CN112379921B - Automatic generation and self-perfecting system and method for dynamic flexible flow - Google Patents
Automatic generation and self-perfecting system and method for dynamic flexible flow Download PDFInfo
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- 238000013499 data model Methods 0.000 claims abstract description 85
- 238000000354 decomposition reaction Methods 0.000 claims description 15
- 238000013500 data storage Methods 0.000 claims description 14
- 238000012546 transfer Methods 0.000 description 6
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- 230000000694 effects Effects 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
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- 238000013523 data management Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000004886 process control Methods 0.000 description 2
- 238000011144 upstream manufacturing Methods 0.000 description 2
- 238000009960 carding Methods 0.000 description 1
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Abstract
The invention belongs to the technical field of knowledge engineering, and relates to a system and a method for automatically generating and perfecting a dynamic flexible flow. According to the invention, each node in the process is decomposed into tasks and data, and a task model and a data model are established. And associating the task model with the data model to form an upstream-downstream relation between tasks, and determining the position of each node in the task model in the process through the generation and the use of data so as to automatically generate the process. When the input and output data model nodes of the task model nodes are changed, the flow is automatically changed, and the functions of building and self-perfecting the dynamic flow based on the model are realized.
Description
Technical Field
The invention belongs to the technical field of knowledge engineering, and relates to a system and a method for automatically generating and perfecting a dynamic flexible flow.
Background
At present, a task flow management platform usually adopts a rigid flow, namely, after a task flow is defined in advance, the task flow is executed strictly according to the flow sequence, mainly the organization system and the responsibility authority are relied on, and carding and writing are needed in advance, but it is often difficult to determine to which layer the nodes of the flow are refined to. Moreover, the traditional rigid flow is suitable for production line type work and is not suitable for product design and research and development. For design flows with greater flexibility, traditional rigid flows are "process constraints" and "flow restrictions.
Disclosure of Invention
The purpose of the invention is that: a system and method for automatic generation and self-perfecting of dynamic flexible flow are provided to solve the incompleteness of flow programming.
In business work, each node in the flow is decomposed into tasks and data, and a task model and a data model are established. A task model is a multi-layer structure of a series of task nodes. The task nodes can correlate knowledge or requirements of input data, standard specifications, and the like according to the AOS specification. The data model is a multi-layer structure of task yield data of a series of tasks. The data model nodes represent a class of data sets, equivalent to a data storage space. The task model nodes are not in direct relation with the nodes, but are associated through a data model, namely, the upstream and downstream relation among tasks is a data relation, and the positions of all the nodes in the task model in the process are determined through the generation and the use of data, so that the process is automatically generated. When the input and output data model nodes of the task model nodes are changed, the flow is automatically changed.
In order to achieve the purpose, the invention adopts the following technical scheme:
the technical scheme is as follows:
a dynamic flexible process auto-generation and self-perfecting system, the system comprising: the system comprises a task decomposition module, a task model module, a task data relation module, a data model module and a data storage module;
the task model module is used for storing a plurality of task model nodes, and the task model nodes are organized according to a tree structure;
the task decomposition module is used for performing task decomposition according to the related task model nodes in the plurality of task model nodes to obtain task instance nodes;
the data model module is used for storing a plurality of data model nodes, and the data model nodes are organized according to a tree structure;
the data storage module is used for carrying out data storage according to the related data model nodes in the plurality of data model nodes to obtain data instance nodes;
and the task data relation module is used for defining the input-output relation between the plurality of task model nodes and the plurality of data model nodes.
The first technical scheme of the invention is characterized in that:
(1) The attributes of each task model node include: task model node name, task model node ID, parent node ID of task model.
(2) The attributes of each data model node include: data model node name, data model node ID, parent node ID of the data model.
(3) The input-output relations between the task model nodes and the data model nodes are embodied in the form of a relation table; a plurality of records are stored in the relation table;
the attributes of each record include: task model node ID, data model node ID, and input/output type; the input/output type is input or output.
The second technical scheme is as follows:
a method for automatically generating and self-perfecting a dynamic flexible flow, wherein the method is applied to the system according to the first technical scheme, and the method comprises the following steps:
s1, acquiring a top-level task, selecting at least one task model node related to the top-level task according to a plurality of task model nodes in a task model module, and determining task instance nodes after task decomposition in a task decomposition module;
s2, establishing complete data instance nodes in a data storage module according to a plurality of data model nodes in the data model module;
s3, determining the input-output relationship between the task instance node and the complete data instance node according to the input-output relationship between a plurality of task model nodes and a plurality of data model nodes defined in the task data relationship module;
s4, obtaining the execution sequence between the task instance nodes according to the input-output relation between the task instance nodes and the complete data instance nodes.
The second technical proposal of the invention has the characteristics and further improvement that:
(1) In S1, when at least one task model node related to the top-level task is selected, if the task model module lacks a related task model node, a corresponding task model node is newly built.
(2) And S2, when necessary data model nodes are absent in the data model module, a corresponding data model node is newly built.
(3) And S3, when the input-output relations between the plurality of task model nodes and the plurality of data model nodes defined in the task data relation module do not meet the requirements of the current top-level task, manually modifying the input-output relations between the plurality of task model nodes and the plurality of data model nodes defined in the task data relation module.
The technical effects of the invention include: flexible flow is flexibly constructed based on a unified task model, so that real collaborative research and development and process control are realized; the data sharing center is flexibly constructed based on the unified data model, so that data sharing and unified data sources are facilitated; providing model-based "process guidance support" rather than "process constraints" or "flow restrictions"; the technical staff naturally perfects the model in the working process, and after the new task is finished and the approval is passed, the related model takes effect in the model library; the requirements and the workload of workers are reduced to the greatest extent possible by the model application and the model perfection, and particularly, the workload is not increased additionally by the model perfection.
Drawings
FIG. 1 is a task model, a data model form and a relationship description diagram provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of automatic generation and self-improvement of a dynamic flexible process according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a task management module-related form;
FIG. 4 is a schematic diagram illustrating a form associated with a data management module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a dynamic flexible flow automatic generation and self-perfecting system, which comprises: the system comprises a task decomposition module, a task model module, a task data relation module, a data model module and a data storage module;
the task model module is used for storing a plurality of task model nodes, and the task model nodes are organized according to a tree structure;
the task decomposition module is used for performing task decomposition according to the related task model nodes in the plurality of task model nodes to obtain task instance nodes;
the data model module is used for storing a plurality of data model nodes, and the data model nodes are organized according to a tree structure;
the data storage module is used for carrying out data storage according to the related data model nodes in the plurality of data model nodes to obtain data instance nodes;
and the task data relation module is used for defining the input-output relation between the plurality of task model nodes and the plurality of data model nodes.
Further:
(1) The attributes of each task model node include: task model node name, task model node ID, parent node ID of task model.
(2) The attributes of each data model node include: data model node name, data model node ID, parent node ID of the data model.
(3) The input-output relations between the task model nodes and the data model nodes are embodied in the form of a relation table; a plurality of records are stored in the relation table;
the attributes of each record include: task model node ID, data model node ID, and input/output type; the input/output type is input or output.
The embodiment of the invention also provides a method for automatically generating and self-perfecting the dynamic flexible flow, which is applied to the system and comprises the following steps:
s1, acquiring a top-level task, selecting at least one task model node related to the top-level task according to a plurality of task model nodes in a task model module, and determining task instance nodes after task decomposition in a task decomposition module;
s2, establishing complete data instance nodes in a data storage module according to a plurality of data model nodes in the data model module;
s3, determining the input-output relationship between the task instance node and the complete data instance node according to the input-output relationship between a plurality of task model nodes and a plurality of data model nodes defined in the task data relationship module;
s4, obtaining the execution sequence between the task instance nodes according to the input-output relation between the task instance nodes and the complete data instance nodes.
Further:
(1) In S1, when at least one task model node related to the top-level task is selected, if the task model module lacks a related task model node, a corresponding task model node is newly built.
(2) And S2, when necessary data model nodes are absent in the data model module, a corresponding data model node is newly built.
(3) And S3, when the input-output relations between the plurality of task model nodes and the plurality of data model nodes defined in the task data relation module do not meet the requirements of the current top-level task, manually modifying the input-output relations between the plurality of task model nodes and the plurality of data model nodes defined in the task data relation module.
The system for automatically generating and self-perfecting the dynamic flexible flow provided by the invention is developed by Java and uses an Oracle database, and mainly comprises a task model, a data model, a task management module and a data management module, so that the functions of constructing and self-perfecting the dynamic flow based on the model are realized. Wherein, the tasks automatically generate a flow according to the data transfer relation.
The task model and data model form relationships are shown in FIG. 1 below.
In the system, each node in the flow is decomposed into tasks and data, and a task model and a data model are established. A task model is a multi-layer structure of a series of task nodes. The task nodes can correlate knowledge or requirements of input data, standard specifications, and the like according to the AOS specification. The data model is a multi-layer structure of task yield data of a series of tasks. The data model nodes represent a class of data sets, equivalent to a data storage space. The task model nodes are not in direct relation with the nodes, but are associated through a data model. I.e., the upstream and downstream relationships between tasks are data relationships, as determined by the generation and use of data, as shown in fig. 2.
When the task is decomposed, the task is established by selecting a task model node. The established task can acquire the data transfer relation of the associated task node. And the platform dynamically constructs a flow according to the data transfer relation.
Task related forms are shown in FIG. 3 and data related forms are shown in FIG. 4.
The dynamic flow self-perfection comprises two aspects:
1) And correcting the data transfer relation among tasks.
During task progress, the user needs the data source as input, and can perform operations of "subscribing" and "un-focusing" on the data model nodes.
And after the task approval is passed, the new data transfer relationship automatically returns to the associated task model node, so that the correction of the data transfer relationship between the tasks is realized.
2) Adding task model nodes
When the task model node is lack or a new type of task appears, the task model node can be temporarily newly built and used when the task is decomposed.
And after the task approval is passed, temporarily creating a task model node and automatically returning the node data relationship and the node knowledge to the task model. The information such as the task model knowledge can be directly used in the next use.
According to the above description, the platform work in practical application is mainly divided into four parts:
the first step: according to task level, input/output data, reference knowledge (standard specification, reference, experience summary, technical expert, risk measure, common problem, method step) and task completion tool/module, a data model and a business model are established, and the task model and the data model are associated (only the data and business model need to be established in the initial stage of model task, and the subsequent task can be started directly from the second step).
And a second step of: and instantiating/creating a model task according to the task model. After model tasks are established, task professional flow relationships can be automatically generated according to data flow relationships defined in the task models and the data models.
And a third step of: and completing model tasks through the related knowledge pushed by the tools/modules and the knowledge pushing module associated with the task model. After receiving the model task, the user can directly start the target tool/module associated with the task model, and in the corresponding tool/module, the model task is completed by referring to the related knowledge pushed by the knowledge pushing module.
Fourth step: and submitting the task. After submitting the task and finishing the examination, automatically returning the newly added knowledge in the task to the affiliated knowledge base according to the dimension relation; automatically returning the data relationship contained in the task to a corresponding task model and a data model; and the task output data is automatically submitted to the corresponding data node. And the knowledge pushing module extracts historical knowledge through task details to serve later related model tasks.
The invention 1) flexibly constructs a flexible flow based on a unified task model, and realizes real collaborative research and development and process control; 2) The data sharing center is flexibly constructed based on the unified data model, so that data sharing and unified data sources are facilitated; 3) Providing model-based "process guidance support" rather than "process constraints" or "flow restrictions"; 4) The technical staff naturally perfects the model in the working process, and after the new task is finished and the approval is passed, the related model takes effect in the model library; 5) The requirements and the workload of workers are reduced to the greatest extent possible by the model application and the model perfection, and particularly, the workload is not increased additionally by the model perfection.
The foregoing is merely a detailed description of the invention, which is not a matter of routine skill in the art. However, the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily contemplated by those skilled in the art within the scope of the present invention should be included in the scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. A method for automatically generating and self-perfecting a dynamic flexible process, wherein the method is applied to a system for automatically generating and self-perfecting the dynamic flexible process, and the system comprises: the system comprises a task decomposition module, a task model module, a task data relation module, a data model module and a data storage module;
the task model module is used for storing a plurality of task model nodes, and the task model nodes are organized according to a tree structure;
the task decomposition module is used for performing task decomposition according to the related task model nodes in the plurality of task model nodes to obtain task instance nodes;
the data model module is used for storing a plurality of data model nodes, and the data model nodes are organized according to a tree structure;
the data storage module is used for carrying out data storage according to the related data model nodes in the plurality of data model nodes to obtain data instance nodes;
the task data relation module is used for defining input-output relations between a plurality of task model nodes and a plurality of data model nodes;
the attributes of each task model node include: task model node name, task model node ID, parent node ID of task model; the attributes of each data model node include: a data model node name, a data model node ID, a parent node ID of the data model; the input-output relations between the task model nodes and the data model nodes are embodied in the form of a relation table; a plurality of records are stored in the relation table; the attributes of each record include: task model node ID, data model node ID, and input/output type; the input/output type is input or output;
the method comprises the following steps:
s1, acquiring a top-level task, selecting at least one task model node related to the top-level task according to a plurality of task model nodes in a task model module, and determining task instance nodes after task decomposition in a task decomposition module;
s2, establishing complete data instance nodes in a data storage module according to a plurality of data model nodes in the data model module;
s3, determining the input-output relationship between the task instance node and the complete data instance node according to the input-output relationship between a plurality of task model nodes and a plurality of data model nodes defined in the task data relationship module;
s4, obtaining the execution sequence between the task instance nodes according to the input-output relation between the task instance nodes and the complete data instance nodes.
2. The method according to claim 1, wherein in S1, when at least one task model node associated with the top-level task is selected, if the task model module lacks an associated task model node, a corresponding task model node is newly built.
3. The method for automatically generating and self-perfecting a dynamic flexible flow according to claim 1, wherein in S2, when a necessary data model node is absent in the data model module, a corresponding data model node is newly built.
4. The method for automatically generating and self-perfecting a dynamic flexible flow according to claim 1, wherein in S3, when the input-output relationship between a plurality of task model nodes and a plurality of data model nodes defined in the task data relationship module does not meet the requirement of the current top-level task, the input-output relationship between a plurality of task model nodes and a plurality of data model nodes defined in the task data relationship module is manually modified.
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