A medicine supply chain management cloud database synchronization method and system

Abstract

This application provides a cloud database synchronization method and system for pharmaceutical supply chain management. Through steps S1-S3, a closed-loop synchronization mechanism is formed, improving the efficiency and reliability of pharmaceutical supply chain data collaboration. Step S1, feature recognition based on a pharmaceutical business rule base, enables the system to understand the business semantics of the data, solving the synchronization blind spots and consistency risks caused by the lack of business logic in traditional methods. Step S2, differentiated synchronization strategy matching, achieves dynamic adaptation of synchronization resources to business criticality, overcoming the real-time deficiencies and resource waste problems of fixed synchronization modes. Step S3, strategy-driven intelligent synchronization execution, ensures the reliability, immediate delivery, and full compliance auditing of key instructions throughout the entire chain through priority routing and integrity verification. The synergy of these three elements ultimately achieves a unity of data consistency, business real-time performance, and operational compliance.

Description

Technical Field

[0001] This application relates to the field of data synchronization technology, specifically to a method and system for synchronizing a cloud database for pharmaceutical supply chain management. Background Technology

[0002] The pharmaceutical supply chain involves the entire process of drug production, warehousing, distribution, and final sales and use, characterized by numerous participants, strict regulation, and high requirements for real-time data. With the development of cloud computing technology, more and more pharmaceutical companies are using cloud databases to manage supply chain data to achieve data sharing and improve collaborative efficiency. However, the current synchronization process of cloud databases for pharmaceutical supply chain management faces the following pressing technical challenges: 1. Pharmaceutical data, such as drug batches, expiration dates, inventory, temperature and humidity monitoring records, and flow documents, are highly correlated and have complex business logic, requiring adherence to principles like First-In-First-Out (FIFO) and First-Out-of-Premium (FIO). Existing general database synchronization technologies, such as log-based incremental synchronization, struggle to guarantee the integrity and consistency of business data packets across multiple tables with complex business dependencies when performing table-level or row-level data synchronization. This can easily lead to inconsistent database states on different nodes, further causing problems such as inaccurate inventory and lost documents.

[0003] 2. The pharmaceutical supply chain has high requirements for the real-time synchronization of critical business events such as emergency procurement and warehousing, and the issuance of drug recall orders. However, traditional synchronization methods usually use fixed-frequency polling or batch synchronization without prioritizing data. When faced with sudden high-frequency business events, the synchronization link is prone to congestion, causing low-priority data to block high-priority data, which cannot meet the real-time synchronization needs of critical business operations. Summary of the Invention

[0004] This application aims to ensure the integrity and consistency of business data packets spanning multiple tables and having complex business dependencies, and to meet the real-time synchronization needs of critical businesses, by providing a method and system for synchronizing a cloud database for pharmaceutical supply chain management.

[0005] To achieve this objective, the following technical solution is adopted in this application: A method for synchronizing a cloud database for pharmaceutical supply chain management is provided, including the following steps: S1, When data changes occur in the source database, the changed data is captured, and based on a predefined medical business rule base, the business characteristics to which the changed data belongs are identified. The business characteristics include at least business type and business priority. S2, based on the identified business characteristics, match and select the corresponding differentiated synchronization strategy from the preset synchronization strategy library; the differentiated synchronization strategy at least defines the synchronization triggering conditions, data compression method and exception handling mechanism; S3. Based on the selected differentiated synchronization strategy, the changed data is encapsulated into a synchronization task, and routed to the target database node for synchronization operation through message middleware.

[0006] Preferably, in step S1, the method for identifying the business characteristics to which the changed data belongs includes the following steps: S11, Extract metadata from the captured changed data, including the source identifier, operation type, and data object of the changed data; S12, Based on the pharmaceutical business rule base, perform business feature matching on the extracted metadata; S13: Based on the business feature matching results of the metadata, filter and aggregate related data, and then output complete business features.

[0007] Preferably, the method for selecting the differentiated synchronization strategy in step S2 includes the following steps: S21, Based on the business type carried in the identified business characteristics, determine the preliminary category of the proposed differentiated synchronization strategy; S22, within the determined preliminary category, and in conjunction with the business priority carried in the business characteristics identified in step S1, select the final differentiated synchronization strategy.

[0008] Preferably, at least one differentiated synchronization strategy is preset under each of the preliminary categories. The differentiated synchronization strategies preset under the same preliminary category have the same strategy execution principle, but the strategy execution parameters have gradient differences. The method by which the system matches the final synchronization strategy from the pre-set differentiated synchronization strategies with parameter gradient differences under the same initial category based on the business priority carried in the business characteristics is as follows: Analyze the priority characteristics of each of the differentiated synchronization strategies preset under the initial category; The business priority carried in the business features identified in step S1 is matched with the parsed priority features to select the differentiated synchronization strategy associated with the priority feature with the highest consistency.

[0009] Preferably, in step S3, the method for encapsulating the changed data into a synchronization task according to the selected differentiated synchronization strategy is as follows: Based on the parameters carried in the differential synchronization strategy finally selected in step S2, a structured synchronization task object is created. The synchronization task object includes one or more of the following: strategy identifier of the finally selected strategy, routing priority label, processing priority label, data integrity verification information, timeout and retry rules. The data integrity verification information is a hash value dynamically generated based on each data element constituting the synchronization task object.

[0010] Preferably, in step S3, the method for the target database node to perform the synchronization task includes the following steps: S31, parse out the policy identifier carried in the synchronization task, and then call the local execution processor corresponding to the policy associated with the parsed policy identifier; S32, the local execution processor uses the previously received hash value to verify whether the data packet is complete and whether it has been tampered with during transmission, and executes the received majority synchronization task when the verification result is complete and not tampered with.

[0011] This application also provides a method for synchronizing a cloud database for pharmaceutical supply chain management, including: The change data capture module is used to capture change data when data changes occur in the source database; The business feature identification module, connected to the change data capture module, is used to identify the business features to which the change data belongs based on a predefined medical business rule base. The strategy matching module is connected to the business feature identification module and is used to match and select the corresponding differentiated synchronization strategy from the preset synchronization strategy library based on the identified business features. A synchronization task encapsulation module, connected to the change data capture module and the strategy matching module, is used to encapsulate the change data into a synchronization task according to the selected differentiated synchronization strategy; The routing module, connected to the synchronization task encapsulation module, is used to route the synchronization task to the target database node via a message middleware. The local execution processor, deployed on the target database node, is used to perform synchronization operations based on the received synchronization tasks.

[0012] This application has the following beneficial effects: 1. This application establishes a closed-loop synchronization mechanism through steps S1-S3, improving the efficiency and reliability of data collaboration in the pharmaceutical supply chain. Step S1, feature recognition based on a pharmaceutical business rule base, enables the system to understand the business semantics of the data, resolving synchronization blind spots and consistency risks caused by the lack of business logic in traditional methods. Step S2, differentiated synchronization strategy matching, achieves dynamic adaptation of synchronization resources to business criticality, overcoming the real-time deficiencies and resource waste problems of fixed synchronization modes. Step S3, strategy-driven intelligent synchronization execution, ensures the reliability, immediate delivery, and full compliance auditing of critical instructions throughout the entire chain through priority routing and integrity verification. The synergy of these three elements ultimately achieves a unified approach to data consistency, business real-time performance, and operational compliance.

[0013] 2. Through step S1, the original database operations are transformed into standardized and complete data packets and their characteristics that are rich in business semantics, providing high-quality data input and decision-making basis for subsequent synchronization of the cloud database.

[0014] 3. Step S2 uses a two-level decision-making mechanism that first roughly categorizes business types and then refines synchronization strategies according to business priorities. This accurately translates the business characteristics identified in Step S1 into an executable solution that includes specific triggering, compression, and exception handling gradient parameters, thus combining decision-making efficiency with control precision.

[0015] 4. The hash value generation process in this application has two dynamic factors: 1. The process of selecting a differentiated synchronization strategy in step S2 is dynamic. The pre-set strategies under each preliminary category are usually different, and the selection of the preliminary category depends on the business characteristics dynamically identified in step S1. Therefore, the process of creating the synchronization task object on which hash value generation depends is also dynamic; 2. The data elements constituting the synchronization task object include one or more of the following: policy identifier, routing priority label, processing priority label, timeout and retry rules. Therefore, the creation of the synchronization task object itself is a random process. These two dynamic factors ensure that each generated hash value has dynamic and random characteristics, thus providing higher security as information for subsequent data integrity verification. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly described below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a diagram illustrating the implementation steps of a cloud database synchronization method for pharmaceutical supply chain management provided in an embodiment of this application. Detailed Implementation

[0018] The technical solution of this application will be further described below with reference to the accompanying drawings and specific embodiments.

[0019] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual images. They should not be construed as limiting the scope of this application. To better illustrate the embodiments of this application, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0020] In the accompanying drawings of the embodiments of this application, the same or similar reference numerals correspond to the same or similar components. In the description of this application, it should be understood that if terms such as "upper," "lower," "left," "right," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting this application. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0021] In the description of this application, unless otherwise expressly specified and limited, the term "connection" or similar designation indicating a connection between components should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0022] This application provides a method for synchronizing a cloud database for pharmaceutical supply chain management, such as... Figure 1 As shown, the steps include: S1. When data changes occur in the source database, capture the changed data and identify the business characteristics to which the changed data belongs based on a predefined medical business rule base. The business characteristics include at least the business type and business priority. In this embodiment, a data capture agent is deployed in the source database. This agent captures data changes caused by operations such as inserts, updates, and deletions in real time by monitoring the database's transaction logs or using trigger mechanisms. After replenishment, the agent not only obtains the changed data itself (including data before and after the update), but also extracts its structured metadata, including the source identifier, operation type, and data object. The source identifier identifies which database and business node the changed data originates from; the operation type includes the type of operation performed on the source data, such as insert, update, or delete; and the data object refers to the specific database table, primary key, and changed fields involved in the operation on the source data.

[0023] For example, a warehouse manager completes an outbound confirmation operation for a certain drug (batch number 123) in the system. The agent captures this outbound update record from the transaction log. The extracted metadata includes, for example: Source: a warehouse in a certain location; Operation: Update; Table: database table a; Record ID: BATCH-20231005; Change field: The inventory quantity of drug b with batch number 123 changed from 1000 to 990; Operation time: November 5, 2023.

[0024] Then, based on a predefined medical business rule base, business feature matching is performed on the extracted metadata. The specific implementation method is as follows: The extracted metadata from the changed data will be matched with a predefined pharmaceutical business rule base. The rules in the pharmaceutical business rule base are preferably structured as a "condition-conclusion" framework. The condition part of the rule is built based on metadata and preferably includes logical judgments on data tables, fields, field values, and cross-table relationships; the conclusion part assigns business characteristics to the condition part, preferably including business type and data priority.

[0025] For example, a rule in the rule base is recorded as follows: The condition section is: if the operation is an update and the database table is database table a, and the changed field includes changes in inventory quantity, and there is a related outbound order record whose status changes to "confirmed", then the conclusion section is: the business type is an outbound inventory deduction for drug b, and the data priority is "high".

[0026] The matching process between metadata and business features is as follows: The extracted metadata is substituted into the condition part of the rule. If it matches all the condition parts, the result part bound to the condition part is output. The result part is the matched business feature.

[0027] Finally, based on the matching results of the business features of the metadata, the associated data is filtered and aggregated, and then the complete business features are output.

[0028] A single business operation, such as confirming an outbound shipment, typically generates multiple related data change records in the database, such as updating the inventory table, inserting an outbound transaction record, and updating the order status table. When matching data based on a predefined pharmaceutical business rule base, the rule engine identifies multiple data changes within a short time window (such as a preset time window) that belong to the same business transaction (determined by the same transaction ID or business order number) as a single logical unit, and assigns this overall unit the same business characteristics.

[0029] For example, in the same outbound operation, in addition to updating the inventory table, an outbound log entry is inserted into the outbound transaction record, and the order status is updated in the order status table. The rule engine associates these three independent database change records using the transaction ID or outbound order number, identifying that they belong to a complete outbound business event. Ultimately, the system outputs not the business characteristics of a single table change, but the complete business characteristics of the outbound business event data packet that includes the aforementioned three database change records. This data packet contains all associated change data.

[0030] This application, through step S1, transforms the original database operations into standardized, complete data packets and their characteristics that are rich in business semantics, providing high-quality data input and a decision-making basis for subsequent synchronization of the cloud database.

[0031] After identifying the business characteristics described in the changed data through step S1, such as Figure 1 As shown in the figure, the following steps are provided in the method for synchronizing a pharmaceutical supply chain management cloud database: S2, based on the identified business characteristics, match and select the corresponding differentiated synchronization strategy from the preset synchronization strategy library. The differentiated synchronization strategy at least defines the synchronization triggering conditions, data compression method and exception handling mechanism. The specific steps include: S21. Based on the business type carried in the business characteristics, determine the preliminary category of the differentiated synchronization strategy to be selected; In this embodiment, the system maintains a synchronization strategy category index with business type as the primary key. When the system receives the business characteristics output in step S1, which carry a clear business type, such as emergency instructions, inventory transactions, batch tracking, etc., the system first queries this index to map the business type to a predefined, broad synchronization strategy category.

[0032] For example, the business type carried in the business characteristics output in step S1 is: Emergency Drug Recall Instruction. In the predefined pharmaceutical business rule base, all "emergency instruction" type businesses, including recall, discontinuation, and major warnings, belong to the "real-time critical" strategy category. Therefore, based on the pre-bound relationship between the business type "recall" and the strategy category "real-time critical," the initial category of the synchronization strategy for the "Emergency Drug Recall Instruction" is matched as "real-time critical." The execution principle of the synchronization strategy for "real-time critical" can be pre-set as follows: disregarding significant bandwidth and computing resource costs, prioritizing the lowest latency and highest reliability.

[0033] S22, within the determined initial categories, and in combination with data priority, select the final differentiated synchronization strategy; In this application, under the preliminary strategy determined in step S21, the system further performs a secondary strategy filtering based on the business priority carried in the business features identified in step S1. Several differentiated synchronization strategies are pre-set under each preliminary category. These pre-set differentiated synchronization strategies under the same preliminary category have the same strategy execution principle, but their strategy execution parameters have gradient differences. The method by which the system matches the final selected synchronization strategy from the pre-set differentiated synchronization strategies under the preliminary category based on the business priority carried in the business features identified in step S1 is as follows: Analyze the priority characteristics of each differentiated synchronization strategy preset under the same initial category. For example, the priority characteristic of the differentiated synchronization strategy "real-time highest strategy a" is "real-time highest". Then, the business priority carried in the business features identified in step S1 is matched with the parsed priority features to select the differentiated synchronization strategy associated with the priority feature with the highest consistency.

[0034] For example, the initial category is "Real-time Critical," and the data priority is "Highest." Under this initial category, the following three synchronization strategies are pre-defined: Real-time highest strategy a: Trigger condition = Immediate; Data compression = No; Confirmation mechanism = Synchronous wait; Retry interval = 100ms; Real-time high-strategy b: Trigger condition = immediate; Data compression = lightweight compression; Confirmation mechanism = asynchronous callback; Retry interval = 1 second; Real-time strategy c: Trigger condition = micro-delay (less than 2s) aggregation; Data compression = yes; Confirmation mechanism = log recording; Retry interval = 5s.

[0035] Since the priority of the business feature identified in step S1 is "highest", the system automatically selects the real-time highest strategy a as the final differentiated synchronization strategy selected for this execution.

[0036] Step S2 uses a two-level decision-making mechanism that first roughly categorizes business types and then refines synchronization strategies according to business priorities. This accurately translates the business characteristics identified in Step S1 into an executable scheme that includes specific triggering, compression, and exception handling gradient parameters, thus combining decision-making efficiency with control precision.

[0037] After selecting the differentiated synchronization strategy in step S2, as follows Figure 1 As shown, the steps of the pharmaceutical supply chain management cloud database synchronization method provided in this application are as follows: S3 encapsulates the changed data into a synchronization task based on the selected differentiated synchronization strategy, and routes it to the target database node for synchronization operation through the message middleware.

[0038] In this application, the method for encapsulating changed data into a synchronization task based on the selected differentiated synchronization strategy is as follows: Based on the parameters in the differentiated synchronization strategy selected in step S2, the system creates a structured synchronization task object. This object not only contains the original changed data itself, but also encapsulates at least the following metadata, including: Policy identifier: Indicates the specific policy followed by this synchronization task. For example, if the final selected differentiated synchronization policy is the real-time highest policy a mentioned above, the policy identifier assigned by the system to the real-time highest policy a might be: Policy a - Emergency Command.

[0039] Routing instructions: Contain a list or group identifier of the target database nodes. For example, specifying that the target database nodes for this synchronization task include all pharmacies in hospitals at level two or above, or all distribution centers in East China, etc.

[0040] Priority label processing: Based on the priority definition carried in the final determined differentiated synchronization strategy, assign the corresponding label to the target task. For example, if the final selected strategy is real-time highest a, then the target task will be labeled "real-time highest".

[0041] Timeout and retry rules: Inherited from the exception handling mechanism defined in the finally selected differentiated synchronization strategy, specifying the successful timeout time of the target task, the number of retries and the interval after failure.

[0042] Data integrity verification information: For business data packets that require strong consistency (such as outbound orders and all their details), the system automatically calculates and encapsulates a data fingerprint (preferably a hash value) for target-side verification. In this embodiment, to improve data security, the numerical fingerprint is preferably a hash value dynamically generated based on the data elements constituting the synchronization task object.

[0043] It should be noted that the hash value generation process is existing technology and will not be elaborated here. The hash value generation process in this application has two dynamic factors: 1. The process of finally selecting a differentiated synchronization strategy in step S2 is dynamic. The pre-set strategies under each preliminary category are usually different, and the selection of the preliminary category depends on the business characteristics dynamically identified in step S1. Therefore, the process of creating the synchronization task object on which hash value generation depends is also dynamic; 2. The data elements constituting the synchronization task object include one or more of the following: policy identifier, routing priority label, processing priority label, timeout and retry rules. Therefore, the creation of the synchronization task object itself is a random process. These two dynamic factors ensure that each generated hash value has dynamic and random characteristics, thus providing higher security as information for subsequent data integrity verification.

[0044] For example, the system matches "real-time highest policy a". In the encapsulated synchronization task object, the policy identifier is, for example, "policy a"; the routing instruction is, for example, "all medical institutions and pharmacies that have received this batch of vaccines"; the priority label is, for example, "highest"; simultaneously, the system generates a hash value as a data fingerprint based on these elements. The timeout rule is, for example, set to: if no confirmation is received within 5 seconds, it is considered a failure and a retry is initiated immediately.

[0045] In step S3, the method for routing the synchronization task to the target database node through the message middleware is as follows: The system submits the encapsulated synchronization task object to the message middleware. The message middleware places the task into different priority queues based on its processing priority tag. Higher priority queues are used for faster consumption and more exclusive transmission resources. Subsequently, the message middleware, according to routing instructions, pushes or sends the task point-to-point to the specified target database node.

[0046] In step S3, the method for the target database node to execute the received synchronization task includes the following steps: S31, parse out the policy identifier carried in the synchronization task, and then call the local execution processor corresponding to the policy associated with the parsed policy identifier; S32, the local execution processor uses pre-received data integrity verification information to verify whether the data packet is complete and untampered during transmission. If the verification result is complete and untampered, the received synchronization task is executed. The verification method is to determine the consistency between the data integrity verification information carried in the synchronization task and the hash value pre-transmitted by the system. If the verification passes, the data packet is determined to be complete and untampered. There are many existing methods for data consistency verification; essentially, they are data consistency matching processes, and will not be specifically described here.

[0047] In summary, this application establishes a closed-loop synchronization mechanism through steps S1-S3, improving the efficiency and reliability of data collaboration in the pharmaceutical supply chain. Step S1, feature recognition based on a pharmaceutical business rule base, enables the system to understand the business semantics of the data, resolving synchronization blind spots and consistency risks caused by the lack of business logic in traditional methods. Step S2, differentiated synchronization strategy matching, achieves dynamic adaptation of synchronization resources to business criticality, overcoming the real-time deficiencies and resource waste problems of fixed synchronization modes. Step S3, strategy-driven intelligent synchronization execution, ensures the reliability, immediate delivery, and full compliance auditing of critical instructions throughout the entire chain through priority routing and integrity verification. The synergy of these three elements ultimately achieves a unified approach to data consistency, business real-time performance, and operational compliance.

[0048] This application also provides a pharmaceutical supply chain management cloud database synchronization system, including: The change data capture module is used to capture change data when data changes occur in the source database; The business feature identification module, connected to the change data capture module, is used to identify the business features to which the change data belongs based on a predefined medical business rule base. The strategy matching module, connected to the business feature identification module, is used to match and select the corresponding differentiated synchronization strategy from the preset synchronization strategy library based on the identified business features. The synchronization task encapsulation module connects the change data capture module and the strategy matching module, and is used to encapsulate the change data into a synchronization task according to the selected differentiated synchronization strategy. The routing module, which connects to the synchronization task encapsulation module, is used to route synchronization tasks to the target database node via message middleware. The local execution processor, deployed on the target database node, is used to perform synchronization operations based on the received synchronization tasks.

[0049] It should be stated that the above-described specific embodiments are merely preferred embodiments and technical principles applied in this application. Those skilled in the art should understand that various modifications, equivalent substitutions, and variations can be made to this application. However, such variations, as long as they do not depart from the spirit of this application, should be within the scope of protection of this application. Furthermore, some terminology used in this application's specification and claims is not limiting but merely for ease of description.

Claims

1. A method for synchronizing a cloud database for pharmaceutical supply chain management, characterized in that, Including the following steps: S1, When data changes occur in the source database, the changed data is captured, and based on a predefined medical business rule base, the business characteristics to which the changed data belongs are identified. The business characteristics include at least business type and business priority. S2, based on the identified business characteristics, match and select the corresponding differentiated synchronization strategy from the preset synchronization strategy library; the differentiated synchronization strategy at least defines the synchronization triggering conditions, data compression method and exception handling mechanism; S3. Based on the selected differentiated synchronization strategy, the changed data is encapsulated into a synchronization task, and routed to the target database node for synchronization operation through message middleware.

2. The method for synchronizing a pharmaceutical supply chain management cloud database according to claim 1, characterized in that, In step S1, the method for identifying the business characteristics to which the changed data belongs includes the following steps: S11, Extract metadata from the captured changed data, including the source identifier, operation type, and data object of the changed data; S12, Based on the pharmaceutical business rule base, perform business feature matching on the extracted metadata; S13: Based on the business feature matching results of the metadata, filter and aggregate related data, and then output complete business features.

3. The method for synchronizing a pharmaceutical supply chain management cloud database according to claim 1, characterized in that, The method for selecting the differentiated synchronization strategy in step S2 includes the following steps: S21, Based on the business type carried in the identified business characteristics, determine the preliminary category of the proposed differentiated synchronization strategy; S22, within the determined preliminary category, and in conjunction with the business priority carried in the business characteristics identified in step S1, select the final differentiated synchronization strategy.

4. The method for synchronizing a pharmaceutical supply chain management cloud database according to claim 3, characterized in that, Each of the aforementioned preliminary categories has at least one pre-defined differential synchronization strategy. The pre-defined differential synchronization strategies under the same preliminary category have the same strategy execution principle, but the strategy execution parameters have gradient differences. The method by which the system matches the final synchronization strategy from the pre-set differentiated synchronization strategies with parameter gradient differences under the same initial category based on the business priority carried in the business characteristics is as follows: Analyze the priority characteristics of each of the differentiated synchronization strategies preset under the initial category; The business priority carried in the business features identified in step S1 is matched with the parsed priority features to select the differentiated synchronization strategy associated with the priority feature with the highest consistency.

5. The method for synchronizing a pharmaceutical supply chain management cloud database according to claim 1, characterized in that, In step S3, the method for encapsulating the changed data into a synchronization task according to the selected differentiated synchronization strategy is as follows: Based on the parameters carried in the differential synchronization strategy finally selected in step S2, a structured synchronization task object is created. The synchronization task object includes one or more of the following: strategy identifier of the finally selected strategy, routing priority label, processing priority label, data integrity verification information, timeout and retry rules. The data integrity verification information is a hash value dynamically generated based on each data element constituting the synchronization task object.

6. The method for synchronizing a pharmaceutical supply chain management cloud database according to claim 5, characterized in that, In step S3, the method for the target database node to execute the synchronization task includes the following steps: S31, parse out the policy identifier carried in the synchronization task, and then call the local execution processor corresponding to the policy associated with the parsed policy identifier; S32, the local execution processor uses the previously received hash value to verify whether the data packet is complete and whether it has been tampered with during transmission, and executes the received majority synchronization task when the verification result is complete and not tampered with.

7. A method for synchronizing a cloud database for pharmaceutical supply chain management, characterized in that, include: The change data capture module is used to capture change data when data changes occur in the source database; The business feature identification module, connected to the change data capture module, is used to identify the business features to which the change data belongs based on a predefined medical business rule base. The strategy matching module is connected to the business feature identification module and is used to match and select the corresponding differentiated synchronization strategy from the preset synchronization strategy library based on the identified business features. A synchronization task encapsulation module, connected to the change data capture module and the strategy matching module, is used to encapsulate the change data into a synchronization task according to the selected differentiated synchronization strategy; The routing module, connected to the synchronization task encapsulation module, is used to route the synchronization task to the target database node via a message middleware. The local execution processor, deployed on the target database node, is used to perform synchronization operations based on the received synchronization tasks.

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