Supervision submission tool rapid deployment method, device and equipment and storage medium

Abstract

The invention relates to a supervision submission tool rapid deployment method and device, equipment and a storage medium. The method comprises the steps that after a to-be-adapted toolkit is received, the to-be-adapted toolkit is analyzed according to an analysis tool matched with a file type of the to-be-adapted toolkit, and multiple keywords in the to-be-adapted toolkit are obtained; according to a mapping relationship in a preset data word library, replacing the plurality of keywords in the to-be-adapted toolkit to obtain an adapted toolkit; the mapping relation is used for representing a corresponding relation between the supervision standard keyword and the in-line environment reference value; pushing the adaptive toolkit to a simulation area, and automatically triggering an arrangement type test process; and after the simulation test is passed, checking the trusted area according to multiple environment checking points in the simulation test, and after the checking is passed, deploying an adaptive toolkit in the trusted area. By adopting the method, the deployment efficiency and reliability of the supervision submission tool can be improved.

Description

Technical Field

[0001] This application relates to the field of financial technology, and in particular to a method, apparatus, device, and storage medium for the rapid deployment of regulatory reporting tools. Background Technology

[0002] In the field of banking regulatory reporting, regulatory agencies regularly update or issue conversion tools based on single-reporting (such as EAST conversion, 1104 conversion, key indicator conversion, etc.). Banks' internal systems need to adapt these toolkits to run in their production environments. Traditional adaptation methods primarily rely on manual methods, where technical personnel manually analyze the code, scripts, or configuration files of the new tools issued by regulators and manually modify the toolkits. However, this method, entirely dependent on manual analysis and modification, requires significant development and time investment for each regulatory update, hindering rapid response and deployment. Manual code and configuration modifications are highly susceptible to errors, such as spelling mistakes, logical omissions, and incomplete configuration updates, which can lead to data reporting errors and regulatory risks. Furthermore, repetitive adaptation work consumes a large amount of time from highly skilled developers, preventing human resources from being invested in more valuable business development. Therefore, there is an urgent need for a method to improve the deployment efficiency and reliability of regulatory reporting tools. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for rapid deployment of regulatory reporting tools to address the aforementioned technical problems, thereby improving the deployment efficiency and reliability of regulatory reporting tools.

[0004] Firstly, this application provides a method for rapid deployment of regulatory reporting tools, including:

[0005] After receiving the toolkit to be adapted, the parsing tool is matched with the file type of the toolkit to be adapted and the toolkit is parsed to obtain multiple keywords in the toolkit to be adapted.

[0006] Based on the mapping relationship in the pre-set data thesaurus, multiple keywords in the adaptation toolkit are replaced to obtain the adaptation toolkit; the mapping relationship is used to characterize the correspondence between regulatory standard keywords and industry environmental benchmark values;

[0007] Push the adaptation toolkit to the simulation area to automatically trigger the orchestrated test process;

[0008] After the simulation test is passed, the trusted zone is checked according to multiple environmental checkpoints in the simulation test. After the check is passed, the adaptation toolkit is deployed in the trusted zone.

[0009] In one embodiment, based on the mapping relationship in a pre-set lexicon, multiple keywords in the adaptation toolkit are replaced to obtain the adaptation toolkit, which includes:

[0010] Based on the mapping relationship in the pre-set data lexicon, the target keywords corresponding to each of the multiple keywords in the toolkit to be adapted are determined by regular expression matching, and the target keywords are used to replace the corresponding keywords in the toolkit to be adapted to obtain the intermediate toolkit.

[0011] Perform semantic analysis and context verification on the intermediate toolkit, and use the intermediate toolkit that passes the verification as the adaptation toolkit.

[0012] In one embodiment, the intermediate tools include multiple components; semantic analysis and context verification of the intermediate toolkit include:

[0013] A pre-trained neural network model is used to perform semantic analysis on multiple intermediate toolkits respectively, and the semantic analysis results of each intermediate toolkit are obtained.

[0014] If the semantic analysis results of multiple intermediate toolkits are all passed, the context consistency of the multiple intermediate toolkits is checked based on their context dependencies, and the check results are obtained.

[0015] In one embodiment, the toolkit to be adapted is parsed using a parsing tool that matches the file type of the toolkit, resulting in multiple keywords in the toolkit, including:

[0016] When the file type of the toolkit to be adapted is a .py script, the toolkit is parsed to obtain multiple environment-sensitive nodes.

[0017] When the file type of the toolkit to be adapted is XML script, tag recognition is performed on the toolkit to be adapted to obtain multiple key tags;

[0018] When the file type of the toolkit to be adapted is SQL script, regularization and syntax parsing are performed on the toolkit to be adapted to obtain several key pieces of information.

[0019] In one embodiment, the adaptation toolkit is pushed to the simulation area, automatically triggering an orchestrated test process, including:

[0020] Identify the regulatory type and technology stack of the toolkit to be adapted, and determine the process template that matches the regulatory type and the environment configuration template that matches the technology stack;

[0021] Based on the process template, environment configuration template, and simulation environment template, the adaptation toolkit is packaged into an executable deployment package and pushed to the simulation area to automatically trigger the orchestrated test process.

[0022] In one embodiment, the method further includes:

[0023] Acceptance testing is performed on the adaptation toolkits deployed in the trusted zone;

[0024] After the acceptance test is passed, the adaptation tool is launched and multiple system metrics of the adaptation toolkit are tested.

[0025] Secondly, this application also provides a rapid deployment device for regulatory reporting tools, including:

[0026] The parsing module is used to parse the toolkit to be adapted after receiving it, according to the file type of the toolkit, and obtain multiple keywords in the toolkit.

[0027] The mapping module is used to replace multiple keywords in the adaptation toolkit according to the mapping relationship in the pre-set data thesaurus, so as to obtain the adaptation toolkit; the mapping relationship is used to represent the correspondence between regulatory standard keywords and industry environmental benchmark values;

[0028] The simulation module is used to push the adaptation toolkit to the simulation area and automatically trigger the orchestrated test process;

[0029] The deployment module is used to check the trusted zone based on multiple environmental checkpoints in the simulation test after the simulation test passes. After the check passes, the adaptation toolkit is deployed in the trusted zone.

[0030] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0031] After receiving the toolkit to be adapted, the parsing tool is matched with the file type of the toolkit to be adapted and the toolkit is parsed to obtain multiple keywords in the toolkit to be adapted.

[0032] Based on the mapping relationship in the pre-set data thesaurus, multiple keywords in the adaptation toolkit are replaced to obtain the adaptation toolkit; the mapping relationship is used to characterize the correspondence between regulatory standard keywords and industry environmental benchmark values;

[0033] Push the adaptation toolkit to the simulation area to automatically trigger the orchestrated test process;

[0034] After the simulation test is passed, the trusted zone is checked according to multiple environmental checkpoints in the simulation test. After the check is passed, the adaptation toolkit is deployed in the trusted zone.

[0035] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0036] After receiving the toolkit to be adapted, the parsing tool is matched with the file type of the toolkit to be adapted and the toolkit is parsed to obtain multiple keywords in the toolkit to be adapted.

[0037] Based on the mapping relationship in the pre-set data thesaurus, multiple keywords in the adaptation toolkit are replaced to obtain the adaptation toolkit; the mapping relationship is used to characterize the correspondence between regulatory standard keywords and industry environmental benchmark values;

[0038] Push the adaptation toolkit to the simulation area to automatically trigger the orchestrated test process;

[0039] After the simulation test is passed, the trusted zone is checked according to multiple environmental checkpoints in the simulation test. After the check is passed, the adaptation toolkit is deployed in the trusted zone.

[0040] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0041] After receiving the toolkit to be adapted, the parsing tool is matched with the file type of the toolkit to be adapted and the toolkit is parsed to obtain multiple keywords in the toolkit to be adapted.

[0042] Based on the mapping relationship in the pre-set data thesaurus, multiple keywords in the adaptation toolkit are replaced to obtain the adaptation toolkit; the mapping relationship is used to characterize the correspondence between regulatory standard keywords and industry environmental benchmark values;

[0043] Push the adaptation toolkit to the simulation area to automatically trigger the orchestrated test process;

[0044] After the simulation test is passed, the trusted zone is checked according to multiple environmental checkpoints in the simulation test. After the check is passed, the adaptation toolkit is deployed in the trusted zone.

[0045] The aforementioned rapid deployment method, apparatus, computer equipment, computer-readable storage medium, and computer program product for regulatory reporting tools, upon receiving a toolkit to be adapted, parses the toolkit according to its file type using a matching parsing tool. This parses the toolkit to obtain multiple keywords. Based on a pre-set mapping relationship in a database, these keywords are then replaced to obtain the adapted toolkit. The mapping relationship characterizes the correspondence between regulatory standard keywords and industry benchmark values. This method automatically parses keywords using a parsing tool and determines the appropriate toolkit based on a pre-set mapping relationship. The keyword matching method enables rapid and accurate replacement of various keywords in the adaptation toolkit, shortening the adaptation cycle and improving deployment efficiency and reliability. Pushing the adaptation toolkit to the simulation area automatically triggers an orchestrated testing process. After the simulation test passes, the trusted zone is checked based on multiple environment checkpoints from the simulation test. Upon successful check, the adaptation toolkit is deployed in the trusted zone, ensuring consistency between the trusted zone environment and the simulation test environment. This avoids cross-environment version deployment differences caused by manual deployment, reduces deployment risks and testing / maintenance difficulties associated with manual operations, and enhances the security and reliability of the deployment process. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is an application environment diagram of a rapid deployment method for regulatory reporting tools in one embodiment;

[0048] Figure 2 This is a flowchart illustrating a method for rapid deployment of regulatory reporting tools in one embodiment;

[0049] Figure 3 This is a schematic diagram of the components of a rapid deployment system for regulatory reporting tools in one embodiment;

[0050] Figure 4 This is a schematic diagram of the overall process of a rapid deployment method for regulatory reporting tools in one embodiment;

[0051] Figure 5 This is a structural block diagram of a rapid deployment device for regulatory reporting tools in one embodiment;

[0052] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0053] 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.

[0054] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0055] The rapid deployment method for regulatory reporting tools provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated on server 104 or placed on the cloud or other network servers. This embodiment uses the method applied to a terminal as an example for illustration. It is understood that this method can also be applied to a server, and can also be applied to a system including both a terminal and a server, and implemented through the interaction between the terminal and the server. After receiving the toolkit to be adapted, terminal 102 parses the toolkit according to the parsing tool that matches the file type of the toolkit, obtaining multiple keywords in the toolkit; it replaces multiple keywords in the toolkit according to the mapping relationship in the pre-set data thesaurus, obtaining the adapted toolkit; the mapping relationship is used to characterize the correspondence between regulatory standard keywords and industry environment benchmark values; the adapted toolkit is pushed to the simulation area, automatically triggering the orchestrated testing process; after the simulation test passes, the trusted area is checked according to multiple environment checkpoints in the simulation test, and after the check passes, the adapted toolkit is deployed in the trusted area. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0056] In one exemplary embodiment, such as Figure 2 As shown, a method for rapid deployment of regulatory reporting tools is provided, which can be applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps 202 to 208. Wherein:

[0057] Step 202: After receiving the toolkit to be adapted, the toolkit is parsed according to the file type of the toolkit to be adapted, and multiple keywords in the toolkit to be adapted are obtained.

[0058] Among them, the toolkits to be adapted refer to the conversion tools based on the single-reporting system that are regularly updated or issued by regulatory agencies in the field of bank regulatory reporting, such as EAST conversion, 1104 conversion, and key indicator conversion. The toolkits to be adapted include core components such as scripts, configurations, and query statements, and can only be used after being adapted to the bank's own production environment.

[0059] File type refers to the format identifier of various files contained in the toolkit to be adapted, which can be distinguished by the file extension. For example, the file types of the toolkit to be adapted include script classes (.py), configuration classes (.xml), database script classes (.sql), executable component classes (.jar), etc.

[0060] Parsing tools refer to technical tools or components pre-defined for different file types, used to parse file content and extract sensitive environmental information and key business information from files. Different parsing tools can be adapted for different file types. For example, .py files can be parsed using the Python ast abstract syntax tree parsing tool. In some embodiments, the terminal pre-stores the correspondence between file types and parsing tools; querying this correspondence automatically associates the parsing tool that matches the file type of the toolkit to be adapted.

[0061] Keywords refer to the key information obtained after parsing the adaptation toolkit. These are the critical nodes that need to be replaced with the bank's actual configuration, including environment parameters, business mapping items, and technical dependencies. For example, environment parameters include: db_host (database address) and db_port (database port); business mapping items include: G01 report (regulatory report name) and customer information table (regulatory business table name); and technical dependencies include: Python 3.8 (version requirement).

[0062] Step 204: Replace multiple keywords in the adaptation toolkit according to the mapping relationship in the pre-set data thesaurus to obtain the adaptation toolkit; the mapping relationship is used to characterize the correspondence between regulatory standard keywords and industry environmental benchmark values.

[0063] The pre-built data thesaurus refers to a structured knowledge base that the terminal pre-builds and maintains, storing the mapping relationships between regulatory standards and the industry environment. For example, the mapping relationships in the pre-built data thesaurus adopt a three-layer mapping structure. The basic layer stores the correspondence between regulatory standard keywords and industry environment benchmark values; for example, the regulatory report name "G01" maps to the industry table name "REP_G01". The middle layer builds the association relationship between "business scenarios and keyword groups"; for example, "EAST reporting scenario" is associated with keyword clusters such as "account information, transaction flow, and counterparty information". The extension layer supports user-defined mapping rules, for example, through a web interface for visual configuration.

[0064] In some embodiments, the pre-built data lexicon supports incremental learning and can automatically mine new mapping relationships through manual annotation or machine learning algorithms, such as the BERT-based semantic similarity model. The pre-built data lexicon is automatically updated once every preset time interval (e.g., weekly) to ensure a rapid response to new regulatory rules.

[0065] The terminal automatically replaces each keyword parsed from the adaptation toolkit with the corresponding keywords from the bank's internal environment based on the mapping relationships in the pre-set data thesaurus, thus obtaining the adaptation toolkit. After keyword replacement, the adaptation toolkit meets the bank's production environment configuration requirements and can be run directly in the bank's system. It includes the replaced scripts, configuration files, query statements, etc.

[0066] Step 206: Push the adaptation toolkit to the simulation area to automatically trigger the orchestrated test process.

[0067] The simulation zone refers to a test environment that is logically consistent with but isolated from the production environment. It is used to verify the functional availability and environmental adaptability of the toolkit, avoiding risks arising from direct testing in the production environment. In some embodiments, the adaptation toolkit is pushed to the simulation zone, and the toolkit is automatically built into a Docker image, creating an isolated simulation environment in the simulation zone based on Kubernetes.

[0068] An orchestrated test process refers to a set of automated test steps arranged in a preset order and with dependencies, ensuring that the toolkit is fully compatible with the simulation environment. For example, an orchestrated test process includes smoke testing and full regression testing. Smoke testing verifies core functionalities, such as data extraction success rate and the correctness of transformation logic; full regression testing calls over 500 preset test cases, covering boundary values, abnormal scenarios, etc. Test results are collected in real time through the Prometheus monitoring system, generating a visual test report. If the pass rate is below 95%, an alarm is automatically triggered and the process terminates.

[0069] Step 208: After the simulation test passes, the trusted zone is checked according to multiple environmental checkpoints in the simulation test. After the check passes, the adaptation toolkit is deployed in the trusted zone.

[0070] After the adaptation toolkit completes the programmed test process in the simulation area, all preset test items meet the qualification standards, confirming that the adaptation toolkit simulation test has passed.

[0071] The Trusted Zone refers to a high-security environment (i.e., the production environment) used for core business data storage and production operations. It possesses strict security policies, data isolation mechanisms, and compliance controls, and is the target environment for the final deployment and operation of regulatory tools. Environment checkpoints refer to environment configuration items that have been verified and passed in simulation testing and are strongly related to the toolkit's operation (essential conditions for the tool's normal operation). These serve as benchmark standards for Trusted Zone environment compliance checks. The Trusted Zone is checked according to multiple environment checkpoints to ensure consistency between the two environments.

[0072] If multiple environmental checkpoints in the Trusted Zone meet the qualification standards, the Trusted Zone environment is determined to be in a state where it is ready to deploy and run the toolkit. Once the Trusted Zone check is passed, the adaptation toolkit is installed and configured into the Trusted Zone using automated deployment tools, making it a production-grade application that can run normally and support the process of reporting regulatory data.

[0073] In the aforementioned rapid deployment method for regulatory reporting tools, after receiving the toolkit to be adapted, the parsing tool is matched with the file type of the toolkit to be adapted and parsed to obtain multiple keywords in the toolkit. Based on the mapping relationship in the pre-set data thesaurus, multiple keywords in the toolkit to be adapted are replaced to obtain the adapted toolkit. The mapping relationship is used to represent the correspondence between regulatory standard keywords and industry environment benchmark values. This method of automatically parsing keywords through the parsing tool and determining the adapted keywords through the pre-set mapping relationship achieves rapid and accurate replacement of each keyword in the toolkit to be adapted, shortens the adaptation cycle, and improves deployment efficiency and reliability. The adapted toolkit is pushed to the simulation area, automatically triggering the orchestrated testing process. After the simulation test passes, the trusted area is checked according to multiple environment checkpoints in the simulation test. After the check passes, the adapted toolkit is deployed in the trusted area. This ensures the consistency between the trusted area environment and the simulation area test environment, avoids cross-environment version deployment differences caused by manual deployment, reduces the deployment risks and testing and maintenance difficulties caused by manual operation, and improves the security and reliability of the deployment process.

[0074] In an exemplary embodiment, multiple keywords in the adaptation toolkit are replaced according to the mapping relationship in a pre-set data lexicon to obtain the adaptation toolkit, which includes:

[0075] Based on the mapping relationship in the pre-set data lexicon, the target keywords corresponding to each of the multiple keywords in the toolkit to be adapted are determined by regular expression matching, and the target keywords are used to replace the corresponding keywords in the toolkit to be adapted to obtain the intermediate toolkit.

[0076] Perform semantic analysis and context verification on the intermediate toolkit, and use the intermediate toolkit that passes the verification as the adaptation toolkit.

[0077] Regular expression matching refers to using pre-compiled regular expressions to quickly locate target keywords in the mapping relationship of a preset database that completely match or have the same format as each keyword in the toolkit to be adapted, achieving efficient and accurate location and initial replacement. After replacing the corresponding keywords in the toolkit with the target keywords, an intermediate toolkit is obtained.

[0078] To ensure the adaptation toolkit conforms to syntactic logic and contextual dependencies, semantic analysis and contextual validation are performed on the intermediate toolkit. Semantic analysis refers to parsing the content of the intermediate toolkit at the semantic level, checking for syntactic errors, and identifying synonyms. For example, "Customer Information Table" and "Personal Customer Basic Information Table" are synonyms. For semantic issues such as syntactic errors or synonyms, semantic alerts should be generated to indicate that the semantic issues should be eliminated. Then, semantic analysis is performed on the toolkit after the semantic issues have been eliminated.

[0079] Contextual validation refers to combining the file dependencies within the intermediate toolkit (such as configuration files called by scripts, database tables referenced by configuration files, and fields associated with SQL statements) to verify the consistency of the replaced keywords across the entire toolkit, thus avoiding logical breaks caused by isolated replacements.

[0080] After semantic analysis and context verification are passed, the corresponding intermediate toolkit will be used as the adaptation toolkit.

[0081] In this embodiment, regular expression matching is used to quickly locate and batch replace target keywords, significantly improving adaptation efficiency and shortening the adaptation cycle of regulatory tools. The semantic analysis stage uses natural language understanding technology to ensure that the replaced keywords match the business scenario and context description, avoiding semantic conflicts caused by differences between regulatory and industry expressions. Context verification covers all file dependencies within the toolkit, eliminating problems such as parameter mismatch, table name conflict, and path inconsistency caused by isolated replacement, ensuring the integrity and operability of the overall logic of the toolkit. The adapted toolkit, after double verification, is more reliable in terms of functional logic and environmental adaptability, reducing data reporting errors caused by tool adaptation issues and improving the reliability of the adapted toolkit.

[0082] In an exemplary embodiment, the intermediate tools include multiple intermediate tools; semantic analysis and context verification of the intermediate toolkits include: performing semantic analysis on the multiple intermediate toolkits respectively using a pre-trained neural network model to obtain the semantic analysis results of each of the multiple intermediate toolkits; if the semantic analysis results of the multiple intermediate toolkits are all passed, performing context consistency verification on the multiple intermediate toolkits according to the context dependency relationship of the multiple intermediate toolkits to obtain the verification result.

[0083] The toolkit to be adapted includes at least one tool, but the example given is that there are multiple tools to be adapted. Each toolkit to be adapted corresponds to a functional module in the regulatory reporting (such as data extraction, transformation, verification, and reporting), and there are multiple intermediate tools, which have context dependencies.

[0084] The pre-trained neural network model can be a natural language processing model, such as the BERT model or a large language model, which has the ability to understand semantics and judge logical consistency. It is used to analyze semantic problems in the content of intermediate toolkits. The pre-trained neural network model performs semantic analysis on multiple intermediate toolkits separately. The output semantic analysis results include "pass" (i.e., no semantic conflict and consistent with business logic) and "fail" (i.e., semantic conflict and logical deviation exist), with explanations of the conflict points.

[0085] If the semantic analysis results of multiple intermediate toolkits all pass, further context consistency verification is performed.

[0086] Context dependencies refer to business process dependencies, data transfer dependencies, and configuration association dependencies among multiple intermediate toolkits, such as configuration files called by scripts and database tables referenced by configuration files, which can be obtained by parsing the toolkit to be adapted. Context consistency checks can verify the consistency of data transfer, configuration parameters, and business logic among multiple intermediate toolkits, avoiding problems such as inconsistent keyword substitutions across modules, incompatible data formats, and configuration conflicts.

[0087] The results of context consistency verification include "pass" (data, configuration, and logic are completely consistent across modules) and "fail" (inconsistency issues exist across modules).

[0088] In this embodiment, a two-layer verification mechanism of single-module semantic analysis and context consistency verification is used to achieve accurate and secure adaptation verification for multi-module regulatory toolkits. The pre-trained neural network model has professional scene semantic understanding capabilities and can accurately identify semantic conflicts after keyword replacement in a single intermediate toolkit, avoiding tool function failure due to semantic misunderstanding. The consistency verification based on context dependency covers cross-module correlation points such as data transmission, configuration parameters, and business logic, ensuring the logical coherence and data consistency of the entire regulatory reporting process.

[0089] In an exemplary embodiment, the toolkit to be adapted is parsed according to the file type matching parsing tool to obtain multiple keywords in the toolkit, including: if the file type of the toolkit to be adapted is a .py script, the toolkit to be adapted is parsed to obtain multiple environment-sensitive nodes; if the file type of the toolkit to be adapted is an .xml script, the toolkit to be adapted is identified by tag recognition to obtain multiple key tags; if the file type of the toolkit to be adapted is a .sql script, the toolkit to be adapted is regularized and parsed to obtain multiple key information.

[0090] For Python scripts, the AST (Abstract Syntax Tree) parsing technology from the Python ecosystem is used to perform syntax-level deconstruction of script files, accurately identifying environment-sensitive nodes such as variable definitions, function calls, and path references.

[0091] For XML scripts, the XPath syntax of the lxml library is used to extract tag-level content, resulting in multiple key tags.

[0092] For SQL script types, key information such as database and table names, field names, and stored procedure calls are identified through a combination of regular expressions and syntax parsing.

[0093] In this embodiment, a differentiated parsing strategy is adopted to automatically complete the parsing of three mainstream regulatory toolkit file types: py, xml, and sql. This accurately extracts core information, laying the foundation for subsequent keyword replacement and adaptation deployment. It significantly reduces parsing time costs and enables rapid response to regulatory tool update needs.

[0094] In an exemplary embodiment, the adaptation toolkit is pushed to the simulation area, automatically triggering an orchestrated testing process, including: identifying the regulatory type and technology stack of the toolkit to be adapted, determining the process template matching the regulatory type and the environment configuration template matching the technology stack; and packaging the adaptation toolkit into an executable deployment package according to the process template, environment configuration template, and simulation environment template, and pushing it to the simulation area to automatically trigger the orchestrated testing process.

[0095] The regulatory type refers to the regulatory reporting business category corresponding to the toolkit to be adapted, such as EAST reporting, 1104 reporting, and key indicator monitoring. Each regulatory type corresponds to different process content; therefore, the terminal has pre-installed process templates for each regulatory type. Each process template contains a typical deployment process for that regulatory type (e.g., the EAST template set contains a pipeline configuration of "data extraction-cleaning-transformation-verification-reporting").

[0096] A technology stack refers to the combination of development languages, dependent frameworks, and runtime environments for the toolkit to be adapted. Examples include Python, Java, and SQL. Different technology stacks have different environment deployment requirements. Therefore, the terminal pre-installs environment configuration templates for each technology stack to adapt to the deployment requirements of different development languages. For example, the Python template integrates virtualenv virtual environment configuration, and the Java template includes JVM parameter tuning.

[0097] The deployment environment in this application embodiment includes a simulation zone and a trusted zone. The network policies (such as allowing test data injection in the simulation zone and enabling data anonymization in the trusted zone) and resource quotas (such as the number of computing nodes and memory allocation) are different in different deployment environments. Therefore, when conducting simulation tests in the simulation zone, a simulation environment template is selected.

[0098] Each template supports parameterized configuration, enabling dynamic injection of environment variables through placeholders (such as {{DB_HOST}}), which are automatically replaced with the actual values ​​of the target environment during the deployment phase.

[0099] Combining process templates, environment configuration templates, and simulation environment templates, the adapted code, configuration files, and dependency libraries are packaged into an executable deployment package (such as a Docker image, Ansible Playbook, or Shell script package). The executable deployment package has a built-in self-verification script that automatically checks environment dependencies (such as database connections and network reachability) upon startup. If the check fails, it outputs detailed error logs and terminates the process.

[0100] Once the executable deployment package is pushed to the simulation area, an orchestrated testing process is automatically triggered. This process includes: first, executing unit tests to verify function-level logic; then, executing integration tests to verify interactions between modules; and finally, executing end-to-end tests to simulate the complete reporting process. During testing, exceptions (such as network latency and dirty reads) can be injected using chaos engineering tools to verify the system's fault tolerance. Test data is automatically archived to the data lake, providing a basis for subsequent analysis.

[0101] In this embodiment, preset templates are matched with regulatory types and technology stacks to avoid manually designing and deploying processes and configuring environments from scratch, thus significantly shortening the deployment cycle of the simulation area. Each template is designed in a standardized manner to ensure the consistency of deployment logic for toolkits of the same regulatory type and the same technology stack. The orchestrated test process is arranged according to regulatory requirements and business logic, covering all dimensions of test points such as environment checks, functional verification, and abnormal scenarios, ensuring comprehensive and thorough testing.

[0102] In one exemplary embodiment, the method further includes: performing acceptance testing on the adaptation toolkit deployed in the trusted zone; and after the acceptance testing is passed, deploying the adaptation toolkit online and detecting multiple system metrics of the adaptation toolkit.

[0103] Once deployed in the Trusted Zone, an automated acceptance test is triggered, where a Robotic Process Automation (RPA) tool simulates the entire regulatory reporting process to verify the accuracy and timeliness of data reporting.

[0104] Once the accuracy and timeliness meet the preset standards, the acceptance test is confirmed to have passed. The adaptation tool is then deployed and automatically integrated into the industry's AIOps platform to monitor multiple system metrics (such as CPU utilization, memory consumption, and reporting time). Machine learning algorithms are used to detect anomalies in these metrics and provide early warnings of potential faults. Simultaneously, a visual operations dashboard is generated, displaying the tool's operational status, historical reporting records, and troubleshooting paths, providing decision support for operations personnel.

[0105] In this embodiment, acceptance testing is conducted after deployment. The acceptance test focuses on the real production environment of the trusted zone, which can accurately identify problems not exposed in the simulation environment. Only after the acceptance test is passed can the system be allowed to go online, ensuring the reliability of the adaptation toolkit. After going online, continuous monitoring through system indicators can quickly respond to toolkit malfunctions and ensure that regulatory reporting tasks are completed on time and smoothly.

[0106] To illustrate in detail the rapid deployment method and effectiveness of the regulatory reporting tool in this solution, a detailed implementation example is provided below:

[0107] like Figure 3 The diagram illustrates the components of a rapid deployment system for regulatory reporting tools in some embodiments. The system includes a regulatory tool receiving and parsing module, an intelligent keyword detection and replacement engine, a configuration template library, and an integrated deployment module.

[0108] The regulatory tool receiving and parsing module employs an intelligent deconstruction hub for multi-source heterogeneous files. This module uses a microservice architecture to support parallel processing of multiple toolkit types (.py scripts, .xml configuration files, .sql query statements, .jar executables, etc.). Through Python's AST abstract syntax tree parsing technology, it performs syntax-level deconstruction on script files, accurately identifying environment-sensitive nodes such as variable definitions, function calls, and path references. For .xml files, it utilizes the XPath syntax of the lxml library to extract tag-level content. For .sql files, it identifies key information such as database table names, field names, and stored procedure calls through a combination of regular expressions and syntax parsing. Simultaneously, the module incorporates a file fingerprint verification system, using the MD5 hash algorithm to uniquely identify received toolkits, ensuring version traceability and providing a fundamental guarantee for consistency in subsequent adaptation and deployment.

[0109] The intelligent keyword detection and replacement engine is the core of the semantic-level mapping, with a pre-built lexicon. The lexicon employs a three-layer mapping structure: the base layer stores "regulatory standard keywords - bank production environment benchmark values" (e.g., the regulatory report name "G01" maps to the internal report name "REP_G01"); the middle layer constructs "business scenario - keyword group" associations (e.g., "EAST reporting scenario" associates with keyword clusters such as "account information, transaction history, and counterparty information"); and the extension layer supports user-defined mapping rules, with visual configuration via a web interface. The lexicon supports incremental learning, automatically mining new mapping relationships through manual annotation or machine learning algorithms (such as a BERT-based semantic similarity model), and is automatically updated weekly to ensure rapid response to new regulatory rules.

[0110] (1) Pattern matching and replacement: A multi-strategy precise execution engine, employing a three-layer matching strategy of "regular expression matching + semantic understanding + context verification":

[0111] (2) Regular expression matching layer: For keywords with fixed format (such as file path / regulatory / east / ), it quickly locates and replaces them through pre-compiled regular expressions.

[0112] (3) Semantic understanding layer: Use pre-trained language models (such as BERT) to perform semantic analysis on the business logic described in natural language, identify synonyms (such as "customer information table" and "personal customer basic information table"), and ensure the accuracy of mapping.

[0113] (4) Context validation layer: Combine the dependencies within the toolkit (such as the configuration file called by the script and the database table referenced by the configuration file) to perform consistency validation on the replaced keywords and avoid logical errors caused by "isolated replacement".

[0114] The replacement process supports version rollback. Each replacement operation generates an operation log and snapshot, allowing for one-click rollback to a previous version.

[0115] The configuration template library serves as a standardized platform for scenario-based deployments. The template library is categorized into three dimensions: "Regulatory Type - Deployment Environment - Technology Stack".

[0116] (1) According to the type of supervision, it is divided into EAST template set, 1104 template set, key indicator monitoring template set, etc. Each template set has a typical deployment process for the supervision scenario (such as the EAST template set containing the pipeline configuration of "data extraction-cleaning-conversion-verification-reporting").

[0117] (2) Based on the deployment environment, the templates are divided into simulation zone templates and trusted zone templates, and the network policies (such as allowing test data injection in the simulation zone and enabling data anonymization in the trusted zone) and resource quotas (such as the number of computing nodes and memory allocation) are distinguished between the environments.

[0118] (3) Based on the technology stack, templates are divided into Python templates, Java templates, SQL templates, etc., to adapt to the deployment requirements of different development languages ​​(e.g., Python templates integrate virtualenv virtual environment configuration, and Java templates include JVM parameter tuning). Templates support parameterized configuration and dynamically inject environment variables through placeholders (e.g., {{DB_HOST}}), which are automatically replaced with the actual values ​​of the target environment during the deployment phase.

[0119] The integrated deployment module serves as the central hub for automated operation and maintenance in the dual-zone collaboration. This module is deeply integrated with the CI / CD toolchain (Jenkins, GitLab CI, ArgoCD, etc.) to achieve full-process automation of "code submission - image building - environment deployment - automated testing - canary release - full deployment".

[0120] (1) Simulation Zone Deployment and Testing: Quality is strictly controlled by Gates, which automatically builds the adapted toolkit into a Docker image and creates an isolated environment in the simulation zone based on Kubernetes. Smoke testing and full regression testing are triggered: Smoke testing verifies core functions (such as data extraction success rate and the correctness of transformation logic); full regression testing calls 500+ preset test cases, covering boundary values, abnormal scenarios, etc. Test results are collected in real time through the Prometheus monitoring system and a visual test report is generated. If the pass rate is lower than 95%, an alarm is automatically triggered and the process is terminated.

[0121] (2) Trusted Zone Synchronization: The ultimate line of defense for consistency assurance, adopting a "image-level synchronization + double verification insurance" mechanism: First, the Docker image verified in the simulation zone is synchronized to the trusted zone image repository; then, the file system, database configuration, and network policy of the trusted zone server are compared using the SSH protocol to ensure complete consistency with the simulation zone. The synchronization process supports canary releases, which can be deployed and verified on 10% of trusted zone nodes first, and then gradually expanded to all nodes to minimize the risk of going online.

[0122] like Figure 4 The diagram illustrates the overall flow of a rapid deployment method for regulatory reporting tools in some embodiments. The method includes:

[0123] 1. Parsing and Detection: Achieving comprehensive deconstruction of multi-dimensional information. After receiving the toolkit, the system initiates three sub-processes in parallel: syntax parsing, semantic analysis, and dependency mining. Syntax parsing generates an abstract syntax tree and marks environment-sensitive nodes; semantic analysis identifies implicit environment variables in business logic; dependency mining analyzes the calling relationships of files within the toolkit (such as scripts calling configuration files, configuration files referencing databases). Finally, an "environment-sensitive information graph" is generated, containing all keywords that need to be replaced and their contextual dependencies.

[0124] 2. Adaptation: Achieving precise execution of semantic-level mapping. The engine invokes the three-layer mapping rules of the data lexicon to perform layered replacement of the "environment-sensitive information graph": basic layer keywords are replaced first, the middle layer supplements related keywords according to business scenarios, and the extended layer applies custom rules. After the replacement is completed, a static code analysis tool (such as SonarQube) is used to perform a quality scan on the adapted code, checking for syntax errors, potential vulnerabilities, code complexity, and other indicators to ensure that the adapted toolkit has production-grade usability.

[0125] 3. Packaging: This process generates standardized deliverables. Combining scenario-based templates from the configuration template library, the adapted code, configuration files, and dependency libraries are packaged into an executable deployment package (such as a Docker image, Ansible Playbook, or Shell script package). The deployment package includes a built-in self-verification script that automatically checks environment dependencies (such as database connections and network reachability) upon startup. If the check fails, it outputs detailed error logs and terminates the process.

[0126] 4. Simulation Area Deployment and Testing: Serving as a quality gate for automated verification. After the deployment package is pushed to the simulation area, an orchestrated testing process is automatically triggered: first, unit tests are executed to verify function-level logic; then, integration tests are executed to verify interactions between modules; and finally, end-to-end tests are executed to simulate the complete reporting process. During testing, exceptions (such as network latency and dirty reads) are injected using chaos engineering tools to verify the system's fault tolerance. Test data is automatically archived to the data lake, providing a basis for subsequent analysis.

[0127] 5. Synchronous Deployment: The ultimate implementation of dual-zone consistency. After the simulation zone test passes, the system automatically generates a "Deployment Consistency Verification Checklist," containing over 200 checkpoints including file hashes, process status, and network configuration. During deployment in the trusted zone, each item is compared against the checklist to ensure complete consistency with the simulation zone. After deployment, automated acceptance testing is triggered, with Robotic Process Automation (RPA) tools simulating the entire regulatory reporting process to verify the accuracy and timeliness of data reporting.

[0128] 6. Ready for Deployment: Serving as the starting point for intelligent monitoring and operations. After deployment, the module automatically connects to the industry's AIOps platform, using machine learning algorithms to detect anomalies in system metrics (such as CPU utilization, memory consumption, and reporting time) and provide early warnings of potential faults. Simultaneously, it generates a visual operations dashboard displaying the tool's operational status, historical reporting records, and troubleshooting paths, providing decision support for operations personnel.

[0129] The above technical solutions achieve "intelligent, standardized, and automated" adaptation and deployment of regulatory reporting tools, enabling one-click, synchronous deployment of the adapted tools in the simulation and trusted zones, ensuring environmental consistency, significantly reducing manual intervention, lowering labor costs and operational risks caused by human error, and improving banks' responsiveness and agility in responding to changes in regulatory policies. They also completely solve the efficiency and quality bottlenecks of the traditional manual mode, providing technical support for banks to cope with high-frequency and highly complex regulatory requirements.

[0130] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0131] Based on the same inventive concept, this application also provides a rapid deployment device for regulatory reporting tools to implement the rapid deployment method for regulatory reporting tools described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the rapid deployment device for regulatory reporting tools provided below can be found in the limitations of the rapid deployment method for regulatory reporting tools described above, and will not be repeated here.

[0132] In one exemplary embodiment, such as Figure 5 As shown, a rapid deployment device 500 for regulatory reporting tools is provided, including: a parsing module 520, a mapping module 540, a simulation module 560, and a deployment module 580, wherein:

[0133] The parsing module 520 is used to parse the toolkit to be adapted after receiving it, according to the parsing tool matched with the file type of the toolkit, to obtain multiple keywords in the toolkit to be adapted.

[0134] The mapping module 540 is used to replace multiple keywords in the adaptation toolkit according to the mapping relationship in the pre-set data thesaurus, so as to obtain the adaptation toolkit; the mapping relationship is used to characterize the correspondence between regulatory standard keywords and industry environment benchmark values;

[0135] Simulation module 560 is used to push the adaptation toolkit to the simulation area and automatically trigger the orchestrated test process;

[0136] Deployment module 580 is used to check the trusted zone based on multiple environmental checkpoints in the simulation test after the simulation test passes, and to deploy the adaptation toolkit in the trusted zone after the check passes.

[0137] The aforementioned rapid deployment device for regulatory reporting tools, upon receiving the toolkit to be adapted, parses it using a parsing tool that matches the file type of the toolkit. This process yields multiple keywords within the toolkit, which are then replaced according to a pre-set mapping relationship in a database. The mapping relationship represents the correspondence between regulatory standard keywords and industry benchmark values. This method, which automatically parses keywords and determines the appropriate keywords based on pre-set mapping relationships, enables rapid and accurate replacement of keywords within the toolkit, shortening the adaptation cycle and improving deployment efficiency and reliability. The toolkit is then pushed to the simulation zone, automatically triggering an orchestrated testing process. After the simulation test passes, the trusted zone is checked based on multiple environment checkpoints. Upon successful check, the toolkit is deployed in the trusted zone, ensuring consistency between the trusted zone environment and the simulation zone test environment. This avoids cross-environment version deployment differences caused by manual deployment, reduces deployment risks and testing / maintenance difficulties associated with manual operations, and enhances the security and reliability of the deployment process.

[0138] In one embodiment, multiple keywords in the toolkit to be adapted are replaced according to the mapping relationship in the preset data lexicon to obtain the adaptation toolkit. The mapping module 540 is further configured to: determine the target keywords corresponding to each of the multiple keywords in the toolkit to be adapted through regular expression matching according to the mapping relationship in the preset data lexicon, and replace the corresponding keywords in the toolkit to be adapted with the target keywords to obtain the intermediate toolkit; perform semantic analysis and context verification on the intermediate toolkit, and use the intermediate toolkit that passes the verification as the adaptation toolkit.

[0139] In one embodiment, the intermediate tools include multiple intermediate tools; the mapping module 540 is further configured to: perform semantic analysis and context verification on the intermediate toolkits respectively using a pre-trained neural network model to obtain the semantic analysis results of each intermediate toolkit; if the semantic analysis results of the multiple intermediate toolkits are all passed, perform context consistency verification on the multiple intermediate toolkits according to the context dependency relationship of the multiple intermediate toolkits to obtain the verification result.

[0140] In one embodiment, the parsing module 520 parses the toolkit to be adapted according to the file type matching parsing tool to obtain multiple keywords in the toolkit. The parsing module 520 is also used to: perform syntax parsing on the toolkit to be adapted when the file type of the toolkit to be adapted is a py script to obtain multiple environment-sensitive nodes; perform tag recognition on the toolkit to be adapted when the file type of the toolkit to be adapted is an xml script to obtain multiple key tags; and perform regularization processing and syntax parsing on the toolkit to be adapted when the file type of the toolkit to be adapted is an sql script to obtain multiple key information.

[0141] In one embodiment, the adaptation toolkit is pushed to the simulation area, automatically triggering an orchestrated test process. The simulation module 560 is also used to: identify the regulatory type and technology stack of the toolkit to be adapted, determine the process template matching the regulatory type and the environment configuration template matching the technology stack; and package the adaptation toolkit into an executable deployment package according to the process template, environment configuration template and simulation environment template, and push it to the simulation area, automatically triggering an orchestrated test process.

[0142] In one embodiment, the deployment module 580 is further configured to: perform acceptance testing on the adaptation toolkit deployed in the trusted zone; and after the acceptance test is passed, deploy the adaptation toolkit online and test multiple system metrics of the adaptation toolkit.

[0143] The modules in the aforementioned rapid deployment device for regulatory reporting tools can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can invoke and execute the corresponding operations of each module.

[0144] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for rapid deployment of regulatory reporting tools. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0145] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0146] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0147] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0148] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0149] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0150] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0151] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0152] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for rapid deployment of a regulatory reporting tool, characterized in that, The method includes: Upon receiving the toolkit to be adapted, the toolkit is parsed using a parsing tool that matches the file type of the toolkit to be adapted, and multiple keywords in the toolkit to be adapted are obtained. Based on the mapping relationship in the pre-set data thesaurus, multiple keywords in the toolkit to be adapted are replaced to obtain the adaptation toolkit; the mapping relationship is used to characterize the correspondence between regulatory standard keywords and industry environmental benchmark values; Push the adaptation toolkit to the simulation area to automatically trigger the orchestrated test process; After the simulation test is passed, the trusted zone is checked according to multiple environmental checkpoints in the simulation test. After the check is passed, the adaptation toolkit is deployed in the trusted zone.

2. The method according to claim 1, characterized in that, The process involves replacing multiple keywords in the toolkit to be adapted based on the mapping relationships in a pre-set lexicon to obtain the adaptation toolkit, including: Based on the mapping relationship in the pre-set data lexicon, the target keywords corresponding to each of the multiple keywords in the toolkit to be adapted are determined by regular expression matching, and the corresponding keywords in the toolkit to be adapted are replaced by the target keywords to obtain the intermediate toolkit; The intermediate toolkit is subjected to semantic analysis and context verification, and the intermediate toolkit that passes the verification is used as the adaptation toolkit.

3. The method according to claim 2, characterized in that, The intermediate tools include multiple components; the semantic analysis and context verification of the intermediate toolkit include: A pre-trained neural network model is used to perform semantic analysis on multiple intermediate toolkits respectively, and the semantic analysis results of each intermediate toolkit are obtained. If the semantic analysis results of multiple intermediate toolkits are all passed, the context consistency of the multiple intermediate toolkits is checked based on their context dependencies, and the check results are obtained.

4. The method according to claim 1, characterized in that, The parsing tool, which matches the file type of the toolkit to be adapted, parses the toolkit to be adapted to obtain multiple keywords in the toolkit, including: When the file type of the toolkit to be adapted is a .py script, the toolkit to be adapted is parsed to obtain multiple environment-sensitive nodes; When the file type of the toolkit to be adapted is XML script, tag recognition is performed on the toolkit to be adapted to obtain multiple key tags; When the file type of the toolkit to be adapted is SQL script, regularization and syntax parsing are performed on the toolkit to be adapted to obtain several key pieces of information.

5. The method according to claim 1, characterized in that, The step of pushing the adaptation toolkit to the simulation area and automatically triggering the orchestrated test process includes: Identify the regulatory type and technology stack of the toolkit to be adapted, and determine the process template that matches the regulatory type and the environment configuration template that matches the technology stack; Based on the process template, the environment configuration template, and the simulation environment template, the adaptation toolkit is packaged into an executable deployment package and pushed to the simulation area to automatically trigger the orchestrated test process.

6. The method according to claim 1, characterized in that, The method further includes: Acceptance testing is performed on the adaptation toolkit deployed in the trusted zone; After the acceptance test is passed, the adaptation tool is launched online, and multiple system metrics of the adaptation toolkit are tested.

7. A rapid deployment device for regulatory reporting tools, characterized in that, The device includes: The parsing module is used to parse the toolkit to be adapted according to the file type of the toolkit after receiving it, and obtain multiple keywords in the toolkit to be adapted. The mapping module is used to replace multiple keywords in the toolkit to be adapted according to the mapping relationship in the pre-set data thesaurus, so as to obtain the adaptation toolkit; the mapping relationship is used to characterize the correspondence between regulatory standard keywords and industry environment benchmark values; The simulation module is used to push the adaptation toolkit to the simulation area and automatically trigger the orchestrated test process. The deployment module is used to check the trusted region based on multiple environmental checkpoints in the simulation test after the simulation test passes, and to deploy the adaptation toolkit in the trusted region after the check passes.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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