Database transformation engineering monitoring method, device, equipment, medium and product

Abstract

The application provides a database transformation project monitoring method, which can be applied to the fields of artificial intelligence and financial technology. The method comprises the following steps: performing data identification on a pre-acquired database transformation project plan, and generating a node early warning event according to the identified key task nodes, task time constraints and task dependency relationships; storing the relevant data of the node early warning event in a preset central data pool in real time; performing risk analysis on the relevant data flowing into the central data pool in real time, and outputting a risk event with a level; performing standard stream batch processing on the risk event according to the level, and obtaining a cross-role collaborative review result; assigning a responsibility task related to the database transformation to a cross-role person in charge according to the cross-role collaborative review result, monitoring the disposal state of the responsibility task, and sending the disposal state to the supervisor of the cross-role person in charge until the responsibility task is completed. The application also provides a database transformation project monitoring device, equipment, medium and product.

Description

Technical Field

[0001] This application relates to the fields of artificial intelligence and financial technology, specifically to a method for processing financial technology databases, and more specifically to a database transformation engineering monitoring method, device, equipment, medium, and product. Background Technology

[0002] With the rapid development of the financial system, financial databases urgently need to undergo transformation projects such as expansion and reconstruction. However, due to the massive amount of data involved in financial databases, database transformation projects require collaboration among multiple departments, have long transformation cycles, and are therefore complex to manage.

[0003] In related technologies, the management technology for financial database transformation projects lacks a systematic and intelligent support system. Data collection and other operations require manual queries across multiple different functional systems, resulting in poor adaptability and inconsistent data formats, leading to low data acquisition efficiency. Task allocation does not consider the matching of personnel workload and skills, easily resulting in poor matching of responsibility allocation, which in turn affects the overall efficiency of database transformation implementation. When conducting risk analysis, risk assessment is limited to basic resource risks such as equipment availability, failing to cover key dimensions such as process compliance review, project progress, and project quality. This leads to scattered risk data, further resulting in poor timeliness and low accuracy of risk identification, which in turn leads to poor cross-departmental collaborative information transmission during process execution, making it impossible to achieve closed-loop monitoring and control of the entire process. Summary of the Invention

[0004] In view of the above problems, this application provides a database transformation engineering monitoring method, apparatus, equipment, medium and product.

[0005] According to the first aspect of this application, a database transformation project monitoring method is provided, comprising: identifying data from a pre-acquired database transformation project plan, and generating node early warning events based on the identified key task nodes, task time constraints, and task dependencies; collecting data from multiple external systems associated with the node early warning events to obtain relevant data, and storing the relevant data in real time to a preset central data pool; performing risk analysis on the relevant data flowing into the central data pool in real time from at least one dimension of resources, processes, and quality, and outputting risk events with assigned levels; performing standard batch processing on the risk events according to their levels to obtain cross-role collaborative review results; assigning relevant database transformation responsibilities to cross-role responsible persons based on the cross-role collaborative review results, monitoring the handling status of the responsibilities, and sending the handling status to the supervisors of the cross-role responsible persons until the responsibilities are completed.

[0006] According to embodiments of this application, data identification is performed on a pre-acquired database transformation project plan to obtain key task nodes, task time constraints, and task dependencies. This includes: splitting the database transformation project plan into a pre-defined format split document, where the pre-defined format includes at least one of text, image, table, and database statement formats; extracting the structure based on the format of the split document to obtain key fields and field mapping relationships; constructing a graph based on the key fields and field mapping relationships using a pre-defined graph neural network to obtain a task dependency directed graph; and extracting features from the task dependency directed graph to obtain key task nodes, task dependencies, and task time constraints.

[0007] According to an embodiment of this application, generating node warning events based on key task nodes, task time constraints, and task dependencies includes: determining the priority of key task nodes based on key task nodes and task dependencies; performing date recursion based on priority and task time constraints to obtain warning lead time, whereby the warning lead time characterizes the completion time of the upper-level task nodes of the key task nodes and the degree of completion of the upper-level task nodes at a specific time; and generating node warning events related to key task nodes based on the warning lead time and task dependencies.

[0008] According to an embodiment of this application, data is collected from multiple external systems associated with a node warning event to obtain relevant data, including: determining the node type of the task node associated with the node warning event, wherein the task node includes the key task node and the upper-level task node of the key task node; performing permission verification on each of the multiple external systems corresponding to the node type sequentially based on a unified data access gateway; in response to the permission verification passing, using a preset distributed lock based on a preset encrypted transmission mechanism to obtain system data extracted from the multiple external systems; wherein the multiple external systems include at least one of a personnel directory system, a database resource system and a database architecture building system, a distributed version control system and a lightweight directory access protocol system; performing data cleaning and format conversion on the system data to obtain progress data related to the warning event, and labeling the progress data with versions to obtain relevant data.

[0009] According to the embodiments of this application, risk analysis is performed on relevant data flowing into the central data pool in real time from the resource dimension, and risk events with grades are output, including: data parsing of relevant data to obtain inventory information and in-transit information of data resources or asset resources; risk assessment is performed based on inventory information, in-transit information and early warning lead time to obtain the grade of relevant resources and risk events related to relevant resources.

[0010] According to an embodiment of this application, risk analysis is performed on relevant data flowing into the central data pool in real time from a process perspective, and risk events with assigned levels are output. This includes: parsing the relevant data to obtain task node flow data; extracting features from the task node flow data using a preset language processing model to obtain the task flow order; and identifying risks based on the task flow order and priority to obtain the level of the relevant process and the risk events of the relevant process.

[0011] According to the embodiments of this application, risk analysis is performed on relevant data flowing into the central data pool in real time from a quality dimension, and risk events with grades are output, including: periodically performing compliance scoring on the construction progress of the newly built transformation database and the newly stored data in the transformation database according to preset transformation scoring rules; and determining the relevant quality level and risk event corresponding to the compliance score according to a preset scoring level table.

[0012] According to an embodiment of this application, risk events are processed in a standard batch process according to the stated level to obtain cross-role collaborative review results, including: identifying relevant parties for the risk event through synchronous or asynchronous review; determining the approval permissions and approval order of relevant parties according to a preset database transformation manual; creating process approval links for all relevant parties based on their approval permissions, and ensuring that the process approval links are only open to each relevant party with an approval interface that is appropriate for the approval order and approval permissions; and outputting cross-role collaborative review results with notes from each relevant party in response to all relevant parties sequentially completing their respective approvals through the approval interface.

[0013] According to the embodiments of this application, determining the cross-role responsible person includes: determining the associated person with each responsibility task in the cross-role collaborative review results; and designating the associated person whose number of uncompleted tasks is less than a preset task threshold and whose skill matching degree with the responsibility task is higher than a preset matching degree threshold as the cross-role responsible person.

[0014] The second aspect of this application provides a database transformation project monitoring device, comprising: an event generation module for identifying data from a pre-acquired database transformation project plan and generating node early warning events based on the identified key task nodes, task time constraints, and task dependencies; a data sharing module for collecting data from multiple external systems associated with the node early warning events, obtaining relevant data, and storing the relevant data in real time to a preset central data pool; a risk analysis module for performing risk analysis on the relevant data flowing into the central data pool in real time from at least one dimension of resources, processes, and quality, and outputting risk events with assigned levels; a collaborative approval module for performing standard batch processing of risk events according to their levels to obtain cross-role collaborative review results; and a task execution module for assigning database transformation-related responsibility tasks to cross-role responsible persons based on the cross-role collaborative review results, monitoring the handling status of the responsibility tasks, and sending the handling status to the supervisors of the cross-role responsible persons until the responsibility tasks are completed.

[0015] A third aspect of this application provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0016] A fourth aspect of this application also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0017] The fifth aspect of this application also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0018] The above one or more embodiments have the following beneficial effects: By identifying data from the pre-acquired database transformation project plan, and generating node early warning events based on the identified key task nodes, task time constraints, and task dependencies, the data related to the early warning events are uniformly placed in the central data pool to aggregate and share data from multiple systems. This facilitates risk analysis based on real-time captured data, improves the timeliness of risk analysis, and thus improves the online processing efficiency and accuracy of project risk and schedule management. After outputting risk events with assigned levels, standard batch processing of risk events according to their levels yields cross-role collaborative review results. Targeted approval of risk events is then conducted, thereby increasing the intensity of risk handling while improving online approval efficiency. Based on the cross-role collaborative review results, relevant database transformation responsibilities are assigned to cross-role responsible persons, and the handling status of these responsibilities is monitored. This achieves a shift from post-event risk discovery to pre-event early warning, improving response speed, forming a closed-loop risk monitoring and control system, ensuring the implementation of risk handling and the efficiency of database transformation, improving cross-departmental online collaboration efficiency, and ultimately enhancing the automated management level and online comprehensive management efficiency of the database transformation project. Attached Figure Description

[0019] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0020] Figure 1 The illustration schematically depicts application scenarios of the database transformation engineering monitoring method, apparatus, device, medium, and program product according to embodiments of this application;

[0021] Figure 2 A flowchart illustrating a database transformation engineering monitoring method according to an embodiment of this application is shown schematically.

[0022] Figure 3 This illustration schematically shows the data flow involved in the database transformation engineering monitoring method according to an embodiment of this application;

[0023] Figure 4 This illustration shows a schematic diagram of the risk analysis operations involved in the database transformation engineering monitoring method according to an embodiment of this application;

[0024] Figure 5 This schematic diagram illustrates the structural block diagram of a database transformation engineering monitoring device according to an embodiment of this application;

[0025] Figure 6 A block diagram schematically illustrates an electronic device suitable for implementing a database transformation engineering monitoring method according to an embodiment of this application. Detailed Implementation

[0026] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0027] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0029] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0030] In the technical solution of this application, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0031] This application provides a database transformation project monitoring method, apparatus, device, medium, and product. The database transformation project monitoring method includes: identifying data from a pre-acquired database transformation project plan and generating node early warning events based on identified key task nodes, task time constraints, and task dependencies; collecting data from multiple external systems associated with the node early warning events to obtain relevant data, and storing the relevant data in real-time in a preset central data pool; performing risk analysis on the relevant data flowing into the central data pool in real-time from at least one dimension—resources, processes, and quality—and outputting risk events with assigned levels; performing standard batch processing on the risk events according to their levels to obtain cross-role collaborative review results; assigning database transformation-related responsibilities to cross-role responsible persons based on the cross-role collaborative review results, monitoring the handling status of the responsibilities, and sending the handling status to the supervisors of the cross-role responsible persons until the responsibilities are completed.

[0032] Figure 1 The diagram illustrates an application scenario of the database transformation engineering monitoring method according to an embodiment of this application.

[0033] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105.

[0034] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0035] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0036] Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables. Network 104 serves as the medium for providing communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0037] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0038] It should be noted that the database transformation engineering monitoring method provided in this application embodiment can generally be executed by server 105. Correspondingly, the database transformation engineering monitoring device provided in this application embodiment can generally be located in server 105. The database transformation engineering monitoring method provided in this application embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the database transformation engineering monitoring device provided in this application embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0039] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0040] The following will be based on Figure 1 The described scene, through Figures 2-4 A database transformation project monitoring method according to embodiments of this application will be described in detail.

[0041] Figure 2 A flowchart illustrating a database transformation engineering monitoring method according to an embodiment of this application is shown schematically.

[0042] like Figure 2 As shown, the database transformation project monitoring method of this embodiment includes S210~S250, as detailed below:

[0043] Operation S210 identifies data from the pre-acquired database transformation project plan and generates node warning events based on the identified key task nodes, task time constraints, and task dependencies. Operation S220 collects data from multiple external systems associated with the node warning events, obtains relevant data, and stores the relevant data in real time to a preset central data pool. Operation S230 performs risk analysis on the relevant data flowing into the central data pool in real time from at least one dimension: resources, processes, and quality, and outputs risk events with assigned levels. Operation S240 performs standard batch processing on the risk events according to their levels to obtain cross-role collaborative review results. Operation S250 assigns database transformation responsibilities to cross-role responsible persons based on the cross-role collaborative review results, monitors the handling status of the responsibilities, and sends the handling status to the supervisors of the cross-role responsible persons until the responsibilities are completed.

[0044] Figure 3 This illustration shows a schematic diagram of the data flow involved in the database transformation engineering monitoring method according to an embodiment of this application.

[0045] exist Figure 3 In the example shown, for operation S210, the database transformation project plan is first parsed to obtain information contained in the plan, such as Gantt charts, text, images, and tables. All key task nodes are identified, such as "equipment procurement," "environment preparation," "code modification," "review meeting," and "stream switching and deployment," along with their related task time constraints and dependencies. Then, the key task nodes, task time constraints, and task dependencies are used as signal sources for the entire database transformation project monitoring method. Based on this, node warning events are generated. It should be noted that each node warning event corresponds to a specific key task node. It can be a one-to-one correspondence or a many-to-one correspondence. For example, a critical task node can have multiple node warning events. For instance, the critical task node of "equipment procurement" can correspond to two node warning events, such as "equipment procurement funds received" and "equipment procurement supplier products delivered". It can even be a one-to-many correspondence, that is, multiple critical task nodes correspond to one node warning event. For example, two critical task nodes, "environment preparation" and "source code preparation", generate a node warning event of "code modification preparation". In this way, risk monitoring and task monitoring are performed on the node warning event of "code modification preparation" before code modification.

[0046] As an example, the multi-source external systems in operation S220 refer to the independent multi-source related business systems / external systems involved in the entire database transformation project, such as asset management systems and personnel directory systems with different functions. After determining the node warning event, the relevant data of each multi-source external system associated with each node warning event is extracted with reference to the node warning event. The relevant data from each multi-source external system is then aggregated into the central data pool in real time through synchronous or asynchronous means. This allows step S230 to directly perform risk analysis on the relevant data flowing into the central data pool in real time, and obtain risk events with different levels. Based on this, the data related to the warning event is uniformly placed in the central data pool so that the data from multiple systems can be aggregated and shared. This facilitates risk analysis based on the real-time captured data, improves the timeliness of risk analysis, and thus improves the online processing efficiency and accuracy of project risk and schedule management.

[0047] As an example, in operation S240, risk events are processed using standard batch processing according to their risk levels to obtain cross-role collaborative review results. Different risk events have different levels, i.e., risk grades. For example, if the risk grade is high, the corresponding preset standard process is more rigorous and redundant. For instance, if the two key task nodes "Environment Preparation" and "Source Code Preparation" generate a node warning event for "Code Modification Preparation", and the risk grade of the "Code Modification Preparation" risk event is high, then the standard batch process / standard procedure for this event needs to be approved sequentially by "Environment Preparation Manager - Environment Preparation Manager - Source Code Preparation Manager - Source Code Preparation Manager - Code Modification Manager". If the risk grade of the risk event is low, the approval / review process can be simplified, such as only requiring approval / review by "Environment Preparation Manager - Source Code Preparation Manager - Code Modification Manager", thus obtaining cross-role collaborative review results. In this way, corresponding batch processing processes are made for different risk grades, thereby avoiding risks and improving the overall batch processing efficiency.

[0048] In this example, the cross-role responsible person in step S240 is the person associated with the cross-role collaborative review result. For example, this person can be one of the above-mentioned "Environment Preparation Manager, Environment Preparation Manager, Source Code Preparation Manager, Source Code Preparation Manager, Code Modification Manager", or the person designated by the above-mentioned "Environment Preparation Manager, Environment Preparation Manager, Source Code Preparation Manager, Source Code Preparation Manager, Code Modification Manager". Based on the cross-role collaborative review result, the cross-role responsible person is assigned a corresponding responsibility task, which is then executed, handled, or supervised by others to complete the responsibility task. The system receives the handling status of the responsibility task and notifies the cross-role responsible person's supervisor of the handling status through social software or interactive software, such as email or SMS, until the responsibility task is completed.

[0049] It should be noted that the above operations S210-S250 are all automatically executed online by the preset database transformation project monitoring system. For example, the event generation module in the system (database transformation project monitoring system) automatically identifies data based on the received database transformation project plan. Similarly, the system approval module in the system automatically pushes approval nodes to relevant personnel of the risk event, so that the relevant personnel of the risk event can perform standard batch processing in sequence. Likewise, the task execution module in the system determines the cross-role responsible person according to the preset allocation rules based on the cross-role collaborative review results, and assigns responsibility tasks to the cross-role responsible person online according to the preset rules. Subsequently, the handling status of the acquired responsibility tasks is sent to the client of the supervisor.

[0050] Based on this, after generating early warning events according to key task nodes, event constraints, and dependencies, the data related to the early warning events is uniformly placed in a central data pool to aggregate and share data from multiple systems. This facilitates risk analysis based on real-time captured data, improves the timeliness of risk analysis, and enhances the efficiency and accuracy of online processing for project risk and schedule management. After outputting risk events with assigned levels, standard batch processing is performed according to the level to obtain cross-role collaborative review results. Targeted approval of risk events is then conducted, thereby increasing the intensity of risk handling while improving online approval efficiency. Based on the cross-role collaborative review results, relevant database transformation responsibilities are assigned to cross-role responsible persons, and the handling status of these responsibilities is monitored. This achieves a shift from post-event risk discovery to pre-event early warning, improving response speed, forming a closed loop of risk management, ensuring the implementation of risk handling, and improving the efficiency of database transformation and cross-departmental online collaboration.

[0051] In this embodiment, data identification is performed on the pre-acquired database transformation project plan to obtain key task nodes, task time constraints, and task dependencies. This includes: splitting the database transformation project plan into a pre-defined format split document, where the pre-defined format includes at least one of text, image, table, and database statement formats; extracting the structure based on the format of the split document to obtain key fields and field mapping relationships; constructing a graph based on the key fields and field mapping relationships using a pre-defined graph neural network to obtain a task dependency directed graph; and extracting features from the task dependency directed graph to obtain key task nodes, task dependencies, and task time constraints.

[0052] As an example, this database transformation project plan is a pre-defined overall plan report for the database transformation project. This plan report can be a plan document in a specific format exported through a specific project reporting system, or it can be a task list file. This file can contain various file formats, not limited to text, image, table, and database statement formats. Upon receiving the plan report, it is first split into split documents of different formats. Then, targeted structure extraction is performed on each of these split documents to obtain key fields and the mapping relationships between fields. In a more specific example, through the process of format recognition, structure extraction, and field mapping, core fields such as task ID, sequence, dependencies, task data, and task attributes are automatically extracted and converted into a unified data format. This unified format of key fields and field mapping relationships is then input into a pre-defined graph neural network. The graph neural network constructs a graph based on these key fields and field mapping relationships, outputting a directed graph of task dependencies with task dependency relationships and attribute characteristics. For example, the graph neural network parses out "The online flow cut dependency flow cut review has passed," which is explicitly marked in the plan. This explicit dependency is identified, and implicit dependencies such as "code modification depends on the environment setup being completed" and "code scanning depends on code submission" that are not explicitly labeled are automatically identified. A complete directed graph of task dependencies is constructed, and then features are extracted from the directed graph of task dependencies to obtain features such as key task nodes, task dependencies, and task event constraints.

[0053] In this example, the graph neural network is pre-defined, meaning it is a pre-trained graph neural network model. This pre-trained graph neural network model can be an independent artificial intelligence model or a large model. Based on this, under the constraint of the prompt words, the pre-defined graph neural network can output a task-dependent directed graph according to the key fields and field mapping relationships, under the constraint of the pre-defined graph prompt words.

[0054] Based on this, data / structure extraction can be performed on the content of database transformation project plans in various formats, reducing data entry costs and improving the overall execution efficiency of database transformation projects. Furthermore, based on the key fields and field mapping relationships obtained, as well as the construction and feature extraction of directed graphs of reverse task dependencies, the extraction accuracy and efficiency of key task nodes, task dependencies, and task time constraints can be improved, thereby providing a good data foundation for subsequent risk event identification and risk warning.

[0055] In this embodiment, generating node warning events based on key task nodes, task time constraints, and task dependencies includes: determining the priority of key task nodes based on key task nodes and task dependencies; performing date recursion based on priority and task time constraints to obtain warning lead time, whereby warning lead time represents the completion time of the upper-level task nodes of the key task nodes and the degree of completion of the upper-level task nodes at a specific time; and generating node warning events related to key task nodes based on warning lead time and task dependencies.

[0056] As an example, this node warning event is generated and confirmed based on task time constraints and task dependencies, revolving around critical task nodes. First, the priority of critical task nodes needs to be determined according to the critical task nodes and task dependencies, that is, the order and degree of priority completion of each critical task node need to be determined. Then, based on the priority and task time constraints, the date is recursively calculated to obtain the warning lead time. This warning lead time can accurately deduce the latest completion time of the upper-level task nodes of the critical task node, as well as the minimum degree of completion of the upper-level task nodes at a specific time. For example, if the priority of a certain critical task node is P0, that is, the core level, the system automatically matches the warning lead time: 10 days in advance for equipment arrival warning, 5 days in advance for review node warning, 24 hours in advance for code scanning warning, etc. When generating node warning events, these events can be triggered in multiple dimensions. This means that node warning events can be generated around key task nodes from various perspectives. For example, they can be triggered by time (e.g., automatic warning 5 days before review), by event (e.g., a scan warning triggered after a certain event occurs, such as code submission), or by status (e.g., a arrival warning triggered after the device status changes to "in transit"). This ensures that no warnings are missed. Based on this, the warning lead time is adjusted according to project priority to improve the accuracy of warning timing and enhance the effectiveness of risk prediction.

[0057] In this embodiment, data is collected from multiple external systems associated with a node warning event to obtain relevant data. This includes: determining the node type of the task node associated with the node warning event, where the task node includes a critical task node and its upper-level task node; sequentially verifying the permissions of each multiple external system corresponding to the node type using a unified data access gateway; and, in response to successful permission verification, acquiring system data from the multiple external systems using a preset distributed lock and a preset encrypted transmission mechanism; wherein the multiple external systems include at least one of a personnel directory system, a database resource system, a database architecture building system, a distributed version control system, and a lightweight directory access protocol system; and performing data cleaning and format conversion on the system data to obtain progress data related to the warning event, and then version-marking the progress data to obtain relevant data.

[0058] As an example, when acquiring relevant data, it is necessary not only to acquire the multi-source external systems associated with the key task nodes involved in the node warning event itself, but also to acquire the multi-source external systems associated with the upper-level task nodes that receive the key task. Different task node types correspond to different associated multi-source external systems, so it is necessary to first determine the node type and then determine the multi-source external systems associated with the threshold. These multi-source external systems include any system with different functions, such as personnel directory systems, database resource systems, database architecture building systems, distributed version control systems, and lightweight directory access protocol systems. After determining the multi-source external systems, permission verification is required. For example, an authorization mechanism can be used to obtain access permissions for each multi-source external system. Based on this permission configuration, a whitelist can be set to restrict access to only servers related to the database transformation project. If the server that issued the verification request is within the whitelist, the permission verification will pass; otherwise, the permission verification will fail. After the permission verification is passed, system data from multiple external systems is obtained and extracted based on an encrypted transmission mechanism. This encryption mechanism is preset. For example, the system data is encrypted based on a preset encryption protocol, and sensitive data such as user passwords are replaced with preset characters. Furthermore, a preset distributed lock is used to obtain and extract system data from multiple external systems based on the preset encrypted transmission mechanism. The use of distributed locks avoids device status data conflicts caused by concurrent acquisition, improves the effectiveness and standardization of data extraction / collection / extraction, and thus improves the overall data acquisition efficiency.

[0059] Based on this, the technical problems of requiring manual login to multiple systems for data collection, poor protocol compatibility, security risks in data transmission and storage, inconsistent data formats, lack of quality assurance, and data conflicts easily arising from concurrent collection are addressed. This solution employs multiple security mechanisms to ensure data transmission and storage security, improves data security during database transformation, avoids conflicts with distributed locks, supports traceability through data version management, and enhances the effectiveness and standardization of data extraction / collection / extraction, thereby improving overall data collection efficiency and providing reliable data support for risk assessment.

[0060] Figure 4 The diagram illustrates the risk analysis operations involved in the database transformation engineering monitoring method according to an embodiment of this application.

[0061] exist Figure 4 In the illustrated embodiment, risk analysis is performed on relevant data flowing into the central data pool in real time from a resource perspective, and risk events with assigned levels are output. This includes: parsing the relevant data to obtain inventory and in-transit information of data resources or asset resources; and conducting risk assessment based on inventory information, in-transit information, and early warning lead time to obtain the level of the relevant resources and the risk events related to the relevant resources.

[0062] As an example, risk analysis of relevant data from a resource perspective involves risk assessment of resources such as computing resources used in database transformation. For instance, if a project requires 20 servers of model X, data parsing of the inventory data in the central data pool yields information on 5 servers already in stock and 15 ordered servers in transit. A pre-defined large language model can be introduced, taking into account features such as supplier ID, equipment type, purchase quantity, order time, and quarter. This model predicts that the actual arrival time of the 15 in-transit servers will be 2 days later than planned, with a delay probability of 35%. Therefore, the equipment preparation event can be considered a risk event. Since 10 servers are expected to be delayed and there are no direct replacements, this can be classified as a "fatal risk," generating a standard approval process requiring "immediately initiating emergency procurement." This large language model can be an independent neural network model or a large model that, under the constraint of pre-defined language prompts, identifies specific types of keywords to determine the inventory and in-transit data. In this way, risk classification standards based on resource availability and quantity gaps make the disposal requirements clearer, accurately predict the actual arrival time and delay probability of resources, and improve the scientificity and accuracy of risk prediction.

[0063] In this embodiment, risk analysis is performed on relevant data flowing into the central data pool in real time from the process dimension, and risk events with grades are output. This includes: parsing the relevant data to obtain task node flow data; extracting features from the task node flow data through a preset language processing model to obtain the task flow order; and identifying risks based on the task flow order and priority to obtain the grade of the relevant process and the risk events of the relevant process.

[0064] As an example, feature extraction is performed on the task node flow data using a pre-defined language processing model. For instance, the following task flow sequence is obtained: "3 days before the flow cut review, the number of review experts is ≥3 and their qualifications include database transformation; the review documents are complete, such as requirement documents, test reports, rollback plans, etc.; among them, after 5 review experts submit their opinions, code inspectors check whether subsequent code submissions include function replacements for the corresponding modules, and the code scan has no related syntax vulnerabilities; for 'supplementary test cases,' it is checked whether test documents have been uploaded to the corresponding directory and approved, but the test cases have not yet been supplemented." Keyword recognition confirms that the task flow sequence conforms to the pre-defined flow process. Then, risk analysis is performed. If rectification is not completed within 7 days, the risk level is confirmed as "medium," and an approval process for supplementary test cases can be generated subsequently.

[0065] It should be noted that the language processing model mentioned above can be a separate pre-trained language model or a large pre-trained model. Even if the large model extracts features from the task node flow data under the constraint of preset prompt words to obtain the task flow order, and identifies risks based on the task flow order and priority to obtain the level of the relevant process and the risk events of the relevant process.

[0066] In this way, data analysis and feature extraction are performed on the task node flow data to improve the targeting of rectification. Furthermore, by using language processing models based on keyword recognition and other technologies, the rectification actions in the task node flow data can be automatically linked and verified with code submissions and document management systems, ensuring that the process is implemented effectively and improving the accuracy of risk event generation, the implementation of responses, and the efficiency of online process processing.

[0067] In this embodiment, risk analysis is performed on relevant data flowing into the central data pool in real time from a quality dimension, and risk events with grades are output. This includes: periodically scoring the construction progress of the newly built transformation database and the newly stored data in the transformation database according to preset transformation scoring rules; and determining the relevant quality level and risk event corresponding to the compliance score according to a preset scoring level table.

[0068] As an example, risk analysis from a quality perspective involves determining the engineering quality of the transformation database's construction progress and the compliance quality of the newly stored data within that database. For instance, if static scanning detects a nested query exceeding three levels, and the compliance score based on this is below a preset threshold, it indicates a medium-risk performance issue when storing data in the newly constructed transformation database. Another example is setting up a test environment with a dataset of 1 million records identical to the production environment and simulating the execution of the transformed database code. If a query takes 1.2 seconds, and this timeout exceeds the 1-second threshold, its compliance score is lowered, thus triggering a high-risk performance issue for the transformation database. Therefore, timely scoring of the newly constructed transformation database and its stored data ensures the timeliness of quality risk analysis and improves the cross-database syntax compatibility and performance adaptability of the transformation database.

[0069] In this embodiment, risk events are processed in a standard batch flow according to their severity level to obtain cross-role collaborative review results. This includes: identifying relevant parties for risk events through synchronous or asynchronous review; determining the approval permissions and order of relevant parties based on a preset database transformation manual; creating process approval links for all relevant parties based on their approval permissions, ensuring that the process approval links only open an approval interface appropriate to the approval order and permissions for each relevant party; and outputting cross-role collaborative review results with notes from each relevant party as all relevant parties sequentially complete their respective approvals through the approval interface.

[0070] As an example, this database transformation manual is a document compiled at the initial stage of a database transformation project. It initially identifies the relevant personnel and deadlines for each process within each project. Therefore, upon receiving a risk event, the relevant personnel can be identified through synchronous or asynchronous review. Then, using technologies such as text recognition, the approval permissions and order for each relevant person are determined within the database transformation manual. Based on these permissions, process approval links are created for all relevant personnel. After all relevant personnel complete their respective approvals through the approval interface, a cross-role collaborative review result is output, including notes and requirements from each relevant person. For example, regarding the risk event of "replacing a unique function," the process for this risk event is found to be "function replacement → code scanning → review approval → deployment." Therefore, the relevant parties are identified one by one according to the above process, and approval processes are created for all relevant parties in the order of the process. Once all relevant parties have completed their approvals, a cross-role collaborative review result with notes from each relevant party can be output. Furthermore, during the approval process, the approval link is only open to each relevant party, with the approval interface appropriate to the approval order and permissions. Based on this, only interfaces appropriate to the permissions and order of relevant parties are opened, improving the rigor of the approval process and information security.

[0071] In this embodiment, determining the cross-role responsible person includes: determining the associated person with each responsibility task in the cross-role collaborative review results; and designating the associated person whose number of uncompleted tasks is less than a preset task threshold and whose skill matching degree with the responsibility task is higher than a preset matching degree threshold as the cross-role responsible person.

[0072] As an example, for the rectification task of "replacing functions", the number of unfinished tasks of the developers responsible for the rectification task can be queried. For example, if developer A currently has 3 tasks and has previously handled function replacement tasks with a skill matching degree of 88%, the rectification task can be assigned to developer A through the algorithm of "load balancing plus skill matching degree". In this way, the determination of cross-role responsible persons not only considers the task matching degree, but also their total workload, improves the online allocation balance of tasks, and intervenes to improve the engineering management efficiency of the overall database transformation.

[0073] As described above, the database transformation project monitoring method provided in this embodiment identifies data from the pre-acquired database transformation project plan and generates node early warning events based on the identified key task nodes, task time constraints, and task dependencies. After generating early warning events, the data related to the early warning events are uniformly placed in a central data pool to aggregate and share data from multiple systems. This facilitates risk analysis based on real-time captured data, improves the timeliness of risk analysis, and thus enhances the online processing efficiency and accuracy of project risk and schedule management. After outputting risk events with assigned levels, standard batch processing of risk events according to their levels yields cross-role collaborative review results. Targeted approval of risk events is then conducted, thereby increasing the intensity of risk handling while improving online approval efficiency. Based on the cross-role collaborative review results, relevant database transformation responsibilities are assigned to cross-role responsible persons, and the handling status of these responsibilities is monitored. This achieves a shift from post-event risk discovery to pre-event early warning, improving response speed, forming a risk control closed loop, ensuring the implementation of risk handling and the efficiency of database transformation, and enhancing cross-departmental online collaboration efficiency. Ultimately, this improves the automation management level and online comprehensive management efficiency of the database transformation project.

[0074] Based on the above-mentioned database transformation project monitoring method, this application also provides a database transformation project monitoring device. The following will combine... Figure 6 The device is described in detail.

[0075] Figure 5 A schematic diagram of a database transformation engineering monitoring device according to an embodiment of this application is shown.

[0076] like Figure 5 As shown, the database transformation project monitoring device 500 in this embodiment includes an event generation module 510, a data sharing module 520, a risk analysis module 530, a collaborative approval module 540, and a task execution module 550.

[0077] The event generation module 510 can execute operation S210 to identify data in the pre-acquired database transformation project plan and generate node warning events based on the identified key task nodes, task time constraints, and task dependencies. The data sharing module 520 can execute operation S220 to collect data from multiple external systems associated with the node warning events, obtain relevant data, and store the relevant data in real time to a preset central data pool. The risk analysis module 530 can execute operation S230 to perform risk analysis on the relevant data flowing into the central data pool in real time from at least one dimension: resources, processes, and quality, and output risk events with assigned levels. The collaborative approval module 540 can execute operation S240 to perform standard batch processing of risk events according to their levels and obtain cross-role collaborative review results. The task execution module 550 can execute operation S250 to assign relevant database transformation responsibilities to cross-role responsible persons based on the cross-role collaborative review results, monitor the handling status of the responsibilities, and send the handling status to the supervisors of the cross-role responsible persons until the responsibilities are completed.

[0078] In this embodiment, the event generation module 510 performs data identification on the pre-acquired database transformation project plan to obtain key task nodes, task time constraints, and task dependencies. This includes: splitting the database transformation project plan into a pre-formatted split document, where the pre-formatted document includes at least one of text, image, table, and database statement formats; extracting the structure based on the format of the split document to obtain key fields and field mapping relationships; constructing a graph based on the key fields and field mapping relationships using a pre-defined graph neural network to obtain a task dependency directed graph; extracting features from the task dependency directed graph to obtain key task nodes, task dependencies, and task time constraints. The event generation module 510 generates node warning events based on the key task nodes, task time constraints, and task dependencies. This includes: determining the priority of key task nodes based on the key task nodes and task dependencies; performing date recursion based on the priority and task time constraints to obtain a warning lead time, where the warning lead time represents the completion time of the upper-level task nodes of the key task nodes and the degree of completion of the upper-level task nodes at a specific time; and generating node warning events related to the key task nodes based on the warning lead time and task dependencies.

[0079] In this embodiment, the data sharing module 520 collects data from multiple external systems associated with the node warning event to obtain relevant data, including: determining the node type of the task node associated with the node warning event, where the task node includes a critical task node and its upper-level task node; performing permission verification on each of the multiple external systems corresponding to the node type sequentially based on a unified data access gateway; and, in response to successful permission verification, using a preset distributed lock and a preset encrypted transmission mechanism to obtain system data extracted from the multiple external systems; wherein, the multiple external systems include at least one of a personnel directory system, a database resource system, a database architecture building system, a distributed version control system, and a lightweight directory access protocol system; performing data cleaning and format conversion on the system data to obtain progress data related to the warning event, and then labeling the progress data with versions to obtain relevant data.

[0080] In this embodiment, the data sharing module 530 performs risk analysis on relevant data flowing into the central data pool in real time from at least one dimension: resources, processes, and quality, and outputs risk events with assigned levels. Specifically, performing risk analysis on relevant data flowing into the central data pool in real time from the resource dimension and outputting risk events with assigned levels includes: parsing the relevant data to obtain inventory and in-transit information of data resources or asset resources; conducting risk assessment based on inventory information, in-transit information, and early warning lead time to obtain the level of the relevant resource and the risk events related to that resource; and performing risk analysis on relevant data flowing into the central data pool in real time from the process dimension and outputting risk events with assigned levels. The process includes: parsing relevant data to obtain task node flow data; extracting features from the task node flow data using a pre-defined language processing model to obtain the task flow order; identifying risks based on the task flow order and priority to obtain the level of relevant processes and risk events related to those processes; and performing risk analysis on relevant data flowing into the central data pool in real time from a quality perspective, outputting risk events with assigned levels, including: periodically conducting compliance scoring on the construction progress of the newly built transformation database and the newly stored data in the transformation database according to pre-defined transformation scoring rules; and determining the relevant quality level and risk events corresponding to the compliance score based on a pre-defined scoring level table.

[0081] In this embodiment, the data sharing module 540 performs standard batch processing on risk events according to their levels to obtain cross-role collaborative review results. This includes: identifying relevant parties for risk events through synchronous or asynchronous review; determining the approval permissions and order of relevant parties according to a preset database transformation manual; creating process approval links for all relevant parties based on their approval permissions; ensuring that the process approval links only open approval interfaces appropriate to the approval order and permissions for each relevant party; and outputting cross-role collaborative review results with notes from each relevant party as all relevant parties sequentially complete their respective approvals through the approval interfaces.

[0082] In this embodiment, the task execution module 550 determines the cross-role responsible person by: determining the associated person with each responsible task in the cross-role collaborative review results; and designating the associated person whose number of uncompleted tasks is less than a preset task threshold and whose skill matching degree with the responsible task is higher than a preset matching degree threshold as the cross-role responsible person.

[0083] It should be noted that the implementation methods, technical problems solved, functions achieved, and technical effects of each module in the device embodiment are the same as or similar to the implementation methods, technical problems solved, functions achieved, and technical effects of each corresponding step in the method embodiment, and will not be repeated here.

[0084] Figure 6 A block diagram schematically illustrates an electronic device suitable for implementing a database transformation engineering monitoring method according to an embodiment of this application.

[0085] like Figure 6 As shown, an electronic device 600 according to an embodiment of this application includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage portion 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0086] RAM 603 stores various programs and data required for the operation of electronic device 600. Processor 601, ROM 602, and RAM 603 are interconnected via bus 604. Processor 601 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 602 and / or RAM 603. It should be noted that programs may also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in one or more memories.

[0087] According to embodiments of this application, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to a bus 604. The electronic device 600 may also include one or more of the following components connected to the input / output (I / O) interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 610 as needed so that computer programs read from it can be installed into the storage section 608 as needed.

[0088] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0089] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include ROM 602 and / or RAM 603 and / or one or more memories other than ROM 602 and RAM 603 described above.

[0090] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the database transformation engineering monitoring method provided in the embodiments of this application.

[0091] When the computer program is executed by the processor 601, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0092] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and / or installed from the removable medium 611. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0093] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, it performs the functions defined in the system of this application embodiment. According to the embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0094] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0096] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. A database transformation project monitoring method, characterized in that, The method includes: Data identification is performed on the pre-acquired database transformation project plan, and node early warning events are generated based on the identified key task nodes, task time constraints, and task dependencies. Data is collected from multiple external systems associated with the node early warning event to obtain relevant data, and the relevant data is stored in real time to a preset central data pool; Risk analysis is performed on the relevant data flowing into the central data pool in real time from at least one dimension: resources, processes, and quality, and risk events with assigned levels are output. The risk events are processed in a standard batch process according to the described level to obtain cross-role collaborative review results. Based on the cross-role collaborative review results, assign relevant database transformation responsibilities to the cross-role responsible persons, monitor the progress of the responsibilities, and send the progress to the supervisors of the cross-role responsible persons until the responsibilities are completed.

2. The method according to claim 1, characterized in that, Data identification is performed on the pre-acquired database transformation project plan to obtain key task nodes, task time constraints, and task dependencies, including: The database transformation project plan is split into a format to obtain a split document with a preset format, wherein the preset format includes at least one of text format, image format, table format and database statement format; Based on the format of the split document, the structure is extracted to obtain key fields and field mapping relationships; A pre-defined graph neural network is used to construct a graph based on the key fields and the mapping relationship between the fields, resulting in a task-dependent directed graph; Feature extraction is performed on the task-dependent directed graph to obtain key task nodes, task dependencies, and task time constraints.

3. The method according to claim 2, characterized in that, Based on the key task nodes, task time constraints, and task dependencies, node early warning events are generated, including: The priority of the critical task nodes is determined based on the critical task nodes and the task dependencies; Based on the priority and the task time constraints, date recursion is performed to obtain the early warning lead time. The early warning lead time represents the completion time of the upper-level task node of the key task node, and the degree of completion of the upper-level task node at a specific time. Based on the advance warning amount and the task dependency relationship, node warning events related to the critical task nodes are generated.

4. The method according to claim 1, characterized in that, The process of collecting data from multiple external systems associated with the node early warning event to obtain relevant data includes: Determine the node type of the task node associated with the node warning event, wherein the task node includes the critical task node and the upper-level task node of the critical task node; Based on the unified data access gateway, the permissions of each multi-source external system corresponding to the node type are sequentially verified. In response to the successful permission verification, the system data of the multi-source external system is obtained and extracted using a preset distributed lock and a preset encrypted transmission mechanism. The multi-source external system includes at least one of the following: personnel directory system, database resource system, database architecture building system, distributed version control system, and lightweight directory access protocol system. The system data is cleaned and format converted to obtain progress data related to the warning event, and the progress data is version-labeled to obtain relevant data.

5. The method according to claim 3, characterized in that, Risk analysis is performed on relevant data flowing into the central data pool in real time from a resource perspective, and risk events with assigned levels are output, including: The relevant data is parsed to obtain inventory and in-transit information of data resources or asset resources; Risk assessment is conducted based on the inventory information, the in-transit information, and the advance warning amount to obtain the level of the relevant resources and the risk events related to the resources.

6. The method according to claim 3, characterized in that, Risk analysis is performed on relevant data flowing into the central data pool in real time from a process perspective, and risk events with assigned levels are output, including: The relevant data is parsed to obtain task node flow data; The task flow order is obtained by extracting features from the task node flow data using a preset language processing model. Risk identification is performed based on the task flow sequence and priority to obtain the level of the relevant process and the risk events of the relevant process.

7. The method according to claim 3, characterized in that, Risk analysis is performed on the relevant data flowing into the central data pool in real time from a quality perspective, and risk events with assigned levels are output, including: According to the preset transformation scoring rules, the construction progress of the newly built transformation database and the newly stored data in the transformation database are periodically evaluated for compliance. The relevant quality level and risk event corresponding to the compliance score are determined based on a preset rating scale.

8. The method according to claim 1, characterized in that, The step of performing standard batch processing on the risk events according to the stated level to obtain cross-role collaborative review results includes: The relevant parties for the risk events are identified through synchronous or asynchronous review methods. The approval permissions and approval order of the event stakeholders are determined according to the preset database transformation manual. Based on the approval permissions, process approval links are created for all event stakeholders, and the process approval links are made to open the approval interface that is appropriate to the approval order and approval permissions only for each event stakeholder. In response to all stakeholders completing their respective approvals through the approval interface, the system outputs a cross-role collaborative review result with notes and requirements from each stakeholder.

9. The method according to claim 1, characterized in that, Identifying cross-role responsible persons includes: Identify the individuals associated with each responsibility task in the cross-role collaborative review results; Individuals whose number of uncompleted tasks is less than a preset task threshold and whose skill matching degree with the assigned task is higher than a preset matching degree threshold are designated as cross-role responsible persons.

10. A database transformation engineering monitoring device, characterized in that, The device includes: The event generation module is used to identify data from the pre-acquired database transformation project plan and generate node warning events based on the identified key task nodes, task time constraints, and task dependencies. The data sharing module is used to collect data from multiple external systems associated with the node warning event, obtain relevant data, and store the relevant data in real time to a preset central data pool. The risk analysis module is used to perform risk analysis on relevant data flowing into the central data pool in real time from at least one dimension: resources, processes, and quality, and output risk events with a level. The collaborative approval module is used to perform standard batch processing on the risk events according to the level, and obtain cross-role collaborative review results. The task execution module is used to assign database transformation responsibility tasks to cross-role responsible persons based on the cross-role collaborative review results, monitor the handling status of the responsibility tasks, and send the handling status to the supervisor of the cross-role responsible person until the responsibility tasks are completed.

11. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 9.

13. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 9.

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