Patent Publication Number: US-2023153651-A1

Title: Enterprise management system and execution method thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the priority benefit of China application serial no. 202111365202.2, filed on Nov. 17, 2021. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification. 
     BACKGROUND 
     Technical Field 
     The present disclosure relates to a process system, and in particular to an enterprise management system and an execution method thereof. 
     Description of Related Art 
     At present, enterprise business behavior management is mostly realized by adopting a business process management (BPM) system. In this regard, the business process management system may be designed to be adapted for defining business processes between members of an organization and solutions of integration between constituent systems (for example, between people, between a person and an application system, and between application systems). However, in the face of an application scenario of a large amount of data, a traditional business process management system cannot effectively perceive data changes and immediately respond and process correctly. Also, since most of the processes in the system still rely on people to make decisions, knowledge of decision-making behaviors cannot be effectively encapsulated and replicated. Therefore, when the traditional business process management system faces the application scenario of a large amount of data, the business processes might not be carried out efficiently. More importantly, the user&#39;s operation habits and operation experience cannot be effectively replicated. 
     SUMMARY 
     The present disclosure relates to an enterprise management system and an execution method thereof, which automatically provide optimized and/or personalized recommendation results of a system function, task, or operation sequence according to user operation behaviors. 
     According to an embodiment of the present disclosure, an enterprise management system of the present disclosure includes a storage device and a processor. The storage device stores a plurality of modules. The processor is coupled to the storage device and is used to execute the modules. The processor obtains user operation behavior data and executes a data collection module according to user operation behavior data to obtain user organization information, a user operation behavior record, and a user operation time record. The data collection module generates inference data according to the user organization information, the user operation behavior record, and the user operation time record. The processor executes a model inference module, and inputs the inference data to a task inference model in the model inference module, so that the task inference model generates inference result data. 
     According to an embodiment of the present disclosure, an execution method of an enterprise management system of the present disclosure includes the following. User operation behavior data are obtained. A data collection module is executed according to the user operation behavior data, so as to obtain user organization information, user operation behavior record, and user operation time record. Inference data are generated according to the user organization information, the user operation behavior record, and the user operation time record through the data collection module. A model inference module is executed, and the inference data are input into a task inference model in the model inference module. Inference result data are generated through the task inference model. 
     Based on the above, the enterprise management system and the execution method thereof of the present disclosure obtain the corresponding user organization information, user operation behavior record, and user operation time record as inference data according to user operation behavior data, and input the inference data into the pre-trained model inference module, so that the model inference module generates inference result data adapted for the current user or current application scenario according to the inference data. 
     To provide a further understanding of the above features and advantages of the disclosure, embodiments accompanied with drawings are described below in details. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a schematic diagram of an enterprise management system according to an embodiment of the present disclosure; 
         FIG.  2    is a flow chart of an execution method of an enterprise management system according to an embodiment of the present disclosure; 
         FIG.  3    is a schematic diagram of executing a plurality of modules of an enterprise management system according to an embodiment of the present disclosure; 
         FIG.  4    is a schematic diagram of an enterprise management system according to another embodiment of the present disclosure; 
         FIG.  5    is a training flow chart of the enterprise management system of  FIG.  4    of the present disclosure; 
         FIG.  6    is an inference flow chart of the enterprise management system of  FIG.  4    of the present disclosure. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     Now, reference will be made to the exemplary embodiment of the present disclosure in detail, and examples of the exemplary embodiment are illustrated in the accompanying drawings. Whenever possible, the same reference numerals are used in the drawings and descriptions to indicate the same or similar parts. 
       FIG.  1    is a schematic diagram of an enterprise management system according to an embodiment of the present disclosure. Referring to  FIG.  1   , an enterprise management 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 a processing circuit such as a central processor (CPU), a microprocessor control unit (MCU), or a field programmable gate array (FPGA), or a chip with data computing function, but the present disclosure is not limited thereto. The storage device  120  may be a memory, and the memory may be a non-volatile memory such as a read only memory (ROM) and an erasable programmable read only memory (EPROM), a volatile memory such as a random access memory (RAM), and a storage device such as a hard disc drive and a semiconductor memory, and the storage device  120  is used to store data including various programs and information mentioned in the present disclosure. In this embodiment, the storage device  120  may store a plurality of specific modules, algorithms, and/or software, etc., for the processor  110  to respectively read and execute. It is worth noting that the modules and units described in each embodiment of the present disclosure may respectively be implemented by one or more algorithms and/or software, and the related function and operation described in the embodiment may be implemented according to the execution result of one or more algorithms and/or software. 
     In this embodiment, the storage device  120  may store a data collection module  121 , a model inference module  122 , a data management module  123 , a model parameter module  124 , and a model training module  125 . The processor  110  may read these modules stored in the storage device  120 , and execute these modules to realize the function of automatically providing optimized and/or personalized recommendation results of a system function, task, or operation sequence according to user operation behaviors. In this embodiment, the enterprise management system  100  may be, for example, a computer host that is disposed in an enterprise, and may provide a user interface for the user to operate so as to obtain user operation behavior data. Or, in an embodiment, the enterprise management system  100  may also be implemented, for example, by the architecture of a cloud server system. The user may connect to the cloud server through executing the user interface (UI) program of an electronic appliance to perform related enterprise management operations. In this regard, the user may operate the content of the user interface displayed on the display screen of the electronic appliance, so that the user interface or related programs may provide corresponding user operation behavior data to the cloud server. The cloud server may execute the aforementioned modules to realize the function of providing optimized and/or personalized recommendation results of a system function, task, or operation sequence according to user operation behaviors. 
     In this embodiment, the data collection module  121  may be configured to collect user organization information, a user operation behavior record, a user operation time record, and related data information stored in an enterprise resource planning (ERP) database, to generate training data and inference data. In this embodiment, the model inference module  122  may be configured to input the inference data into a specific task inference model, and allow the specific task inference model to output optimized and/or personalized operation recommendation results. 
     The operation recommendation results may be, for example, but not limited to a system function recommendation, a user commonly used function recommendation, a best exception elimination solution recommendation, a user operation habit recommendation, etc. In this embodiment, the data management module  123  may be configured to clean, store, and update and maintain the multi-source training data information collected by the data collection module  121 . In this embodiment, the model parameter module  124  may store one or more task inference models and the corresponding characteristic engineering parameters, respectively. In this embodiment, the model training module  125  may continuously learn through the iterative training of artificial intelligence machine learning algorithms, and gain insight into the user&#39;s operation experience from the data, and further save (store) the operation experience into the model parameter module  124  in the form of an artificial intelligence model. 
     In this embodiment, the user organization information is, for example, the user&#39;s corresponding authority, level, and/or related identity information in the enterprise organization architecture. The user operation behavior record may refer to the same or similar operation behavior record performed by the user in the past. The user operation time record may refer to the time when the user performed the same or similar operation behavior in the past. 
       FIG.  2    is a flow chart of an execution method of an enterprise management system according to an embodiment of the present disclosure.  FIG.  3    is a schematic diagram of executing a plurality of modules of an enterprise management system according to an embodiment of the present disclosure. Referring to  FIGS.  1  to  3   , the enterprise management system  100  may execute the following steps S 210  to S 250 . In step S 210 , the processor  110  may obtain user operation behavior data. In this embodiment, the user may perform a relevant enterprise management operation behavior, for example, through an inputting apparatus (such as a mouse, a keyboard, or a touch screen, etc.) and/or an application programming interface (API) of the enterprise management system  100 , so that the processor  110  may obtain the user operation behavior data corresponding to the user operation behavior. In step S 220 , the processor  110  may execute the data collection module  121  according to the user operation behavior data to obtain the user organization information, the user operation behavior record, and the user operation time record. In step S 230 , the processor  110  may generate inference data  301  according to the user organization information, the user operation behavior record, and the user operation time record through the data collection module  121 . 
     In step S 240 , the processor  110  may execute the model inference module  122 , and input the inference data  301  into a task inference model in the model inference module  122 . As shown in  FIG.  3   , the data collection module  121  may include an inference data extracting unit  1211  and a training data collecting unit  1212 . In this embodiment, the training data collecting unit  1212  may collect training data  302  through an enterprise resources planning database and/or a platform data management unit in advance, and provide the training data  302  to the model training module  125 . The model training module  125  may iteratively train the task inference model according to different training data according to the training data  302 . 
     Specifically, the inference data extracting unit  1211  may, for example, query the enterprise resources planning database and/or the platform data management unit according to the user operation behavior data, so as to obtain the user organization information, the user operation behavior record, and the user operation time record that may be used as the inference data  301 , and the inference data extracting unit  1211  may perform data cleaning and data transformation on the extracted data, so as to input the appropriate inference data  301  to the model inference module  122 . The model inference module  122  may select a corresponding one of a plurality of task inference models in the model parameter module  124  according to the inference data  301 , and may input the inference data  301  into the task inference model selected by the model inference module  122 . Therefore, in step S 250 , the processor  110  may generate inference result data  303  through the selected task inference model. The enterprise management system  100  of this embodiment may automatically generate the inference result data  303  adapted for the current user or the current application scenario according to the user operation behavior. In this embodiment, the processor  110  may perform engineering package transfer on the inference result data  303  to output a recommendation result list. The engineering package transfer may refer to, for example, transferring and/or arranging the data of a plurality of items of the inference result data  303  into a list according to a preset or specific list format. In this way, the user may decide and perform an appropriate next operation behavior according to the information and suggestions in the recommendation result list, so that the user may appropriately and correctly implement an enterprise management process. 
     In this embodiment, the enterprise management system  100  may further set an automatic scheduling program, and may record user operation result data  304  generated through an actual operation executed by the user according to the inference result data  303 , so as to use the inference result data  303  and the user operation result data  304  as the next training data  302  to iteratively train the task inference model. In other words, the user may execute the same recommended information provided by the recommendation result list, or execute the same or different recommended information provided by the recommendation result list according to other considerations. In this regard, the enterprise management system  100  adaptively modifies and iteratively trains the task inference model, and may provide a personalized recommendation service. 
     It is worth noting that before executing the inference operation, the enterprise management system  100  may first collect relevant data information in an enterprise management software database to recommend the system to input. The data format of the aforementioned relevant data information may, for example, include but is not limited to supplier credit rating, supplier supply quality rating, and manufacturer consultation records, etc., and the aforementioned rating data may be continuous values or ordered discrete values. In addition, the enterprise management system  100  may construct user profile data according to user information and organization information. The enterprise management system  100  may record user operation behaviors, such as unstructured data such as business decision records and decision reasons, and may also record operation time information, such as operation start time and dwell time of the user under a certain function interface. Next, the training data collecting unit  1212  of the data collection module  121  may perform data collection, data cleaning, and data maintenance on the above multi-source information to update the enterprise resources planning database. The training data collecting unit  1212  may gain insight into the data characteristic information of the training data  302 , and require the model training module  125  to perform model training. The model training module  125  may automatically select a suitable machine learning algorithm according to the data type of the training data  302  to construct characteristic engineering and an algorithm model structure. Finally, the model training module  125  may repeatedly train and test the model and optimize the model to obtain the task inference model with a current best parameter network. In this way, the enterprise management system  100  may provide artificial intelligence services in an enterprise management software system, and especially provide applications of personalized recommendation services. 
       FIG.  4    is a schematic diagram of an enterprise management system according to another embodiment of the present disclosure. Referring to  FIG.  4   , an enterprise management system  400  may include a processor  410 , a storage device  420 , and an enterprise resources planning database  430 . The processor  410  is coupled to the storage device  420  and the enterprise resources planning database  430 . The storage device  420  may store a data collection module  421 , a model inference module  422 , a data management module  423 , a model parameter module  424 , and a model training module  425 . In this embodiment, the enterprise resources planning database  430  may be stored in the storage device  420 , or stored in another external storage device, and the present disclosure is not limited thereto. In this embodiment, the data collection module  421  may include an inference data extracting unit  4211 , a training data collecting unit  4212 , a platform data management unit  4213 , and a user behavior recording unit  4214 . The model inference module  422  may include an inference characteristic engineering unit  4221 , a model prediction unit  4222 , and a model selection unit  4223 . The model parameter module  424  may include a characteristic parameter management unit  4241  and an inference model management unit  4242 . The data training module  425  may include a training characteristic engineering unit  4251 , a model training unit  4252 , a model construction engineering unit  4253 , and a model test unit  4254 . The description of the above-mentioned embodiments of  FIG.  1    to  FIG.  3    may be referred to for the specific hardware features and implementation of the enterprise management system  400  of this embodiment. 
       FIG.  5    is a training flow chart of the enterprise management system of  FIG.  4    of the present disclosure. Referring to  FIG.  4    and  FIG.  5   , the enterprise management system  400  may execute the following steps S 501  to S 511 . In step S 501 , the processor  410  may execute the training data collecting unit  4212  to obtain the behavior attribute of the user and the data sample of the behavior target from the user behavior recording unit  4214 . In step S 502 , the training data collecting unit  4212  may obtain the user information and organization data corresponding to the current operation behavior from the platform data management unit  4213  according to the data sample. In step S 503 , the training data collecting unit  4212  may obtain relevant information and records corresponding to the current operation behavior from the enterprise resources planning database  430  according to the data sample. In this embodiment, the training data collecting unit  4212  may use the data obtained in steps S 501  to S 503  as training data and perform storing, and the data may at least include the user organization information, the user operation behavior record, and the user operation time record. In step S 504 , the training data collecting unit  4212  may provide the training data to the data management module  423 . In step S 505 , the processor  410  may execute the data management module  423  to perform data cleaning and regularization on the training data provided by the training data collecting unit  4212 , and provide the training data after data cleaning and regularization to the training characteristic engineering unit  4251 . 
     In step S 506 , the processor  410  may execute the model construction engineering  4253  to automatically select an appropriate algorithm according to the user&#39;s setting or according to the training data, so that the model training unit  4252  may perform a model network construction on the task inference model. In step S 507 , the processor  410  may execute the training characteristic engineering unit  4251  to generate the characteristic parameter according to the input requirements of the task inference model, and provide the characteristic parameter to the model training unit  4252 . The processor  410  may execute the model training unit  4252  to train the task inference model according to the characteristic parameter. In step S 508 , the model training unit  4252  may provide the trained task inference model to the model test unit  4254 . In step S 509 , the model test unit  4254  may determine whether the task inference model has completed training according to an evaluation index of the task inference model on the test set. If not, in step S 510 , the processor  410  may re-execute steps S 505  to S 509  to cycle through the training process; and if so, in step S 511 , the model training unit  4252  may output the task inference model and the corresponding characteristic parameter to the inference model management unit  4242  and the characteristic parameter management unit  4241  of the model parameter module  424  to save the model and the parameter. 
     It is worth noting that the model test unit  4254  may perform determining according to the evaluation index of the task inference model on the test set, and the evaluation index may be determined according to different task types, and may be, for example, classification accuracy, regression analysis mean square error, or area under the curve of receiver operating characteristic (ROC) curve. In addition, the model training module  425  may iteratively execute the training characteristic engineering unit  4251 , the model training unit  4252 , and the model construction engineering unit  4253  to iteratively train the task inference model. 
       FIG.  6    is an inference flow chart of the enterprise management system of  FIG.  4    of the present disclosure. Referring to  FIG.  4    and  FIG.  6   , the enterprise management system  400  may execute the following steps S 601  to S 609 . In step S 601 , the processor  410  may transmit user current behavior attribute data to the inference data extracting unit  4211  through the user behavior recording unit  4214  according to the user operation behavior data. In step S 602  and step S 603 , the processor  410  may execute the inference data extracting unit  4211  to extract the user organization information, the user operation behavior record, and the user operation time record from the platform data management unit  4213  and the enterprise resources planning database  430 . In step S 604 , the processor  410  may execute the inference data extracting unit  4211  to provide the user organization information, the user operation behavior record, and the user operation time record to the inference characteristic engineering unit  4221  of the model inference module  422 . In step S 605 , the processor  410  may execute the inference characteristic engineering unit  4221  to obtain corresponding characteristic engineering parameters from the characteristic parameter management unit  4241  of the model parameter module  424  according to the user organization information, the user operation behavior record, and the user operation time record, and perform characteristic extraction on the user organization information, the user operation behavior record, and the user operation time record to generate inference data according to the characteristic engineering parameters. In step S 606 , the inference characteristic engineering unit  4221  provides the inference data to the model prediction unit  4222  and the model selection unit  4223 . In step S 607 , the processor  410  may execute the model selection unit  4223  to select one of a plurality of models stored in the inference model management unit  4242  of the model parameter module  424  as the task inference model according to the inference data. The model selection unit  4223  may provide the model network data of the task inference model to the model prediction unit  4222 . In step S 608 , the processor  410  may execute the model prediction unit  4222  to input the inference data to the task inference model, so that the task inference model performs inference calculation according to the inference data. In step S 609 , the model prediction unit  4222  may generate inference result data  600 . In this embodiment, the processor  410  may further perform engineering package transfer on the inference result data  600  to output a recommendation result list. 
     In summary, the enterprise management system and the execution method thereof of the present disclosure may collect and analyze user information, user operation behavior, and operation time, and infer the user&#39;s operation habits through the artificial intelligence model, and realize system functions and personalized recommendation functions of tasks and operation sequence. The enterprise management system of the present disclosure may recommend common functions according to the user&#39;s role and organization information, so as to effectively reduce the user&#39;s learning threshold and enterprise employee training costs. The enterprise management system of the present disclosure may collect user&#39;s choices and judgments in the event of decision-making, and perform operation behavior classification and analysis to achieve the optimal operation recommendation for the enterprise system in decision-making scenarios. 
     Lastly, it is to be noted that: the embodiments described above are only used to illustrate the technical solutions of the disclosure, and not to limit the disclosure; although the disclosure is described in detail with reference to the embodiments, those skilled in the art should understand: it is still possible to modify the technical solutions recorded in the embodiments, or to equivalently replace some or all of the technical features; the 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.