation rule includes metadata, and the metadata is as follows: {"rule_id":"data_access_01", "applicable_roles":["auditor","risk_control"], "task_types":["marketing_analysis","credit_evaluation"], "field_category":"personal_sensitive"}.

[0051] Execute when the selected validation rules are filtered out: ActiveRules"="PolicyDB.query(user_role,task_type) ; Here, `task_typ` represents the task category, determined based on data field labels and the data output scenario, while `user_role` represents the information of the first execution object. Therefore, only selection validation rules that match both "first execution object information + task category" are loaded, and these selection validation rules form a set of rules. Consequently, different execution objects executing the same data analysis node will use different selection validation rules; the same execution object executing different data analysis nodes will also use different selection validation rules. This achieves dynamic rule filtering for execution objects and task categories, selecting selection validation rules that meet the requirements of the data analysis nodes.

[0052] In step S402 of some embodiments, this embodiment uses a rule compiler to mutate the selected verification rule into an executable abstract syntax tree, defined as an AST, and then dynamically injects and executes it at runtime to obtain the target verification component.

[0053] In steps S401 to S402 of this embodiment, the selected verification rules are first filtered out, and then the selected verification rules are injected into the selected verification component to build a target verification component that adapts to the data analysis node, thereby improving the data security of the data analysis node execution process.

[0054] It should be noted that the target verification component in this embodiment is pluggable, specifically a "compliance checker" that can be automatically embedded between any data analysis nodes, allowing for flexible setting of compliance checks between data analysis nodes.

[0055] In step S104 of some embodiments, after the target verification component is constructed, it is inserted after the corresponding data analysis node according to the node category to construct the target data analysis process. It should be noted that the target data analysis process configures a "compliance checker" on each data analysis node, which can improve the security of data analysis and result output. Furthermore, the target data analysis process supports exporting the complete process script as a target script in DSL mode. Executing the target script is equivalent to completing data analysis through the target data analysis process, specifically including data reading, model training, data transformation, etc. This embodiment does not limit the applicable scenarios of the target data analysis process.

[0056] In some embodiments, to enhance the security of the target data analysis process, a risk assessment is required before execution. Therefore, a risk assessment component is set up to perform a risk assessment on the entire target data analysis process, following the same procedure as the risk assessment component's assessment of each data analysis node.

[0057] Please see Figure 5 In some embodiments, after step S104, the data analysis workflow construction method may also include, but is not limited to, steps S501 to S504: Step S501: Obtain the input and output data required for the execution of the target data analysis process; Step S502: Perform an execution risk assessment on the target data analysis process based on the input and output data to obtain execution risk assessment data; Step S503: Extract risk analysis nodes from the target data analysis process based on the execution risk assessment data; Step S504: Mark the risk in the target data analysis process according to the risk analysis nodes.

[0058] In step S501 of some embodiments, the input data is the data input to each data analysis node of the target data analysis process, and the output data is the data output by the target data analysis. The input data and output data can serve as key data for assessing the risk of the target data analysis process.

[0059] In step S502 of some embodiments, this embodiment uses a risk assessment component to perform risk assessment on the input and output data. The assessment mainly focuses on three aspects: the data sensitivity of the input and output data, the risk of the execution object, and the output risk, to determine the execution risk assessment data. This execution risk assessment data serves as the risk score for the execution of the target data analysis process. It should be noted that a higher risk score indicates a higher execution risk for the target data analysis process, and vice versa.

[0060] In step S503 of some embodiments, the execution risk assessment data includes the execution risk score of each data analysis node, and the data analysis node whose execution risk score exceeds a preset risk threshold is designated as a risk analysis node.

[0061] In step S504 of some embodiments, the target data analysis process is marked with risks according to the risk analysis nodes, so that staff can review the execution risks of the target data analysis process again. Risk analysis nodes can also be replaced or modified in a targeted manner to repair the target data analysis process and obtain an updated data analysis process.

[0062] In steps S501 to S504 of this embodiment, before each target data analysis process is executed, the risk assessment component performs a risk assessment on the input and output data, selects and marks risk analysis nodes belonging to the high-risk category, so that the staff on the system can further review them, thereby improving the security of the target data analysis process execution.

[0063] In some embodiments, if the risk assessment data exceeds a preset assessment threshold, the target data analysis process is blocked and a risk warning message is output to reduce the occurrence of data security risks caused by the execution of the target data analysis process.

[0064] In some embodiments, the input data includes data metadata, second execution object information, and operation category. The data metadata represents the data content of the input data, the second execution object information is the input object of the input data and also the execution object of the target data analysis process, and the operation category is the operation category of the target data analysis process, including reading, aggregation, and output.

[0065] Please see Figure 6 In some embodiments, step S502 may include, but is not limited to, steps S601 to S604: Step S601: Perform field sensitivity assessment on data element information and operation categories to obtain sensitivity assessment data; Step S602: Perform an object risk assessment on the second execution object information to obtain object risk assessment data; Step S603: Perform a risk assessment on the sensitive information in the output data to obtain output risk assessment data; Step S604: The sensitivity assessment data, object risk assessment data, and output risk assessment data are concatenated to obtain the execution risk assessment data.

[0066] In step S601 of some embodiments, the data element information includes data field labels, data source information, and data granularity information; sensitivity assessment data is obtained by performing field sensitivity assessment on operation category, data field labels, data source information, and data granularity information, and the field sensitivity assessment mainly assesses whether the operation, data content, source, and data granularity of the target data analysis process input involve sensitive fields, which is specifically accomplished through preset sensitivity assessment rules.

[0067] In step S602 of some embodiments, an object risk assessment is performed on the second execution object information, and the second execution object information includes the historical violation rate, access frequency and department risk coefficient of the second execution object. The object risk assessment data is obtained by performing a risk assessment on the second execution object through the historical violation rate, access frequency and department risk coefficient.

[0068] In step S603 of some embodiments, the risk assessment of the output data mainly involves assessing the risk of leakage of potentially sensitive information in the output data to obtain output risk assessment data.

[0069] In step S604 of some embodiments, in this embodiment, the weights of the sensitivity assessment data, object risk assessment data, and output risk assessment data are dynamically adjusted by the strategy. The sensitivity assessment data, object risk assessment data, and output risk assessment data are then weighted and summed according to each weight to obtain execution risk assessment data. Execution risk assessment data represents the execution risk of the entire target data analysis process. Specifically, execution risk assessment data includes the execution risk score of at least one data analysis node. The execution risk score can be used to identify data analysis nodes with higher risks.

[0070] In steps S601 to S604 of this embodiment, sensitivity assessment data is determined by evaluating the sensitivity of data metadata and operation categories; object risk assessment data is determined by evaluating the object risk of the second execution object information; and output risk assessment data is obtained by evaluating the risk of sensitive information leakage in the output data. Then, the sensitivity assessment data, object risk assessment data, and output risk assessment data are weighted and summed together to obtain the execution risk assessment data. Therefore, assessing the execution risk of the target data analysis process from three aspects—input field sensitivity, object risk, and output risk—can more accurately assess the execution risk of the target data analysis process.

[0071] Please refer to Figure 7 When a user wants to invoke the pre-built target data analysis process 2.0, the execution risk of the target data analysis process 2.0 is pre-assessed to obtain execution risk assessment data. This risk assessment is conducted in real-time during the execution of the target data analysis process 2.0, specifically at each data analysis node, to output real-time execution risk assessment data. Then, the execution risk assessment data is compared with a preset risk threshold. If the execution risk assessment data exceeds the preset risk threshold, the target data analysis process 2.0 is directly blocked, and a message is displayed on the interface indicating that the target data analysis process 2.0 has execution risks and cannot be invoked. Therefore, performing a risk assessment before the target data analysis process is applied can reduce potential security risks in actual application.

[0072] In some embodiments, if the risk assessment data of the target data analysis process during pre-execution is less than a preset risk threshold, the target data analysis process can be directly invoked for use in a specific scenario. It should be noted that after each execution of the target data analysis process, a risk assessment is performed on the executed process. This assessment specifically examines the actual access fields, actual output content, whether anonymization / masking was triggered, and the risks associated with intermediate compliance events. Essentially, it verifies the actual execution risk of the target data analysis process to obtain post-execution risk assessment data, which is then stored. It should be noted that the post-execution risk assessment data is used for subsequent auditing, record keeping, and accountability, serving as an audit record and risk tracing tool for the target data analysis process. The risk assessment of the target data analysis process after execution in this embodiment is consistent with the aforementioned risk assessment and will not be repeated here.

[0073] This application's embodiment adopts a process orchestration centimeter that prioritizes business orchestration and injects compliance capabilities later. It sets up risk assessments at key data analysis nodes through a unified risk assessment component and performs risk control and audit recording before and after the execution of the target data analysis process, thereby achieving high compliance and traceable operation of low-code analysis.

[0074] In some embodiments, as disclosed above, the candidate verification component includes a field desensitization component, a permission verification component, a risk assessment component, and an output verification component. The field desensitization component desensitizes the fields involved in the data analysis node. Specifically, it determines whether the fields involved in the data analysis node are sensitive fields based on preset sensitive field rules. These rules include: whether the data belongs to personal sensitive information, whether the current role lacks access permissions, and whether the output inference risk is hit. If the data in the data analysis node meets the sensitive field rules, a corresponding sensitivity method is selected as the target desensitization method for each field type within the data analysis node, and then the fields of the data analysis node are desensitized according to the target desensitization method. Specifically, for a field type of ID card number, the desensitization method is hash + partial retention; for a field type of mobile phone number, the desensitization method is a middle four-digit mask; for a field type of address, the desensitization method is generalization to the district / county level; and for a field type of income, the desensitization method is segmented intervalization. Therefore, setting the field desensitization to be completed within the target data analysis process does not affect the subsequent operation of the data analysis node, thus improving the security of data analysis.

[0075] In some embodiments, traditional data analysis processes only save execution results, lacking version management and traceable execution logs. Therefore, after the target data analysis process is successfully built, the execution logs and release information of the target data analysis process are collected and stored in a database, allowing relevant data to be retrieved for analysis when the target data analysis process is used.

[0076] Please see Figure 8 In some embodiments, the data analysis workflow construction method further includes, but is not limited to, steps S801 to S803: Step S801: Obtain the release information and execution log of the target data analysis process; Step S802: Set the identification code for the target data analysis process according to the published information to obtain the analysis identification code; Step S803: Store the execution log to a preset database according to the analysis identifier code.

[0077] In step S801 of some embodiments, the distribution information is the distribution content of the target data analysis process, recording the process information of the target data analysis process. The distribution information includes: graph structure information, component parameters, and process rule versions. The execution log is the log data obtained after the target data analysis process is executed, and the execution log includes at least one execution record. The execution record includes execution object identification information, execution task identification information, input source information, verification rule version, execution risk assessment data, and trigger rule information.

[0078] In step S802 of some embodiments, the distribution information is hashed (e.g., SHA256) to obtain an analysis identifier code, which is defined as pipeline_id. The analysis identifier code serves as a unique identifier code, making it convenient to quickly find the execution log through the analysis identifier code.

[0079] In step S803 of some embodiments, the execution log is stored according to the analysis identifier code. First, each execution record is hashed according to the analysis identifier code to obtain hash data, and then the hash data is stored in the database. Specifically, according to Determine the hash data, and Let be the i-th execution record. Therefore, hash data is constructed as an immutable audit chain.

[0080] In steps S801 to S803 of this embodiment, a unique analysis identifier is set for each execution log, and the execution log is converted into hash data storage according to the analysis identifier. This allows the execution log to be quickly extracted through the analysis identifier, supporting post-event penetration auditing and meeting the regulatory requirements of "reproducible behavior and verifiable results".

[0081] In some embodiments, after a traditional data analysis process is executed, business personnel, who are not professional analysts, cannot understand which operations violated regulations during the process, making real-time intervention or review difficult. Therefore, this application provides an interpretable compliance report that can automatically generate natural language compliance explanations for auditors and regulatory authorities to review.

[0082] Please see Figure 9 In some embodiments, the data analysis workflow construction method may also include, but is not limited to, steps S901 to S904: Step S901: Parse the execution log to obtain log parsing data; wherein, the log parsing data includes: execution risk assessment data, execution verification rules, and rule description information of the execution verification rules; Step S902: Generate a summary based on the execution risk assessment data and execution verification rules to obtain a risk assessment summary; Step S903: Based on the execution risk assessment data and rule description information, filter the preset candidate rule adjustment information to obtain the selected rule adjustment information; Step S904: Input the execution risk assessment data, execution verification rules, rule description information, risk assessment summary, and selected rule adjustment information into the preset visualization template to obtain the risk analysis view.

[0083] In step S901 of some embodiments, as described above, the execution log includes at least one verification rule. The verification rule further includes execution risk assessment data, the execution verification rule itself, and rule description information for the execution verification rule. The execution verification rule is represented by a set, defined as {rule_1, rule_2, ...}. The execution risk assessment data includes: field risk scores, user risk scores, output content scores, and a comprehensive score. Each field risk score is defined as R_field, the user risk score as R_user, the output content risk score as R_output, and the comprehensive risk score as R_final.

[0084] In step S902 of some embodiments, the risk assessment data and rule description information are entered into a summary template to form a risk assessment summary. The risk assessment summary allows for an intuitive understanding of the risk analysis nodes in the current target data analysis process and the triggered execution verification rules.

[0085] In step S903 of some embodiments, the risk level is determined according to the execution risk assessment data, and the candidate rule adjustment information corresponding to each risk level is different. Therefore, preliminary rule adjustment information is first filtered from the candidate rule adjustment information according to the rule description information, and then the selected rule adjustment information is filtered from the preliminary rule adjustment information according to the risk level.

[0086] In step S904 of some embodiments, the visualization template is a pre-set template, and staff can adjust it according to needs. Following the requirements of each visualization area of ​​the visualization template, risk assessment data, execution verification rules, rule description information, risk assessment summary, and selected rule adjustment information are filled in to obtain the risk analysis view. Therefore, the risk analysis view records information from multiple areas, allowing for a more comprehensive and intuitive understanding of the target data analysis process.

[0087] Specifically, the risk analysis view in this embodiment involves six blocks. In addition to the five blocks mentioned above, a block for the execution information of the target data analysis process is also set. Therefore, as Figure 10As shown, this application sets up a risk analysis view with six blocks. The first block records the process execution information, including the ID of the target data analysis process, the execution object, and the execution time. The second block records a risk assessment summary, including a comprehensive risk score and risk level. The third block records the triggered execution verification rules, specifically including the triggering information for the execution verification rules, such as sensitive fields and output reverse inference risks. The fourth block records field-level execution risk assessment data, including risk assessment scores for ID card numbers and mobile phone numbers. The fifth block records selected rule adjustment information, used to prompt adjustments to the target data analysis process, such as desensitizing fields, automatically blocking external exports, and setting up manual audits. The sixth block records rule description information, such as the version information of the current execution verification rule. Therefore, by constructing a rich and comprehensive representation of the risks, triggering rules, and adjustment suggestions for the execution of the target data analysis process, auditors can understand the triggering reasons without reading the execution logs, reducing the difficulty of tracing the causes after the execution of the target data analysis process.

[0088] In steps S901 to S904 of this embodiment, after the target data analysis process is executed, the execution log of the execution process is parsed to obtain execution risk assessment data, execution verification rules, and rule description information of the execution verification rules. The selected rule adjustment information for the execution verification rules is also determined. Then, the execution risk assessment data, execution verification rules, rule description information, and selected rule adjustment information are displayed in a visual view. Therefore, an interpretable report is automatically generated for approval by the compliance department or auditors, reducing communication costs and achieving a more understandable transformation from "technical compliance" to "business compliance."

[0089] Please refer to Figure 11 , Figure 11This is the overall flowchart. In this embodiment, the first step is for business personnel to drag and drop data analysis nodes on the process encoder via a graphical interface, such as "Import customer claims data → Aggregate → Model → Report Output," to construct the original data analysis process. The second step involves inserting corresponding target validation components (such as field desensitization components, permission validation components, risk assessment components, etc.) between each data analysis node to form the target data analysis process. The third step involves loading the corresponding selected validation rules from the strategy library and dynamically compiling and injecting them into each target validation component based on the first execution object information, data field labels, and data output scenario. The fourth step involves a pre-run check of the target data analysis process: calculating the initial execution risk score of the target data analysis process. If the initial execution risk score exceeds a preset risk threshold, the execution of the target data analysis process is blocked, and a compliance warning is issued. The fifth step involves... Target Data Analysis Process Execution and Real-time Monitoring: During execution, input and output data of the target data analysis process are collected, and abnormal fields within the data are anonymized in real time; Step 6: Risk assessment and recording of the executed target data analysis process: After execution, the final execution risk score of the target data analysis process is calculated and written into the audit ledger; Step 7: An interpretable report is automatically generated based on the execution log of the target data analysis process for approval by the compliance department or auditors; Step 8: The execution log of the target data analysis process is archived according to the analysis identifier code, which can be reused or replayed in future projects.

[0090] Please see Figure 12 This application also provides a data analysis workflow construction apparatus, which can implement the above-described data analysis workflow construction method. The apparatus includes: The acquisition module 1201 is used to acquire the original data analysis process; wherein the original data analysis process includes at least one data analysis node and node attribute parameters of the data analysis node; wherein the node attribute parameters include: node category, data attribute information and first execution object information; The component filtering module 1202 is used to filter the selected verification component from the preset candidate verification components according to the node category; The rule setting module 1203 is used to set the verification rules for the selected verification component based on the data attribute information and the first execution object information, so as to obtain the target verification component; The component insertion module 1204 is used to insert target validation components into each data analysis node of the original data analysis process according to the node category, so as to obtain the target data analysis process.

[0091] The specific implementation of this data analysis workflow construction device is basically the same as the specific implementation of the data analysis workflow construction method described above, and will not be repeated here.

[0092] This application also provides a computer 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 data analysis process construction method. This computer device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0093] Please see Figure 13 , Figure 13 The hardware structure of a computer device according to another embodiment is illustrated. The computer device includes: The processor 1301 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1302 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1302 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1302 and is called and executed by the processor 1301 to implement the data analysis process construction method of the embodiments of this application. The input / output interface 1303 is used to implement information input and output; The communication interface 1304 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1305 transmits information between various components of the device (e.g., processor 1301, memory 1302, input / output interface 1303, and communication interface 1304); The processor 1301, memory 1302, input / output interface 1303 and communication interface 1304 are connected to each other within the device via bus 1305.

[0094] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described data analysis process construction method.

[0095] 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.

[0096] The data analysis workflow construction method, apparatus, computer equipment, and storage medium provided in this application propose an approach that adds a target verification component for security checks to the existing data analysis workflow. The target verification component is selected and compiled according to the node category, data attribute information, and first execution node information of each data analysis node in the original data analysis workflow, thereby constructing a target verification component that meets the security requirements of each data analysis node. Finally, the target verification component is inserted before each data analysis node according to the node category to construct the target data analysis workflow. Therefore, by automatically injecting a security verification component into each data analysis node, security checks are ensured at every stage, improving the security of data analysis. Furthermore, the entire workflow setup is automated, saving manpower.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 a computer 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.

[0107] 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. A method for constructing a data analysis workflow, characterized in that, The method includes: Obtain the original data analysis process; wherein the original data analysis process includes at least one data analysis node and node attribute parameters of the data analysis node; wherein the node attribute parameters include: node category, data attribute information and first execution object information; Selected verification components are selected from the preset candidate verification components based on the node category; Based on the data attribute information and the first execution object information, the selected verification component is configured with verification rules to obtain the target verification component; According to the node category, the target verification component is inserted into each of the data analysis nodes in the original data analysis process to obtain the target data analysis process.

2. The method according to claim 1, characterized in that, The step of selecting a verification component from a preset pool of candidate verification components based on the node category includes: The data analysis node is analyzed and risk assessment is performed according to the node category to obtain analysis risk assessment information; wherein, the analysis risk assessment information indicates whether the data analysis node needs or does not need risk assessment; Determine the security requirements of the data analysis node based on the node category; The selected verification component is selected from the candidate verification components based on the risk assessment information and the security requirement characteristics.

3. The method according to claim 1, characterized in that, The data attribute information includes: data field labels and data output scenarios; the step of setting verification rules for the selected verification component based on the data attribute information and the first execution object information to obtain the target verification component includes: Selected verification rules are selected from preset candidate verification rules based on the data field labels, the data output scenario, and the first execution object information; The selected verification rule is compiled into the selected verification component to obtain the target verification component.

4. The method according to any one of claims 1 to 3, characterized in that, After inserting the target validation component into each data analysis node of the original data analysis flow according to the node category to obtain the target data analysis flow, the process includes: Obtain the input and output data during the execution of the target data analysis process; Based on the input data and the output data, an execution risk assessment is performed on the target data analysis process to obtain execution risk assessment data. Based on the execution risk assessment data, risk analysis nodes are extracted from the target data analysis process; The target data analysis process is risk-marked based on the risk analysis nodes.

5. The method according to claim 4, characterized in that, The input data includes data metadata, second execution object information, and operation category; the execution risk assessment of the target data analysis process based on the input data and the output data, to obtain execution risk assessment data, includes: Field sensitivity assessments are performed on the data metadata and the operation categories to obtain sensitivity assessment data; Perform an object risk assessment on the second execution object information to obtain object risk assessment data; A risk assessment is performed on the sensitive information in the output data to obtain output risk assessment data; The sensitivity assessment data, the object risk assessment data, and the output risk assessment data are concatenated to obtain the execution risk assessment data.

6. The method according to any one of claims 1 to 3, characterized in that, After inserting the target validation component into each data analysis node of the original data analysis flow according to the node category to obtain the target data analysis flow, the method further includes: Obtain the release information and execution logs of the target data analysis process; Based on the published information, an identification code is set for the target data analysis process to obtain an analysis identification code; The execution log is stored in a preset database according to the analysis identifier.

7. The method according to claim 6, characterized in that, After inserting the target validation component into each data analysis node of the original data analysis flow according to the node category to obtain the target data analysis flow, the method further includes: The execution log is parsed to obtain log parsing data; wherein, the log parsing data includes: execution risk assessment data, execution verification rules, and rule description information of the execution verification rules; A risk assessment summary is generated based on the execution risk assessment data and the execution verification rules. Based on the execution risk assessment data and the rule description information, the preset candidate rule adjustment information is filtered to obtain the selected rule adjustment information; Input the execution risk assessment data, the execution verification rules, the rule description information, the risk assessment summary, and the selected rule adjustment information into a preset visualization template to obtain a risk analysis view.

8. A data analysis workflow construction device, characterized in that, The device includes: An acquisition module is used to acquire the original data analysis process; wherein the original data analysis process includes at least one data analysis node and node attribute parameters of the data analysis node; wherein the node attribute parameters include: node category, data attribute information, and first execution object information; The component filtering module is used to filter out selected verification components from preset candidate verification components according to the node category; The rule setting module is used to set verification rules for the selected verification component based on the data attribute information and the first execution object information, so as to obtain the target verification component; The component insertion module is used to insert the target verification component into each data analysis node of the original data analysis process according to the node category, so as to obtain the target data analysis process.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the data analysis process construction method according to 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 the processor, it implements the data analysis process construction method according to any one of claims 1 to 7.

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