Data processing method and device, computer device, and storage medium

Abstract

The application relates to the technical field of artificial intelligence, and discloses a data processing method and device, computer equipment and a storage medium, which comprise the following steps: receiving entity definition instructions, attribute configuration instructions and entity relationship definition instructions, and configuring a storage model; monitoring database access tracks generated by a target program accessing a target database, identifying operation intentions and data dependency relationships, and capturing change data for the target database; determining entity types, attribute information and operation types of the change data, and constructing a change influence propagation graph; predicting a data conflict risk and generating a conflict resolution strategy, generating a data synchronization strategy according to the change influence propagation graph, the conflict resolution strategy and the storage model, and storing the change data into the target database. The application can be applied to data processing scenes of medical health and financial technology, and can solve problems such as the difficulty of existing data processing methods in adapting to multi-source heterogeneous data storage integration, meeting the real-time requirements of industry data and taking into account data processing efficiency.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and more specifically to a data processing method, apparatus, computer equipment, and computer-readable storage medium. Background Technology

[0002] Currently, with the accelerated pace of digital transformation, the healthcare and fintech sectors, as data-intensive industries, heavily rely on the efficient management, real-time synchronization, and secure storage of massive amounts of data. The accuracy, real-time performance, and compliance of data processing directly impact business quality, service security, and even the implementation of industry regulatory requirements. However, these two sectors generally face technical challenges in data processing, such as poor storage model adaptability, untimely capture of changed data, and rigid synchronization strategies. Existing data processing methods are no longer sufficient to meet the demands of high-quality development in these industries.

[0003] In the healthcare field, with the continuous improvement of medical informatization, data sources are becoming increasingly diversified, encompassing electronic medical records (EMR), medical imaging, laboratory test data, genomic data, health monitoring equipment data, and various structured and unstructured data collected by regional health information platforms. The volume of data is experiencing explosive growth, and the formats are complex and diverse. Currently, data processing in the healthcare field mainly relies on traditional fixed storage models. These models require pre-defined underlying database structures and cannot flexibly adjust to dynamic changes in clinical operations (such as adding disease diagnosis entities, expanding examination indicator attributes, and adjusting doctor-patient relationship definitions). This leads to redundant data storage, chaotic relationships, and difficulty in achieving efficient integration of multi-source medical data. Simultaneously, the real-time requirements for medical data are extremely high. Scenarios such as chronic disease monitoring, surgical support, and epidemic prevention and control require real-time capture of data changes in the database (such as updates to patient vital signs, modifications to medical records, and adjustments to drug information). However, existing methods often employ batch capture or periodic queries, resulting in delays and missed captures of changed data, which can easily lead to lags in medical decision-making. Furthermore, medical data involves patient privacy and must strictly comply with regulatory requirements. Existing data processing methods lack sophisticated synchronization strategies for changed data, making it difficult to ensure real-time data synchronization while also protecting data privacy and ensuring compliance. This can easily lead to risks such as data leakage and synchronization anomalies, affecting the orderly conduct of clinical diagnosis and treatment and public health management.

[0004] In the fintech sector, business systems are complex and data is highly heterogeneous, encompassing core banking systems, payment platforms, and credit approval processes. The underlying databases are diverse, and the data involves multiple dimensions, including transaction logs, customer profiles, risk assessments, and credit information. Data silos are prominent, and data integration is challenging. Existing data processing methods often employ fixed storage models, failing to dynamically adapt to innovative financial business needs (such as adding new wealth management products, expanding customer credit attributes, and adjusting transaction relationship definitions). This leads to a disconnect between data storage and business requirements, resulting in low efficiency in data querying and statistics, and hindering support for core business operations such as risk control decisions and compliance audits. Furthermore, financial businesses have extremely stringent requirements for data real-time performance and accuracy. For example, anti-fraud scenarios require second-level capture of transaction data changes, while credit approval scenarios require real-time synchronization of customer credit data changes. However, existing methods for capturing change data suffer from high latency and packet loss, potentially leading to delayed risk identification and flawed credit decisions. Furthermore, the financial industry faces stringent regulatory requirements, with end-to-end data traceability and compliance auditing being core demands. Existing data processing methods lack flexibility in their synchronization strategies, failing to dynamically adjust based on changes in data types (such as new transactions, modifications to customer information, and deletion of invalid records) and storage model characteristics. This makes it difficult to achieve traceable and verifiable data synchronization, increasing compliance risks and failing to meet the dual needs of financial business innovation and regulatory compliance.

[0005] In summary, existing data processing methods in both the healthcare and fintech sectors suffer from the following shortcomings: First, storage models cannot be dynamically configured according to business needs, exhibiting poor adaptability and failing to meet the storage and integration requirements of multi-source heterogeneous data. Second, data change capture is untimely and inaccurate, failing to meet industry requirements for real-time data processing. Third, data synchronization strategies are rigid, unable to dynamically adjust based on the changing data type and storage model characteristics, making it difficult to balance data processing efficiency, data security, and compliance. Therefore, there is an urgent need for a data processing method capable of dynamically configuring storage models, capturing changing data in real time, and flexibly generating synchronization strategies to address the deficiencies of existing technologies. Summary of the Invention

[0006] In view of the shortcomings of the prior art, the purpose of this invention is to provide a data processing method, apparatus, computer equipment and computer-readable storage medium, which aims to solve the problems that existing data processing methods are difficult to adapt to the integration of multi-source heterogeneous data storage, meet the real-time data requirements of industries and take into account data processing efficiency.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a data processing method, comprising: Receive entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user, and configure the storage model of the target database according to the entity definition instructions, attribute configuration instructions, and entity relationship definition instructions; Real-time monitoring of the database access trajectory generated by the target program accessing the target database; semantic context analysis of the database access trajectory; identification of operation intent and data dependency relationship; and capture of change data for the target database based on the identified operation intent and data dependency relationship. The change data is parsed to determine the entity type, attribute information, and operation type corresponding to the change data, and a change impact propagation map is constructed based on the entity type, attribute information, and operation type. Based on the change impact propagation map, predict the data conflict risk and generate a conflict resolution strategy. Based on the change impact propagation map, the conflict resolution strategy, and the storage model, generate a data synchronization strategy for the changed data. According to the data synchronization strategy, the changed data is stored in the target database.

[0008] In a second aspect, the present invention provides a data processing apparatus, comprising: The receiving module is used to receive entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user, and configure the storage model of the target database according to the entity definition instructions, attribute configuration instructions, and entity relationship definition instructions; The monitoring module is used to monitor the database access trajectory generated by the target program accessing the target database in real time, perform semantic context analysis on the database access trajectory, identify the operation intention and data dependency relationship, and capture the change data for the target database based on the identified operation intention and data dependency relationship. The parsing module is used to parse the changed data, determine the entity type, attribute information and operation type corresponding to the changed data, and construct a change impact propagation map based on the entity type, the attribute information and the operation type; The generation module is used to predict data conflict risks based on the change impact propagation map and generate conflict resolution strategies, and to generate data synchronization strategies for the changed data based on the change impact propagation map, the conflict resolution strategies, and the storage model. The storage module is used to store the changed data to the target database according to the data synchronization strategy.

[0009] Thirdly, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data processing method described above.

[0010] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the data processing method described above.

[0011] Compared to existing technologies, this invention provides a data processing method, apparatus, computer device, and computer-readable storage medium, comprising: receiving entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by a target user; configuring a storage model of a target database according to the entity definition instructions, attribute configuration instructions, and entity relationship definition instructions; monitoring in real time the database access trajectory generated by a target program accessing the target database, performing semantic context analysis on the database access trajectory, identifying operation intentions and data dependencies, and capturing changed data for the target database according to the identified operation intentions and data dependencies; parsing the changed data, determining the entity type, attribute information, and operation type corresponding to the changed data, and constructing a change impact propagation graph based on the entity type, attribute information, and operation type; predicting data conflict risks and generating conflict resolution strategies based on the change impact propagation graph, the conflict resolution strategies, and the storage model; and storing the changed data in the target database according to the data synchronization strategy. First, this invention receives entity definitions, attribute configurations, and entity relationship definition instructions from the target user and configures the target database's storage model. This enables dynamic adaptation of the storage model without pre-fixing the underlying database structure. Entities, attributes, and relationships can be flexibly defined according to business needs, effectively solving the storage integration problem of multi-source heterogeneous data. Second, this invention monitors the database access trajectory generated by the target program accessing the target database in real time, performs semantic context analysis on the database access trajectory, identifies the operation intent and data dependencies, and captures changed data targeting the target database based on the identified operation intent and data dependencies. This achieves real-time and accurate capture of changed data, avoiding the delay and missed capture problems of traditional batch capture or timed query methods. Third, this invention parses the changed data to determine the entity type corresponding to the changed data. This invention identifies attribute information and operation types, and constructs a change impact propagation map based on entity type, attribute information, and operation types. Then, it predicts data conflict risks and generates conflict resolution strategies based on the change impact propagation map, conflict resolution strategies, and storage models. This generates a data synchronization strategy for the changed data, enabling dynamic adaptation of the data synchronization strategy. Differentiated data synchronization schemes are formulated for different types of changed data and storage model characteristics, breaking the rigidity of traditional synchronization strategies and improving the targeting and efficiency of data synchronization. Finally, by storing changed data in the target database according to the data synchronization strategy, this invention ensures accurate and efficient synchronization of changed data to the target database. Combined with the dynamically configured storage model and differentiated data synchronization strategies described above, it further guarantees the accuracy and integrity of data storage, while also considering data processing efficiency and data security. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram illustrating the application environment of a data processing method provided in an embodiment of the present invention.

[0014] Figure 2 This is a flowchart illustrating a data processing method according to an embodiment of the present invention.

[0015] Figure 3 This is a schematic diagram of a program module of a data processing device provided in an embodiment of the present invention.

[0016] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention.

[0017] Figure 5 This is another structural schematic diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0020] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0022] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0024] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0025] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0026] An embodiment of the present invention provides a data processing method that can be applied to, for example... Figure 1In the application environment shown, the client and server communicate via a network. The client includes, but is not limited to, handheld computers, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud computing devices, and personal digital assistants (PDAs). The server can be a standalone server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0027] Please see Figure 2 An embodiment of the present invention provides a data processing method, wherein the method includes the following steps: S100: Receive entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user; configure the storage model of the target database according to the entity definition instructions, attribute configuration instructions, and entity relationship definition instructions. S200. Monitor the database access trajectory generated by the target program accessing the target database in real time, perform semantic context analysis on the database access trajectory, identify the operation intention and data dependency relationship, and capture the change data for the target database based on the identified operation intention and data dependency relationship. S300. Analyze the change data, determine the entity type, attribute information and operation type corresponding to the change data, and construct a change impact propagation map based on the entity type, the attribute information and the operation type; S400. Based on the change impact propagation map, predict the data conflict risk and generate a conflict resolution strategy. Based on the change impact propagation map, the conflict resolution strategy, and the storage model, generate a data synchronization strategy for the changed data. S500. According to the data synchronization strategy, the changed data is stored in the target database.

[0028] In practical implementation, the data processing method of this embodiment, through step-by-step implementation and closed-loop linkage design, can achieve significant technical effects in many aspects and effectively solve many pain points in existing data processing: First, by receiving the entity definition, attribute configuration, and entity relationship definition instructions input by the target user in step S100 and configuring the storage model of the target database, dynamic adaptation of the storage model can be achieved. There is no need to pre-fix the underlying database structure; entities, attributes, and relationships can be flexibly defined according to business needs. This effectively solves the problem of storing and integrating multi-source heterogeneous data, reduces data redundancy, clarifies data relationships, improves the rationality and standardization of target database storage, and adapts to medical... This system addresses the storage needs of multi-dimensional medical data in the healthcare field and diverse business data in the fintech field. Secondly, step S200 involves real-time monitoring of the database access trajectory generated by the target program accessing the target database. Semantic context analysis of this trajectory identifies operational intent and data dependencies. Based on these identified intents and dependencies, it captures changed data targeting the target database. This real-time and accurate capture of changed data avoids the delays and missed captures inherent in traditional batch capture or scheduled query methods, ensuring data timeliness and providing a reliable foundation for subsequent data synchronization. This meets the real-time update requirements of diagnostic and treatment data in the healthcare field. The core requirement in the fintech field is second-level synchronization of transaction data. Next, step S300 analyzes the changed data to determine the corresponding entity type, attribute information, and operation type. Based on these, a change impact propagation graph is constructed. Step S400 then predicts data conflict risks based on the change impact propagation graph and generates conflict resolution strategies. Based on the change impact propagation graph, conflict resolution strategies, and storage models, a data synchronization strategy for the changed data is generated. This enables dynamic adaptation of the data synchronization strategy, allowing for differentiated data synchronization solutions tailored to different types of changed data and storage model characteristics, breaking away from traditional synchronization strategies. The rigidity of the data synchronization mechanism can be improved to enhance its targeting and efficiency. Finally, by storing the changed data in the target database according to the data synchronization strategy in step S500, it is ensured that the changed data is accurately and efficiently synchronized to the target database. Combined with the dynamically configured storage model and differentiated data synchronization strategy mentioned above, the accuracy and integrity of data storage are further guaranteed, while taking into account data processing efficiency and data security. This helps the healthcare field achieve efficient management of clinical data and the fintech field achieve compliant control of business data, thereby improving the overall level of intelligent data processing, reducing data management costs, and providing reliable data support for business innovation and high-quality development in both fields.

[0029] Understandably, the data processing method provided in this embodiment of the invention can be applied to data processing scenarios related to the medical and health field. The following is a simple example: Target users (such as medical staff) access the model configuration interface through the client, input relevant commands for entity definition, attribute configuration, and relationship definition. The system configures and stores the model according to these commands, monitors changes in medical data in real time, captures changes in patient diagnosis and treatment-related data (such as medical record updates, test result modifications, etc.), and accurately writes the changed data into the target database according to the appropriate data synchronization strategy. This achieves real-time synchronization and precise management of medical data, ensures the integrity and timeliness of diagnosis and treatment data, and provides reliable data support for clinical diagnosis and disease tracking.

[0030] It is understood that the data processing method provided in this embodiment of the invention can also be applied to data processing scenarios related to the fintech field. The following is a simple example: Target users (such as financial professionals) configure the storage model through the client. The system monitors the running status of financial transaction-related programs in real time, captures changes in transaction data (such as adjustments to transaction amounts, updates to transaction status, etc.), and generates an appropriate data synchronization strategy based on the storage model. The system accurately writes the changed data into the target database, achieving real-time synchronization and efficient management of financial transaction data, ensuring the traceability and security of transaction data, and meeting the core requirements of the financial sector for data real-time performance and accuracy.

[0031] Furthermore, in one embodiment, the data processing method, wherein receiving entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user, and configuring the storage model of the target database according to the entity definition instructions, attribute configuration instructions, and entity relationship definition instructions, specifically includes: It receives entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user through the client, performs structured parsing on the corresponding entities, attributes, and entity relationships, and generates dynamic modeling metadata; Based on the dynamic modeling metadata, create a target storage set in the target database corresponding to the entity and the relationship between the entities; Monitor the historical query load characteristics and data distribution characteristics of the target storage set, and dynamically adjust the index structure based on the historical query load characteristics and data distribution characteristics to complete the storage model configuration of the target database.

[0032] Furthermore, in the data processing method, receiving entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user through the client, and performing structured parsing of the corresponding entities, attributes, and entity relationships to generate dynamic modeling metadata, specifically includes: The client responds to the target user's interface start command to launch and display the model configuration interface; On the model configuration interface, at least one authentication method selected from biometrics, dynamic verification code, or digital certificate is used to verify the identity information of the target user; When the identity information of the target user is verified, the entity definition instruction, attribute configuration instruction and entity relationship definition instruction input by the target user are received through the model configuration interface. The corresponding entities, attributes and entity relationships are structured and parsed to generate dynamic modeling metadata.

[0033] In practical implementation, this embodiment achieves several key technical effects through a progressive, closed-loop design: First, the client responds to the target user's interface initiation command to enter the model configuration interface, and verifies the target user's identity using at least one authentication method among biometrics, dynamic verification codes, or digital certificates. This effectively ensures the legitimacy of the target user's identity, prevents unauthorized user operations from tampering with the database storage model, and enhances the security and compliance of the data modeling process, especially suitable for fields with high data security requirements such as healthcare and fintech. After successful identity verification, various commands input by the target user are received, and entities, attributes, and relationships between entities are structurally parsed to generate dynamic modeling metadata. This enables accurate parsing of modeling commands, ensuring that the metadata accurately and completely reflects the target user's modeling needs, laying the foundation for subsequent... Storage model configuration provides a reliable foundation; then, based on dynamic modeling metadata, a corresponding target storage set is created in the target database, enabling precise matching between the target storage set and entities, and the relationships between entities, avoiding redundancy or missing storage structures and improving the rationality of database storage; finally, the historical query load characteristics and data distribution characteristics of the target storage set are monitored, and the index structure is dynamically adjusted based on these characteristics to complete the storage model configuration of the target database. This improves data query efficiency, facilitates rapid location and retrieval of target data, and through a complete modeling process, achieves standardized and personalized configuration of the target database storage model, enabling the storage model to flexibly adapt to the needs of different business scenarios. This lays a solid foundation for the capture and synchronization of subsequent changed data, and overall improves the security, accuracy, and efficiency of data processing.

[0034] The specific implementation process of this embodiment is roughly as follows: 1. Startup Configuration Interface: When the client receives the interface start command from the target user, it immediately responds and jumps to the model configuration interface. This interface is a dedicated operation interface for configuring the database storage model. It supports users to perform operations such as entity definition, attribute setting and relationship adjustment, providing a dedicated operating environment for subsequent command input and model configuration.

[0035] 2. User authentication: In the model configuration interface, the system uses at least one authentication method among biometrics (such as fingerprints and faces), dynamic verification codes, and digital certificates to strictly verify the identity information of the target user. Only users who have passed the authentication can obtain subsequent command input permissions, effectively preventing illegal operations, ensuring data security, and preventing unauthorized personnel from tampering with the stored model or entering incorrect commands.

[0036] 3. Command Reception and Parsing: After successful authentication, the system receives entity definition commands, attribute configuration commands, and entity relationship definition commands input by the target user through the interface. Then, it performs structured parsing on these commands, filters out valid configuration information, and generates dynamic modeling metadata that meets business requirements, ensuring that the metadata accurately reflects the user's configuration requirements for the storage model.

[0037] 4. Storage Model Construction: Based on the dynamically generated modeling metadata after parsing, a corresponding target storage collection is created in the target database. This collection will accurately match the user-defined entity type. At the same time, based on the attribute information in the metadata, a reasonable structure is built for the target storage collection to ensure the standardization and orderliness of data storage.

[0038] 5. Index configuration complete: Monitor the historical query load characteristics and data distribution characteristics of the target storage set, and dynamically adjust the index structure according to the historical query load characteristics and data distribution characteristics to complete the storage model configuration of the target database. Among them, the adjustment of the index structure can adapt to subsequent data query, modification and other operations, improve data processing efficiency, and finally complete the configuration of the entire storage model, making full preparations for the synchronous storage of subsequent changed data.

[0039] Further, in one embodiment, the data processing method, wherein the real-time monitoring of the database access trajectory generated by the target program accessing the target database, performing semantic context analysis on the database access trajectory to identify operation intentions and data dependencies, and capturing change data for the target database based on the identified operation intentions and data dependencies, specifically includes: The operation logs of the target database are monitored in real time to obtain the data operation records of the target program on the target database, and the data operation records are aggregated in time and associated with the context to generate a database access trajectory. Extract the contextual information of the database access trajectory, determine the operation intent category corresponding to the data operation record based on the preset operation intent recognition model, analyze the dependency relationship between the data objects involved in the database access trajectory, and construct a data dependency relationship graph; Based on the operation intent category and the data dependency graph, identify the source change data that will trigger a change in the state of the associated data, and determine the source change data as the change data of the target database.

[0040] Further, in the data processing method, the step of parsing the changed data, determining the entity type, attribute information, and operation type corresponding to the changed data, and constructing a change impact propagation map based on the entity type, the attribute information, and the operation type; predicting data conflict risks and generating conflict resolution strategies based on the change impact propagation map; and generating a data synchronization strategy for the changed data based on the change impact propagation map, the conflict resolution strategy, and the storage model, specifically includes: The changed data is parsed at the field level to determine the entity type, attribute information, and operation type corresponding to the changed data; The synchronization mode of the changed data is determined based on the entity type, the attribute information, and the operation type. Based on the entity type, attribute information, operation type, and synchronization mode, the impact propagation path of the changed data in the storage model is constructed, and a change impact propagation map is generated.

[0041] Furthermore, the data processing method, wherein predicting data conflict risk based on the change impact propagation map and generating a conflict resolution strategy, and generating a data synchronization strategy for the changed data based on the change impact propagation map, the conflict resolution strategy, and the storage model, specifically includes: Based on the aforementioned change impact propagation map, the evolution of data state under multi-source concurrent scenarios is simulated to predict potential data conflict risk points; Based on the predicted data conflict risk points, a conflict resolution strategy including priority arbitration rules and merging strategies is generated; Based on the synchronization mode, the change impact propagation map, the conflict resolution strategy, and the storage model, a data synchronization strategy for the changed data is generated.

[0042] In practical implementation, this embodiment achieves several key technical effects through a progressive and precisely adapted design: By real-time monitoring of the target database's operation logs, it obtains data operation records executed by the target program on the target database, and performs time-series aggregation and contextual association on these records to generate a database access trajectory. This enables real-time monitoring of the entire database operation process, ensuring that no data operation-related information is missed. This provides a comprehensive and reliable foundation for the accurate capture of changed data, effectively solving problems such as delays and missed captures in traditional monitoring methods, and ensuring the real-time and completeness of changed data capture. Furthermore, by extracting the contextual loop of the database access trajectory... Based on a pre-defined operation intent recognition model, the system identifies the operation intent categories corresponding to data operation records and analyzes the dependencies between data objects involved in the database access trajectory to construct a data dependency graph. Then, according to the operation intent categories and the data dependency graph, it identifies the source change data that will trigger changes in the state of related data. This source change data is then identified as the change data in the target database, enabling precise differentiation of different types of database operations. This allows for targeted capture of change data specific to the target database, avoiding interference from invalid data, improving the accuracy of change data capture, and ensuring the efficiency of subsequent synchronization processing. The captured change data undergoes fine-grained field-level parsing. Precisely identifying the entity type, attribute information, and operation type corresponding to changed data is more targeted and detailed than ordinary parsing methods. It can clearly grasp the core characteristics of changed data, providing a precise basis for generating data synchronization strategies. Based on the parsed entity type, attribute information, and operation type, the synchronization mode is determined. Then, based on the entity type, attribute information, operation type, and synchronization mode, the impact propagation path of changed data in the storage model is constructed, generating a change impact propagation map. Then, based on the change impact propagation map, the data state evolution under multi-source concurrent scenarios is simulated, potential data conflict risk points are predicted, and based on the predicted data conflict risk points, a data synchronization strategy including optimal... The system employs conflict resolution strategies between prior arbitration rules and merging strategies. Finally, based on synchronization patterns, change impact propagation maps, conflict resolution strategies, and storage models, it generates a data synchronization strategy for changed data. This breaks through the rigid limitations of traditional data synchronization strategies, enabling the data synchronization strategy to accurately match the characteristics of changed data with the storage model structure. This improves the efficiency and accuracy of data synchronization while avoiding data redundancy and synchronization anomalies during the synchronization process. It provides strong support for the efficient and accurate storage of changed data in the target database, thereby enhancing the overall intelligence and precision of data processing and meeting the core requirements of real-time data accuracy in fields such as healthcare and fintech.

[0043] The specific implementation process of this embodiment is roughly as follows: 1. Enable monitoring and log listening: Enable real-time monitoring of the target database, listen to the operation logs of the target database in real time, obtain the data operation records of the target program on the target database, and perform time-series aggregation and context association on the data operation records to generate database access traces, laying the foundation for subsequent change data capture and ensuring that no operation records are missed.

[0044] 2. Change Data Capture: The system extracts the contextual information of the database access trajectory, determines the operation intent category corresponding to the data operation record based on the preset operation intent recognition model, analyzes the dependency relationship between data objects involved in the database access trajectory, and constructs a data dependency relationship graph. Then, according to the operation intent category and the data dependency relationship graph, it identifies the source change data that will trigger the change of related data status, determines the source change data as the change data of the target database, that is, captures the corresponding change data, clarifies the change content, change scope and related entity information, and ensures the integrity and accuracy of the change data.

[0045] 3. Change Data Parsing and Strategy Generation: The captured change data is parsed at the field level to clarify the entity type, attribute information, and operation type corresponding to the change data. Based on this, the synchronization mode of the change data is determined. Then, based on the entity type, attribute information, operation type, and synchronization mode, the impact propagation path of the change data in the storage model is constructed, generating a change impact propagation map. Then, based on the change impact propagation map, the data state evolution under multi-source concurrent scenarios is simulated to predict potential data conflict risk points. Based on the predicted data conflict risk points, a conflict resolution strategy including priority arbitration rules and merging strategies is generated. Finally, based on the synchronization mode, change impact propagation map, conflict resolution strategy, and storage model, a data synchronization strategy for the change data is generated to ensure that the data synchronization strategy is highly matched with the characteristics of the storage model and the change data, avoiding synchronization deviation.

[0046] Furthermore, in one embodiment, the data processing method, wherein storing the changed data in the target database according to the data synchronization strategy, specifically includes: According to the data synchronization strategy, the changed data is format-converted and data-adapted, and the changed data after format conversion and data adaptation is written into the target storage set in the target database, and the index information corresponding to the target storage set is updated synchronously. The source, processing, and synchronization results of the changed data are recorded in a chain to generate a data traceability audit chain, and the data consistency between the data traceability audit chain and the target storage set is verified. When the verification passes, a corresponding data synchronization completion result is generated; wherein the data synchronization completion result includes the synchronization time, the changed content, and the traceability identifier; The data synchronization results are fed back to the client and displayed to the target user according to the preset result display strategy.

[0047] In practical implementation, this embodiment strictly follows the data synchronization strategy to perform format conversion and adaptation processing on changed data, ensuring that the changed data meets the storage requirements of the target database and effectively avoiding synchronization failures caused by format incompatibility. The processed changed data is accurately written into the corresponding target storage set, and index information is updated synchronously to ensure data and index consistency and prevent data corruption. Simultaneously, a data traceability audit chain is generated by chaining the source, processing, and synchronization results of the changed data. The consistency between the data traceability audit chain and the target storage set is verified, and when the verification passes, a corresponding data synchronization completion result is generated. This result includes the synchronization time, changed content, and traceability identifier. Finally, the data synchronization completion result is fed back to the client and displayed to the target user according to a preset result display strategy, achieving traceability and verifiability of changed data. This improves the standardization and efficiency of data processing, ensures the security and integrity of data storage, forms a complete data update closed loop, further strengthens the compliance and stability of data management, meets the core data processing needs of various fields, and provides reliable data support for business operations.

[0048] The specific implementation process of this embodiment is roughly as follows: 1. Data preprocessing: The system first calls the data synchronization strategy. According to the strategy requirements, the captured changed data is format converted (unified data format, adapted to the target database storage specifications) and data adapted (adjusted data fields, supplemented related information) to ensure that the changed data meets the target database storage requirements and avoids storage failure due to format incompatibility.

[0049] 2. Data writing and index update: The changed data after format conversion and adaptation is accurately written into the target storage set in the target database (matching the entity and attribute corresponding to the data), and the index information corresponding to the target storage set is updated synchronously to ensure that the data and index are synchronized in real time, thus ensuring the accuracy of subsequent data queries and retrieval.

[0050] 3. Consistency Verification: After data writing is completed, the source, processing, and synchronization results of the changed data are recorded in a chain to generate a data traceability audit chain. The consistency between the data traceability audit chain and the target storage set is verified. When the verification passes, the corresponding data synchronization completion result is generated. The data synchronization completion result includes the synchronization time, changed content, and traceability identifier. If an anomaly occurs, a rollback mechanism is triggered to ensure data consistency.

[0051] 4. Result Feedback: After successful verification, the system generates a synchronization completion result including synchronization time and changed content, and simultaneously feeds the result back to the client. According to the preset result display strategy, the synchronization completion result is clearly displayed to the target user.

[0052] As can be seen from the above method embodiments, the data processing method provided by the present invention includes: receiving entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by a target user; configuring a storage model of a target database according to the entity definition instructions, attribute configuration instructions, and entity relationship definition instructions; monitoring the database access trajectory generated by a target program accessing the target database in real time; performing semantic context analysis on the database access trajectory to identify operation intentions and data dependencies; capturing changed data for the target database according to the identified operation intentions and data dependencies; parsing the changed data to determine the entity type, attribute information, and operation type corresponding to the changed data; constructing a change impact propagation graph based on the entity type, attribute information, and operation type; predicting data conflict risks and generating conflict resolution strategies based on the change impact propagation graph; generating a data synchronization strategy for the changed data according to the change impact propagation graph, the conflict resolution strategy, and the storage model; and storing the changed data in the target database according to the data synchronization strategy. Thus, the method of the present invention can solve the problems of existing data processing methods being unable to adapt to the integration of multi-source heterogeneous data storage, meet the real-time data requirements of industries, and balance data processing efficiency.

[0053] It should be understood that although this application provides the method operation steps as described in the embodiments or flowcharts, conventional or non-inventive labor may include more or fewer operation steps, and these operation steps are not necessarily executed sequentially according to the order of the embodiments or flowcharts. The order of steps listed in the embodiments or flowcharts is merely one way of executing many steps and does not represent the only execution order. It should be noted that there is no necessary sequential order between the above steps. Those skilled in the art can understand from the description of the embodiments of the present invention that the above steps may have different execution orders in different embodiments, that is, they may be executed in parallel or in exchange, etc. Moreover, at least some steps in the embodiments or flowcharts may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn, alternately, or synchronously with other steps or at least a part of the sub-steps or stages of other steps.

[0054] Based on the above method embodiments, please refer to Figure 3Another embodiment of the present invention also provides a data processing apparatus, wherein the apparatus includes: The receiving module 11 is used to receive entity definition instructions, attribute configuration instructions and entity relationship definition instructions input by the target user, and configure the storage model of the target database according to the entity definition instructions, attribute configuration instructions and entity relationship definition instructions; The monitoring module 12 is used to monitor the database access trajectory generated by the target program accessing the target database in real time, perform semantic context analysis on the database access trajectory, identify the operation intention and data dependency relationship, and capture change data for the target database based on the identified operation intention and data dependency relationship. The parsing module 13 is used to parse the change data, determine the entity type, attribute information and operation type corresponding to the change data, and construct a change impact propagation map based on the entity type, the attribute information and the operation type; The generation module 14 is used to predict data conflict risks based on the change impact propagation map and generate conflict resolution strategies, and to generate a data synchronization strategy for the changed data based on the change impact propagation map, the conflict resolution strategies and the storage model. Storage module 15 is used to store the changed data to the target database according to the data synchronization strategy.

[0055] Further, in one embodiment, the data processing apparatus, wherein receiving entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user, and configuring the storage model of the target database according to the entity definition instructions, attribute configuration instructions, and entity relationship definition instructions, specifically includes: It receives entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user through the client, performs structured parsing on the corresponding entities, attributes, and entity relationships, and generates dynamic modeling metadata; Based on the dynamic modeling metadata, create a target storage set in the target database corresponding to the entity and the relationship between the entities; Monitor the historical query load characteristics and data distribution characteristics of the target storage set, dynamically adjust the index structure based on the historical query load characteristics and data distribution characteristics, and complete the storage model configuration of the target database.

[0056] Furthermore, in the data processing device, the step of receiving entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user through a client, and performing structured parsing on the corresponding entities, attributes, and entity relationships to generate dynamic modeling metadata, specifically includes: The client responds to the target user's interface start command, launching and displaying the model configuration interface; On the model configuration interface, at least one authentication method selected from biometrics, dynamic verification code, or digital certificate is used to verify the identity information of the target user; When the identity information of the target user is verified, the entity definition instruction, attribute configuration instruction and entity relationship definition instruction input by the target user are received through the model configuration interface. The corresponding entities, attributes and entity relationships are structured and parsed to generate dynamic modeling metadata.

[0057] Further, in one embodiment, the data processing apparatus, wherein the real-time monitoring of the database access trajectory generated by the target program accessing the target database, performing semantic context analysis on the database access trajectory to identify operation intentions and data dependencies, and capturing change data for the target database based on the identified operation intentions and data dependencies, specifically includes: The operation logs of the target database are monitored in real time to obtain the data operation records of the target program on the target database, and the data operation records are aggregated in time and associated with the context to generate a database access trajectory. Extract the contextual information of the database access trajectory, determine the operation intent category corresponding to the data operation record based on the preset operation intent recognition model, analyze the dependency relationship between the data objects involved in the database access trajectory, and construct a data dependency relationship graph; Based on the operation intent category and the data dependency graph, identify the source change data that will trigger a change in the state of the associated data, and determine the source change data as the change data of the target database.

[0058] Furthermore, in the data processing apparatus, the step of parsing the changed data, determining the entity type, attribute information, and operation type corresponding to the changed data, and constructing a change impact propagation map based on the entity type, the attribute information, and the operation type specifically includes: The changed data is parsed at the field level to determine the entity type, attribute information, and operation type corresponding to the changed data; The synchronization mode of the changed data is determined based on the entity type, the attribute information, and the operation type. Based on the entity type, attribute information, operation type, and synchronization mode, the impact propagation path of the changed data in the storage model is constructed, and a change impact propagation map is generated.

[0059] Furthermore, in the data processing apparatus, the step of predicting data conflict risk and generating conflict resolution strategies based on the change impact propagation map, and generating a data synchronization strategy for the changed data according to the change impact propagation map, the conflict resolution strategy, and the storage model, specifically includes: Based on the aforementioned change impact propagation map, the evolution of data state under multi-source concurrent scenarios is simulated to predict potential data conflict risk points; Based on the predicted data conflict risk points, a conflict resolution strategy including priority arbitration rules and merging strategies is generated; Based on the synchronization mode, the change impact propagation map, the conflict resolution strategy, and the storage model, a data synchronization strategy for the changed data is generated.

[0060] Furthermore, in one embodiment, the data processing apparatus, wherein storing the changed data in the target database according to the data synchronization strategy specifically includes: According to the data synchronization strategy, the changed data is format-converted and data-adapted, and the changed data after format conversion and data adaptation is written into the target storage set in the target database, and the index information corresponding to the target storage set is updated synchronously. The source, processing, and synchronization results of the changed data are recorded in a chain to generate a data traceability audit chain, and the data consistency between the data traceability audit chain and the target storage set is verified. When the verification passes, a corresponding data synchronization completion result is generated; wherein the data synchronization completion result includes the synchronization time, the changed content, and the traceability identifier; The data synchronization results are fed back to the client and displayed to the target user according to the preset result display strategy.

[0061] It should be noted that, in the device embodiments of the present invention, the information interaction and execution process between the above modules are based on the same concept as in the method embodiments of the present invention. For details on their specific functions and the resulting technical effects, please refer to the aforementioned method embodiments section, which will not be repeated here.

[0062] Based on the above method embodiments, another embodiment of the present invention also provides a computer device, which can be a server, and its internal structure diagram can be as follows. Figure 4As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the data processing method on the server side as described in any of the above method embodiments.

[0063] Based on the above method embodiments, another embodiment of the present invention also provides a computer device, which can be a client, and its internal structure diagram can be as follows. Figure 5 As shown, the computer device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When executed by the processor, the computer program implements the functions or steps of the data processing method on the client side as described in any of the above method embodiments.

[0064] Those skilled in the art will understand that Figure 4 and Figure 5 The structural schematic diagram shown is only a schematic diagram of a part of the structure related to the present invention and does not constitute a limitation on the computer device on which the present invention is applied. The specific computer device may include more components than shown in the figure, or combine certain components, or have different component arrangements.

[0065] The processor referred to herein can be a CPU, but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0066] The memory includes readable storage media, internal memory, etc., where internal memory can be the RAM of a computer device. Internal memory provides an environment for the operation of the operating system and computer-readable instructions stored in the readable storage media. The readable storage media can be the hard drive of the computer device, or in other embodiments, it can be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory can include both internal storage units and external storage devices of the computer device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as program code for computer programs. The memory can also be used to temporarily store data that has been output or will be output.

[0067] Based on the above method embodiments, another embodiment of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the data processing method as described in any of the above method embodiments. The computer-readable storage medium may be non-volatile or volatile.

[0068] It should be noted that the functions or steps that can be achieved by the computer-readable storage medium or computer device, and the technical effects brought about by the functions / steps, can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0069] 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, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc. The disclosed memory components or memories of the operating environment described herein are intended to include one or more of these and / or any other suitable types of memory.

[0070] Those skilled in the art will understand that, for the sake of convenience and brevity, the embodiments of the device of the present invention are only illustrated by the division of the above-mentioned functional units and modules. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the present invention. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0071] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0072] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0073] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0075] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A data processing method, characterized in that, include: Receive entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user, and configure the storage model of the target database according to the entity definition instructions, attribute configuration instructions, and entity relationship definition instructions; Real-time monitoring of the database access trajectory generated by the target program accessing the target database; semantic context analysis of the database access trajectory; identification of operation intent and data dependency relationship; and capture of change data for the target database based on the identified operation intent and data dependency relationship. The change data is parsed to determine the entity type, attribute information, and operation type corresponding to the change data, and a change impact propagation map is constructed based on the entity type, attribute information, and operation type. Based on the change impact propagation map, predict the data conflict risk and generate a conflict resolution strategy. Based on the change impact propagation map, the conflict resolution strategy, and the storage model, generate a data synchronization strategy for the changed data. According to the data synchronization strategy, the changed data is stored in the target database.

2. The data processing method according to claim 1, characterized in that, The step of receiving entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user, and configuring the storage model of the target database according to the entity definition instructions, attribute configuration instructions, and entity relationship definition instructions, includes: It receives entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user through the client, performs structured parsing on the corresponding entities, attributes, and entity relationships, and generates dynamic modeling metadata; Based on the dynamic modeling metadata, create a target storage set in the target database corresponding to the entity and the relationship between the entities; Monitor the historical query load characteristics and data distribution characteristics of the target storage set, and dynamically adjust the index structure based on the historical query load characteristics and data distribution characteristics to complete the storage model configuration of the target database.

3. The data processing method according to claim 2, characterized in that, The process involves receiving entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user through the client, performing structured parsing on the corresponding entities, attributes, and entity relationships, and generating dynamic modeling metadata, including: The client responds to the target user's interface start command to launch and display the model configuration interface; On the model configuration interface, at least one authentication method selected from biometrics, dynamic verification code, or digital certificate is used to verify the identity information of the target user; When the identity information of the target user is verified, the entity definition instruction, attribute configuration instruction and entity relationship definition instruction input by the target user are received through the model configuration interface. The corresponding entities, attributes and entity relationships are structured and parsed to generate dynamic modeling metadata.

4. The data processing method according to claim 1, characterized in that, The method involves real-time monitoring of the database access trajectory generated by the target program accessing the target database, performing semantic context analysis on the database access trajectory to identify operational intentions and data dependencies, and capturing change data related to the target database based on the identified operational intentions and data dependencies, including: The operation logs of the target database are monitored in real time to obtain the data operation records of the target program on the target database, and the data operation records are aggregated in time and associated with the context to generate a database access trajectory. Extract the contextual information of the database access trajectory, determine the operation intent category corresponding to the data operation record based on the preset operation intent recognition model, analyze the dependency relationship between the data objects involved in the database access trajectory, and construct a data dependency relationship graph; Based on the operation intent category and the data dependency graph, identify the source change data that will trigger a change in the state of the associated data, and determine the source change data as the change data of the target database.

5. The data processing method according to claim 1, characterized in that, The process of parsing the changed data, determining the entity type, attribute information, and operation type corresponding to the changed data, and constructing a change impact propagation map based on the entity type, attribute information, and operation type includes: The changed data is parsed at the field level to determine the entity type, attribute information, and operation type corresponding to the changed data; The synchronization mode of the changed data is determined based on the entity type, the attribute information, and the operation type. Based on the entity type, attribute information, operation type, and synchronization mode, the impact propagation path of the changed data in the storage model is constructed, and a change impact propagation map is generated.

6. The data processing method according to claim 5, characterized in that, The step of predicting data conflict risk based on the change impact propagation map and generating a conflict resolution strategy, and generating a data synchronization strategy for the changed data based on the change impact propagation map, the conflict resolution strategy, and the storage model, includes: Based on the aforementioned change impact propagation map, the data state evolution under multi-source concurrent scenarios is simulated to predict potential data conflict risk points; Based on the predicted data conflict risk points, a conflict resolution strategy including priority arbitration rules and merging strategies is generated; Based on the synchronization mode, the change impact propagation map, the conflict resolution strategy, and the storage model, a data synchronization strategy for the changed data is generated.

7. The data processing method according to claim 2, characterized in that, The step of storing the changed data in the target database according to the data synchronization strategy includes: According to the data synchronization strategy, the changed data is format-converted and data-adapted, and the changed data after format conversion and data adaptation is written into the target storage set in the target database, and the index information corresponding to the target storage set is updated synchronously. The source, processing, and synchronization results of the changed data are recorded in a chain to generate a data traceability audit chain, and the data consistency between the data traceability audit chain and the target storage set is verified. When the verification passes, a corresponding data synchronization completion result is generated; wherein the data synchronization completion result includes the synchronization time, the changed content, and the traceability identifier; The data synchronization results are fed back to the client and displayed to the target user according to the preset result display strategy.

8. A data processing apparatus, characterized in that, include: The receiving module is used to receive entity definition instructions, attribute configuration instructions, and entity relationship definition instructions input by the target user, and configure the storage model of the target database according to the entity definition instructions, attribute configuration instructions, and entity relationship definition instructions; The monitoring module is used to monitor the database access trajectory generated by the target program accessing the target database in real time, perform semantic context analysis on the database access trajectory, identify the operation intention and data dependency relationship, and capture the change data for the target database based on the identified operation intention and data dependency relationship. The parsing module is used to parse the changed data, determine the entity type, attribute information and operation type corresponding to the changed data, and construct a change impact propagation map based on the entity type, the attribute information and the operation type; The generation module is used to predict data conflict risks based on the change impact propagation map and generate conflict resolution strategies, and to generate a data synchronization strategy for the changed data based on the change impact propagation map, the conflict resolution strategies, and the storage model. The storage module is used to store the changed data to the target database according to the data synchronization strategy.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the data processing method as described in any one of claims 1-7.

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

Images