Knowledge management system and knowledge management method

Abstract

The present application generates a knowledge management system and a knowledge management method. The knowledge management system includes an electronic device and a server. The electronic device acquires target input data and application input data. The server includes a data model, an inspection item model, an execution model, and a control and decision model. The data model generates a plurality of active data nodes according to the target input data and source data. The inspection item model calculates the plurality of active data nodes according to standard data to generate inspection results. The execution model performs encapsulation operations according to the inspection results to generate scheme tasks. The control and decision model calculates the application input data based on the scheme tasks to generate feedback results to the electronic device. Therefore, the knowledge management system can automatically establish executable tasks to improve work efficiency.

Description

Technical Field

[0001] This invention relates to a management system, and more particularly to a knowledge management system and knowledge management method that utilizes data-driven programming. Background Technology

[0002] Systems employing data-driven programming can create executable tasks by representing data as conditions and actions. During task execution, variable factors in the external environment may cause flow anomalies. Generally, systems can build an enterprise knowledge base to propose solutions for these anomalies. Current systems can leverage this knowledge base, utilizing modules such as semantic search, question answering, and recommendation to provide a large number of candidate solutions. However, these candidate solutions still require manual evaluation to determine the most suitable solution, thus reducing operational efficiency. Summary of the Invention

[0003] This invention relates to a knowledge management system that can automatically create executable tasks using data-driven programming and knowledge graphs to improve operational efficiency.

[0004] According to embodiments of the present invention, the knowledge management system includes an electronic device and a server. The electronic device executes an interface module to acquire target input data and application input data. The server is coupled to the electronic device. The server includes a data model, a check item model, an execution model, and a control and decision model. The data model generates multiple active data nodes based on the target input data and source data. The check item model calculates multiple active data nodes based on standard data to generate at least one check result. The execution model performs encapsulation operations based on the check result to generate a solution task. The control and decision model calculates application input data based on the solution task to generate feedback results to the electronic device.

[0005] According to embodiments of the present invention, the knowledge management method of the present invention includes the following steps: An interface module is executed through an electronic device to obtain target input data and application input data. A server's data model generates multiple active data nodes based on the input data and source data. A server's inspection item model calculates the multiple active data nodes based on standard data to generate at least one inspection result. A server's execution model performs encapsulation operations based on the inspection result to generate a solution task. A server's control and decision model calculates application input data based on the solution task to generate feedback results to the electronic device.

[0006] Based on the above, the knowledge management system and method of the present invention can collect a large amount of data (i.e., target input data, source data, and standard data) to construct a graph knowledge base represented by multiple nodes. Furthermore, it can automatically generate solution tasks based on the aforementioned data and nodes. Therefore, the knowledge management system can execute solution tasks based on the current application input data to adapt to various variable factors. In this way, the knowledge management system can transfer knowledge and experience and improve operational efficiency.

[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. Attached Figure Description

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

[0009] Figure 2 This is a flowchart of a knowledge management method according to an embodiment of the present invention;

[0010] Figure 3 This is a schematic diagram of the operation of a knowledge management system according to an embodiment of the present invention;

[0011] Figure 4 This is a schematic diagram of the operation of a knowledge management system according to another embodiment of the present invention;

[0012] Figure 5 This is a flowchart of a knowledge management method according to another embodiment of the present invention;

[0013] Figure 6 This is the invention Figure 4 A schematic diagram of the execution model in the embodiment;

[0014] Figure 7 This is a schematic diagram of the operation of a knowledge management system according to another embodiment of the present invention.

[0015] Explanation of reference numerals in the attached figures

[0016] 10, 40, 70: Knowledge Management System;

[0017] 100, 400: Server;

[0018] 110, 410: Control and Design Models;

[0019] 111, 411: Data model;

[0020] 1111: Data building block;

[0021] 1112: Data processing unit;

[0022] 112, 412: Inspection item model;

[0023] 1121: Expressions and formulas;

[0024] 113, 413: Execution model;

[0025] 1131: Action library;

[0026] 120, 420: Control and Decision Models;

[0027] 120_1: Control model;

[0028] 120_2: Decision Model;

[0029] 121: Anomaly Checking Module;

[0030] 1211: Script execution unit;

[0031] 1212: Rule execution unit;

[0032] 1213: Algorithm execution unit;

[0033] 122: Exception handling module;

[0034] 1221: Scheme selection unit;

[0035] 1222: Solution Execution Unit;

[0036] 123: Decision-making suggestion module;

[0037] 1231: Rule enforcement unit;

[0038] 1232: Algorithm execution unit;

[0039] 200: Electronic devices;

[0040] 414: Target Model;

[0041] 711: Mechanism encapsulation tool;

[0042] 721: Deliver the designer;

[0043] 731: Mechanism Execution Engine;

[0044] D1: Target input data;

[0045] D2: Application input data;

[0046] D3: Solution Task;

[0047] D4: Feedback Results;

[0048] D01~D0n: Tasks;

[0049] S210~S250, S510~S550: Steps;

[0050] S311~S350, S411~S415, S421~S426, S610~S620, S710~S730: Modules. Detailed Implementation

[0051] 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.

[0052] Figure 1 This is a circuit block diagram of a knowledge management system according to an embodiment of the present invention. (Reference) Figure 1 The knowledge management system 10 applies data-driven programming and knowledge graphs. The knowledge management system 10 is used to automatically maintain a knowledge base and to automatically propose and execute tasks that can resolve abnormal processes. In this embodiment, the knowledge management system 10 may include a server 100 and an electronic device 200. The electronic device 200 is coupled to the server 100. Users can operate the electronic device 200 and access the server 100 through the electronic device 200. The electronic device 200 may be, for example, a mobile phone, tablet computer, laptop computer, or desktop computer.

[0053] In this embodiment, server 100 may include a control and design model 110, a control and decision model 120, memory (not shown), and a processor (not shown). The memory may store the control and design model 110, the control and decision model 120, and the various models and related algorithms mentioned in the embodiments of this invention. It may also store computational software and other related algorithms, programs, and data used to implement the automatic generation and execution of schemes and tasks of this invention. The memory may be, for example, Dynamic Random Access Memory (DRAM), Flash memory, or Non-Volatile Random Access Memory (NVRAM), and this invention is not limited thereto.

[0054] In this embodiment, the processor is coupled to the control and design model 110, the control and decision model 120, and memory. The processor can access data in the memory, as well as data transmitted between the various models and the electronic device 200. The processor may 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, digital signal processor (DSP), programmable controller, application-specific integrated circuit (ASIC), programmable logic device (PLD), or other similar device or combination thereof, which can load and execute computer program-related firmware or software to perform functions such as data acquisition, calculation, data structuring, encapsulation, and execution.

[0055] In this embodiment, the control and design model 110 is operable at design time and is used to generate and provide a solution task D3 that can resolve anomalies based on the target input data D1. The control and design model 110 may include a data model 111, a check item model 112, and an execution model 113.

[0056] In this embodiment, the control and decision model 120 is operable in the practical application stage and is used to execute tasks (e.g., solution task D3) based on application input data D2 and provide results after task execution (e.g., feedback result D4). The control and decision model 120 may include a control model 120_1 and a decision model 120_2. The control model 120_1 may include an anomaly checking module 121 and an anomaly handling module 122. The decision model 120_2 may include a decision suggestion module 123.

[0057] Figure 2 This is a flowchart of a knowledge management method according to an embodiment of the present invention. (Reference) Figure 1 as well as Figure 2 The knowledge management system 10 can execute steps S210 to S250. The order of these steps S210 to S250 is merely illustrative and not intended to be limiting. In this embodiment, steps S210 to S250 can be applied to the following exemplary situations.

[0058] In this embodiment, a user operates electronic device 200 to execute an interface module (not shown) stored in electronic device 200. Electronic device 200 accesses server 100 through the interface module. The interface module may be, for example, an application programming interface (API). In some embodiments, electronic device 200 executes an enterprise system to cooperate with server 100 through the interface module. The enterprise system may be, for example, an enterprise resource planning (ERP) system.

[0059] In step S210, the electronic device 200 executes the interface module to obtain target input data D1 and application input data D2. Specifically, during design time, the user operates the electronic device 200 to input information, causing the electronic device 200 to obtain target input data D1 through the interface module. Target input data D1 may, for example, be a matter that the user (or enterprise) wishes to resolve and / or control.

[0060] In practical applications, the user operates the electronic device 200 to generate application input data D2, or the electronic device 200 executes the enterprise system to generate application input data D2, so that the electronic device 200 obtains the application input data D2 through the interface module. The application input data D2 may be, for example, routine data generated during the execution of the enterprise system, such as a purchase order or requisition.

[0061] In step S220, the processor accesses the target input data D1 and executes data model 111 to generate multiple active data nodes based on the target input data D1 and source data. The source data may be, for example, reference data associated with the target input data D1. The active data nodes may be, for example, structured and processed knowledge graph data.

[0062] In detail, data model 111 performs data structuring on the target input data D1 and the source data through data construction unit 1111 to establish knowledge graph data. Data model 111 performs data processing on the aforementioned knowledge graph data through data processing unit 1112 to generate multiple active data nodes. Data processing may include mathematical calculations, sampling, or combinations thereof, and other unconditional calculations.

[0063] In step S230, the processor executes the check item model 112, causing the check item model 112 to calculate multiple active data nodes based on standard data to generate one or more check results. The standard data may be, for example, reference data such as expressions and formulas 1121. For example, the standard data may be abstracted reference data from a standard knowledge base, or semantically analyzed and defined reference data. The check results may be, for example, data-structured judgment results, represented by nodes.

[0064] In detail, the check item model 112 accesses standard data (e.g., expressions and formulas 1121) in memory. The check item model 112 calculates multiple active data nodes with reference to the expressions and formulas 1121 and generates check results corresponding to these nodes. That is, the check item model 112 can perform conditional calculations on multiple active data nodes.

[0065] In step S240, the processor executes execution model 113, which performs encapsulation operations based on the inspection results to generate scheme task D3. Specifically, execution model 113 accesses the action library 1131 in memory. Based on the action library 1131, execution model 113 translates the inspection results into an executable data structure (i.e., scheme task D3).

[0066] It should be noted that Solution Task D3 conforms to data-driven programming and has been compiled. Solution Task D3 may include data to represent information and instructions to calculate (or process) the aforementioned data. Furthermore, Solution Task D3 is associated with target input data D1 to define a standard process for the matters that the user wishes to resolve and / or control.

[0067] In step S250, the processor accesses the scheme task D3 and the application input data D2, and executes the control and decision model 120 to calculate the application input data D2 based on the scheme task D3 to generate a feedback result D4 to the electronic device 200. The feedback result D4 may, for example, be the result of executing a standard procedure (e.g., scheme task D3) for an established event, in relation to the current situation (e.g., the application input data D2). For example, suppose the standard procedure includes sending an email to a supervisor via the processor when an event anomaly occurs. In this case, the control and decision model 120 sends the email as feedback result D4 to the electronic device 200 operated by the supervisor.

[0068] In detail, the anomaly checking module 121 checks whether the event indicated by the application input data D2 is abnormal through the script execution unit 1211, rule execution unit 1212, and algorithm execution unit 1213, which will trigger an anomaly process. When the aforementioned check result is normal, the anomaly handling module 122 and the decision suggestion module 123 are not triggered. Conversely, when the aforementioned check result is abnormal, the anomaly handling module 122 and the decision suggestion module 123 are triggered. At this time, the decision suggestion module 123 generates one or more tasks that can resolve the anomaly process based on the application input data D2 through the rule execution unit 1231 and the algorithm execution unit 1232. The anomaly handling module 122 selects a task (e.g., solution task D3) through the solution selection unit 1221. The anomaly handling module 122 executes the selected task through the solution execution unit 1222 and generates the execution result (e.g., feedback result D4) to be fed back to the electronic device 200.

[0069] It is worth mentioning that by collecting a large amount of data (such as target input data D1, source data, and standard data) through data model 111 and check item model 112, knowledge graph data (such as activity data nodes) can be automatically constructed. This data can be encapsulated and stored within the knowledge graph model, enabling the transfer of knowledge and experience. On the other hand, by automatically generating solution task D3 based on the knowledge graph data through execution model 113, users do not need to fully understand the connotation of the knowledge, and it can ensure that enterprises can execute business processes according to management experience and methods, thereby improving operational efficiency.

[0070] Figure 3 This is a schematic diagram illustrating the operation of a knowledge management system according to an embodiment of the present invention. (Reference) Figure 1 as well as Figure 3 The knowledge management system 10 can execute multiple modules S311 to S350 to illustrate how the control and design model 110 uses knowledge graph technology to manage, identify, and control anomalies. In this embodiment, the design time and the actual application phase can be executed continuously, so the knowledge management system 10 can treat the matters to be solved and / or controlled as the current matters in the actual application. That is, the target input data D1 can replace the application input data D2 and be regarded as the actual routine data.

[0071] In modules S311-S312, data model 111 accesses items (e.g., target input data D1) and current conditions (e.g., source data) to identify the problem that needs to be solved. In modules S321-S322, inspection item model 112 accesses standards (e.g., standard data) in the knowledge base to obtain standard data about the problem. In modules S331-S333, inspection item model 112 uses formulas (e.g., expressions and formula 1121) in the knowledge base to calculate the known standards and current conditions, and compares the calculation results of the two to compare the standards with the situation that needs to be controlled.

[0072] When check item model 112 detects a condition that meets the standard and is normal, execution model 113 executes module S341. Conversely, when check item model 112 detects a condition that does not meet the standard and is abnormal, execution model 113 executes module S342. In module S341, when the current condition is normal, execution model 113 is disabled and does not perform encapsulation operations. In module S342, when the current condition is abnormal, execution model 113 is enabled to perform encapsulation operations based on the comparison results (e.g., check results) to generate a solution (e.g., solution task D3). In module S350, control and decision model 120 processes the current items in modules S311-S312 based on the solution (e.g., solution task D3) to execute the solution based on the comparison results in module S331.

[0073] For example, in an application of field equipment schedule management, the goal of the enterprise system is to control abnormal tasks. An abnormal task might be, for instance, any equipment or material whose processing time exceeds the standard working hours. Controlling this abnormal task could be done, for example, by sending an email to the supervisor.

[0074] Specifically, the task in module S312 could be an exception handling task, which involves processing data related to a specific piece of equipment (e.g., a purchase requisition for the first piece of equipment). The current status in module S311 could be the current time and start time of the purchase requisition. The knowledge in module S322 could be the exception standard base data in the knowledge base. The standard in module S321 could be the standard exception time in the exception standard base data. The formula in module S332 could be "a + b > c", where a, b, and c can represent the start time, the standard exception time, and the current time, respectively. In other words, the exception handling process of a purchase requisition is defined as an exception when the sum of the start time and the standard exception time during processing is greater than the current time during processing.

[0075] Continuing the above explanation, the calculation in module S333 can be, for example, a mathematical calculation. That is, this calculation includes referring to the formula in module S332, substituting the start time into parameter a, substituting the standard exception time into parameter b, and calculating the sum of parameters a and b. The comparison in module S331 can be, for example, a conditional calculation and judgment. That is, this comparison includes referring to the formula in module S332 and comparing whether the sum in module S333 is greater than the current time (i.e., parameter c). When the sum of parameters a and b is greater than parameter c, it indicates that the processing period for the purchase requisition has exceeded the standard working hours. At this time, the exception in module S342 can be, for example, that the processing period for the purchase requisition has expired. The solution in module S350 can be, for example, sending an email to the supervisor.

[0076] Figure 4 This is a schematic diagram illustrating the operation of a knowledge management system according to another embodiment of the present invention. (Reference) Figure 4 The knowledge management system 40 may include a server 400, wherein the server 400 may include a control and design model 410 and a control and decision model 420. Figure 4 In this embodiment, the control and design model 410 may include a target model 414, a data model 411, a check item model 412, and an execution model 413. The control and decision model 420 can access and execute the scheme task D3. The data model 411, check item model 412, execution model 413, and control and decision model 420 can be deduced from the relevant description of the knowledge management system 10, and therefore will not be repeated here.

[0077] Please refer to the above. Figure 5 , Figure 5 This is a flowchart of a knowledge management method according to another embodiment of the present invention. The knowledge management system 40 can execute steps S510 to S550 and multiple modules S411 to S426. The order of these steps S510 to S550 is only for illustrative purposes and is not intended to be limiting. In this embodiment, steps S510 to S550 can be applied to the following exemplary situations.

[0078] In this embodiment, server 400 accesses the electronic device through an interface module. Control and design model 410 obtains events (e.g., events) from the electronic device through the interface module. Figure 3 (Items in module S312). The control and design model 410 generates an executable solution task D3 based on the aforementioned events, so that the control and decision model 420 executes the solution task D3 based on the aforementioned events.

[0079] Specifically, in step S510, the target model 414 creates events. In this embodiment, the target model 414 will generate events or tasks performed by the electronic device (e.g., events or tasks). Figure 3The events in module S312 are defaulted to newly created data-driven events, and the changed data state corresponding to this event is introduced into the newly created data state.

[0080] In detail, target model 414 executes module S411 to control the mount point of the active target. The active target can be a data source provided by an electronic device to provide a supply of raw data. The active target can be, for example, a... Figure 3 The items in module S312 may include target input data D1 and / or application input data D2 provided by the electronic device. The mount point controlled by the target model 414 may be a task or project, and may be, for example, a task or project based on data-driven programming.

[0081] In step S520, data model 411 constructs data (e.g., knowledge graph data) based on the event. In this embodiment, data model 411 constructs data about the event (e.g., knowledge graph data). Figure 3 The events in module S312 and the current status of module S311 are used to construct preliminary activity nodes. Therefore, data model 411 can structure events to combine the various processes within an event.

[0082] In detail, data model 411 executes module S412 to configure the parameters of the event. Figure 3 The embodiment is for illustrative purposes only. In module S412, data model 411 configures the items of module S312 (i.e., Figure 1 The target input data D1 of the embodiment and the current status of module S311 (i.e., Figure 1 The source data of the embodiment contains multiple parameters. Data model 411 configures these parameters to establish multiple initial activity data nodes. That is, data model 411 is able to deconstruct the individual parameters in the event and the coupling relationships between these parameters, and represent the aforementioned deconstruction results as multiple data nodes.

[0083] It should be noted that, in addition to extracting data provided by the electronic device (i.e., events and current status), data model 411 can also obtain external resources as reference data through the interface module. Therefore, data model 411 can provide a model-based integration method to extract and process large amounts of data to provide valuable judgment criteria.

[0084] In step S530, data model 411 processes the constructed data. In this embodiment, data model 411 uses built-in data processing tools (e.g., ...). Figure 3 The formula in module S322 needs to process the result data (e.g., represented by a script) that meets the requirements of programming. Figure 3 (Calculation results of module S333).

[0085] Specifically, data model 411 executes module S413. (The sentence is incomplete and requires more context to translate accurately.) Figure 3 The embodiment is for illustrative purposes. In module S413, data model 411 calculates multiple preliminary active data nodes in module S412 based on the formula of module S322 to generate the calculation result of module S333 (i.e., multiple active data nodes). In other words, data model 411 processes data with reference to the formula and represents the aforementioned calculation result as multiple data nodes.

[0086] In step S540, the check item model 412 forms a judgment based on the processed data. In this embodiment, the check item model 412 considers check items (e.g., ...) Figure 3 The standard data from module S321 is used to form gateway routing nodes, and these nodes are compared with multiple nodes processed by data model 411 to generate inspection results (e.g., ...). Figure 3 The comparison results of module S331). Inspection item model 412 determines whether the inspection results meet the expectations of the event to generate a judgment result (e.g., is...). Figure 3 (Check results of module S341 or S342).

[0087] Specifically, the inspection item model 412 executes module S414. Figure 3 The embodiment is for illustrative purposes. In module S414, the check item model 412 refers to multiple multi-strategy options in the standard data of module S321 and calculates the calculation results (i.e., multiple active data nodes) from module S333 of data model 411 to establish multiple linked data nodes. The check item model 412 calculates these linked data nodes to generate a normal check result for module S341 and / or an abnormal check result for module S342. In this embodiment, these multi-strategy options may correspond to one or more check results. These multi-strategy options may, for example, be calculation formulas with different conditional expressions.

[0088] It should be noted that the inspection item model 412 can accumulate a batch of valuable management knowledge (e.g., multiple linked data nodes) through the knowledge graph. The inspection item model 412 can correct the process of events using the processed control data (e.g., multiple multi-strategy options) and the aforementioned management knowledge, and represent it as inspection results.

[0089] In step S550, execution model 413 executes the determined result. In this embodiment, execution model 413 forms operation execution nodes based on the expected control flow to form a solution to the problem (e.g., ...). Figure 3 (Solution for module S350). It should be noted that execution model 413 intervenes in the inspection results through encapsulation to ensure the enterprise's expectations for the process.

[0090] Specifically, execution model 413 executes module S415. Figure 3 The embodiment is for illustrative purposes only. In module S415, when the inspection result compared by inspection item model 412 in module S331 is abnormal, execution model 413 executes the target expected data (e.g., ...). Figure 1 In this embodiment, the action library 1131) establishes multiple operation execution nodes via API calls and encapsulates these operation execution nodes into a scheme task D3. The control and decision model 420 executes the scheme task D3 to implement the solution of module S350.

[0091] Please refer to the above. Figure 6 , Figure 6 This is the invention Figure 4 A schematic diagram of the execution model in this embodiment. Execution model 413 can execute multiple modules S610 to S620. This example illustrates how execution model 413 establishes and encapsulates a solution task D3 via API calls.

[0092] In module S610, execution model 413 is based on Figure 1 The action library 1131 shown in the embodiment acquires one or more software components that can resolve exceptions. The software components may include... Figure 6 The squares marked "sourceWidget", "activityWidget", "checkWidget", and "planWidget" serve as multiple operation execution nodes.

[0093] In module S620, execution model 413 accesses the current solution task. The current solution task may include multiple tasks D01 to D0n, where n is a positive integer. The data structure of each task D01 to D0n is implemented as a knowledge graph. For example, task D01 includes first data represented as "F", second data represented as "T", and task data represented as "FlowGraph". The task data is associated with the first data and the second data, and represents the coupling relationship between the two data. The coupling relationship may include one or more consecutive programs, software components, or combinations thereof. The second data represented as "T" in task D01 can be linked with the first data represented as "F" in task D02, and the second data represented as "T" in task D02 can be linked with the first data represented as "F" in task D03, and so on, to sequentially link multiple consecutive tasks.

[0094] In this embodiment, the execution model 413 is based on Figure 1The action library 1131 shown in the embodiment disconnects the link between tasks D01 and D02, and extracts the second data represented as "T" in task D01 and the first data represented as "F" in task D02. The execution model 413 uses the software components in module S610 as task data between the extracted first and second data to establish and encapsulate a new scheme task D3. That is, scheme task D3 includes the data represented as "T" in task D01 as the first data (i.e., "F" in scheme task D3), the data represented as "F" in task D02 as the second data (i.e., "T" in scheme task D3), and task data represented as multiple "activities". The task data is associated with the first and second data.

[0095] Refer again Figure 4 In this embodiment, the method by which the control and decision model 420 executes the scheme task D3 may include multiple operations corresponding to modules S411 to S415. The operation of the control and decision model 420 accessing the scheme task D3 may correspond to the operation of module S411 controlling the activity target.

[0096] The execution of task D3 begins in module S421 and ends in module S426. Control and decision model 420 executes module S422, corresponding to module S412, to configure event parameters and list them into multiple preliminary active data nodes. Control and decision model 420 executes module S423, corresponding to module S413, to calculate multiple preliminary active data nodes using formulas and embed the calculation results into a script (i.e., multiple active data nodes). Control and decision model 420 executes modules S424_1 to S424_2, corresponding to module S414, to list multiple multi-strategy options and check multiple active data nodes based on these options to generate check results. Control and decision model 420 executes modules S425_1 to S425_2, corresponding to module S415, to make API calls or re-initiate task D3 or other task schemes based on the check results.

[0097] Figure 7 This is a schematic diagram illustrating the operation of a knowledge management system according to another embodiment of the present invention. (Reference) Figure 7 The knowledge management system 70 can execute multiple modules S710 to S730, which are used as examples of the application operation of the knowledge management system 70.

[0098] In module S710, the knowledge management system 70 encapsulates the tool 711 (e.g., a mechanism in the server) through a mechanism encapsulation tool 711. Figure 4The execution model 413 of the embodiment performs an encapsulation operation on the overall mechanism of the enterprise system. In module S720, the knowledge management system 70 delivers the encapsulated mechanism to the enterprise system through the delivery designer 721 in the server. In module S730, the knowledge management system 70 collaborates with the enterprise system through the mechanism execution engine 731 in the server to run the overall mechanism of the enterprise system.

[0099] In summary, the knowledge management system and method of the present invention, by automatically constructing knowledge graph data (e.g., activity data nodes), can realize the inheritance of knowledge and experience through the knowledge graph model. Furthermore, by automatically generating solution tasks, the knowledge management system can respond to various variable factors, thereby improving operational efficiency.

[0100] 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 therein. Such 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.

Claims

1. A knowledge management system, characterized in that, include: Electronic device used to execute interface modules to acquire target input data and application input data; as well as A server, coupled to the electronic device, includes: A data model is used to generate multiple active data nodes based on the target input data and source data; The inspection item model is used to calculate the multiple active data nodes based on conditional expressions and formulas in standard data to generate at least one inspection result. An execution model is configured to perform encapsulation operations based on the at least one check result to translate the at least one check result into an executable scheme task associated with the target input data, wherein the execution model disconnects links between multiple scheme tasks and extracts different data from the multiple scheme tasks to create and encapsulate a new task as the scheme task; and A control and decision model is used to calculate the application input data based on the scheme task to generate feedback results to the electronic device.

2. The knowledge management system according to claim 1, characterized in that, The server also includes: A target model for controlling the mount point of an active target, wherein the active target includes at least one of the target input data and the application input data.

3. The knowledge management system according to claim 1, characterized in that, The data model is used to configure multiple parameters of the target input data and the source data to establish multiple preliminary active data nodes, and to calculate the multiple preliminary active data nodes based on the built-in script to generate the multiple active data nodes.

4. The knowledge management system according to claim 1, characterized in that, The standard data includes multiple multi-strategy options, and the inspection item model is used to calculate the multiple active data nodes with reference to the multiple multi-strategy options to establish multiple linked data nodes, and to calculate the multiple linked data nodes to generate the at least one inspection result corresponding to the multiple multi-strategy options.

5. The knowledge management system according to claim 1, characterized in that, The execution model is used to establish multiple operation execution nodes based on the target expected data when the at least one check result is abnormal, and to encapsulate the multiple operation execution nodes into the scheme task.

6. The knowledge management system according to claim 1, characterized in that, The scheme task includes first data, task data, and second data, and the task data is associated with the first data and the second data.

7. The knowledge management system according to claim 6, characterized in that, The execution model is used to disconnect the links between multiple schemes and extract the first data and the second data from the multiple schemes respectively.

8. A knowledge management method, characterized in that, include: The interface module is executed via electronic devices to obtain target input data and application input data. Based on the input data and source data, multiple active data nodes are generated using the server's data model. The server's inspection item model is used to calculate the multiple active data nodes based on conditional expressions and formulas in the standard data to generate at least one inspection result. The execution model of the server performs an encapsulation operation based on the at least one check result to translate the at least one check result into an executable scheme task associated with the target input data. The execution model disconnects the links between multiple scheme tasks and extracts different data from the multiple scheme tasks to create and encapsulate a new task as the scheme task. as well as The server's control and decision-making model calculates the application input data based on the proposed task to generate feedback results for the electronic device.

9. The knowledge management method according to claim 8, characterized in that, Also includes: The target model of the server controls the mount point of the active target, wherein the active target includes at least one of the target input data and the application input data.

10. The knowledge management method according to claim 8, characterized in that, The step of generating the plurality of active data nodes based on the input data and the source data includes: Using the data model, multiple parameters are configured for both the target input data and the source data to establish multiple initial active data nodes; and Using the data model, the multiple preliminary active data nodes are calculated based on the built-in script to generate the multiple active data nodes.

11. The knowledge management method according to claim 8, characterized in that, The standard data includes multiple multi-strategy options, and the step of calculating the multiple active data nodes based on the standard data to generate the at least one inspection result includes: Using the inspection item model, and referring to the multiple multi-strategy options, the multiple active data nodes are calculated to establish multiple linked data nodes; and The plurality of linked data nodes are calculated using the inspection item model to generate at least one inspection result corresponding to the plurality of multi-strategy options.

12. The knowledge management method according to claim 8, characterized in that, The step of performing a packaging operation to generate the solution task based on the at least one inspection result includes: Through the execution model, when the at least one check result is abnormal, multiple operation execution nodes are established based on the target expected data, and the multiple operation execution nodes are encapsulated into the scheme task.

13. The knowledge management method according to claim 8, characterized in that, The scheme task includes first data, task data, and second data, and the task data is associated with the first data and the second data.

14. The knowledge management method according to claim 13, characterized in that, Also includes: The execution model disconnects the links between multiple schemes. as well as The execution model extracts the first data and the second data from the multiple schemes respectively.

Images