Decision processing system and decision processing method

Abstract

The invention provides a decision processing system and a decision processing method. The decision processing system comprises an electronic device and a server. The electronic device obtains target input data and application input data. The server comprises a memory and a processor. The memory stores a plurality of models and task metadata. The processor executes a plurality of models. The design decision model computes task metadata according to the target input data to generate a plurality of heterogeneous data nodes, and performs an encapsulation operation on the selected plurality of heterogeneous data nodes to generate a decision item. The action decision model executes a decision item according to the application input data to generate a decision result to the electronic device, so as to establish a decision item conforming to the abnormal condition to improve the work efficiency.

Description

Technical Field

[0001] The present invention relates to a processing system, in particular to a decision processing system and a decision processing method using data driven programming. Background Art

[0002] Systems that use data-driven programming can create executable tasks through data. Since the external environment such as personnel and materials will affect the tasks being executed, the process of the task will be abnormal. In order to solve the aforementioned abnormalities, the current system uses semantic search, question and answer, and recommendation applications based on the enterprise knowledge base to obtain a variety of possible solutions. However, these solutions still require manual judgment to obtain useful solutions, which causes the abnormalities in the process to be unable to be effectively eliminated, thereby reducing the efficiency of executing tasks. Summary of the invention

[0003] The present invention is directed to a decision processing system, which applies data-driven programming and is capable of establishing decision projects that fit abnormal conditions to improve operating efficiency.

[0004] According to an embodiment of the present invention, the decision processing system of the present invention includes an electronic device and a server. The electronic device obtains target input data and application input data. The server is coupled to the electronic device. The server includes a memory and a processor. The memory stores multiple models and task metadata. The processor is coupled to the memory and executes multiple models. The multiple models include a design decision model and an action decision model. The design decision model calculates the task metadata according to the target input data to generate multiple heterogeneous data nodes, and performs encapsulation operations on the selected multiple heterogeneous data nodes to generate decision items. The action decision model executes the decision item according to the application input data to generate a decision result to the electronic device.

[0005] According to an embodiment of the present invention, the decision processing method of the present invention includes the following steps. Obtain target input data and application input data through an electronic device. Execute multiple models stored in the memory of the server through a processor of the server. The multiple models include a design decision model and an action decision model. The step of executing multiple models includes the following steps. Calculate the task metadata stored in the memory according to the target input data through the design decision model to generate multiple heterogeneous data nodes. Perform encapsulation operations on the selected multiple heterogeneous data nodes through the design decision model to generate decision items. Execute the decision items according to the application input data through the action decision model to generate decision results to the electronic device.

[0006] Based on the above, the decision processing system and decision processing method of the present invention can obtain various structured data related to abnormal conditions by generating heterogeneous data nodes according to target input data and task metadata. By encapsulating these selected structured data (i.e., heterogeneous data nodes), the decision processing system can obtain executable decision items so that corresponding anomalies can be eliminated based on the decision items in the execution of any application task. In this way, the knowledge management system can transfer knowledge and experience and improve the efficiency of executing tasks.

[0007] In order to make the above features and advantages of the present invention more clearly understood, embodiments are given below with reference to the accompanying drawings for detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a circuit block diagram of a decision processing system according to an embodiment of the present invention;

[0009] Figure 2 is a flow chart of a decision processing method according to an embodiment of the present invention;

[0010] Figure 3A is an action diagram of a decision processing system according to an embodiment of the present invention;

[0011] Figure 3B The present invention Figure 3A A schematic diagram of multiple heterogeneous data nodes of an embodiment;

[0012] Figure 4 is an action diagram of a decision processing system according to an embodiment of the present invention;

[0013] Figure 5 is an action schematic diagram of a decision processing system according to another embodiment of the present invention;

[0014] Figure 6 It is a schematic diagram of decision items according to an embodiment of the present invention.

[0015] Description of Reference Numerals

[0016] 10, 50: Decision processing system;

[0017] 100, 500: server;

[0018] 110, 510: Design decision model;

[0019] 11: Memory;

[0020] 12: Processor;

[0021] 111, 511: data model;

[0022] 1111: data construction unit;

[0023] 1112: data processing unit;

[0024] 112, 412: Check item model;

[0025] 1121: Expressions and formulas;

[0026] 113, 513: Execution model;

[0027] 1131: Action library;

[0028] 120, 520: Action decision model;

[0029] 120_1: control model;

[0030] 120_2: Decision model;

[0031] 121: abnormality checking module;

[0032] 1211: script execution unit;

[0033] 1212: rule execution unit;

[0034] 1213: Algorithm execution unit;

[0035] 122: exception handling module;

[0036] 1221: Scheme selection unit;

[0037] 1222: Program execution unit;

[0038] 123: Decision suggestion module;

[0039] 1231: rule execution unit;

[0040] 1232: Algorithm execution unit;

[0041] 200: electronic devices;

[0042] 210: user;

[0043] 410: Delivery Designer;

[0044] 420: Mechanism Designer (KG);

[0045] 430: mechanism tool (datamap);

[0046] 514: target model;

[0047] D1: target input data;

[0048] D2: application input data;

[0049] D3: Decision-making items;

[0050] D31, D32_1~D32_2: Project tasks;

[0051] D33: Link;

[0052] D4: Decision results;

[0053] Dt: task metadata;

[0054] N1~N3: heterogeneous data nodes;

[0055] N1'~N3'_2, N61~N63: data nodes;

[0056] N621: child data node;

[0057] S210-S240, S41-S420: steps;

[0058] S511~S526: module. DETAILED DESCRIPTION

[0059] 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.

[0060] Figure 1 FIG. 1 is a circuit block diagram of a decision processing system according to an embodiment of the present invention. Figure 1 , the decision processing system 10 applies data-driven programming and knowledge graph. The decision processing system 10 automatically proposes and executes a decision item D3 that can resolve the abnormal process. In this embodiment, the decision processing system 10 may include a server 100 and an electronic device 200. The electronic device 200 is coupled to the server 100.

[0061] In this embodiment, the user can operate the electronic device 200 to call the server 100 through the application programming interface (API). The user can also operate the electronic device 200 to call the enterprise system through the API, and then execute various business services through the enterprise system. The electronic device 200 can be, for example, a mobile phone, a tablet computer, a laptop computer, and a desktop computer. The enterprise system can be, for example, an enterprise resource planning (ERP) system (hereinafter, the ERP system is used as an example).

[0062] In this embodiment, the server 100 may include a memory 11 and a processor 12. The memory 11 stores a plurality of models 110-120 and task metadata Dt. These models may include a design decision model 110 and an action decision model 120. The task metadata Dt may be, for example, reference data associated with various processes used by the ERP system to provide business services, and is structured and defined reference data.

[0063] In this embodiment, the memory 11 may also store computing software and the like for implementing the algorithms, programs and data related to the functions of the present invention regarding data-driven programming, software design, software packaging, various calculations, and software execution. The memory 11 may be, for example, a dynamic random access memory (DRAM), a flash memory or a non-volatile random access memory (NVRAM), but the present invention is not limited thereto.

[0064] In this embodiment, the processor 12 is coupled to the memory 11. The processor 12 accesses the memory 11 and can execute the data in the memory 11 and the models 110-120, as well as the data from the electronic device 200 (e.g., the target input data D1 and the application input data D2). The processor 12 can be, for example, a signal converter, a field programmable gate array (FPGA), a central processing unit (CPU), or other programmable general-purpose or special-purpose microprocessor (Microprocessor), a digital signal processor (DSP), a programmable controller, an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or other similar devices or a combination of these devices, which can load and execute computer program related firmware or software to implement data-driven programming, software design, software packaging, various calculations, and software execution functions.

[0065] In this embodiment, the processor 12 executes the design decision model 110 during software design so that the design decision model 110 generates and provides decision items D3 that can solve the anomaly according to the target input data D1. The design decision model 110 may include a data model 111, a check item model 112, and an execution model 113.

[0066] In this embodiment, the processor 12 executes the action decision model 120 during the actual application, so that the action decision model 120 executes the executable project (for example, the decision project D3) according to the application input data D2 and provides the result after the project execution (for example, the decision result D4). The action decision model 120 may include a control model 120_1 and a decision model 120_2. The control model 120_1 may include an abnormality checking module 121 and an abnormality handling module 122. The decision model 120_2 may include a decision suggestion module 123.

[0067] Figure 2 is a flowchart of a decision processing method according to an embodiment of the present invention. Figure 1 as well as Figure 2 , the decision processing system 10 may execute steps S210 to S250. The order of these steps S210 to S250 is only an example and is not limited thereto. In this embodiment, steps S210 to S250 may be applied to the following exemplary situations.

[0068] In step S210, the electronic device 200 obtains target input data D1 and application input data D2. In detail, during the software design period, the electronic device 200 accesses the ERP system to obtain the target input data D1. The target input data D1 may be, for example, an item that the user (or enterprise) wants to solve and / or control. During the actual application period, the electronic device 200 accesses the ERP system to obtain the application input data D2. The application input data D2 may be, for example, routine data generated when an abnormality occurs in the process of the ERP system during execution, such as an abnormal purchase order or purchase requisition.

[0069] During software design, the processor 12 executes the design decision model 110 stored in the memory 11. The processor 12 operates in coordination with the electronic device 200 so that the server 100 and the user can interactively design a decision item D3 that can resolve the abnormal process.

[0070] Specifically, in step S220, the processor 12 executes the design decision model 110 so that the design decision model 110 calculates the task metadata Dt according to the target input data D1 to generate a plurality of heterogeneous data nodes (e.g. Figure 3A or Figure 3B The heterogeneous data nodes may be, for example, structured and processed knowledge graph data.

[0071] In detail, the processor 12 executes the data model 111 and the check item model 112 in the design decision model 110. The data model 111 performs data structuring on the task metadata Dt according to the target input data D1 through the data construction unit 1111 to establish the knowledge graph data. The data model 111 performs data processing on the aforementioned knowledge graph data through the data processing unit 1112 to generate a plurality of activity data nodes. The data processing may include unconditional calculations such as mathematical calculations, sampling, or a combination of the aforementioned.

[0072] Continuing with the above description, the check item model 112 accesses the expression and formula 1121 in the memory 11. The check item model 112 calculates multiple active data nodes with reference to the expression and formula 1121 to check whether these data nodes meet the process specifications of the corresponding business service. When the check passes, the check item model 112 outputs the multiple active data nodes as multiple heterogeneous data nodes.

[0073] In step S230, the processor 12 executes the design decision model 110, so that the design decision model 110 performs a packaging operation on the selected plurality of heterogeneous data nodes to generate a decision project D3. The decision project D3 may be, for example, an executable program associated with the target input data D1 to indicate the process of the matter that the user (or enterprise) wants to solve and / or control. The decision project D3 complies with data-driven programming and has been compiled.

[0074] In detail, the user operates the electronic device 200 to select a portion of the plurality of heterogeneous data nodes from the plurality of heterogeneous data nodes in step S220 through the server 100. The selected heterogeneous data nodes are used as data structures for solving abnormal processes. The processor 12 executes the execution model 113 in the design decision model 110. The execution model 113 translates the selected heterogeneous data nodes into executable data structures (i.e., decision items D3).

[0075] During actual application, the processor 12 executes the action decision model 120 stored in the memory 11. The processor 12 and the electronic device 200 operate in coordination so that the server 100 executes a corresponding exception handling program (ie, decision item D3) for the abnormal process.

[0076] Specifically, in step S240, the processor 12 executes the action decision model 120 so that the action decision model 120 executes the decision item D3 according to the application input data D2 to generate a decision result D4 to the electronic device 200. The decision result D4 may be, for example, the result of executing the standard process (for example, decision item D3) of the established matter for an abnormal condition (for example, the application input data D2). For example, it is assumed that the decision item D3 includes sending an email to the supervisor through the processor when an abnormal event occurs. At this time, the execution control and decision model 120 sends an email as the decision result D4 to the electronic device 200 operated by the supervisor.

[0077] In detail, the processor 12 executes the abnormality checking module 121, the abnormality handling module 122, and the decision suggestion module 123 in the action decision model 120. The abnormality checking module 121 checks whether the event indicated by the application input data D2 is abnormal through the script execution unit 1211, the rule execution unit 1212, and the algorithm execution unit 1213, and triggers the abnormal process. When the aforementioned inspection result is normal, the abnormality handling module 122 and the decision suggestion module 123 are not triggered. On the contrary, when the aforementioned inspection result is abnormal, the abnormality handling module 122 and the decision suggestion module 123 are triggered.

[0078] Continuing with the above description, the decision suggestion module 123 accesses one or more items that can solve the abnormal process through the rule execution unit 1231 and the algorithm execution unit 1232. The abnormal handling module 122 selects the decision item D3 according to the application input data D2 through the solution selection unit 1221. The abnormal handling module 122 executes the selected decision item D3 through the solution execution unit 1222, and generates a result after execution (for example, a feedback result D4) to feed back to the electronic device 200.

[0079] It is worth mentioning here that by designing the decision model 110 to establish structured multiple heterogeneous data nodes according to the target input data D1 and the task metadata Dt, the decision processing system 10 can precipitate and encapsulate the abnormal process with the knowledge graph, thereby realizing the inheritance of knowledge and experience. In addition, by designing the decision model 110 and interactively designing executable decision items D3 with the user according to multiple heterogeneous data nodes, the decision processing system 10 can eliminate the corresponding anomalies based on the decision items when an abnormality occurs in the process of the ERP system. In this way, the user does not need to fully understand the knowledge connotation, and the decision processing system 10 can transmit knowledge and experience, and can also ensure that the ERP system executes business processes according to management experience and methods, thereby improving work efficiency.

[0080] Figure 3A FIG. 1 is a schematic diagram of the operation of a decision processing system according to an embodiment of the present invention. Figure 1 as well as Figure 3A , the decision processing system 10 operates during the software design period, and establishes a plurality of heterogeneous data nodes N1-N3 to illustrate the implementation details of step S220.

[0081] In this embodiment, the processor 12 executes the data model 111 so that the data model 111 establishes a plurality of data nodes according to the target input data D1. These data nodes indicate various entities of the abnormal process to implement various business logics. These data nodes may include a data node N1' indicated as "when the task is opened", a data node N2' indicated as "option", a data node N3'_1 indicated as "according to the empirical rule", and a data node N3'_2 indicated as "according to the algorithm evaluation".

[0082] In this embodiment, the processor 12 executes the data model 111, so that the data model 111 structures the task metadata Dt into multiple heterogeneous data nodes according to the data nodes N1'-N3'_2. These heterogeneous data nodes may include at least one associated opportunity node N1, at least one solution node N2, and at least one condition node N3.

[0083] In detail, the solution node N2 may be, for example, a data node indicated as a "decision solution" to indicate a certain solution and provide it to the user for selection. The timing node N1 may be, for example, a data node indicated as "timing" to indicate when to select the aforementioned solution (i.e., N2). The condition node N3 may be, for example, a data node indicated as "basis" to indicate intervention in the solution according to a certain rule. In other words, there is an option relationship between the timing node N1 and the solution node N2, so that the corresponding abnormal matters can be handled by a certain solution in a certain scenario. In addition, there is an applicable relationship between the solution node N2 and the condition node N3, so that the process of the solution can be standardized by a certain rule.

[0084] For example, Figure 3B The present invention Figure 3A A schematic diagram of multiple heterogeneous data nodes of an embodiment. Figure 3B In the abnormal scenario of material shortage, the data model 111 provides multiple associated heterogeneous data nodes as possible processing logic. Figure 3B In the embodiment of the present invention, the plurality of heterogeneous data nodes may include a timing node N1 indicating "abnormal out of stock", a condition node N3 indicating "alternative supplier available", and a solution node N2 indicating "supplier replacement". That is, in the aforementioned scenario, the processing logic may be, for example, to replace the supplier to eliminate the abnormality of material shortage under the condition that an alternative supplier is available.

[0085] Figure 4FIG. 1 is a schematic diagram of the operation of a decision processing system according to an embodiment of the present invention. Figure 1 as well as Figure 4 The decision processing system 10 operates during software design and interactively establishes a decision project D3 with the user 210 to illustrate the implementation details of steps S220 to S230.

[0086] In this embodiment, the memory 11 of the server 100 may also store a delivery designer 410, a mechanism designer (denoted as "KG") 420, and a mechanism tool (denoted as "datamap") 430. The processor 12 may execute these tools 410-430 to implement various functions related to software design. In this embodiment, the delivery designer 410, the mechanism designer 420, and the mechanism tool 430 may be configured as a design decision model 110.

[0087] In this embodiment, the user 210 operates the electronic device 200 to interactively cooperate with the server 100. The decision processing system 10 can execute steps S41 to S420 by executing the processor 12. The order of these steps S41 to S420 is only an example and is not limited to this. In this embodiment, the user 210 operates the electronic device 200 to log in to the account and corresponding operation authority in the server.

[0088] In step S41, the user 210 selects a newly added decision capability to the delivery designer 410 through the electronic device 200. The newly added decision capability indicates a newly established software design to design a decision item D3 that can resolve an abnormal process for the target input data D1.

[0089] In step S42, the delivery designer 410 accesses the mechanism tool 420 to query the task list related to the target input data D1. The task list may, for example, be a list indicating various tasks. In step S43, the mechanism tool 420 returns a task list including manual type tasks to the delivery designer 410. Manual type tasks may, for example, indicate various tasks designed interactively by the user 210 and the server 100. In step S44, the delivery designer 410 returns the task list accessed in step S43 to the user 210 so that the electronic device 200 displays this task list. That is, the delivery designer 410 is able to set up a mechanism for abnormal processes and coach the user 210 on the design operation of the mechanism through the task list.

[0090] In this embodiment, the design decision model 110 (ie, the plurality of components 410-430) selects a plurality of solution nodes (eg, Figure 3B solution node N2), timing node (e.g. Figure 3Btiming node N1), and multiple conditional nodes (e.g. Figure 3B The conditional node N3) is used as a plurality of selected heterogeneous data nodes, and these heterogeneous data nodes are encapsulated into a decision item D3.

[0091] In detail, in step S45, the user 210 selects a task requiring a decision from the displayed task list through the electronic device 200 to design the required task in the decision project D3. In step S46, the delivery designer 410 receives the selection instruction from the electronic device 200 to access the identifier of the selected task, and transmits the identifier to the mechanism designer 420. The identifier may be, for example, a unique identifier of the selected task, and may be indicated as taskId.

[0092] In step S47, the mechanism designer 420 queries the data displayed as valid in the browsing component (e.g., pageview) identifier according to the task identifier taskId to obtain the task metadata Dt related to the target designed task. In step S48, the mechanism designer 420 returns the task metadata Dt that matches the identifier taskId to the delivery designer 410. In step S49, the delivery designer 410 returns the task metadata Dt accessed in step S48 to the user 210 so that the electronic device 200 displays multiple fields in this task metadata Dt. These fields indicate various reference attributes in the task metadata Dt.

[0093] In step S410, the user 210 selects a scenario that matches the target input data D1 from the displayed fields through the electronic device 200, so that the delivery designer 410 selects this scenario accordingly. The scenario may be, for example, a field indicating how to trigger a task in the decision item D3. In this embodiment, the scenario may be, for example, Figure 3B The timing node N1 is shown.

[0094] In step S411, the delivery designer 410 automatically selects multiple conditions associated with the scenario from multiple fields in the task metadata Dt according to the selected scenario. These conditions may be, for example, rules or intervention conditions for enabling or disabling the task. In this embodiment, the conditions may be, for example, Figure 3B Conditional node N3 is shown.

[0095] In step S412, the mechanism designer 420 queries the Encapsulating Security Payload (ESP) metadata in the data source according to the condition selected in step S411 to select and obtain multiple solutions related to the condition. These solutions may be, for example, process components of the task. In this embodiment, the solution may be, for example, Figure 3B The scheme node N2 is shown.

[0096] In step S413, the mechanism designer 420 returns the selected solution to the delivery designer 410. In step S414, the delivery designer 410 returns the solution accessed in step S413 to the user 210, so that the electronic device 200 displays the selected solution. In step S415, the user 210 confirms the solution through the electronic device 200 and instructs the server 100 to execute the validation mechanism for the solution.

[0097] In step S416, the delivery designer 410 compiles the solution according to the instruction from the electronic device 200 to generate executable tasks (e.g. Figure 6 In step S417, the mechanism designer 420 performs secondary compilation on the executable task to generate an executable decision item D3 (e.g. Figure 6 Decision item D3 shown).

[0098] In step S418, the mechanism tool 430 generates the corresponding mechanism logic (mechanismLogic) according to the decision item D3 generated in step S417 to complete the packaging operation of the decision item D3. In step S419, the mechanism designer 420 saves the parts of the mechanism logic. In step S420, the delivery designer 410 returns the execution end information to the user 210 so that the electronic device 200 displays the effective result information.

[0099] Figure 5 FIG. 1 is a schematic diagram of the operation of a decision processing system according to another embodiment of the present invention. Figure 5 , the decision processing system 50 may include a server 500 and an electronic device. The server 500 may include a processor and a memory, wherein the memory may store a design decision model 510 and an action decision model 520. Figure 5 In an embodiment, the design decision model 510 may include a target model 514, a data model 511, a check item model 512, and an execution model 513. The action decision model 520 may access and execute the solution task D31. The data model 511, the check item model 512, the execution model 513, and the action decision model 520 may refer to the relevant description of the decision processing system 10 and be deduced by analogy, so they are not repeated here.

[0100] In this embodiment, the server 500 accesses events (such as Figure 1 The execution design decision model 510 generates an executable solution task D31 (for example, Figure 4In step S45, the action decision model 520 executes the solution task D31 according to the aforementioned event. In addition, the execution design decision model 510 generates an executable decision item (e.g., Figure 4 The decision item D3 designed in steps S46 to S20).

[0101] In this embodiment, the target model 514 executes module S511 so that the target model 514 controls the mount point of the active target. The active target may be, for example, a data source provided by an electronic device to provide raw data. Figure 1 The target input data D1 and / or application input data D2. The mounting point controlled by the target model 514 may be a task (eg, a solution task D31) or a project (eg, a decision project D3), and may be a task or project based on data-driven programming.

[0102] In this embodiment, the data model 511 executes module S512 so that the data model 511 can generate a task based on the target input data D1 and the task metadata (for example, Figure 1 The data model 511 generates multiple active data nodes according to the configured multiple parameters of the target input data D1 and the task metadata Dt. In detail, the data model 511 configures the parameters of the target input data D1 and the parameters of the accessed task metadata Dt. The data model 511 configures these parameters to establish multiple active data nodes. In other words, the data model 511 can deconstruct the various parameters in the target input data D1 and the task metadata Dt and the coupling relationship between these parameters, and represent the aforementioned deconstruction results with multiple data nodes.

[0103] In this embodiment, the data model 511 executes module S513, so that the data model 511 calculates multiple active data nodes in module S512 according to the built-in data processing tool (for example, a defined formula) to generate a calculation result. The calculation result can be represented by a corresponding multiple active data nodes. In other words, the data model 511 processes the data according to the formula and represents the aforementioned calculation result by multiple data nodes.

[0104] In the present embodiment, the inspection item model 512 executes module S514 so that the data model 511 calculates multiple active data nodes with reference to multiple multi-strategy options to generate multiple link data nodes. In detail, the inspection item model 512 calculates the calculation results (i.e., multiple active data nodes) from the data model 511 with reference to the multiple multi-strategy options in the task metadata Dt to establish multiple link data nodes. The inspection item model 512 calculates these link data nodes to generate inspection results representing normal or abnormal conditions. In the present embodiment, multiple multi-strategy options may, for example, be calculation formulas with different conditional expressions. These multi-strategy options may correspond to one or more inspection results. Multiple link data nodes may, for example, be selection logic associated with a gateway routing node.

[0105] It should be noted that the check item model 512 can accumulate a batch of valuable management knowledge (e.g., multiple link data nodes) through the knowledge graph. The check item model 512 can correct the process associated with the target input data D1 through the processed control data (e.g., multiple multi-strategy options) and the aforementioned management knowledge, and express it as a check result.

[0106] In this embodiment, the execution model 513 executes module S515 so that the execution model 513 executes module S515 according to the target expected data (for example, Figure 1 The action library 1131 of the module S512 establishes multiple operation execution nodes in the form of API calls, and encapsulates multiple activity data nodes in the module S512, multiple link data nodes in the module S514, and these operation execution nodes into multiple solution tasks ( Figure 5 Take a single solution task D31 as an example).

[0107] In this embodiment, the execution of the solution task D31 starts from module S521 and ends from module S526. The action decision model 520 executes module S522 corresponding to module S512 to configure the parameters of the target input data D1 and the task metadata Dt, and lists the parameters into multiple active data nodes. The action decision model 520 executes module S523 corresponding to module S513 to calculate multiple active data nodes with reference to the formula, and the calculation results are built into a script (i.e., active data nodes). The action decision model 520 executes modules S524_1~S524_2 corresponding to module S514 to list multiple multi-strategy options, and check multiple active data nodes based on these multi-strategy options to generate multiple link data nodes. The action decision model 520 executes modules S525_1~S525_2 corresponding to module S515 to execute or re-initiate the solution task D31 through API calls.

[0108] Figure 6FIG. 1 is a schematic diagram of a decision-making project according to an embodiment of the present invention. Figure 5 as well as Figure 6 During software design, the decision processing system 50 and the user interactively design decision items D3 that can resolve abnormal processes. Figure 6 In the embodiment of the present invention, the decision project D3 may include a plurality of solution tasks D31, D31_1 to D32_2, and a plurality of links D33. The decision processing system 50 may Figure 5 The multiple modules S511~S515 in the design correspond to the solution tasks D31, D32_1~D32_2.

[0109] In this embodiment, the decision processing system 50 executes the design decision model 510 so that the design decision model 510 calls the target expected data (for example, Figure 1 The action library 1131 of the module S515 is used to obtain multiple software components that can solve the exception. These software components may include a sourceWidget component, a strategyWidget component, a chooseWidget component, and a planWidget component. These software components can be used as multiple operation execution nodes in the module S515.

[0110] In detail, the design decision model 510 executes the sourceWidget component to generate a task model by default (for example, a model corresponding to the solution task D32_1). In the model corresponding to the solution task D32_1, the sourceWidget component generates a data node N61 represented as "from" and a data node N63 represented as "to" as the flow of data status. In addition, the sourceWidget component generates a data node N62 represented as "solution task". The sourceWidget component presets the attribute of this data node N62 to flowGraph, so that the data node N62 includes a child data node N621 represented as "activity". The design decision model 510 performs a packaging operation on these data nodes N61~N63 to generate the solution task D32_1. Another solution task D32_2 can refer to the description of the solution task D32_1 and make an analogy.

[0111] In this embodiment, the decision processing system 50 executes the design decision model 510 so that the design decision model 510 is based on multiple condition nodes (for example, Figure 3B The conditional node N3) forms multiple links D33 between multiple solution tasks D31, D32_1~D32_2 to illustrate the implementation details of steps S410~S415.

[0112] In detail, the design decision model 510 executes the StrategyWidget component to generate a plurality of selection data with various conditions through the chooseWidget component. The design decision model 510 executes the StrategyWidget component to generate gateway attribute data corresponding to a plurality of solution tasks D31, D32_1~D32_2 through the planWidget component. The design decision model 510 executes the StrategyWidget component to generate a plurality of links D33 according to the aforementioned plurality of selection data and the gateway attribute data corresponding to the plurality of solution tasks D31, D32_1~D32_2.

[0113] In this embodiment, the link D33 with the first condition may be, for example, linked between the solution task D31 and the solution task D32_1. The link D33 with the second condition may be, for example, linked between the solution task D31 and the solution task D32_2. That is, for the solution task D31, multiple links D33 with selection conditions (such as whether the amount is sufficient) may correspond to different recommendation processes to respectively go to different solution tasks D32_1 and D32_2.

[0114] In this embodiment, the decision processing system 50 executes the design decision model 510 so that the design decision model 510 is based on the timing nodes (e.g. Figure 3B The timing node N1) encapsulates multiple solution tasks D31, D32_1~D32_2, and multiple links D33 into a decision project D3 to illustrate the implementation details of steps S416~S417.

[0115] In detail, when the design decision model 510 performs overall compilation, the design decision model 510 calls another mechanism object. At this time, the design decision model 510 links multiple solution tasks D31, D32_1-D32_2, and multiple links D33 in the project model corresponding to the timing node through the three decision elements (i.e., timing node, solution node, and condition node) to form a compiled code (i.e., decision project D3).

[0116] In summary, the decision processing system and decision processing method of the present invention can precipitate and encapsulate abnormal processes with knowledge graphs to achieve the inheritance of knowledge and experience. In addition, the decision processing system interactively designs executable decision projects based on multiple heterogeneous data nodes through the server and the user, and can eliminate corresponding anomalies based on decision projects when an abnormality occurs in the process of the ERP system. In this way, the decision processing system can transmit knowledge and experience, and can also establish decision projects that fit the abnormal conditions to improve work efficiency.

[0117] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or perform equivalent replacements on some or all of the technical features therein. However, 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 decision processing system, characterized in that: include: An electronic device, acquiring target input data and application input data; as well as A server is coupled to the electronic device and includes a memory and a processor, wherein the memory stores a plurality of models and task metadata, and the processor is coupled to the memory and executes the plurality of models, wherein the plurality of models include a design decision model and an action decision model. wherein the design decision model calculates the task metadata according to the target input data to generate a plurality of heterogeneous data nodes, and performs a packaging operation on the selected plurality of heterogeneous data nodes to generate a decision item, The action decision model executes the decision item according to the application input data to generate a decision result to the electronic device.

2. The decision processing system according to claim 1, characterized in that: The plurality of heterogeneous data nodes include at least one associated solution node, at least one opportunity node, and at least one condition node.

3. The decision processing system according to claim 1, characterized in that: The design decision model selects a plurality of solution nodes, a timing node, and a plurality of condition nodes from the plurality of heterogeneous data nodes as the selected plurality of heterogeneous data nodes according to a selection instruction from the electronic device.

4. The decision processing system according to claim 1, characterized in that: The design decision model includes a data model, a check item model, and an execution model. wherein the data model generates a plurality of active data nodes according to a plurality of parameters configured respectively of the target input data and the task metadata, wherein the check item model calculates the plurality of active data nodes with reference to a plurality of multi-strategy options to generate a plurality of link data nodes, The execution model generates a plurality of operation execution nodes according to target expected data, and encapsulates the plurality of activity data nodes, the plurality of link data nodes, and the plurality of operation execution nodes into a plurality of solution tasks in the decision project.

5. The decision processing system according to claim 4, characterized in that: The design decision model forms a plurality of links between the plurality of solution tasks according to a plurality of condition nodes in the plurality of heterogeneous data nodes.

6. The decision processing system according to claim 5, characterized in that: The design decision model encapsulates the multiple solution tasks and the multiple links into the decision items according to the timing nodes in the multiple heterogeneous data nodes.

7. A decision processing method, characterized in that: include: Obtaining target input data and application input data through an electronic device; as well as Executing, by a processor of the server, a plurality of models stored in a memory of the server, wherein the plurality of models include a design decision model and an action decision model, including: By means of the design decision model, the task metadata stored in the memory is calculated according to the target input data to generate a plurality of heterogeneous data nodes; By means of the design decision model, performing a packaging operation on the selected plurality of heterogeneous data nodes to generate a decision item; and The action decision model is used to execute the decision items according to the application input data to generate decision results to the electronic device.

8. The decision-making method according to claim 7, characterized in that: The plurality of heterogeneous data nodes include at least one associated solution node, at least one opportunity node, and at least one condition node.

9. The decision-making method according to claim 7, characterized in that: The step of executing the plurality of models further comprises: Through the design decision model, a plurality of solution nodes, opportunity nodes, and a plurality of condition nodes are selected from the plurality of heterogeneous data nodes according to a selection instruction from the electronic device to serve as the selected plurality of heterogeneous data nodes.

10. The decision-making method according to claim 7, characterized in that: The design decision model includes a data model, a check item model, and an execution model, wherein the step of executing the multiple models further includes: Generate multiple active data nodes according to the target input data and the configured multiple parameters of the task metadata through the data model; By means of the inspection item model, the plurality of active data nodes are calculated with reference to a plurality of multi-strategy options to generate a plurality of link data nodes; Generate multiple operation execution nodes according to target expected data through the execution model; and Through the execution model, the multiple active data nodes, the multiple link data nodes, and the multiple operation execution nodes are encapsulated into multiple solution tasks in the decision project.

11. The decision-making method according to claim 10, characterized in that: The step of executing the plurality of models further comprises: Through the design decision model, multiple links are formed between the multiple solution tasks according to the multiple condition nodes in the multiple heterogeneous data nodes.

12. The decision-making method according to claim 11, characterized in that: The step of executing the plurality of models further comprises: By means of the design decision model, the plurality of solution tasks and the plurality of links are encapsulated into the decision items according to the timing nodes in the plurality of heterogeneous data nodes.

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