CN117649107A - Automatic decision node creation method, device, system and readable medium - Google Patents
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Abstract
The application provides an automatic decision node creation method, an automatic decision node creation device, an automatic decision node creation system and a readable medium. The automatic decision node creation method is applied to the Fab flow system and comprises the following steps: extracting historical manual decision node data from the obtained historical flow data, wherein the historical manual decision node data comprises historical node input parameters, historical context semantic data and historical decision information corresponding to each manual decision node; analyzing the historical manual decision node data to determine a target manual decision node which accords with the automatic decision node creation condition; analyzing the target historical decision information corresponding to the target manual decision node, and determining the branch decision conditions and the corresponding branch decision contents corresponding to each decision branch; based on the branch decision condition and the branch decision content, creating a mutual exclusion gateway, and completing the creation of the automatic decision node so as to enable the automatic decision node to replace the manual decision node. According to the technical scheme, the dependence on manpower is reduced, and the approval efficiency of the process is improved.
Description
Technical Field
The present disclosure relates to the field of semiconductor technologies, and in particular, to a method, an apparatus, a system, and a readable medium for creating an automatic decision node in a process engine when the process engine is used to connect a semiconductor CIM (Computer Integrated Manufacturing) system in series.
Background
Wafer manufacturers are responsible for converting silicon wafers into chips, one chip needs to plug all circuit elements into the wafer in a line width which is not reached by one head, and thousands of complicated process procedures such as film deposition, photoresist coating, photoetching development, etching, measurement, cleaning, ion implantation and the like are performed, during which a large amount of production process data is generated, and various related systems such as a manufacturing execution system (Manufacturing Execution System, MES), a statistical process control system (Statistical Process Control, SPC), an equipment automation scheme (Equipment Automation Programming, EAP), a formula management system (recipe management sys-tem, RMS), a yield management system (Yield Management System, YMS), a defect management system (Defect Management System, DMS), an automatic defect classification (Automatic Defect Classification, ADC), a maintenance management system (planned maintenance system, PMS) and the like are involved to monitor the production process conditions, complete the production and improve the yield.
FFS can connect the business systems in series, and operator work is guided according to the flow direction through the cooperation of the flow direction and the condition control system. When an operator makes a manual decision, the operator mainly depends on experience values and information transmitted offline, and past experience cannot be well summarized when a branch is selected, so that accidental judgment errors can occur. In addition, if the operator temporarily cannot make a manual decision in time, the problem that the process cannot be advanced due to no manual decision approval can be caused, the operation efficiency of the process is greatly influenced, and many simple and repeated application scenes still need the operator to participate in the decision, so that the operator is heavy and redundant, and the labor cost and the time cost are increased.
The present application is specifically directed to this problem.
Disclosure of Invention
An object of the present application is to provide a method, apparatus, system and readable medium for creating an automatic decision node, at least to solve the technical problem that simple repeated business processes still depend on manual decision in the wafer manufacturing business processes.
To achieve the above object, some embodiments of the present application provide the following aspects:
in a first aspect, some embodiments of the present application provide an automatic decision node creation method applied to a Fab flow system, the method comprising:
Extracting historical manual decision node data from the obtained historical flow data, wherein the historical manual decision node data comprises historical node input parameters, historical context semantic data and historical decision information corresponding to each manual decision node;
analyzing the historical manual decision node data to determine a target manual decision node which accords with the automatic decision node creation condition;
analyzing the target historical decision information corresponding to the target manual decision node, and determining the branch decision conditions and the corresponding branch decision contents corresponding to each decision branch;
and creating a mutual exclusion gateway based on the branch decision condition and the branch decision content to finish the creation of the automatic decision node so that the automatic decision node replaces the manual decision node.
In a second aspect, some embodiments of the present application further provide an automatic decision node creation apparatus, provided in a Fab flow system, the apparatus comprising:
the historical manual decision node data extraction module is used for extracting historical manual decision node data from the obtained historical flow data, wherein the historical manual decision node data comprises historical node input parameters, historical context semantic data and historical decision information corresponding to each manual decision node;
The target manual decision node determining module is used for analyzing the historical manual decision node data and determining target manual decision nodes which accord with the automatic decision node creating conditions;
the decision branch determining module is used for analyzing the target historical decision information corresponding to the target manual decision node and determining branch decision conditions and corresponding branch decision contents corresponding to each decision branch;
and the automatic decision node creation module is used for creating a mutual exclusion gateway based on the branch decision condition and the branch decision content to finish the automatic decision node creation so that the automatic decision node replaces the manual decision node.
In a third aspect, some embodiments of the present application further provide a Fab flow system, the system comprising:
one or more processors; and
a memory storing computer program instructions that, when executed, cause the processor to perform the method as described above.
In a fourth aspect, some embodiments of the present application also provide a computer readable medium having stored thereon computer program instructions executable by a processor to implement a method as described above.
Compared with the prior art, in the scheme provided by the embodiment of the application, the automatic decision node creation method applied to the Fab flow system comprises the following steps: extracting historical manual decision node data from the obtained historical flow data, wherein the historical manual decision node data comprises historical node input parameters, historical context semantic data and historical decision information corresponding to each manual decision node; analyzing the historical manual decision node data to determine a target manual decision node which accords with the automatic decision node creation condition; analyzing the target historical decision information corresponding to the target manual decision node, and determining the branch decision conditions and the corresponding branch decision contents corresponding to each decision branch; and creating a mutual exclusion gateway based on the branch decision condition and the branch decision content to finish the creation of the automatic decision node so that the automatic decision node replaces the manual decision node. By creating the automatic decision nodes to replace the corresponding manual decision nodes, the dependence of the business process on the manual nodes is reduced, the labor cost and the time cost are reduced, the decision approval efficiency of the business process is improved, meanwhile, the manual decision nodes which can be replaced by the automatic decision nodes are determined based on a large amount of historical process data for analysis, the accuracy of the decision approval of the corresponding nodes can be improved, the operation and monitoring of the consistency in the decision approval can be ensured, and the stability of the business process is ensured.
Drawings
Fig. 1 is a schematic flow chart of an automatic decision node creation method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of decision making by a PQA spot check yield manual decision node according to an embodiment of the present application;
fig. 3 is a schematic flow chart of decision making by a PQA spot check yield automatic decision node according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an automatic decision node creating device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a Fab flow system according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Example 1
Fig. 1 is a flow chart of an automatic decision node creation method according to an embodiment of the present application. The embodiment can be used for converting a manual decision node in a wafer manufacturing business process in a Fab process system into an automatic decision node. The method may be performed by an automatic decision node creation means, which may be implemented in hardware and/or software, e.g. which may be configured in a Fab flow system. As shown in fig. 1, the method includes:
Step S101, extracting historical manual decision node data from the obtained historical flow data, wherein the historical manual decision node data comprises historical node input parameters, historical context semantic data and historical decision information corresponding to each manual decision node.
The historical flow data in the embodiment comprises historical flow instance data, and the historical flow instance data comprises historical task instance data corresponding to each node in the flow and historical form data corresponding to each task; the historical process instance data comprises a process type, a node type, a task type, instance content, starting parameters corresponding to the instance content, input parameters of each node and processing time information, specifically, the process type can be classified according to scenes, the process type can comprise a monitoring process, an abnormal processing process and the like, the node type can comprise a starting node, an ending node, a user node, a service node and the like, and the task type can comprise a user task, a service task and the like. The history form data comprises parameter information, result analysis, task decision information, countersign transfer data and the like in the corresponding task execution process.
The historical manual decision node data is historical data corresponding to each manual decision node in the historical flow data, wherein the input parameters of the historical node data can be historical parameter information received by the corresponding node, the historical context semantic data can be data used for determining historical execution sequence or causal relation of tasks corresponding to the corresponding node, and the historical decision information can be branch circulation information of the corresponding node when the corresponding task is executed. Specifically, the historical context semantic data may include historical form data corresponding to the manual decision node and historical task instance content, and the historical decision information may be extracted based on the historical task instance content and/or the historical form data. Preferably, the manual decision node in the historical flow data can be determined first, and then the historical manual decision node data corresponding to the manual decision node is extracted, wherein the manual decision node can be determined directly through the node type, and also can be determined through whether the decision information corresponding to the node is executed manually or not.
Step S102, analyzing the historical manual decision node data to determine a target manual decision node which accords with the automatic decision node creation condition.
The automatic decision node is a node which can automatically complete the decision without manually participating in the decision and can carry out the branch circulation of the subsequent decision. In this embodiment, not all the manual decision nodes can be converted into automatic decision nodes, and after some manual decision nodes obtain the node input parameters, decision staff can complete the decision corresponding to the node without other parameters, so that the manual decision nodes can be considered to meet the conditions for creating the automatic decision nodes. The manual decision node is a PQA (Product Quality Assurance, product quality detection) spot check yield manual decision node, the corresponding node input parameter is a PQA spot check yield, and the manual decision node can be determined through a plurality of historical context semantic data and historical decision information, and after the PQA spot check yield is obtained, decision-making personnel all execute the following decisions: and confirming whether the yield of the PQA spot inspection is qualified or not, if so, informing a next site responsible person by the mail, and if not, informing a previous site responsible person by the mail. In this example, after the PQA spot check yield artificial decision node obtains the PQA spot check yield, the decision maker completes the decision corresponding to the node without using other parameters, so the PQA spot check yield artificial decision node is considered to conform to the automatic decision node creation condition. And after some manual decision nodes obtain the node input parameters, decision staff still need to finish the decision corresponding to the node by means of other parameters, and then the manual decision nodes can be considered to be not in accordance with the automatic decision node creation conditions. The manual decision node is a defect management manual decision node, the input parameters of the corresponding node are partial log codes, the input parameters can be determined through a plurality of historical context semantic data and historical decision information, and after the partial log codes are acquired, decision-making personnel execute the following decisions: the method can not directly determine the corresponding defect types, and needs to trace back information or give decision judgment according to experience. In this example, after obtaining the partial log code, the decision maker needs to complete the decision corresponding to the node by using other parameters, so that the defect management manual decision node is considered to be not in accordance with the automatic decision node creation condition.
Therefore, to determine whether the manual decision node meets the automatic decision node creation condition, it is necessary to analyze the historical manual decision node data. Preferably, the analysis can be performed by combining the historical node input parameters, the historical context semantic data and the historical decision information corresponding to the manual decision node.
Step S103, analyzing the target historical decision information corresponding to the target manual decision node, and determining the branch decision conditions and the corresponding branch decision contents corresponding to each decision branch.
Generally, the decision information may include different decision branches, each decision branch may include a branch decision condition and a branch decision content, and each branch decision condition corresponds to one branch decision content, where the branch decision condition is a judgment condition in the decision branch, and the branch decision content is a decision content corresponding to the judgment condition in the decision branch, and only if the data meets the branch decision condition, the corresponding branch decision content may be executed. And taking the manual decision node as a PQA sampling rate manual decision node, taking the PQA sampling rate as an example of the corresponding input parameter, and taking the target historical decision information corresponding to the manual decision node as decision staff after acquiring the PQA sampling rate no matter how much historical task instance content exists, confirming whether the PQA sampling rate is qualified or not, if so, informing a next site responsible person by a mail, and if not, informing a previous site responsible person by the mail. It may be determined that there are two decision branches in the above example, where judging that the pass rate and fail rate of the PQA spot inspection are branch decision conditions of the two decision branches, respectively, the mail notifies the next site responsible person and the mail notifies the last site responsible person of branch decision contents of the two decision branches, respectively.
Step S104, based on the branch decision condition and the branch decision content, creating a mutual exclusion gateway, and completing the creation of the automatic decision node so that the automatic decision node replaces the manual decision node.
Preferably, the automatic decision node in this embodiment is in the form of a mutually exclusive gateway, where the mutually exclusive network management includes different decision branches, and each decision branch corresponds to a branch decision condition and a corresponding branch decision content. In this embodiment, after the automatic decision node is created, the corresponding task can be executed instead of the manual decision node. It can be understood that after the automatic decision node is created, the corresponding manual decision node can be deleted, the corresponding manual decision node can be reserved, if the manual decision node is reserved, the priority can be set for the automatic decision node and the corresponding manual decision node, the automatic decision node is set as a default node, the manual decision node is set as a spare node, once the automatic decision node has a problem, the manual decision node can be immediately converted, and the business process is ensured not to be interrupted.
The manual decision node is taken as a PQA sampling rate manual decision node, the corresponding input parameter is taken as a PQA sampling rate as an example, and the above process is specifically described with reference to fig. 2 and 3: fig. 2 is a schematic flow diagram of a decision making by a manual decision node of a PQA sampling rate provided in an embodiment of the present application, and fig. 3 is a schematic flow diagram of a decision making by an automatic decision node of a PQA sampling rate provided in an embodiment of the present application, as shown in fig. 2, historical manual decision node data includes that a Fab flow system obtains the PQA sampling rate in a yield management system through a service node, and sends the obtained PQA sampling rate to the manual decision node of the PQA sampling rate, after obtaining the PQA sampling rate, a decision maker confirms whether the PQA sampling rate is qualified, if so, a mail notifies a next site responsible person, and if not, a mail notifies a previous site responsible person. Through analysis, two decision branches exist in the corresponding target historical decision information, wherein, the qualification and the disqualification of the PQA sampling inspection are judged to be the branch decision conditions of the two decision branches respectively, a mail informs a next site responsible person and a mail informs a last site responsible person of the branch decision contents of the two decision branches respectively, a mutual exclusion gateway can be established based on the branch decision contents, and the establishment of the automatic decision node of the PQA sampling inspection yield is completed. As shown in fig. 3, the Fab flow system obtains the PQA spot check yield in the yield management system through the service node, the automatic PQA spot check yield decision node obtains the PQA spot check yield, and determines whether the PQA spot check yield is qualified based on a preset spot check yield threshold, if so, the next site responsible person is notified by the mail, and if not, the previous site responsible person is notified by the mail.
According to the technical scheme provided by the embodiment of the application, the historical manual decision node data are extracted from the obtained historical flow data, and the historical manual decision node data comprise the historical node input parameters, the historical context semantic data and the historical decision information corresponding to each manual decision node; analyzing the historical manual decision node data to determine a target manual decision node which accords with the automatic decision node creation condition; analyzing the target historical decision information corresponding to the target manual decision node, and determining the branch decision conditions and the corresponding branch decision contents corresponding to each decision branch; and creating a mutual exclusion gateway based on the branch decision condition and the branch decision content to finish the creation of the automatic decision node so that the automatic decision node replaces the manual decision node. By creating the automatic decision nodes to replace the corresponding manual decision nodes, the dependence of the business process on the manual nodes is reduced, the labor cost and the time cost are reduced, the decision approval efficiency of the business process is improved, meanwhile, the manual decision nodes which can be replaced by the automatic decision nodes are determined based on a large amount of historical process data for analysis, the accuracy of the decision approval of the corresponding nodes can be improved, the operation and monitoring of the consistency in the decision approval can be ensured, and the stability of the business process is ensured.
In some embodiments of the present application, the analyzing the historical manual decision node data to determine a target manual decision node that meets an automatic decision node creation condition includes:
and determining whether the current manual decision node accords with the automatic decision node creation condition according to the data structure of the history node input parameters corresponding to the current manual decision node.
The complexity of the node input parameters can be intuitively determined based on the data structure of the node input parameters, so that some more complex manual decision nodes which do not accord with the automatic decision node creation conditions can be conveniently eliminated.
The data structures may include one-dimensional, two-dimensional, and multi-dimensional data structures, and may also include nested data structures, in which case other data structures than one-dimensional data structures are relatively complex.
Preferably, determining whether the current manual decision node meets an automatic decision node creation condition according to a data structure of a history node input parameter corresponding to the current manual decision node includes:
if the data structure is not a one-dimensional nested data structure, determining that the current manual decision node does not accord with an automatic decision node creation condition;
If the data structure is a one-dimensional nested data structure, determining whether the current manual decision node accords with an automatic decision node creation condition according to the historical context semantic data and the historical decision information of the current manual decision node.
In addition to considering the data structure of the node input parameters, the embodiment fully combines the context semantic data and the decision information of the manual decision node to more accurately determine the target manual decision node which accords with the automatic decision node creation condition.
Preferably, determining whether the current manual decision node meets an automatic decision node creation condition according to the historical context semantic data and the historical decision information of the current manual decision node comprises:
if the decision of the current manual decision node is determined according to the historical context semantic data and the historical decision information and is determined only according to the historical node input parameters, determining that the current manual decision node accords with an automatic decision node creation condition;
if the decision of the current manual decision node is determined according to the historical context semantic data and the historical decision information and according to the historical node input parameters and the node data corresponding to at least one node before the current manual decision node, determining that the current manual decision node does not accord with the automatic decision node creation condition.
The context semantic data and the decision information can provide the sequence or the causal relation of the manual decision executing process and final decision information, and the accuracy of the target manual decision node determined by referring to the context semantic data and the decision information is higher.
Still regard artificial decision node as the artificial decision node of PQA selective examination qualification rate, its correspondent node input parameter is the example of the selective examination qualification rate of PQA, can confirm through a plurality of historical context semantic data and historical decision information, after obtaining the selective examination qualification rate of PQA, decision maker all carries out the following decision: and confirming whether the yield of the PQA spot inspection is qualified or not, if so, informing a next site responsible person by the mail, and if not, informing a previous site responsible person by the mail. In this example, after the PQA sampling rate is obtained, the decision maker only uses the PQA sampling rate to complete the decision corresponding to the node, and no other parameters are used, so that the PQA sampling rate manual decision node is considered to meet the automatic decision node creation condition.
Still take the manual decision node as the defect management manual decision node, the input parameters of the corresponding nodes are taken as partial log codes for example, the decision can be determined through a plurality of historical context semantic data and historical decision information, and after the partial log codes are acquired, decision staff execute the following decisions: the decision maker cannot directly determine the corresponding defect type, and needs to trace back the node data corresponding to at least one node before the current manual decision node or give decision judgment according to experience. In this example, after the defect management manual decision node obtains the partial log code, the decision maker can complete the decision corresponding to the node by using the partial log code and the node data (or experience) corresponding to at least one node before the current manual decision node, so that the defect management manual decision node is considered to be not in accordance with the automatic decision node creation condition.
In some embodiments of the present application, after completing the automatic decision node creation, the method further includes: and matching the node input parameters of the automatic decision node with the branch decision conditions of the corresponding mutual exclusion gateway, and executing corresponding branch decision contents according to the matching result so as to promote the subsequent flow.
After the automatic decision node is established, the stability and the reliability of the automatic decision node can be determined by actual application. If there is no problem, the subsequent flow can be advanced.
With continued reference to fig. 3, as shown in fig. 3, the mutual exclusion gateway includes two decision branches, wherein, it is judged that the PQA sampling rate is qualified and the mail notifies the next site of being responsible for a decision branch, it is judged that the PQA sampling rate is unqualified and the mail notifies the last site of being responsible for a decision branch, concretely, the Fab flow system acquires the PQA sampling rate in the yield management system through the service node, the automatic decision node of the PQA sampling rate acquires the PQA sampling rate, and it is determined whether the PQA sampling rate is qualified or not based on a preset sampling rate threshold, if the PQA sampling rate is judged to be qualified, the mail notifies the next site of being responsible, and if the PQA sampling rate is judged to be unqualified, the mail notifies the last site of being responsible.
In some embodiments of the present application, further comprising: and if the node input parameters of the automatic decision node cannot be matched with the decision branch conditions in the corresponding mutual exclusion gateway, sending alarm information.
After the automatic decision node is established, if the automatic decision node has a problem, the automatic decision node needs to be informed of manual inspection and repair through alarm information, and meanwhile, the automatic decision node can be converted into a manual decision node to continue the flow.
Taking the automatic decision node of the yield of the PQA as an example, if the yield data of the PQA is damaged and the data cannot be identified after the automatic decision node of the yield of the PQA acquires the yield of the PQA, the automatic decision node of the yield of the PQA needs to be notified to manually participate in inspection and repair, and at the moment, the automatic decision node of the yield of the PQA can be notified by sending alarm information.
Example two
Fig. 4 is a schematic structural diagram of an automatic decision node creating device according to an embodiment of the present application. As shown in fig. 4, the apparatus includes:
a historical manual decision node data extraction module 410, configured to extract historical manual decision node data from the obtained historical flow data, where the historical manual decision node data includes a historical node input parameter, historical context semantic data and historical decision information corresponding to each manual decision node;
The target manual decision node determining module 420 is configured to analyze the historical manual decision node data and determine a target manual decision node that meets the automatic decision node creation condition;
the decision branch determining module 430 is configured to analyze the target historical decision information corresponding to the target manual decision node, and determine a branch decision condition and a corresponding branch decision content corresponding to each decision branch;
and an automatic decision node creation module 440, configured to create a mutually exclusive gateway based on the branching decision condition and the branching decision content, and complete the automatic decision node creation, so that the automatic decision node replaces the manual decision node.
According to the technical scheme provided by the embodiment of the application, the historical manual decision node data extraction module is used for extracting historical manual decision node data from the acquired historical flow data, wherein the historical manual decision node data comprises historical node input parameters, historical context semantic data and historical decision information corresponding to each manual decision node; the target manual decision node determining module is used for analyzing the historical manual decision node data and determining target manual decision nodes which accord with the automatic decision node creating conditions; the decision branch determining module is used for analyzing the target historical decision information corresponding to the target manual decision node and determining the branch decision condition corresponding to each decision branch and the corresponding branch decision content; the automatic decision node creation module is used for creating a mutual exclusion gateway based on the branch decision condition and the branch decision content to complete the automatic decision node creation so that the automatic decision node replaces the manual decision node. By creating the automatic decision nodes to replace the corresponding manual decision nodes, the dependence of the business process on the manual nodes is reduced, the labor cost and the time cost are reduced, the decision approval efficiency of the business process is improved, meanwhile, the manual decision nodes which can be replaced by the automatic decision nodes are determined based on a large amount of historical process data for analysis, the accuracy of the decision approval of the corresponding nodes can be improved, the operation and monitoring of the consistency in the decision approval can be ensured, and the stability of the business process is ensured.
Optionally, on the basis of the above solution, the target manual decision node determining module 420 includes a target manual decision node determining unit, which may specifically be used for: and determining whether the current manual decision node accords with the automatic decision node creation condition according to the data structure of the history node input parameters corresponding to the current manual decision node.
Optionally, based on the above scheme, the target manual decision node determining unit may specifically be configured to: if the data structure is not a one-dimensional nested data structure, determining that the current manual decision node does not accord with an automatic decision node creation condition; if the data structure is a one-dimensional nested data structure, determining whether the current manual decision node accords with an automatic decision node creation condition according to the historical context semantic data and the historical decision information of the current manual decision node.
Optionally, on the basis of the above scheme, the target manual decision node determining unit may be further specifically configured to: if the decision of the current manual decision node is determined according to the historical context semantic data and the historical decision information and is determined only according to the historical node input parameters, determining that the current manual decision node accords with an automatic decision node creation condition; if the decision of the current manual decision node is determined according to the historical context semantic data and the historical decision information and according to the historical node input parameters and the node data corresponding to at least one node before the current manual decision node, determining that the current manual decision node does not accord with the automatic decision node creation condition.
Optionally, on the basis of the above solution, the automatic decision node creating device may further include an automatic decision node executing module, and specifically may be configured to match, after the automatic decision node is created, a node input parameter of the automatic decision node with each branch decision condition of the corresponding mutually exclusive gateway, and execute corresponding branch decision content according to a matching result, so as to advance a subsequent procedure.
Optionally, on the basis of the above scheme, the automatic decision node creating device may further include an alarm device, and may specifically be used for: and if the node input parameters of the automatic decision node cannot be matched with the decision branch conditions in the corresponding mutual exclusion gateway, sending alarm information.
Optionally, on the basis of the above scheme, the historical flow data includes historical flow instance data, where the historical flow instance data includes historical task instance data corresponding to each node in the flow and historical form data corresponding to each task; the history flow instance data comprises a flow type, a node type, a task type, instance content, starting parameters corresponding to the instance content, input parameters of each node and processing time information, and the history form data comprises parameter information, result analysis, task decision information and countersign transfer data in the corresponding task executing process.
The automatic decision node creation device provided by the embodiment of the invention can execute the automatic decision node creation method applied to the Fab flow system provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example III
In addition, the embodiment of the present application further provides a Fab flow system, fig. 5 is a schematic structural diagram of the Fab flow system provided in the embodiment of the present application, and the structure of the system is shown in fig. 5, where the Fab flow system includes a first memory 51 for storing computer readable instructions and a first processor 52 for executing the computer readable instructions, where the computer readable instructions when executed by the first processor 52 trigger the first processor 52 to execute an automatic decision node creation method applied to the Fab flow system.
The methods and/or embodiments of the present application may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. The above-described functions defined in the method of the present application are performed when the computer program is executed by a processing unit.
It should be noted that, the computer readable medium described in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowchart or block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As another aspect, the present application also provides a computer readable medium, which may be contained in the Fab flow system described in the above embodiment; or may exist alone without being assembled into the system. The above-described computer readable medium carries one or more computer readable instructions executable by a processor to implement the steps of the automated decision node creation method and/or technique scheme applied to a Fab flow system in the various embodiments of the present application described above.
In a typical configuration of the present application, the terminals, the devices of the services network each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer-readable media include both permanent and non-permanent, removable and non-removable media, and information storage may be implemented by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device.
In addition, the embodiment of the application also provides a computer program which is stored in the Fab flow system, so that the Fab flow system executes the automatic decision node creation method executed by the control code.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, using Application Specific Integrated Circuits (ASIC), a general purpose computer or any other similar hardware device. In some embodiments, the software programs of the present application may be executed by a processor to implement the above steps or functions. Likewise, the software programs of the present application (including associated data structures) may be stored on a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. In addition, some steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the apparatus claims can also be implemented by means of one unit or means in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Claims (10)
1. An automatic decision node creation method applied to a Fab flow system, the method comprising:
extracting historical manual decision node data from the obtained historical flow data, wherein the historical manual decision node data comprises historical node input parameters, historical context semantic data and historical decision information corresponding to each manual decision node;
analyzing the historical manual decision node data to determine a target manual decision node which accords with the automatic decision node creation condition;
analyzing the target historical decision information corresponding to the target manual decision node, and determining the branch decision conditions and the corresponding branch decision contents corresponding to each decision branch;
and creating a mutual exclusion gateway based on the branch decision condition and the branch decision content to finish the creation of the automatic decision node so that the automatic decision node replaces the manual decision node.
2. The method of claim 1, wherein analyzing the historical manual decision node data to determine a target manual decision node that meets an automatic decision node creation condition comprises:
and determining whether the current manual decision node accords with the automatic decision node creation condition according to the data structure of the history node input parameters corresponding to the current manual decision node.
3. The method of claim 2, wherein determining whether the current manual decision node meets an automatic decision node creation condition based on a data structure of historical node input parameters corresponding to the current manual decision node comprises:
if the data structure is not a one-dimensional nested data structure, determining that the current manual decision node does not accord with an automatic decision node creation condition;
if the data structure is a one-dimensional nested data structure, determining whether the current manual decision node accords with an automatic decision node creation condition according to the historical context semantic data and the historical decision information of the current manual decision node.
4. A method according to claim 3, wherein determining whether the current manual decision node meets an automatic decision node creation condition based on historical context semantic data and historical decision information of the current manual decision node comprises:
if the decision of the current manual decision node is determined according to the historical context semantic data and the historical decision information and is determined only according to the historical node input parameters, determining that the current manual decision node accords with an automatic decision node creation condition;
If the decision of the current manual decision node is determined according to the historical context semantic data and the historical decision information and according to the historical node input parameters and the node data corresponding to at least one node before the current manual decision node, determining that the current manual decision node does not accord with the automatic decision node creation condition.
5. The method of any of claims 1-4, further comprising, after completing the automatic decision node creation:
and matching the node input parameters of the automatic decision node with the branch decision conditions of the corresponding mutual exclusion gateway, and executing corresponding branch decision contents according to the matching result so as to promote the subsequent flow.
6. The method as recited in claim 5, further comprising:
and if the node input parameters of the automatic decision node cannot be matched with the decision branch conditions in the corresponding mutual exclusion gateway, sending alarm information.
7. The method of any one of claims 1-4, wherein the historical process data includes historical process instance data, the historical process instance data including historical task instance data corresponding to each node in the process and historical form data corresponding to each task; the history flow instance data comprises a flow type, a node type, a task type, instance content, starting parameters corresponding to the instance content, input parameters of each node and processing time information, and the history form data comprises parameter information, result analysis, task decision information and countersign transfer data in the corresponding task executing process.
8. An automatic decision node creation device, provided in a Fab flow system, comprising:
the historical manual decision node data extraction module is used for extracting historical manual decision node data from the obtained historical flow data, wherein the historical manual decision node data comprises historical node input parameters, historical context semantic data and historical decision information corresponding to each manual decision node;
the target manual decision node determining module is used for analyzing the historical manual decision node data and determining target manual decision nodes which accord with the automatic decision node creating conditions;
the decision branch determining module is used for analyzing the target historical decision information corresponding to the target manual decision node and determining branch decision conditions and corresponding branch decision contents corresponding to each decision branch;
and the automatic decision node creation module is used for creating a mutual exclusion gateway based on the branch decision condition and the branch decision content to finish the automatic decision node creation so that the automatic decision node replaces the manual decision node.
9. A Fab flow system, said system comprising:
One or more processors; and
a memory storing computer program instructions that, when executed, cause the processor to perform the method of any of claims 1-7.
10. A computer readable medium having stored thereon computer program instructions executable by a processor to implement the method of any of claims 1-7.
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