CN115858111B - Intelligent scheduling method based on dynamic scene - Google Patents
Intelligent scheduling method based on dynamic scene Download PDFInfo
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- CN115858111B CN115858111B CN202211435362.4A CN202211435362A CN115858111B CN 115858111 B CN115858111 B CN 115858111B CN 202211435362 A CN202211435362 A CN 202211435362A CN 115858111 B CN115858111 B CN 115858111B
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Abstract
The invention relates to the technical field of natural robots, in particular to an intelligent scheduling method based on a dynamic scene, which comprises the following steps of according to standard agreements, in the process of trial operation of a user; reading all execution instructions of the whole canvas area, and constructing a complete instruction tree; recursively traversing the instruction tree, analyzing each node in the traversing process, judging the environment in which the current node can operate, and marking the node with a corresponding mark; determining the priority execution environment of the whole flow as a server or a local; and performing intelligent scheduling according to the determined priority execution environment. The invention adopts the thought of dynamic planning, can analyze the most suitable execution environment of each specific real execution instruction, adopts the mode of searching the optimal solution, dynamically adjusts in the process, and well solves the difference caused by rpa and ipaas fusion; and assists some standard conventions, fundamentally solving the diversity of process arrangement.
Description
Technical Field
The invention relates to the technical field of natural robots, in particular to an intelligent scheduling method based on a dynamic scene.
Background
The current instruction is divided into two main types of cloud instructions and local instructions, the cloud instructions can be executed in a remote server environment, and the local instructions need to depend on the environment of a local computer to operate some application programs of a local computer. In order to provide the user with richer capability and freedom, the user can freely select the instruction to build the own flow when the flow is arranged, so that the purpose of the user is achieved. In order to meet the needs of user diversity, algorithm analysis is required to be performed on the overall arranged flow, and the running environment of the current instruction can be intelligently judged when the application is executed.
Most rpa vendors and ipaas vendors currently provide single-capability flow scheduling and execution, which limits the implementation of many user requirements.
Disclosure of Invention
The invention aims to provide an intelligent scheduling method based on a dynamic scene, which is characterized in that rpa and ipaas are fused at a starting point, each instruction in a user flow is analyzed under the fused scene, the running environment and the form of the instruction are analyzed, and the scheduling scheme is determined under the condition of combining the whole flow, so that the defects in the background technology are overcome.
The technical scheme adopted by the invention is as follows:
the intelligent scheduling method based on the dynamic scene comprises the following steps:
according to standard convention, in the process of trial operation by a user; reading all execution instructions of the whole canvas area, and constructing a complete instruction tree;
recursively traversing the instruction tree, analyzing each node in the traversing process, judging the environment in which the current node can operate, and marking the node with a corresponding mark;
determining the priority execution environment of the whole flow as a server or a local;
and performing intelligent scheduling according to the determined priority execution environment.
As a preferred technical scheme of the invention: the intelligent scheduling according to the determined priority execution environment specifically comprises the following steps:
when the priority execution environment is a server, the schema data of all nodes are sent to the server for analysis by the server during execution, and the schema data are sent back to the in-process message through websocket;
when the preferential execution environment is local, the optimal execution environment of the current instruction can be dynamically adjusted according to the marked mark in the specific execution process.
As a preferred technical scheme of the invention: the standard convention comprises the type of the instruction, the api route of the corresponding server side of the instruction, the output model of the instruction and the schema structure of the instruction.
As a preferred technical scheme of the invention: and converting each node into a single-chain table structure when analyzing in the traversing process.
As a preferred technical scheme of the invention: 3 pointers are arranged on each node, and the pointers point to the first child node of the node respectively; the nesting pointer points to the next sibling node of the child node; until the last node there is a return pointer to the parent node.
As a preferred technical scheme of the invention: and analyzing the capacity model aiming at each node when analyzing each node.
As a preferred technical scheme of the invention: the preferential execution environment of the decision overall process is subjected to combined analysis by the capability model of the single node.
Compared with the prior art, the intelligent scheduling method based on the dynamic scene has the beneficial effects that:
the invention adopts the thought of dynamic planning, can analyze the most suitable execution environment of each specific real execution instruction, adopts the mode of searching the optimal solution, dynamically adjusts in the process, and well solves the difference caused by rpa and ipaas fusion; and assists some standard conventions, fundamentally solving the diversity of process arrangement.
Drawings
FIG. 1 is a system block diagram of a preferred embodiment of the present invention;
fig. 2 is a diagram illustrating a tree structure of nodes according to a preferred embodiment of the present invention.
Detailed Description
It should be noted that, under the condition of no conflict, the embodiments of the present embodiments and features in the embodiments may be combined with each other, and the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and obviously, the described embodiments 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.
Referring to fig. 1-2, a preferred embodiment of the present invention provides an intelligent scheduling method based on dynamic scenarios, comprising the steps of:
according to standard convention, in the process of trial operation by a user; reading all execution instructions of the whole canvas area, and constructing a complete instruction tree;
recursively traversing the instruction tree, analyzing each node in the traversing process, judging the environment in which the current node can operate, and marking the node with a corresponding mark;
determining the priority execution environment of the whole flow as a server or a local;
and performing intelligent scheduling according to the determined priority execution environment.
Specifically, the main concept of the invention is that a set of relatively standard conventions are needed on the premise that cloud instructions and local instructions can be distinguished by the conventions, and the conventions can better serve the execution of the instructions and the dynamic environment judgment of the instructions;
in this embodiment, based on the premise, when the user performs trial operation, a complete instruction tree is constructed by reading all execution instructions of the whole canvas area; performing recursive traversal on the instruction tree, analyzing each node in the traversal process, judging the environment in which the current node can operate, marking the node with a corresponding mark, finally comprehensively determining the priority execution environment of the whole flow, and if the priority execution environment is a server, sending the schema data of all the nodes to the server for analysis by the server and sending back in-process information through websocket; if the preferential execution environment is local, dynamically adjusting the optimal execution environment of the current instruction according to the marked mark in the specific execution process; finally, intelligent scheduling after instruction fusion is achieved.
Specifically, the implementation method is as follows:
1) Standard convention
In the scene of the invention, the cloud instruction has a corresponding account system, and the output of the cloud instruction is dynamically generated according to the actual application scene, so that in order to be different from the local instruction, some standard conventions are required to be carried out on the cloud instruction, including but not limited to the type of the instruction, the api route of the corresponding service end of the instruction, the output model of the instruction, the schema structure of the instruction and the like; with such a set of conventions, the operating environment and the matched data required by the operation can be defined when the instruction analysis is performed.
2) Node tree structure conversion
In the canvas area, nodes are displayed according to a tree structure, but in order to analyze the optimal running environment of the whole process more accurately and more efficiently, the tree structure is converted into a single-chain table structure as follows when being analyzed, each node is provided with 3 pointers, namely child pointers point to the first child node of the node; the nesting pointer points to the next sibling node of the child node; until the last node has a return pointer to its parent; thus, a single linked list structure is formed, and the analysis of the node tree by us is satisfied.
3) Node capability analysis
In the process of analyzing the node tree, the capacity model needs to be analyzed aiming at each node, the analysis and decision is made whether the current node can run in the cloud or can run locally or can only run locally, and finally, the capacity model of a single node is used for carrying out combined analysis, so that the running mode and environment of the current whole flow are determined, and the efficient execution of the flow is further carried out.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention 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 invention 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 should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (5)
1. An intelligent scheduling method based on dynamic scene is characterized in that: the method comprises the following steps:
according to standard convention, in the process of trial operation by a user; reading all execution instructions of the whole canvas area, and constructing a complete instruction tree;
recursively traversing the instruction tree, analyzing each node in the traversing process, judging the environment in which the current node can operate, and marking the node with a corresponding mark;
determining the priority execution environment of the whole flow as a server or a local;
intelligent scheduling is carried out according to the determined priority execution environment;
the intelligent scheduling according to the determined priority execution environment specifically comprises the following steps:
when the priority execution environment is a server, the schema data of all nodes are sent to the server for analysis by the server during execution, and the schema data are sent back to the in-process message through websocket;
when the preferential execution environment is local, dynamically adjusting the optimal execution environment of the current instruction according to the marked mark in the specific execution process;
the standard convention comprises the type of the instruction, the api route of the corresponding service end of the instruction, the output model of the instruction and the schema structure of the instruction.
2. The intelligent scheduling method based on dynamic scene as set forth in claim 1, wherein: and converting each node into a single-chain table structure in the process of traversing.
3. The intelligent scheduling method based on dynamic scene as set forth in claim 2, wherein: 3 pointers are arranged on each node, and the pointers point to the first child node of the node respectively; the nesting pointer points to the next sibling node of the child node; until the last node there is a return pointer to the parent node.
4. The intelligent scheduling method based on dynamic scene as set forth in claim 2, wherein: and analyzing the capacity model aiming at each node when analyzing each node.
5. The intelligent scheduling method based on dynamic scene as set forth in claim 4, wherein: the preferential execution environment of the decision overall process is subjected to combined analysis by the capability model of the single node.
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| CN111279304A (en) * | 2017-09-29 | 2020-06-12 | 甲骨文国际公司 | Method and system for configuring communication decision tree based on locatable elements connected on canvas |
| CN114173355A (en) * | 2021-10-25 | 2022-03-11 | 科大国创云网科技有限公司 | Dynamic execution method and system for network instruction with separated design operation state |
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| US7580948B2 (en) * | 2004-05-03 | 2009-08-25 | Microsoft Corporation | Spooling strategies using structured job information |
| US8363232B2 (en) * | 2004-05-03 | 2013-01-29 | Microsoft Corporation | Strategies for simultaneous peripheral operations on-line using hierarchically structured job information |
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| CN111279304A (en) * | 2017-09-29 | 2020-06-12 | 甲骨文国际公司 | Method and system for configuring communication decision tree based on locatable elements connected on canvas |
| CN110969184A (en) * | 2018-09-28 | 2020-04-07 | 甲骨文国际公司 | Directed trajectory through communication decision trees using iterative artificial intelligence |
| CN114173355A (en) * | 2021-10-25 | 2022-03-11 | 科大国创云网科技有限公司 | Dynamic execution method and system for network instruction with separated design operation state |
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