Data driven system based on data driven model

Abstract

This invention provides a data-driven system and method based on a data-driven model. The data-driven system includes a storage device and a processor. The storage device stores a data-driven module, a knowledge graph module, and a data footprint module. The processor is coupled to the storage device. The processor executes the data-driven module to provide the knowledge graph module with the current data state and feature description information corresponding to the changing business data. The processor executes the knowledge graph module to parse the current data state and feature description information, and sends the corresponding data-driven model back to the data-driven module. The data-driven module executes the tasks in the data-driven model. The processor executes the data footprint module to record the task execution results generated by the data-driven module.

Description

Technical Field

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

[0002] Enterprises possess a wide variety of data and business activities, and most use systems to help employees complete daily tasks. However, currently, data is primarily entered into systems post-hoc, or even recorded offline. Systems are passively operated by users, or they assist in manual data processing rather than replacing it. In other words, traditional business operations still require a significant amount of manual initiation. Therefore, how to enable systems to automatically drive business execution is a key topic in this field.

[0003] Furthermore, due to the numerous different management methods and mechanisms within an enterprise, these often exist only within the minds of experienced personnel, leading to staff turnover and loss. To record and pass on employee knowledge and experience, a common approach in existing technology is to establish an enterprise knowledge base, but this approach presents high barriers to entry and is time-consuming. Furthermore, as scale grows, maintenance becomes increasingly difficult. A key issue with existing technology is that knowledge cannot be automatically parsed and utilized directly by the system, leaving the enterprise reliant on human operators. Therefore, how to effectively accumulate, pass on, and utilize business processing experience and knowledge remains a key topic in this field. Summary of the Invention

[0004] The present invention is directed to a data-driven system and a data-driven method based on a data-driven model, which can automatically drive corresponding business operations for changing business data.

[0005] According to an embodiment of the present invention, a data-driven system includes a storage device and a processor. The storage device stores a data-driven module, a knowledge graph module, and a data footprint module. The processor is coupled to the storage device. The processor executes the data-driven module to provide the knowledge graph module with the current data state and feature description information corresponding to the changing business data. The processor executes the knowledge graph module to analyze the current data state and feature description information and returns the corresponding data-driven model to the data-driven module. The data-driven module executes tasks in the data-driven model. The processor executes the data footprint module to record task execution results generated by the data-driven module executing the tasks.

[0006] According to an embodiment of the present invention, the data-driven method based on a data-driven model of the present invention includes the following steps: executing a data-driven module by a processor to provide the current data status and feature description information corresponding to the changed business data to a knowledge graph module; executing a knowledge graph module by a processor to parse the current data status and feature description information, and returning the corresponding data-driven model to the data-driven module; executing tasks in the data-driven model by the data-driven module; and executing a data footprint module by a processor to record task execution results generated by the data-driven module executing the tasks.

[0007] Based on the above, the data-driven system and data-driven method based on the data-driven model of the present invention can automatically initiate tasks for changing business data to obtain task execution results, and can effectively record the data processing history for use in future business tasks.

[0008] To make the above features and advantages of the present invention more clearly understood, embodiments are given below and described in detail with reference to the accompanying drawings. Simple diagram description

[0009] FIG. 1 is a circuit diagram of a data driving system according to an embodiment of the present invention. FIG. 2 is a module diagram of a data driving system according to an embodiment of the present invention. FIG3 is a flow chart of a data-driven method according to an embodiment of the present invention. FIG4 is a schematic diagram of the system architecture of a data driven system according to an embodiment of the present invention. FIG5 is a schematic diagram of data pulling operations according to an embodiment of the present invention. FIG6 is a schematic diagram of data footprint recording operations according to an embodiment of the present invention. Implementation Method

[0010] 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, like reference numerals are used in the drawings and the description to refer to the same or like parts.

[0011] FIG1 is a circuit diagram of a data-driven system according to an embodiment of the present invention. FIG2 is a module diagram of a data-driven system according to an embodiment of the present invention. Referring to FIG1 and FIG2 , the data-driven system 100 includes a processor 110 and a storage device 120. The processor 110 is coupled to the storage device 120. In this embodiment, the processor 110 may include, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose microprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a programmable logic device (PLD), other similar processing circuits, or a combination of these devices. The storage device 120 may include memory and / or a database. The storage device 120 may store a data-driven module 121, a knowledge map module 122, and a data footprint module 123. The storage device 120 may be, for example, a non-volatile memory (NVM). The storage device 120 may store programs, modules, systems, or algorithms for implementing various embodiments of the present invention, for access and execution by the processor 110 to implement the functions and operations described in various embodiments of the present invention. In this embodiment, the data-driven system 100 is implemented based on a data-driven model.

[0012] In this embodiment, the data-driven system 100 includes a data-driven core consisting of a data-driven module 121, a knowledge map module 122, and a data footprint module 123. The data-driven module 121, the knowledge map module 122, and the data footprint module 123 may be implemented using programming languages ​​such as JSON (JavaScript Object Notation), Extensible Markup Language (XML), or YAML, but the present invention is not limited thereto. In this embodiment, the data-driven system 100 may be configured or implemented on a cloud server or a ground server, or on an internal enterprise information system or an external information system. In one embodiment, the data-driven module 121, the knowledge map module 122, and the data footprint module 123 may be stored on different storage devices, and the processor 110 may include multiple processing units with data processing capabilities. The processor 110 and the storage device 120 may also be located on different servers or devices. In this embodiment, the data drive system 100 can be connected to the external service module and the external interactive system through a wired connection and / or a wireless connection, or can be connected to the external service module and the external interactive system through an intranet and / or the Internet.

[0013] The aforementioned external service modules may, for example, include an enterprise-built Customer Relationship Management (CRM) system, Enterprise Resource Planning (ERP) system, Manufacturing Execution System (MES), and / or Product Lifecycle Management (PLM) system, and may provide a variety of corresponding business service functions. These services may be implemented, for example, in the form of Platform as a Service (PAAS) or Software as a Service (SAAS), but the present invention is not limited thereto. The aforementioned external interactive systems may, for example, include a user interface, a human-machine interface, or an operating interface of an automated system installed on a terminal device, and may provide relevant task information, data, or requests to the user.

[0014] Figure 3 is a flow chart of a data-driven method according to an embodiment of the present invention. Referring to Figures 1 to 3, the data-driven system 100 may execute steps S310 to S340 to implement data-driven operations. In this embodiment, the data-driven system 100 may obtain changed business data, for example, through an external interactive system or an external service module. In one embodiment, the data-driven system 100 may also include a data change detection module (detection engine), which may automatically detect whether the external service module has changed business data. Alternatively, the data-driven system 100 may receive changed business data input by a user through an external interactive system, or use the data change detection module to detect whether the external interactive system has changed business data. In this embodiment, the data-driven module 121 may obtain the changed business data and implement data-driven operations based on the changed business data. In step S310, the processor 110 may execute the data-driven module 121 to provide the current data status and feature description information corresponding to the changed business data to the knowledge graph module 122. In step S320, processor 110 may execute knowledge graph module 122 to analyze the current data state and feature description information, and return the corresponding data-driven model to data-driven module 121. In step S330, data-driven module 121 may execute the tasks in the data-driven model. In step S340, processor 110 may execute data footprint module 123 to record the task execution results generated by data-driven module 121.

[0015] The data-driven system 100 and data-driven method of this embodiment can automatically initiate tasks for changing business data to drive associated business logic and execute associated business operations, and can also automatically record task execution results generated during the execution of associated tasks.

[0016] Figure 4 is a schematic diagram of the system architecture of a data-driven system according to an embodiment of the present invention. Referring to Figures 1 to 4 , this embodiment further illustrates the specific module architecture and implementation examples of the data-driven system 100. In this embodiment, the data-driven module 121 includes a task engine module 1211 and a data pull engine module 1212 , a knowledge graph module 122 , a management knowledge graph 1221 , and an action logic graph 1222 . The data-driven module 121, the knowledge graph module 122, and the data footprint module 123 can perform the following operations S401-S414. It should be noted that the operations S401-S414 described below are not necessarily executed in sequence, but rather primarily illustrate the relevant operations and functions that can be performed and implemented by each module.

[0017] In operation S401, the task engine module 1211 may receive changed business data. In operation S402, the task engine module 1211 may provide a corresponding event number based on the changed business data to the management knowledge graph 1221 of the knowledge graph module 122. In operation S403, the task engine module 1211 may retrieve project information based on the event number through the management knowledge graph 1221, so that the management knowledge graph 1221 returns the project information to the task engine module 1211. In step S404, the task engine module 1211 may obtain the current data state and feature description information corresponding to the changed business data based on the project information, and provide the current data state and feature description information to the management knowledge graph 1221. In operation S405, the task engine module 1211 may analyze the current data state and feature description information through the management knowledge graph 1221 to generate a data-driven model, so that the management knowledge graph 1221 returns the data-driven model to the task engine module 1211. In this regard, in one embodiment, the task engine module 1211 can execute the tasks in the data-driven model to obtain another business data corresponding to the task execution results.

[0018] In operation S406, the task engine module 1211 may also provide the action number and input parameters to the data pull engine module 1212. In operation S407, the data pull engine module 1212 may provide the action number and context data description based on the action number and input parameters to the action logic graph 1222 of the knowledge graph module 122. In operation S408, the data pull engine module 1212 may search the action logic graph 1222 based on the action number and parse the context data description to generate action logic. The action logic graph 1222 may then return the action logic to the data pull engine module 1212. In step S409, the data pull engine module 1212 may execute the action logic based on the action number and input parameters to generate target data. In step S410, the data pull engine module 1212 may return the target data to the task engine module 1211. In this way, the task engine module 1211 can also be used with the target data to execute the task in the data-driven model to generate a task execution result. In addition, the action logic described in this embodiment can also be implemented using a data-driven model.

[0019] In operation S411, the task engine module 1211 may provide the processing data generated during the execution of the task to the data footprint module 123. In operation S412, the data footprint module 123 may associate the processing data with the corresponding historical data. In other words, in operation S413, the task engine module 121 may also obtain previous processing data (optionally combined with target data) from the data footprint module 123 to execute the task in the data-driven model and generate a task execution result. In operation S414, the task engine module 1211 may determine whether the business data of the task execution result is in a completed state. If so, the task engine module 1211 may, for example, provide the task execution result to an external interactive interface to notify the user. If not, the management knowledge graph 1221 of the task engine module 1211 can analyze the next business data of the task execution result through the knowledge graph module 122 to obtain the next data state and next feature description information of the next business data, so that the management knowledge graph 1221 of the knowledge graph module 122 can return the corresponding next data-driven model to the task engine module 1211 of the data-driven module 121. In other words, the task engine module 1211 can re-execute steps S404-S413 to gradually advance task processing until the state and features of the business data reach a completed state. Therefore, the data-driven system 100 of this embodiment can automatically initiate tasks based on changing business data to drive related business logic and execute related business operations, thereby efficiently carrying out business activities within the enterprise. It also allows users (employees) to automatically obtain to-do tasks based on data changes. In this way, users (employees) can complete tasks according to system guidance and gradually achieve business goals, effectively reducing the workload of users (employees) and simultaneously passing on the enterprise's business processing experience.

[0020] In this embodiment, the management knowledge graph 1221 and the action logic graph 1222 can each be constructed based on a data-model-data (DMD) model to implement operations such as retrieval, parsing, and conversion. These models can include tasks, actions (action logic), or projects. Furthermore, the management knowledge graph 1221 and the action logic graph 1222 can each define the operational logic of each model based on enterprise management knowledge. For example, for specific documents (such as purchase requisitions and purchase orders), corresponding operational logic can be defined, allowing the system to automatically execute these operational logic without requiring manual operation by experienced personnel.

[0021] In this embodiment, the task executed by the task engine module 1211 is a complete series of activities for business processing, and it modifies the state or structure of the received business data before outputting it. The data-driven model refers to a logical data architecture of (input) data, tasks, and (output) data, used to encapsulate the knowledge of an enterprise's business processes. The elements of the data (input data and output data) include data types (e.g., purchase requisition data), data state characteristics, and data structures. The elements of a task include input data and output data, and include one or more activities. An activity is the smallest item that can be completed in a single operation and cannot be broken down. For example, input data can be a signed and confirmed purchase order. Output data can be a responded purchase order. A task can include multiple activities, such as obtaining supplier information, sending emails, and responding to delivery deadlines. Input data can be fed into a task to implement the relevant business operations, and upon completion of the relevant business operations within the task, the corresponding output data can be output. In other words, the data-driven module 121 can execute corresponding tasks based on changing business data, automatically executing business tasks through the aforementioned data-driven model and obtaining task execution results (i.e., other business data desired to be obtained during enterprise management or operations). Furthermore, in this embodiment, the action logic executed by the data pull engine module 1212 can be implemented using a data logic architecture consisting of data (metadata) - action logic (action) - data (metadata).

[0022] For example, the task engine module 1211 can, through the data change sensing module, detect project event information generated by a newly added project in an external enterprise resource planning (ERP) system. Based on the newly added project event, the task engine module 1211 can locate the corresponding project information in the management knowledge graph 1221 of the knowledge graph module 122. Then, based on the current data status (e.g., pending project initiation) and feature description information corresponding to the event, it can search for subsequent execution paths to obtain the project and subsequent task execution paths. Next, the task engine module 1211 can retrieve the user corresponding to the ERP project leader as the project responsible (this requires obtaining the project leader based on existing data) and construct the data using a specific structure. The data pull engine module 1212 can retrieve the action logic from the action logic graph 1222 based on the action identifier and existing data fields. Furthermore, after interpreting the execution logic, the data pull engine module 1212 can automatically invoke the employee-to-user interface to return the corresponding user information of the ERP employee through the action logic. Furthermore, the required data structure can be constructed through mapping and returned to the task engine module 1211. After the task engine module 1211 initiates a project, the data status can be changed (e.g., to an initiated project) to continue searching for the subsequent execution path corresponding to the current data status and executing a series of action data to change the status (e.g., to a pending maintenance project). To this end, when the task engine module 1211 performs a project information maintenance task, it notifies the data footprint module 123 of the project number, the corresponding task name, and the task status. Once the project information maintenance is complete, the task engine module 1211 changes the data status (e.g., to a maintained project) and updates the task status corresponding to the project number recorded in the data footprint module 123 to completed. Furthermore, the task engine module 1211 can issue the corresponding task based on the project information and record the corresponding project number and task number data in the data footprint module 123. Finally, when the project is closed, the task engine module 1211 can find the corresponding task name recorded in the data footprint module 123 according to the project number and the ongoing task number and perform a closing action to change the data status (for example, change it to a closed project).

[0023] Figure 5 is a schematic diagram of data pulling operations according to an embodiment of the present invention. Referring to Figures 1 to 3 and 5, this embodiment further illustrates a specific implementation of the data pulling engine module 1212. In this embodiment, the data pulling engine module 1212 may perform the following operations S501 to S505 to pull data. In operation S501, the data pulling engine module 1212 may obtain input parameters for the requested data description from the task engine module 1211. These input parameters may include, for example, metadata representing an action number and specific business data (i.e., context data). The data pulling engine module 1212 may analyze the data and action number to obtain corresponding metadata and input this metadata into the action logic graph 1222 to generate the data pulling execution path (action logic). In operation S502, the data pulling engine module 1212 may provide a data description and a context data description. In this embodiment, the context data description is a type of metadata that can be used to describe the type and structure of the context data. In this embodiment, context data is environmental data that can be called and obtained by the data pulling engine module 1212, including, for example, the data in the response information returned by the caller's previous business logic call service, or the currently logged-in user information or organization information, etc., which can all be used as context data.

[0024] In operation S503, the data pull engine module 1212 can search the data description using the action logic graph 1222 and parse the contextual data description to generate optional data pull execution paths (action logic). The action logic graph 1222 can return the optional data pull execution paths (action logic) to the data pull engine module 1212. In this embodiment, the action logic graph 1222 can be a business logic representation model based on a graph model. Specifically, business logic is abstracted into actions as a primary node type in the graph, and data can be added as another primary node type. The relationship between actions and data is described through edges connecting action nodes and data nodes. Furthermore, based on the characteristics of the graph structure, multiple nodes can be connected arbitrarily, thereby better describing the logical correspondence between actions and data. This allows the integration of existing information system functions, operations requiring manual completion, and even more types of business logic into the action logic graph 1222. Furthermore, various business logic applicable to different scenarios is also incorporated into the action logic graph 1222 and can be freely combined according to actual application scenarios.

[0025] In step S504, the data pull engine module 1212 executes the data pull execution path (action logic) to obtain the target data (i.e., the requested data). In step S505, the data pull engine module 1212 outputs (or returns) the target data to the task engine module 1211. The target data may also be metadata, for example. Thus, the data pull engine module 1212 can implement efficient data pull operations, effectively providing the associated metadata for the task engine module 1211 to use in executing tasks.

[0026] Figure 6 is a schematic diagram of data footprint recording operations according to an embodiment of the present invention. Referring to Figures 1 to 3 and Figure 6, this embodiment further illustrates a specific implementation of the data footprint module 123. In this embodiment, the data footprint module 123 may perform the following operations S601 to S604 to implement data footprint recording. In this embodiment, the knowledge graph module 122 may also include a metadata graph 1223. In operation S601, the metadata graph 1223 of the knowledge graph module 122 may provide the data footprint module 123 with business metadata definitions of tasks and data transformations. In operation S602, the data footprint module 123 may obtain the business data and data status (i.e., processing data) of the current task execution result from the task engine module 1211. In operation S603, the data footprint module 123 may establish a data state change history and transformation relationship based on the business metadata definition, business data, and data status (i.e., associate the processing data with the corresponding historical data). In operation S604, the data footprint module 123 can provide corresponding historical business data according to the requirements of the task engine module 1211. Therefore, the data footprint module 123 can effectively record the data processing history, effectively reducing the data processing volume of the task and speeding up the execution of the task.

[0027] In summary, the data-driven system and method of the present invention can automatically initiate tasks in response to changing business data to drive associated business logic and execute related business operations. Specifically, based on enterprise management knowledge, they can link data with associated tasks to automatically drive tasks directly related to the results of data changes. The data-driven system and method of the present invention can also effectively record the data processing history for subsequent use in related business tasks, thereby reducing data computation workload and increasing data computation speed. The data-driven system and method of the present invention can achieve efficient business processing results.

[0028] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements 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.

[0029] 100: Electronic devices 110: Processor 120: Storage device 121: Data-driven module 1211: Mission Engine Module 1212: Action Execution Engine Module 122:Knowledge Map Module 1221: Management Knowledge Graph 1222: Action Logic Diagram 1223: Metadata Graph 123:Data Footprint Module S310~S340, S401~S414, S501~S505, S601~S604: Steps

Claims

1. A data-driven system based on a data-driven model, comprising: A storage device for storing a data-driven module, a knowledge graph module, and a data footprint module; The system includes a processor coupled to the storage device. The processor executes the data-driven module to provide a current data state and feature description information corresponding to changed business data to the knowledge graph module. The processor executes the knowledge graph module to parse the current data state and the feature description information, and returns the corresponding data-driven model to the data-driven module. The data-driven module executes tasks within the data-driven model. The processor executes the data footprint module to record a task execution result generated by the data-driven module executing the tasks. The data-driven module includes a task engine module. The data-driven module provides a corresponding event number to the knowledge graph module based on the changed business data, and the knowledge graph module returns item information to the task engine module based on the event number, so that the task engine module obtains the current data state and feature description information corresponding to the changed business data based on the item information. The knowledge graph module includes a management knowledge graph, which retrieves project information based on the event number, and parses the current data state and feature description information to generate the data-driven model.

2. The data-driven system as described in claim 1, wherein the data-driven module further includes a data retrieval engine module, the task engine module provides an action number and an input parameter to the data retrieval engine module, the data retrieval engine module executes an action logic according to the action number and the input parameter to send back destination data to the task engine module, and the task engine module executes the task according to the destination data to generate the task execution result.

3. The data-driven system as described in claim 2, wherein the input parameters include metadata.

4. The data-driven system as described in claim 2, wherein the data retrieval engine module provides the action number and a contextual data description to the knowledge graph module according to the input parameters, so that the knowledge graph module returns the action logic to the data retrieval engine module according to the action number and the contextual data description.

5. The data-driven system as described in claim 4, wherein the knowledge graph module includes an action logic graph, the action logic graph being retrieved based on the action number and parsing the context data description to generate the action logic.

6. The data-driven system as described in claim 1, wherein the task engine module provides the processing data generated during the execution of the task to the data footprint module, and the data footprint module establishes an association between the processing data and the corresponding historical data.

7. The data-driven system as claimed in claim 1, wherein the task engine module obtains previous processing data through the data footprint module for performing the task.

8. The data-driven system as described in claim 1, wherein the task engine module parses the next business data of the task execution result through the knowledge graph module to obtain the next data status and next feature description information of the next business data, and sends the corresponding next data-driven model back to the data-driven module so that the data-driven module executes the next task in the next data-driven model.

9. A data-driven method based on a data-driven model, comprising: A processor executes a data-driven module to provide a current data state and feature description information corresponding to changing business data to a knowledge graph module; the processor executes the knowledge graph module to parse the current data state and feature description information, and returns the corresponding data-driven model to the data-driven module; the data-driven module executes tasks in the data-driven model; the processor executes a data footprint module to record a task execution result generated by the data-driven module executing the task; a task engine module of the data-driven module provides a corresponding event number to the knowledge graph module based on the changing business data; the knowledge graph module returns project information to the task engine module based on the event number; and the task engine module obtains the current data state and feature description information corresponding to the changing business data based on the project information. The knowledge graph module includes a management knowledge graph, which retrieves the project information based on the event number and parses the current data state and feature description information to generate the data-driven model.

10. The data-driven method as described in claim 9, wherein the steps of performing the task in the data-driven model include: The task engine module of the data-driven module provides an action number and an input parameter to a data retrieval engine module of the data-driven module. The data retrieval engine module executes action logic based on the action number and the input parameters to send target data back to the task engine module; and the task engine module executes the task based on the target data to generate the task execution result.

11. The data-driven method as described in claim 10, wherein the input parameters include metadata.

12. The data-driven method as described in claim 10, wherein the step of performing the task in the data-driven model further comprises: The data retrieval engine module provides the action number and a contextual data description to the knowledge graph module based on the input parameters. And the action logic is sent back to the data retrieval engine module through the knowledge graph module based on the action number and the context data description.

13. The data-driven method as described in claim 12, wherein the knowledge graph module includes an action logic graph, the action logic graph being retrieved based on the action number and parsing the context data description to generate the action logic.

14. The data-driven method as described in claim 9 further includes: The task engine module provides the processing data generated during the execution of the task to the data footprint module. And the data footprint module is used to establish a connection between the processing data and the corresponding historical data.

15. The data-driven method as described in claim 9, wherein the steps of performing the task in the data-driven model include: The task engine module obtains previous processing data from the data footprint module to perform the task.

16. The data-driven method as described in claim 9 further includes: The task engine module parses the next business data of the task execution result through the knowledge graph module to obtain the next data status and next feature description information of the next business data, and sends the corresponding next data-driven model back to the data-driven module; and executes the next task in the next data-driven model through the data-driven module.

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