Business data processing system and business data processing method

Abstract

A business data processing system and a business data processing method are provided. The business data processing system includes a storage device and a processor. The processor executes a sensor module, an executor module, a message queue management module, a data graph module, a metadata management module, and a 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 a schedule set by the schedule 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 executor module executes a corresponding business service according to metadata provided by the metadata management module to generate new business data, and the executor module updates the new business data to the message queue management module.

Description

Technical Field

[0001] This invention relates to a data processing technology, and more particularly to a business data processing system and a business data processing method. Prior Technology

[0002] In current enterprise automation processes, applications required by customers can be assembled through the orchestration of Process-Based Components (PBCs). However, the relatively rigid orchestration of PBCs involves complex relationships that require customers to spend time understanding. Furthermore, rigid PBC orchestrations are not easily adjusted, resulting in a lack of flexibility in orchestration changes. Summary of the Invention

[0003] This invention relates to a business data processing system and method, which can achieve highly efficient data-driven functions.

[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, actuator module, message queue management module, data graph module, metadata management module, and scheduling management module. The message queue management module establishes a message queue. The sensor module senses changed 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 based on the changed business data to drive the actuator module. The actuator module executes corresponding business services based on the metadata provided by the metadata management module to generate new business data. The actuator module updates the message queue management module with the new business data.

[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 a message queue management module; sensing changed business data from the message queue through a sensor module according to a schedule set by a scheduling management module; performing data feature matching through a data graph module based on the changed business data by the sensor module to drive an executor module; executing corresponding business services through the executor module based on metadata provided by a metadata management module to generate new business data; and updating the new business data to the message queue management module through the executor 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 changed business data through a predetermined schedule to run corresponding business services. 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-sensing operation to retrieve. In this way, the business data processing system and method of the present invention can achieve highly efficient data-driven functionality.

[0007] To make the above features and advantages of the present invention more apparent and understandable, specific embodiments are described below in conjunction with the accompanying drawings for detailed explanation. Simple Explanation of the Diagram

[0008] Figure 1 is a schematic diagram of a business data processing system according to an embodiment of the present invention. Figure 2 is a schematic diagram of the business data processing architecture of the present invention. Figure 3 is a flowchart of a business data processing method according to an embodiment of the present invention. Figure 4 is a schematic diagram of the operation of message queue management according to an embodiment of the present invention. Figure 5 is a schematic diagram of the operation of the sensor according to an embodiment of the present invention. Figure 6 is a schematic diagram of the operation of the actuator according to an embodiment of the present invention. Figure 7 is a schematic diagram of the operation of metadata management according to an embodiment of the present invention. Figure 8 is a schematic diagram of a data map of an embodiment of the present invention. Figure 9 is a schematic diagram of the operation of business data processing according to an embodiment of the present invention. Implementation

[0009] Reference will now be made in detail to exemplary embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same element symbols are used in the drawings and description to denote the same or similar parts.

[0010] 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 may be located in a cloud server, for example, so that system developers or users can connect and operate it via wired or wireless communication methods, but the present invention is not limited thereto. In one embodiment, the business data processing system 100 may also be implemented using multiple processors and storage devices, and the multiple processors and multiple storage devices may be separately located in different cloud devices and / or ground computing devices.

[0011] 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 thereof. The storage device 120 may include, for example, dynamic random access memory (DRAM), flash memory, or non-volatile random access memory (NVRAM).

[0012] Figure 2 is a schematic diagram of the business data processing architecture of an embodiment of the present invention. Referring to Figures 1 and 2, in this embodiment, the storage device 120 can store multiple modules to implement (run) the business data processing architecture 200 shown in Figure 2. The business data processing architecture 200 includes an intelligent application layer 210, an intelligent component 220, a framework layer 230, and a data layer 240. In this embodiment, the intelligent component 220 may include a message queue management module 221, a sensor module 222, a data graph module 223, an executor module 224, a metadata (or metadata) management module 225, and a scheduling management module 226.

[0013] In this embodiment, the message queue management module 221, the sensor module 222, the data graph module 223, the executor module 224, the metadata management module 225, and the scheduling management module 226 can 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 executor module 224, the metadata management module 225, and the scheduling management module 226 can respectively execute the relevant algorithms and programs for the service and functional elements described in the following embodiments.

[0014] In this embodiment, the message queue management module 221, sensor module 222, data graph module 223, executor module 224, metadata management module 225, and scheduling management module 226 can be used to implement one or more intelligent agents. The intelligent agent is used to autonomously perceive changes in business data and execute corresponding business logic based on the changed business data to complete the corresponding business tasks and generate new business data.

[0015] In this embodiment, the intelligent application layer 210 includes related intelligent application service programs and user interfaces, etc., to realize interactive functions with external user operations or applications.

[0016] In this embodiment, the framework layer 230 may include, for example, the Spring Boot development framework, the MyBatis data access framework, and / or the Dubbo Remote Procedure Call (RPC) framework, and the present invention is not limited thereto. The Spring Boot development framework is used to implement the relevant configuration and deployment of Spring applications. The MyBatis data access framework is used to implement interaction with relational databases. The Dubbo remote procedure call framework is used to build a microservice architecture.

[0017] In this embodiment, the data layer 240 may include, for example, a Relational Database Management System (RDBMS) (e.g., MySQL), a key-value store (DB) (e.g., Redis), a document database (e.g., MongoDB), a graph database (e.g., Neo4j), a message queue (e.g., RabbitMQ), and / or a distributed stream processing platform (e.g., Kafka), and the invention is not limited thereto. In this embodiment, the data layer 240 may also include external databases and internal storage databases (e.g., internal memory) as mentioned in the following embodiments.

[0018] Figure 3 is a flowchart of a business data processing method according to an embodiment of the present invention. Referring 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 establishes a message queue (MQ). In step S320, the sensor module 222 senses the changed business data from the message queue according to the schedule set by the scheduling management module 226. In step S330, the sensor module 222 performs data feature matching through the data graph module 223 based on the changed business data to drive the executor module 224. In step S340, the executor 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 executor module 224 updates the new business data to the message queue management module 221.

[0019] In this embodiment, the business data processing system 100 can be constructed with multiple intelligent agents, and the sensor module 222 and the actuator module 224 can construct the sensor function and actuator function of each of the multiple intelligent agents to realize the above-mentioned process. In this way, the multiple intelligent agents can automatically sense changes in business data, run corresponding business services, and generate different new business data. These different new business data can be updated to corresponding message queues for other intelligent agents to retrieve and execute the corresponding next business task or business data processing logic. In this way, the business data processing system of the present invention can achieve highly efficient data-driven functions through the multiple intelligent agents.

[0020] Figure 4 is a schematic diagram of the operation of message queue management according to an embodiment of the present invention. Referring to Figures 2 and 4, the following embodiment specifically describes the operation flow of the message queue management module 221 in Figure 2. In this embodiment, the executor module 224 can deliver changed data to the message queue management module 221. The executor module 224 can execute the data receiving service 401 and send a message (send changed data) to the message queue cluster 402 to store the changed business data in 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 listen to the corresponding message queue in the message queue cluster 402. The changed data listener 403 can receive changed data and write the changed data to a local message table to save the message to the database 404 (e.g., MySQL).

[0021] In this embodiment, the change data listener 403 can trigger sensing to send change data to the sensor module 222 and perform sensing. The sensor module 222 can return the sensing result (the sensing 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 sensing retry task 405 to query the failure record from the database 404. The sensing retry task 405 can obtain the sensing failure record and re-sensitize the changed business data. The sensing retry task 405 can send the change data to the sensor module 222, and the sensor module 222 can return the sensing result to the sensing retry task 405. If the sensing result of the changed business data is still a failure, the sensing retry task 405 can send an email or other notification to the user.

[0022] In this embodiment, the message queue management module 221 can also execute the queue management service 406 to obtain intelligent entity 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 intelligent entity definition data, and dynamically create corresponding change data listeners 403.

[0023] Figure 5 is a schematic diagram of the operation of the sensor according to an embodiment of the present invention. Referring to Figures 2 and 5, the following embodiment specifically describes the operation flow of the sensor module 222 in Figure 2. In this embodiment, the sensor 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 perform an idempotent check service to query the database 502 (e.g., MySQL) to check whether a perception operation has been processed. The perception trigger service 501 can perform log recording to save perception records to the database 502. The perception trigger service 501 can obtain a perception strategy and execute perception. The perception trigger service 501 can perform passive perception to execute the passive perception processor 505. The passive perception processor 505 can perform log recording and feature matching. The sensor module 222 can execute the feature matching service 506 to query the disturbed intelligent agent. The sensor module 222 can query the intelligent 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 disturbed intelligent agent to the passive sensing processor 505. The passive sensing processor 505 can activate the corresponding intelligent agent to drive the actuator module 224 to run the intelligent agent. In this regard, the sensor module 222 can drive the actuator of the intelligent agent according to the changing business data.

[0024] In this embodiment, during the execution of the perception trigger service, the sensor module 222 can also perform proactive perception to obtain the changed business data according to the perception strategy. The sensor module 222 can execute the proactive perception processor 503. The proactive perception processor 503 can perform log recording and implement the perception strategy. The sensor module 222 can execute the intelligent perception execution service 504 to intelligently perceive and obtain data, and return the business data to the proactive perception processor 503. The proactive perception processor 503 can activate the corresponding intelligent agent to drive the executor module 224 to run the intelligent agent. In this regard, the sensor module 222 can drive the executor of the intelligent agent according to the changed business data.

[0025] Figure 6 is a schematic diagram of the operation of the actuator according to an embodiment of the present invention. Referring to Figures 2 and 6, the following embodiment specifically describes the operation flow of the actuator module 224 in Figure 2. In this embodiment, the actuator module 224 can execute the intelligent entity triggering service 601 and obtain intelligent entity metadata from the metadata management module 225 through the intelligent entity creator 602 to create an intelligent entity. The intelligent entity triggering service 601 can create and cache intelligent entities through the intelligent entity creator 602. The intelligent entity creator 602 can read the intelligent entity definition from the metadata management module 225 so that the metadata management module 225 returns the intelligent entity metadata to the intelligent entity creator 602. The intelligent entity creator 602 can cache intelligent entities, create intelligent entities, and refresh intelligent entities. The intelligent entity creator 602 can return an intelligent entity instance to the intelligent entity triggering service 601. The intelligent entity triggering service 601 can write the context to write the activated intelligent entity information and the delivered context data into the internal storage database 241.

[0026] The executor module 224 can execute the intelligent agent to read contextual business data and execute the corresponding business services. Specifically, the intelligent agent creator 602 can execute the intelligent agent. The executor module 224 can execute the intelligent agent execution service 603 to perform operations such as data preparation, business decision-making, and business execution to execute corresponding business tasks. The executor module 224 can execute the intelligent agent interpreter 604 to analyze task checkpoints and execute task checkpoints. The executor module 224 can read previously stored intelligent agent context data from the internal storage database 241 to execute specific activities, such as EAI (Enterprise Application Integration) execution services, script execution services, REST (Representational State Transfer) execution services, human interaction execution services, and / or message execution services. It can also write application interface (API), HTTP requests, and business data after script execution to the internal storage database 241.

[0027] Next, the intelligent agent interpreter 604 determines whether data delivery is required. If no data delivery is needed, the intelligent agent interpreter 604 determines whether this is the final stage and terminates the current intelligent agent. If data delivery is required, the executor module 224 executes the data delivery service 605 to read contextual business data from the internal storage database 241 and assemble the data structure for delivery. The executor module 224 assembles new business data based on the contextual business data and delivers the data to the message queue management module 221. Next, the intelligent agent interpreter 604 determines whether this is the final stage and terminates the current intelligent agent. If not, the intelligent agent interpreter 604 analyzes the next task stage.

[0028] Figure 7 is a schematic diagram of the operation of metadata management according to an embodiment of the present invention. Referring to Figures 2 and 7, the following embodiment specifically describes the operation flow of the metadata management module 225 in Figure 2. In this embodiment, the metadata management module 225 can publish intelligent entities to save the intelligent entity definition to the database 702 (e.g., MongoDB) through the metadata synchronization service 701. Both the perceptron module 222 and the executor module 224 can query the intelligent entity definition through the metadata query service 703. The metadata query service 703 can first query the cache through the internal storage 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 intelligent entity definition data to the perceptron module 222 or the executor module 224 to perform intelligent entity creation or execution of related intelligent entity operations.

[0029] Figure 8 is a schematic diagram of the data graph of an embodiment of the present invention. Referring to Figures 2 and 8, the data graph module 223 can store the (intelligent agent) data graph 800 as shown in Figure 8. The data graph 800 can be composed, for example, of intelligent agents 801-803, model attributes 804-805, and a business model 806. In this regard, the business model 806 can correspond to the model of payment information, and its model attributes belong to model attributes 804-805. Model attribute 804 corresponds to the processing type. Model attribute 805 corresponds to the claim status. Model attribute 804 can be of interest to intelligent agent 801 whose processing type is manual processing, intelligent agent 802 whose processing type is accounts receivable, and intelligent agent 803 whose processing type is other accounts.

[0030] In other words, the sensor module 222 can query the data map module 223 based on the changing business data, and determine the corresponding business model after performing data feature matching. It can then drive the actuator function of the corresponding intelligent entity based on the associated model attributes and the associated business model.

[0031] Figure 9 is a schematic diagram of the operation of business data processing according to an embodiment of the present invention. Referring to Figures 1, 2, and 9, in one embodiment, the business data processing system 100 can construct multiple intelligent agents 901_1 to 901_M, where M is a positive integer. Intelligent agents 901_1 to 901_M can run multiple sensors 922_1 to 922_M and executors 924_1 to 924_M. The message queue management module 221 can establish multiple message queues 902_1 to 902_N to form a message queue cluster 902, where N is a positive integer. The business data processing system 100 can execute intelligent agents 901_1 to 901_M according to the data graph module 223 and the metadata management module 225, so that intelligent agents 901_1 to 901_M can perceive different message queues respectively.

[0032] In response, the sensors 922_1~922_M of the intelligent agents 901_1~901_M can connect to external enterprise service systems 903, such as Enterprise Resource Planning (ERP) systems, or message queues 902_1~902_N, to obtain changed business data. The enterprise service system 903 can obtain changed business data through a business platform (or business middleware) 904. The executors 924_1~924_M of the intelligent agents 901_1~901_M can return new business data (i.e., data generated after executing relevant business processing logic based on changed business data) to message queues 902_1~902_N or update the external enterprise service system 903.

[0033] Specifically, taking Intelligent Agent 901_1 and Intelligent Agent 901_M as examples, the sensor 922_1 of Intelligent Agent 901_1 can, for example, sense message queue 902_1 to obtain changed business data. Intelligent Agent 901_1 can generate new changed business data based on the changed business data, and the executor 924_1 of Intelligent Agent 901_1 can update the new business data to message queue 902_N, so that the sensor 922_M of Intelligent Agent 901_M can sense message queue 902_N to obtain the new business data.

[0034] For example, Agent 901_1 can be applied to bank cash flow processing. Agent 901_2 can be applied to bank cash flow diversion processing. Agent 901_3 can be applied to accounts receivable recognition processing. Agent 901_4 can be applied to other accounts recognition. Agent 901_4 can be applied to unknown accounts recognition. In this regard, Agent 901_1 can proactively sense (detect) the bank cash flow database of the Enterprise Resource Planning (ERP) system to obtain bank cash flow data (i.e., changed business data). Furthermore, Agent 901_1 can generate undiverted bank cash flow data after performing relevant business processing based on the bank cash flow data, and deliver the undiverted bank cash flow data to message queue 902_1. Then, Agent 901_2 can sense message queue 902_1 to obtain the undiverted bank cash flow data (i.e., changed business data). Furthermore, the intelligent system 901_2 can generate the diverted unclaimed accounts receivable information, the diverted unclaimed other accounts receivable information, and the diverted unclaimed unknown accounts receivable information after performing relevant business processing based on the undiverted bank cash flow information, and deliver them to 902_2~902_4 respectively.

[0035] Next, Agent 901_3 can sense message queue 902_2 to obtain the distributed, unclaimed accounts receivable data (i.e., changed business data). Agent 901_3 can perform relevant business processing based on the distributed, unclaimed accounts receivable data to generate claimed accounts receivable data and return it to message queue 902_2. Agent 901_4 can sense message queue 902_3 to obtain the distributed, unclaimed other accounts receivable data (i.e., changed business data). Agent 901_4 can perform relevant business processing based on the distributed, unclaimed other accounts receivable data to generate claimed other accounts receivable data and return it to message queue 902_3. Agent 901_5 can sense message queue 902_4 to obtain the distributed, unclaimed, unknown accounts receivable data (i.e., changed business data). Intelligent Agent 901_5 can generate claimed unknown account information after performing relevant business processing based on the diverted unclaimed unknown account information, and then return it to message queue 902_4. Furthermore, Intelligent Agents 901_3 and 901_4 can also deliver the claimed data to the receipt and accounting voucher database of the Enterprise Resource Planning system. Therefore, the business data processing system 100 in this application example can automatically detect changes in bank cash flow data and assemble it into a payment claim application through intelligent agents.

[0036] In summary, the business data processing system and method of the present invention can realize the automatic assembly of intelligent entities to achieve flexible business data processing, so as to adapt to various data processing requirements and data changes, and can realize highly efficient data-driven functions.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

[0038] 100: Business Data Processing System 110: Processor 120: Storage device 200: Business Data Processing Architecture 210: Intelligent Body Application Layer 220: Intelligent Body Component 221: Message Queue Management Module 222: Sensor Module 223: Data Map Module 224: Actuator Module 225: Metadata Management Module 226: Scheduling Management Module 230: Framework Layer 240: Data Layer 241: Internal Storage Database 401: Data Receiving Service 402: Message Queue Cluster 403: Change Data Listener 404, 502, 702: Database 405: Perceiving Retry Task 406: Queue Management Service 501: Perception Triggered Service 503: Active Sensing Processor 504: Intelligent Sensing Execution Service 505: Passive Sensing Processor 506: Feature Matching Service 601: Intelligent Body Trigger Service 602: Intelligent Entity Creator 603: Intelligent Body Execution Service 604: Intelligent Interpreter 605: Document Delivery Service 701: Metadata Synchronization Service 703: Metadata Query Service 704: Internal Storage Database 801~803, 902_1~902_N: Intelligent Body 804, 805: Model Attributes 806: Business Model 902: Message Queue Cluster 903: Enterprise Service System 904: Business Platform 922_1~922_M: Perceptron 924_1~924_M: Actuator S310~S350: Steps

Claims

1. A business data processing system, comprising: Storage device, including storage sensor module, actuator module, message queue management module, data map module, metadata management module, and scheduling management module; The system includes a processor coupled to the storage device, which 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, and the sensor module senses changed 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 based on the changed business data to drive the actuator module. The actuator module executes corresponding business services based on metadata provided by the metadata management module to generate new business data, and updates the message queue management module with the new business data. The actuator module executes an intelligent entity trigger service and obtains intelligent entity metadata from the metadata management module through an intelligent entity creator to create an intelligent entity. The actuator module executes the intelligent entity to read contextual business data and execute the corresponding business service. The actuator module assembles the new business data based on the contextual business data.

2. The business data processing system as described in claim 1, wherein the message queue management module performs a data receiving service to receive the changed business data and stores the changed business data in the message queue, wherein the message queue management module performs a change data listener to listen to the message queue, the message queue management module saves messages to a database, and sends the changed business data to the sensor module.

3. The business data processing system as described in claim 2, wherein the message queue management module performs a queue management service to obtain intelligent entity definition data from the metadata management module, the metadata management module initializes the message queue according to the intelligent entity definition data, and dynamically creates the change data listener.

4. The business data processing system as described in Request 2, wherein the message queue management module performs a retry-aware task to query failure records from the database and re-aware of changed business data.

5. The business data processing system as described in claim 1, wherein the sensor module executes a perception trigger service and performs passive perception, wherein the sensor module queries the intelligent entity metadata through the data graph module to perform data feature matching and activate the corresponding intelligent entity, wherein the sensor module drives the executor of the intelligent entity according to the changed business data.

6. The business data processing system as described in claim 1, wherein the sensor module performs a perception trigger service and actively perceives to obtain the changed business data according to a perception strategy.

7. The business data processing system as described in claim 1, wherein the message queue management module establishes multiple message queues, and the business data processing system executes multiple intelligent agents according to the data graph module and the metadata management module, so that the multiple intelligent agents respectively perceive different message queues.

8. The business data processing system as described in claim 7, wherein one of the plurality of intelligent agents senses one of the plurality of message queues to obtain the changed business data, and one of the plurality of intelligent agents updates the new business data to another of the message queues, so that another of the plurality of intelligent agents senses the other of the message queues to obtain the new business data.

9. The business data processing system as described in claim 1, wherein the sensor module is connected to an external enterprise resource planning system to obtain the changed business data.

10. A method for processing business data, comprising: Establish a message queue through the message queue management module; The sensor module senses changes in 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 based on the changed business data to drive the actuator module; the actuator module executes corresponding business services based on the metadata provided by the metadata management module to generate new business data. The process of generating the new business data includes updating the message queue management module with the new business data via the executor module, wherein the steps of generating the new business data include: executing a smart agent trigger service via the executor module and obtaining smart agent metadata from the metadata management module via a smart agent creator to create a smart agent; executing the smart agent via the executor module to read context business data and execute the corresponding business service; and assembling the new business data via the executor module based on the context business data.

11. The business data processing method as described in claim 10, wherein the step of sensing the changed business data includes: The message queue management module performs a data receiving service to receive the changed business data and store the changed business data in the message queue. The message queue management module executes a change data listener to monitor the message queue; and saves messages to a database through the message queue management module, and sends the changed business data to the sensor module.

12. The business data processing method as described in request item 11 further includes: The message queue management module executes a queue management service to obtain intelligent entity definition data from the metadata management module. The message queue is initialized based on the intelligent entity definition data through the metadata management module, and the change data listener is dynamically created.

13. The business data processing method as described in claim 11, wherein the step of sensing the changed business data further includes: The message queue management module executes a retry awareness task to query failure records from the database and re-aware of changed business data.

14. The business data processing method as described in claim 10, wherein the step of driving the actuator module includes: The sensor module executes a perception trigger service and performs passive perception. The sensor module queries the intelligent entity metadata through the data graph module to perform data feature matching and activate the corresponding intelligent entity; and the sensor module drives the actuator of the intelligent entity according to the changed business data.

15. The business data processing method as described in claim 10, wherein the step of driving the actuator module includes: The sensor module executes a perception trigger service and performs proactive perception to obtain the changed business data according to the perception strategy.

16. The business data processing method as described in request item 10 further includes: Multiple message queues are established through the message queue management module; And execute multiple intelligent agents according to the data graph module and the metadata management module, so that the multiple intelligent agents can perceive different message queues respectively.

17. The business data processing method as described in claim 16 further includes: The system senses one of the multiple message queues through one of the multiple intelligent agents to obtain the changed business information; updates the new business information to another of the message queues through one of the multiple intelligent agents; and senses another of the message queues through another of the multiple intelligent agents to obtain the new business information.

18. The business data processing method as described in request item 10 further includes: The sensor module is connected to an external enterprise resource planning system to obtain the changed business data.

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