Business data processing system and business data processing method

Abstract

The invention provides a business data processing system and a business data processing method. The business data processing system comprises a storage device and a processor. The processor executes the perceptron module, the actuator module, the message queue management module, the data graph module, the metadata management module and the scheduling management module. The message queue management module establishes a message queue. The sensor module senses the changed service data from the message queue according to the schedule set by the schedule management module. And the sensor module performs data feature matching through the data atlas module according to the changed service data so as to drive the actuator module. And the actuator module executes the corresponding business service according to the metadata provided by the metadata management module so as to generate new business data, and the actuator module updates the new business data to the message queue management module. According to the business data processing system and the business data processing method, a high-efficiency data driving function can be realized.

Description

Technical Field

[0001] The present invention relates to a data-driven technology, in particular to a business data processing system and a business data processing method. Background Art

[0002] In the current enterprise automation process, applications required by customers can be assembled through the orchestration of process-based components (PBCs). However, the relatively fixed orchestration of process-based components has relatively complex orchestration relationships, which require customers to spend time understanding. Moreover, the fixed orchestration of process-based components is not easily adjusted, resulting in a lack of flexibility in orchestration changes. Summary of the Invention

[0003] The present invention is directed to a business data processing system and a business data processing method, which can achieve a high-efficiency data-driven function.

[0004] According to an embodiment of the present invention, the business data processing system of the present invention includes a storage device and a processor. The storage device stores a sensor module, an actuator module, a message queue management module, a data graph module, a metadata management module, and a scheduling management module. The processor is coupled to the storage device. The processor executes the sensor module, the actuator module, the message queue management module, the data graph module, the metadata management module, and the scheduling management module. The message queue management module establishes a message queue. The sensor module senses changing business data from the message queue according to the schedule set by the scheduling management module. The sensor module performs data feature matching through the data graph module according to the changing business data to drive the actuator module. The actuator module executes corresponding business services according to the metadata provided by the metadata management module to generate new business data. The actuator module updates the new business data to the message queue management module.

[0005] According to an embodiment of the present invention, the business data processing method of the present invention includes the following steps: establishing a message queue through the message queue management module; sensing changing business data from the message queue through the sensor module according to the schedule set by the scheduling management module; performing data feature matching through the data graph module according to the changing business data by the sensor module to drive the actuator module; executing corresponding business services according to the metadata provided by the metadata management module by the actuator module to generate new business data; and updating the new business data to the message queue management module by the actuator module.

[0006] Based on the above, the business data processing system and method of the present invention can store changed business data through a message queue, and can automatically detect the changed business data through a predetermined schedule to run the corresponding business service. The corresponding business service can generate new business data, and the new business data can be updated to the corresponding message queue for another data detection operation to obtain. In this way, the business data processing system and method of the present invention can achieve a high-efficiency data-driven function.

[0007] To make the above features and advantages of the present invention more obvious and understandable, specific embodiments are given below and described in detail in conjunction with the accompanying drawings as follows. Description of the Drawings

[0008] Figure 1 Schematic diagram of the business data processing system according to an embodiment of the present invention;

[0009] Figure 2 Schematic diagram of the architecture of the business data processing according to an embodiment of the present invention;

[0010] Figure 3 Flowchart of the business data processing method according to an embodiment of the present invention;

[0011] Figure 4 Schematic diagram of the operation of the message queue management according to an embodiment of the present invention;

[0012] Figure 5 Schematic diagram of the operation of the sensor according to an embodiment of the present invention;

[0013] Figure 6 Schematic diagram of the operation of the actuator according to an embodiment of the present invention;

[0014] Figure 7 Schematic diagram of the operation of the metadata management according to an embodiment of the present invention;

[0015] Figure 8 Schematic diagram of the data graph according to an embodiment of the present invention;

[0016] Figure 9 Schematic diagram of the operation of the business data processing according to an embodiment of the present invention.

[0017] Explanation of the Reference Numerals in the Drawings

[0018] 100: Business data processing system;

[0019] 110: Processor;

[0020] 120: Storage device;

[0021] 200: Business data processing architecture;

[0022] 210: Intelligent Agent Application Layer;

[0023] 220: Intelligent Agent Components;

[0024] 221: Message Queue Management Module;

[0025] 222: Perceptor Module;

[0026] 223: Data Atlas Module;

[0027] 224: Executor Module;

[0028] 225: Metadata Management Module;

[0029] 226: Scheduling Management Module;

[0030] 230: Framework Layer;

[0031] 240: Data Layer;

[0032] 401: Data Receiving Service;

[0033] 402: Message Queue Cluster;

[0034] 403: Changed Data Listener;

[0035] 404, 502, 702: Database;

[0036] 405: Perception Retry Task;

[0037] 406: Queue Management Service;

[0038] 501: Perception Trigger Service;

[0039] 503: Active Perception Processor;

[0040] 504: Intelligent Perception Execution Service;

[0041] 505: Passive Perception Processor;

[0042] 506: Feature Matching Service;

[0043] 601: Intelligent Agent Trigger Service;

[0044] 602: Intelligent Agent Creator;

[0045] 603: Intelligent Agent Execution Service;

[0046] 604: Intelligent Agent Interpreter;

[0047] 605: Data Delivery Service;

[0048] 701: Metadata Synchronization Service;

[0049] 703: Metadata Query Service;

[0050] 704: In-Memory Database;

[0051] 801 - 803, 902_1 - 902_N: Agents;

[0052] 804, 805: Model Attributes;

[0053] 806: Business Model;

[0054] 902: Message Queue Cluster;

[0055] 903: Enterprise Service System;

[0056] 904: Business Platform;

[0057] 922_1 - 922_M: Sensors;

[0058] 924_1 - 924_M: Actuators;

[0059] S310 - S350: Steps. Detailed Embodiment

[0060] Reference will now be made in detail to exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Whenever possible, the same reference numerals are used in the drawings and the description to refer to the same or like parts.

[0061] Figure 1 is a schematic diagram of a business data processing system according to an embodiment of the present invention. Referring to Figure 1 , the business data processing system 100 includes a processor 110 and a storage device 120. The processor 110 is coupled to the storage device 120. The processor 110 and the storage device 120 can be implemented as a server. In this embodiment, the business data processing system 100 can be, for example, set up in a cloud server for system developers or users to connect and operate through wired or wireless communication methods, but the present invention is not limited thereto. In one embodiment, the business data processing system 100 can also be implemented by multiple processors and storage devices, and the multiple processors and the multiple storage devices can be separately disposed in different cloud devices and / or on-premises computing devices.

[0062] In this embodiment, the processor 110 may be a system on a chip (SOC), or may include, for example, a central processing unit (CPU) or other programmable general-purpose or special-purpose microprocessors, digital signal processors (DSPs), programmable controllers, application specific integrated circuits (ASICs), programmable logic devices (PLDs), other similar processing devices, or combinations of these devices. The storage device 120 may include, for example, dynamic random access memory (DRAM), flash memory, or non-volatile random access memory (NVRAM), etc.

[0063] Figure 2 Schematic diagram of the architecture for business data processing in the implementation of the present invention. Refer to Figure 1 and Figure 2 , in this embodiment, the storage device 120 may store multiple modules to implement (run) the business data processing architecture 200 as Figure 2 shown. The business data processing architecture 200 includes an intelligent body application layer 210, intelligent body components 220, a framework layer 230, and a data layer 240. In this embodiment, the intelligent body components 220 may include a message queue management module 221, a sensor module 222, a data graph module 223, an actuator module 224, a metadata management module 225, and a scheduling management module 226.

[0064] In this embodiment, the message queue management module 221, the sensor module 222, the data graph module 223, the actuator module 224, the metadata management module 225, and the scheduling management module 226 may be implemented, for example, in programming languages such as JSON (JavaScript Object Notation), Extensible Markup Language (XML), or YAML. The message queue management module 221, the sensor module 222, the data graph module 223, the actuator module 224, the metadata management module 225, and the scheduling management module 226 may respectively execute the relevant algorithms and programs of the service and functional elements described in the following embodiments.

[0065] In this embodiment, the message queue management module 221, the sensor module 222, the data graph module 223, the actuator module 224, the metadata management module 225, and the scheduling management module 226 can be used to implement one or more intelligent agents (or intelligent agents). The intelligent agent is used to autonomously sense changes in business data, execute corresponding business logics according to the changed business data to complete corresponding business tasks, and generate new business data.

[0066] In this embodiment, the intelligent agent application layer 210 includes relevant intelligent agent application service programs and user interfaces, etc., to implement interactive functions with external user operations or application programs.

[0067] In this embodiment, the framework layer 230 may include, for example, the springboot development framework, the mybatis data access framework, and / or the dubbo remote procedure call (RPC) framework, etc., and the present invention is not limited thereto. The springboot development framework is used to implement relevant configurations and deployments of Spring applications. The mybatis data access framework is used to interact with relational databases. The dubbo remote procedure call framework is used to build a microservices architecture.

[0068] In this embodiment, the data layer 240 may include, for example, a relational database management system (RDBMS) (such as MySQL), a key-value storage database (DB) (such as Redis), a document database (such as MongoDB), a graph database (such as Neo4j), a message middleware (such as RabbitMQ), and / or a distributed stream processing platform (such as Kafka), etc., and the present invention is not limited thereto. In this embodiment, the data layer 240 may also include external databases and in-memory databases mentioned in the following embodiments.

[0069] Figure 3 It is a flowchart of the business data processing method according to the embodiment of the present invention. Refer to Figures 1 to 3, the business data processing system 100 can perform the following steps S310 to S350. In step S310, the message queue management module 221 creates a message queue (Message Queue, MQ). In step S320, the sensor module 222 senses the changing business data from the message queue according to the schedule set by the schedule management module 226. In step S330, the sensor module 222 performs data feature matching through the data graph module 223 according to the changing business data to drive the actuator module 224. In step S340, the actuator module 224 executes the corresponding business service according to the metadata provided by the metadata management module 225 to generate new business data. In step S350, the actuator module 224 updates the new business data to the message queue management module 221.

[0070] In this embodiment, the business data processing system 100 can be constructed with multiple agents, and the sensor module 222 and the actuator module 224 can construct the sensor function and the actuator function of each of the multiple agents to implement the above process. In this regard, the multiple agents can automatically sense the changing business data respectively to run the corresponding business services and generate different new business data, and the different new business data can be updated to the corresponding message queues respectively for other agents to obtain and execute the corresponding next business tasks or business data processing logics respectively. In this way, the business data processing system of the present invention can achieve high-efficiency data-driven functions through the multiple agents.

[0071] Figure 4 It is a schematic diagram of the operation of the message queue management of the embodiment of the present invention. Refer to Figure 2 and Figure 4 , the following embodiments specifically illustrate Figure 2 the operation process of the message queue management module 221. In this embodiment, the actuator module 224 can deliver the changed data to the message queue management module 221. The actuator module 224 can execute the data receiving service 401 and send a message (send the changed data) to the message queue cluster 402 to store the changed business data into the corresponding message queue. The message queue cluster 402 includes multiple message queues. The message queue management module 221 can execute the changed data listener 403 to monitor the corresponding message queue in the message queue cluster 402. The changed data listener 403 can receive the changed data and write the changed data into the local message table to save the message to the database 404 (such as mysql).

[0072] In this embodiment, the change data listener 403 can trigger perception to send the change data to the perception module 222 and perform perception. The perception module 222 can return the perception result (the perception result of the changed business data) to the change data listener 403, so that the change data listener 403 can update the message status to the database 404. In this embodiment, the message queue management module 221 can execute the perception retry task 405 to query the failure records from the database 404. The perception retry task 405 can obtain the perception failure records and re-perceive the changed business data. The perception retry task 405 can send the change data to the perception module 222, and the perception module 222 can return the perception result to the perception retry task 405. If the perception result of the changed business data is still a failure, the perception retry task 405 can send a notification such as an email to the user.

[0073] In this embodiment, the message queue management module 221 can also execute the queue management service 406 to obtain the agent definition data from the metadata management module 225. The metadata management module 225 can initialize the message queues in the message queue cluster 402 according to the agent definition data and dynamically create the corresponding change data listener 403.

[0074] Figure 5 It is a schematic diagram of the operation of the perception module of the embodiment of the present invention. Refer to Figure 2 and Figure 5 , the following embodiments specifically illustrate Figure 2 the operation process of the perception module 222. In this embodiment, the perception module 222 can execute the perception trigger service 501 according to the settings of the message queue management module 221 and the scheduling management module 226. The perception trigger service 501 can be an idempotent check service to query the database 502 (such as mysql) to check whether the perception operation has been processed. The perception trigger service 501 can perform logging to save the perception records to the database 502. The perception trigger service 501 can obtain the perception policy and perform perception. The perception trigger service 501 can perform passive perception to execute the passive perception processor 505. The passive perception processor 505 can perform logging and feature matching. The perception module 221 can execute the feature matching service 506 to query the perturbed agent. The perception module 221 can query the agent metadata through the data graph module 223 to perform data feature matching. The feature matching service 506 can obtain the return result and return the perturbed agent to the passive perception processor 505. The passive perception processor 505 can activate the corresponding agent to drive the actuator module 224 to run the agent. In this regard, the perception module 221 can drive the actuator of the agent according to the changed business data.

[0075] In this embodiment, during the process of the perceptron module 224 executing the perception trigger service, active perception can also be performed to obtain the changed service data according to the perception policy. The perceptron module 224 can execute the active perception processor 503. The active perception processor 503 can perform logging and the perception policy. The perceptron module 224 can execute the intelligent perception execution service 504 to obtain data through intelligent perception and return the service data to the active perception processor 503. The active perception processor 503 can activate the corresponding agent to drive the actuator module 224 to run the agent. In this regard, the perceptron module 221 can drive the actuator of the agent according to the changed service data.

[0076] Figure 6 Schematic diagram of the operation of the actuator according to the embodiment of the present invention. Refer to Figure 2 and Figure 6 , the following embodiments specifically illustrate Figure 2 the operation process of the actuator module 224. In this embodiment, the actuator module 224 can execute the agent trigger service 601 and obtain agent metadata from the metadata management module 225 through the agent creator 602 to create an agent. The agent trigger service 601 can create and cache the agent through the agent creator 602. The agent creator 602 can read the agent definition from the metadata management module 225 to enable the metadata management module 225 to return the agent metadata to the agent creator 602. The agent creator 602 can perform agent caching, create agents, and refresh agents. The agent creator 602 can return the agent instance to the agent trigger service 601. The agent trigger service 601 can write the context to write the activated agent information and the delivered context data into the in-memory database 241.

[0077] The actuator module 224 can execute the agent to read the context business data and execute the corresponding business services. Specifically, the agent creator 602 can execute the agent. The actuator module 224 can execute the service 603 of the agent to perform operations such as data preparation, business decision-making, and business execution to execute the corresponding business tasks. The actuator module 224 can execute the agent interpreter 604 to analyze the task level and execute the task level. The actuator module 224 can read the previously stored agent context data from the memory database 241 to execute specific activities, such as EAI (Enterprise Application Integration) execution service, script execution service, rest (Representational State Transfer) execution service, human interaction execution service, and / or message execution service, and can write the business data after the application interface (API), http request, and script execution to the memory database 241.

[0078] Next, the agent interpreter 604 can determine whether to deliver data. If there is no need to deliver data, the agent interpreter 604 can determine whether it is the last level to end the current agent. If data delivery needs to be executed, the actuator module 224 can execute the data delivery service 605 to read the context business data from the memory database 241 to assemble the data structure for delivery. The actuator module 224 can assemble new business data according to the context business data and deliver the data to the message queue management module 221. Next, the agent interpreter 604 can determine whether it is the last level to end the current agent. If not, the agent interpreter 604 can analyze the next task level.

[0079] Figure 7 It is a schematic diagram of the operation of the metadata management of the embodiment of the present invention. Refer to Figure 2 and Figure 7 , the following embodiments specifically illustrate Figure 2 the operation process of the metadata management module 225. In this embodiment, the metadata management module 225 can perform agent publishing to save the agent definition to the database 702 (such as Mongo DB) through the metadata synchronization service 701. Both the sensor module 222 and the actuator module 224 can query the agent definition through the metadata query service 703. The metadata query service 703 can first query the cache through the memory database 704. When the cache does not exist, the metadata query service 703 can query the database 702. The metadata query service 703 can return the agent definition data to the sensor module 222 or the actuator module 224 for related operations such as agent creation or agent execution.

[0080] Figure 8 Schematic diagram of the data graph of the embodiment of the present invention. Refer to Figure 2 and Figure 8 , the data graph module 223 can store the (agent) data graph 800 as shown in Figure 8 . The data graph 800 can be composed of, for example, agents 801-803, model attributes 804-805, and business models 806. In this regard, the business model 806 can correspond to the model of the collection information, and its model attributes belong to the model attributes 804-805. The model attribute 804 corresponds to the processing type. The model attribute 805 corresponds to the claim status. The model attribute 804 can be concerned by the agent 801 with the processing type of manual processing, can be concerned by the agent 802 with the processing type of accounts receivable, and can be concerned by the agent 803 with the processing type of other accounts.

[0081] In other words, the perceptron module 222 can query the data graph module 223 according to the changing business data, and after performing data feature matching, determine the corresponding business model, and drive the actuator function of the corresponding agent according to the associated model attributes and the associated business model.

[0082] Figure 9 Schematic diagram of the operation of the business data processing of the embodiment of the present invention. Refer to Figure 1 , Figure 2 and Figure 9 , in one embodiment, the business data processing system 100 can be constructed with multiple agents 901_1-901_M, where M is a positive integer. The agents 901_1-901_M can run multiple perceptrons 922_1-922_M and actuators 924_1-924_M. The message queue management module 221 can establish multiple message queues 902_1-902_N to form a message queue cluster 902, where N is a positive integer. The business data processing system 100 can execute the agents 901_1-901_M according to the data graph module 223 and the metadata management module 225, so that the agents 901_1-901_M can respectively perceive different message queues.

[0083] In this regard, the sensors 922_1 to 922_M of the intelligent agents 901_1 to 901_M can be connected to an external enterprise service system 903, such as an Enterprise Resource Planning (ERP) system, or message queues 902_1 to 902_N, to obtain changing business data. The enterprise service system 903 can obtain changing business data through a business platform (or business middle platform) 904. The actuators 924_1 to 924_M of the intelligent agents 901_1 to 901_M can return new business data (i.e., generated after executing relevant business processing logics based on the changing business data) to the message queues 902_1 to 902_N or update it to the external enterprise service system 903.

[0084] Specifically, taking the intelligent agent 901_1 and the intelligent agent 901_M as examples, the sensor 922_1 of the intelligent agent 901_1 can sense, for example, the message queue 902_1 to obtain changing business data. The intelligent agent 901_1 can generate new changing business data based on the changing business data, and the actuator 924_1 of the intelligent agent 901_1 can update the new business data to the message queue 902_N, so that the sensor 922_M of the intelligent agent 901_M can sense the message queue 902_N to obtain the new business data.

[0085] For example, the intelligent agent 901_1 can be applied to, for example, bank cash flow sorting. The intelligent agent 901_2 can be applied to, for example, bank cash flow sub - sorting. The intelligent agent 901_3 can be applied to, for example, accounts receivable claim sorting. The intelligent agent 901_4 can be applied to, for example, other accounts claim. The intelligent agent 901_4 can be applied to, for example, unknown accounts claim. In this regard, the intelligent agent 901_1 can actively sense (detect) the bank cash flow database of the enterprise resource planning system to obtain bank cash flow data (i.e., changing business data). And, the intelligent agent 901_1 can generate unsorted bank cash flow data after executing relevant business processing based on the bank cash flow data, and deliver the unsorted bank cash flow data to the message queue 902_1. Then, the intelligent agent 901_2 can sense the message queue 902_1 to obtain the unsorted bank cash flow data (i.e., changing business data). And, the intelligent agent 901_2 can generate sorted unclaimed accounts receivable data, sorted unclaimed other accounts data, and sorted unclaimed unknown accounts data after executing relevant business processing based on the unsorted bank cash flow data, and deliver them to 902_2 to 902_4 respectively.

[0086] Next, the intelligent agent 901_3 can sense the message queue 902_2 to obtain the unclaimed accounts receivable data that has been shunted (i.e., the changing business data). The intelligent agent 901_3 can perform relevant business processing based on the unclaimed accounts receivable data that has been shunted to generate the claimed accounts receivable data, and return and deliver it to the message queue 902_2. The intelligent agent 901_4 can sense the message queue 902_3 to obtain the unclaimed other accounts data that has been shunted (i.e., the changing business data). The intelligent agent 901_4 can perform relevant business processing based on the unclaimed other accounts data that has been shunted to generate the claimed other accounts data, and return and deliver it to the message queue 902_3. The intelligent agent 901_5 can sense the message queue 902_4 to obtain the unclaimed unknown accounts data that has been shunted (i.e., the changing business data). The intelligent agent 901_5 can perform relevant business processing based on the unclaimed unknown accounts data that has been shunted to generate the claimed unknown accounts data, and return and deliver it to the message queue 902_4. Moreover, the intelligent agents 901_3 and 901_4 can also deliver the claim result data to the collection note and accounting voucher databases of the enterprise resource planning system. Therefore, the business data processing system 100 of this application example can realize automatically sensing the changes in bank cash flow data, and through the self-assembly of intelligent agents, it can be assembled into an application for claiming incoming payments.

[0087] In summary, the business data processing system and the business data processing method of the present invention can realize the automatic assembly of intelligent agents to achieve flexible business data processing to meet various data processing requirements and data changes, and can realize a high-efficiency data-driven function.

[0088] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A business data processing system, characterized in that, Including: A storage device that stores a sensor module, an actuator module, a message queue management module, a data graph module, a metadata management module, and a scheduling management module; And A processor coupled to the storage device and executing the sensor module, the actuator module, the message queue management module, the data graph module, the metadata management module, and the scheduling management module, wherein the message queue management module establishes a message queue, and the sensor module senses changing business data from the message queue according to a schedule set by the scheduling management module, wherein the sensor module performs data feature matching through the data graph module based on the changing business data to drive the actuator module, wherein the actuator module executes corresponding business services according to the metadata provided by the metadata management module to generate new business data, and the actuator module updates the new business data to the message queue management module.

2. The service data processing system according to claim 1, wherein The message queue management module executes a data reception service to receive the changing business data and stores the changing business data in the message queue, wherein the message queue management module executes a change data listener to monitor the message queue, the message queue management module saves messages to a database, and sends the changing business data to the sensor module.

3. The service data processing system according to claim 2, wherein The message queue management module executes a queue management service to obtain agent definition data from the metadata management module, and the metadata management module initializes the message queue according to the agent definition data and dynamically creates the change data listener.

4. The business data processing system according to claim 2, wherein The message queue management module executes a sensing retry task to query failed records from the database and re-sense the changing business data.

5. The service data processing system according to claim 1, characterized in that, The sensor module executes a sensing trigger service and performs passive sensing, wherein the sensor module queries agent metadata through the data graph module for the data feature matching and activates corresponding agents, and the sensor module drives the actuator of the agent according to the changing business data.

6. The service data processing system according to claim 1, wherein The sensor module executes a sensing trigger service and performs active sensing to obtain the changing business data according to a sensing strategy.

7. The service data processing system according to claim 1, wherein The actuator module executes an agent trigger service and obtains agent metadata from the metadata management module through an agent creator to create an agent, wherein the actuator module executes the agent to read context business data and execute the corresponding business services, wherein the actuator module assembles the new business data according to the context business data.

8. The service data processing system according to claim 1, wherein, The message queue management module establishes multiple message queues, and the business data processing system executes multiple agents according to the data graph module and the metadata management module so that the multiple agents respectively sense different message queues.

9. The service data processing system according to claim 8, wherein One of the multiple agents senses one of the multiple message queues to obtain the changed business data, and one of the multiple agents updates the new business data to another one of the message queues, so that another one of the multiple agents senses another one of the message queues to obtain the new business data.

10. The service data processing system according to claim 1, characterized in that, The sensor module is connected to an external enterprise resource planning system to obtain the changed business data.

11. A method for processing service data, characterized in that, Including: Establish a message queue through the message queue management module; Sense the changed business data from the message queue through the sensor module according to the schedule set by the schedule management module; Perform data feature matching through the data graph module according to the changed business data through the sensor module to drive the actuator module; Execute the corresponding business service according to the metadata provided by the metadata management module through the actuator module to generate new business data; And Update the new business data to the message queue management module through the actuator module.

12. The service data processing method according to claim 11, characterized in that, The steps of sensing the changed business data include: Execute a data reception service through the message queue management module to receive the changed business data and store the changed business data in the message queue; Execute a change data listener through the message queue management module to monitor the message queue; and Save the message to the database through the message queue management module and send the changed business data to the sensor module.

13. The service data processing method according to claim 12, wherein Also included: Execute a queue management service through the message queue management module to obtain agent definition data from the metadata management module; And Initialize the message queue according to the agent definition data through the metadata management module and dynamically create the change data listener.

14. The service data processing method according to claim 12, wherein The steps of sensing the changed business data further include: Execute a sensing retry task through the message queue management module to query the failure record from the database and re-sense the changed business data.

15. The service data processing method according to claim 11, characterized in that, The steps of driving the actuator module include: Execute a sensing trigger service through the sensor module and perform passive sensing; Query agent metadata through the data graph module through the sensor module to perform the data feature matching and activate the corresponding agent; and Drive the actuator of the agent according to the changed business data through the sensor module.

16. The service data processing method according to claim 11, wherein The steps of driving the actuator module include: Execute a sensing trigger service through the sensor module and perform active sensing to obtain the changed business data according to the sensing strategy.

17. The service data processing system according to claim 11, wherein The steps of generating the new business data include: Execute an agent trigger service through the actuator module and obtain agent metadata from the metadata management module through an agent creator to create an agent; Execute the agent through the actuator module to read the context business data and execute the corresponding business service; and Assemble the new business data according to the context business data through the actuator module.

18. The service data processing method according to claim 11, wherein Also included: Establish multiple message queues through the message queue management module; And Execute multiple agents through the service data processing system according to the data graph module and the metadata management module, so that the multiple agents respectively sense different message queues.

19. The service data processing method according to claim 18, wherein It further includes: Sense one of the multiple message queues through one of the multiple agents to obtain the changed service data; Update the new service data to the other one of the message queues through one of the multiple agents; and Sense the other one of the message queues through another one of the multiple agents to obtain the new service data.

20. The service data processing method according to claim 11, characterized in that It further includes: Connect to an external enterprise resource planning system through the sensor module to obtain the changed service data.

Images