CN111695880B - Production flow monitoring method and system - Google Patents
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 266
- 238000012544 monitoring process Methods 0.000 title claims abstract description 144
- 238000000034 method Methods 0.000 title claims abstract description 112
- 239000011159 matrix material Substances 0.000 claims abstract description 46
- 238000010586 diagram Methods 0.000 claims abstract description 38
- 238000000605 extraction Methods 0.000 claims description 25
- 230000004931 aggregating effect Effects 0.000 claims description 23
- 238000005070 sampling Methods 0.000 claims description 4
- 238000004891 communication Methods 0.000 description 4
- 238000004590 computer program Methods 0.000 description 4
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- 238000005516 engineering process Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The embodiment of the invention provides a production flow monitoring method and a system, which are characterized in that a flow node production line diagram corresponding to a production flow sequence where a target production flow is located is obtained, then monitoring behavior information of an associated flow section corresponding to the target production flow section is extracted from the flow node production line diagram, the extracted monitoring behavior information is aggregated to generate matrix characteristic information corresponding to the target production flow section, the target production flow is classified according to the matrix characteristic information, and a monitoring passing result of the target production flow is determined according to the obtained classification result. Thus, the accuracy of the monitoring passing result of the target production flow can be improved.
Description
Technical Field
The invention relates to the technical field of information monitoring, in particular to a production flow monitoring method and system.
Background
In the traditional technology, the production flow is monitored and identified usually by a manual observation mode, the mode is limited by manual experience, and the accuracy of the obtained monitoring and identifying result is not high.
Disclosure of Invention
Accordingly, an object of the embodiments of the present invention is to provide a method and a system for monitoring a production process, which can improve the accuracy of the monitoring result of the target production process.
According to an aspect of the embodiment of the present invention, there is provided a production flow monitoring method, applied to a server, the method including:
acquiring a process node production line diagram corresponding to a production process sequence where a target production process is located; the process node production line diagram is obtained according to a production process sequence corresponding to the production process sequence in a preset time period;
extracting monitoring behavior information of an associated process section corresponding to a target production process section from the process node production line graph, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section; the target production flow section is a flow section corresponding to the target production flow;
classifying the target production process according to the matrix characteristic information, and determining a monitoring passing result of the target production process according to the obtained classification result.
Optionally, before the process node production line diagram corresponding to the production process sequence where the target production process is located is obtained, the method further includes:
acquiring a flow configuration parameter sequence corresponding to the production flow sequence within a preset time period, and acquiring a plurality of production process sequences according to the flow configuration parameter sequence;
determining the production flow in a plurality of production process sequences as a flow section of the flow node production line graph;
and constructing association modes among the process sections according to the arrangement relation among the production processes in the production process sequences, and determining the monitoring service oriented of each association mode to obtain the process node production line graph.
Optionally, after constructing the association manner between the process segments according to the arrangement relationship between the production processes in the plurality of production process sequences and determining the monitoring-oriented service of each association manner, the method further includes:
for each association mode, counting the target feedback times of the source production flow corresponding to the association mode to the target production flow corresponding to the association mode according to a plurality of production process sequences;
counting the total feedback times of the source production flow corresponding to the association mode to other production flows according to a plurality of production process sequences;
and determining the weight of the association mode according to the target feedback times and the total feedback times.
Optionally, before extracting the monitoring behavior information of the associated process segment corresponding to the target production process segment from the process node production line graph, and aggregating the extracted monitoring behavior information to generate the matrix feature information corresponding to the target production process segment, the method further includes:
acquiring a target flow monitoring script; the extracting the monitoring behavior information of the associated process section corresponding to the target production process section from the process node production line graph, aggregating the extracted monitoring behavior information, and generating the matrix characteristic information corresponding to the target production process section comprises the following steps:
inputting the flow node production line graph into a monitoring behavior information extraction node of the target flow monitoring script, extracting monitoring behavior information of an associated flow section corresponding to a target production flow section through the monitoring behavior information extraction node, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production flow section;
the step of classifying the target production process according to the matrix characteristic information, and determining the monitoring passing result of the target production process according to the obtained classification result comprises the following steps:
and inputting the matrix characteristic information into a classification node of the target flow monitoring script to obtain a classification result.
Optionally, the monitoring behavior information extraction node includes a plurality of network nodes; the step of extracting the monitoring behavior information of the associated process section corresponding to the target production process section through the monitoring behavior information extracting node, and the step of aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section comprises the following steps:
starting from the top node of the monitoring behavior information extraction node, sequentially sampling candidate associated flow sections corresponding to each network node in the flow node production line graph until the bottom node of the monitoring behavior information extraction node, and obtaining a sub-graph corresponding to the flow node production line graph;
and sequentially extracting and aggregating the monitoring behavior information of the target associated flow sections corresponding to each network node in the subgraph from the bottom node of the monitoring behavior information extraction node to update the monitoring behavior information of the corresponding previous-stage associated flow section until the previous-stage associated flow section is the target production flow section, and generating matrix characteristic information corresponding to the target production flow section.
According to another aspect of the embodiment of the present invention, there is provided a production flow monitoring system, which is applied to a server, the system including:
the acquisition module is used for acquiring a process node production line diagram corresponding to a production flow sequence where the target production flow is located; the process node production line diagram is obtained according to a production process sequence corresponding to the production process sequence in a preset time period;
the extraction module is used for extracting monitoring behavior information of an associated process section corresponding to a target production process section from the process node production line graph, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section; the target production flow section is a flow section corresponding to the target production flow;
and the classification module is used for classifying the target production flow according to the matrix characteristic information and determining the monitoring passing result of the target production flow according to the obtained classification result.
According to another aspect of the embodiments of the present invention, there is provided a readable storage medium having stored thereon a computer program which, when executed by a processor, can perform the steps of the above-described production flow monitoring method.
Compared with the prior art, the production flow monitoring method and system provided by the embodiment of the invention have the advantages that the flow node production line diagram corresponding to the production flow sequence where the target production flow is located is obtained, then the monitoring behavior information of the associated flow section corresponding to the target production flow section is extracted from the flow node production line diagram, the extracted monitoring behavior information is aggregated, the matrix characteristic information corresponding to the target production flow section is generated, the target production flow is classified according to the matrix characteristic information, and the monitoring passing result of the target production flow is determined according to the obtained classification result. Thus, the accuracy of the monitoring passing result of the target production flow can be improved.
In order to make the above objects, monitoring behavior information and advantages of the embodiments of the present invention more comprehensible, the embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows a schematic diagram of components of an electronic device provided by an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for monitoring a production flow according to an embodiment of the present invention;
FIG. 3 shows a functional block diagram of a process flow monitoring system provided by an embodiment of the present invention.
Detailed Description
In order to make the present invention better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, 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 terms first, second, third and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 shows an exemplary component diagram of a server 100. The server 100 may include one or more processors 104, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The server 100 may also include any storage medium 106 for storing any kind of information such as code, settings, data, etc. For example, and without limitation, storage medium 106 may include any one or more of the following combinations: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage medium may store information using any technique. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent fixed or removable components of server 100. In one case, the server 100 may perform any of the operations of the associated instructions when the processor 104 executes the associated instructions stored in any storage medium or combination of storage media. The server 100 also includes one or more drive units 108, such as a hard disk drive unit, an optical disk drive unit, etc., for interacting with any storage media.
The server 100 also includes input/output 110 (I/O) for receiving various inputs (via input unit 112) and for providing various outputs (via output unit 114). One particular output mechanism may include a presentation device 116 and an associated Graphical User Interface (GUI) 118. The server 100 may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the components described above together.
The communication unit 122 may be implemented in any manner, for example, via a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. The communication unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers 100, etc., governed by any protocol or combination of protocols.
Fig. 2 is a flow chart illustrating a production flow monitoring method according to an embodiment of the present invention, which may be executed by the server 100 shown in fig. 1, and detailed steps of the production flow monitoring method are described below.
Step S110, a process node production line diagram corresponding to a production flow sequence where the target production flow is located is obtained. The process node production line graph is obtained according to a production process sequence corresponding to the production process sequence in a preset time period.
And step S120, extracting monitoring behavior information of an associated process section corresponding to the target production process section from the process node production line graph, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section. The target production flow section is a flow section corresponding to the target production flow.
And step S130, classifying the target production flow according to the matrix characteristic information, and determining a monitoring passing result of the target production flow according to the obtained classification result.
Based on the above steps, in this embodiment, the process node production line diagram corresponding to the production flow sequence in which the target production flow is located is obtained, then the monitoring behavior information of the associated flow section corresponding to the target production flow section is extracted from the process node production line diagram, the extracted monitoring behavior information is aggregated to generate matrix characteristic information corresponding to the target production flow section, then the target production flow is classified according to the matrix characteristic information, and the monitoring passing result of the target production flow is determined according to the obtained classification result. Thus, the accuracy of the monitoring passing result of the target production flow can be improved.
Optionally, before step S110, the embodiment may specifically further obtain a flow configuration parameter sequence corresponding to the production flow sequence in a preset time period, obtain a plurality of production process sequences according to the flow configuration parameter sequence, and then determine a production flow in the plurality of production process sequences as a flow section of the flow node production line graph. Therefore, according to the arrangement relation among the production flows in the production process sequences, the association modes among the flow sections are constructed, the monitoring service oriented to each association mode is determined, and the flow node production line diagram is obtained.
Optionally, after the correlation manner between process segments is constructed according to the arrangement relationships between the production flows in the production process sequences, and the monitoring service facing each correlation manner is determined, after the process node production line diagram is obtained, for each correlation manner, the target feedback times of the source production flow corresponding to the correlation manner to the target production flow corresponding to the correlation manner can be counted according to the production process sequences, and then the total feedback times of the source production flow corresponding to the correlation manner to other production flows can be counted according to the production process sequences, so that the weight of the correlation manner can be determined according to the target feedback times and the total feedback times.
Optionally, before extracting the monitoring behavior information of the associated process section corresponding to the target production process section from the process node production line graph and aggregating the extracted monitoring behavior information to generate the matrix characteristic information corresponding to the target production process section, a target process monitoring script may also be acquired. Therefore, in the process of extracting the monitoring behavior information of the associated process section corresponding to the target production process section from the process node production line diagram, aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section, the process node production line diagram can be input into a monitoring behavior information extracting node of the target process monitoring script, the monitoring behavior information of the associated process section corresponding to the target production process section is extracted through the monitoring behavior information extracting node, and the extracted monitoring behavior information is aggregated to generate the matrix characteristic information corresponding to the target production process section.
Meanwhile, in the process of classifying the target production flow according to the matrix characteristic information and determining the monitoring passing result of the target production flow according to the obtained classification result, the matrix characteristic information can be input into the classification node of the target flow monitoring script to obtain the classification result.
Optionally, the monitoring behavior information extraction node comprises a plurality of network nodes. And in the process of extracting the monitoring behavior information of the associated process section corresponding to the target production process section through the monitoring behavior information extraction node, aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section, starting from the top node of the monitoring behavior information extraction node, sequentially sampling the candidate associated process sections corresponding to each network node in the process node production line diagram until the bottom node of the monitoring behavior information extraction node to obtain a sub-graph corresponding to the process node production line diagram. And then, starting from the bottom node of the monitoring behavior information extraction node, sequentially extracting and aggregating the monitoring behavior information of the target associated flow section corresponding to each network node in the subgraph, so as to update the monitoring behavior information of the corresponding previous-stage associated flow section until the previous-stage associated flow section is the target production flow section, and generating matrix characteristic information corresponding to the target production flow section.
Fig. 3 shows a functional block diagram of a production flow monitoring system 200 according to an embodiment of the present invention, where functions implemented by the production flow monitoring system 200 may correspond to steps performed by the above-described method. The process monitoring system 200 may be understood as the server 100, or a processor of the server 100, or may be understood as a component that is independent of the server 100 or the processor and performs the functions of the present invention under the control of the server 100, as shown in fig. 3, the process monitoring system 200 may include an obtaining module 210, an extracting module 220, and a classifying module 230, and the functions of each functional module of the process monitoring system 200 are described in detail below.
An obtaining module 210, configured to obtain a process node production line graph corresponding to a production flow sequence where the target production flow is located; the process node production line diagram is obtained according to a production process sequence corresponding to the production process sequence in a preset time period;
the extracting module 220 is configured to extract monitoring behavior information of an associated process segment corresponding to a target production process segment from the process node production line graph, aggregate the extracted monitoring behavior information, and generate matrix feature information corresponding to the target production process segment; the target production flow section is a flow section corresponding to the target production flow;
the classification module 230 is configured to classify the target production process according to the matrix feature information, and determine a monitoring passing result of the target production process according to the obtained classification result.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus and method embodiments are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. 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.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
Alternatively, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, but that the present invention may be embodied in other specific forms without departing from the spirit or essential monitoring behavior information of the present invention. 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.
Claims (2)
1. A method for monitoring a production process, applied to a server, the method comprising:
acquiring a process node production line diagram corresponding to a production process sequence where a target production process is located; the process node production line diagram is obtained according to a production process sequence corresponding to the production process sequence in a preset time period;
extracting monitoring behavior information of an associated process section corresponding to a target production process section from the process node production line graph, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section; the target production flow section is a flow section corresponding to the target production flow;
classifying the target production flow according to the matrix characteristic information, and determining a monitoring passing result of the target production flow according to the obtained classification result;
before the process node production line diagram corresponding to the production flow sequence where the target production flow is located is obtained, the method further includes:
acquiring a flow configuration parameter sequence corresponding to the production flow sequence within a preset time period, and acquiring a plurality of production process sequences according to the flow configuration parameter sequence;
determining the production flow in a plurality of production process sequences as a flow section of the flow node production line graph;
according to the arrangement relation among the production flows in the production process sequences, constructing association modes among the flow sections, determining the monitoring service oriented of each association mode, and obtaining the flow node production line diagram;
after constructing association modes among process sections according to the arrangement relation among the production processes in the plurality of production process sequences and determining the monitoring-oriented service of each association mode, the method further comprises the steps of:
for each association mode, counting the target feedback times of the source production flow corresponding to the association mode to the target production flow corresponding to the association mode according to a plurality of production process sequences;
counting the total feedback times of the source production flow corresponding to the association mode to other production flows according to a plurality of production process sequences;
determining the weight of the association mode according to the target feedback times and the total feedback times;
before the monitoring behavior information of the associated process section corresponding to the target production process section is extracted from the process node production line diagram, the extracted monitoring behavior information is aggregated, and matrix characteristic information corresponding to the target production process section is generated, the method further comprises:
acquiring a target flow monitoring script; the extracting the monitoring behavior information of the associated process section corresponding to the target production process section from the process node production line graph, aggregating the extracted monitoring behavior information, and generating the matrix characteristic information corresponding to the target production process section comprises the following steps:
inputting the flow node production line graph into a monitoring behavior information extraction node of the target flow monitoring script, extracting monitoring behavior information of an associated flow section corresponding to a target production flow section through the monitoring behavior information extraction node, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production flow section;
the step of classifying the target production process according to the matrix characteristic information, and determining the monitoring passing result of the target production process according to the obtained classification result comprises the following steps:
inputting the matrix characteristic information into a classification node of the target flow monitoring script to obtain a classification result;
the monitoring behavior information extraction node comprises a plurality of network nodes; the step of extracting the monitoring behavior information of the associated process section corresponding to the target production process section through the monitoring behavior information extracting node, and the step of aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section comprises the following steps:
starting from the top node of the monitoring behavior information extraction node, sequentially sampling candidate associated flow sections corresponding to each network node in the flow node production line graph until the bottom node of the monitoring behavior information extraction node, and obtaining a sub-graph corresponding to the flow node production line graph;
and sequentially extracting and aggregating the monitoring behavior information of the target associated flow sections corresponding to each network node in the subgraph from the bottom node of the monitoring behavior information extraction node to update the monitoring behavior information of the corresponding previous-stage associated flow section until the previous-stage associated flow section is the target production flow section, and generating matrix characteristic information corresponding to the target production flow section.
2. A production flow monitoring system for use with a server, the system comprising:
the acquisition module is used for acquiring a process node production line diagram corresponding to a production flow sequence where the target production flow is located; the process node production line diagram is obtained according to a production process sequence corresponding to the production process sequence in a preset time period;
the extraction module is used for extracting monitoring behavior information of an associated process section corresponding to a target production process section from the process node production line graph, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section; the target production flow section is a flow section corresponding to the target production flow;
the classification module is used for classifying the target production flow according to the matrix characteristic information and determining a monitoring passing result of the target production flow according to the obtained classification result;
before a process node production line diagram corresponding to a production process sequence where a target production process is located is obtained, obtaining a process configuration parameter sequence corresponding to the production process sequence in a preset time period, and obtaining a plurality of production process sequences according to the process configuration parameter sequence; determining the production flow in a plurality of production process sequences as a flow section of the flow node production line graph; according to the arrangement relation among the production flows in the production process sequences, constructing association modes among the flow sections, determining the monitoring service oriented of each association mode, and obtaining the flow node production line diagram;
after the correlation modes among the process sections are constructed according to the arrangement relation among the production flows in the production process sequences, and the monitoring-oriented service of each correlation mode is determined, and the process node production line diagram is obtained, for each correlation mode, counting the target feedback times of the source production flow corresponding to the correlation mode to the target production flow corresponding to the correlation mode according to the production process sequences; counting the total feedback times of the source production flow corresponding to the association mode to other production flows according to a plurality of production process sequences; determining the weight of the association mode according to the target feedback times and the total feedback times;
extracting monitoring behavior information of an associated process section corresponding to a target production process section from the process node production line graph, and acquiring a target process monitoring script before aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section;
the extracting the monitoring behavior information of the associated process section corresponding to the target production process section from the process node production line graph, aggregating the extracted monitoring behavior information, and generating the matrix characteristic information corresponding to the target production process section comprises the following steps: inputting the flow node production line graph into a monitoring behavior information extraction node of the target flow monitoring script, extracting monitoring behavior information of an associated flow section corresponding to a target production flow section through the monitoring behavior information extraction node, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production flow section;
the step of classifying the target production process according to the matrix characteristic information, and determining the monitoring passing result of the target production process according to the obtained classification result comprises the following steps: inputting the matrix characteristic information into a classification node of the target flow monitoring script to obtain a classification result;
the monitoring behavior information extraction node comprises a plurality of network nodes; the step of extracting the monitoring behavior information of the associated process section corresponding to the target production process section through the monitoring behavior information extracting node, and the step of aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section comprises the following steps: starting from the top node of the monitoring behavior information extraction node, sequentially sampling candidate associated flow sections corresponding to each network node in the flow node production line graph until the bottom node of the monitoring behavior information extraction node, and obtaining a sub-graph corresponding to the flow node production line graph; and sequentially extracting and aggregating the monitoring behavior information of the target associated flow sections corresponding to each network node in the subgraph from the bottom node of the monitoring behavior information extraction node to update the monitoring behavior information of the corresponding previous-stage associated flow section until the previous-stage associated flow section is the target production flow section, and generating matrix characteristic information corresponding to the target production flow section.
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