CN117519035A - Optimization method and system applied to tubular pile production control system - Google Patents
Optimization method and system applied to tubular pile production control system Download PDFInfo
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- CN117519035A CN117519035A CN202311577754.9A CN202311577754A CN117519035A CN 117519035 A CN117519035 A CN 117519035A CN 202311577754 A CN202311577754 A CN 202311577754A CN 117519035 A CN117519035 A CN 117519035A
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 141
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000005457 optimization Methods 0.000 title claims abstract description 20
- 230000002159 abnormal effect Effects 0.000 claims abstract description 71
- 238000013528 artificial neural network Methods 0.000 claims abstract description 60
- 230000006399 behavior Effects 0.000 claims abstract description 29
- 239000013598 vector Substances 0.000 claims abstract description 27
- 230000004927 fusion Effects 0.000 claims abstract description 25
- 230000009471 action Effects 0.000 claims abstract description 13
- 238000002372 labelling Methods 0.000 claims description 18
- 230000006870 function Effects 0.000 claims description 16
- 238000004891 communication Methods 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 7
- 230000003542 behavioural effect Effects 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 206010000117 Abnormal behaviour Diseases 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32368—Quality control
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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
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides an optimization method and system applied to a tubular pile production control system, wherein a plurality of production control behaviors in production control instance data of the tubular pile production control system are loaded to a first neural network to generate label data of different abnormal nodes corresponding to each production control behavior, and the first neural network is an auxiliary neural network generated by knowledge learning according to training marking requests of different abnormal nodes. Then, a corresponding fusion embedded vector is generated based on the tag data of each production control action. Finally, the fusion embedded vector is loaded to a second neural network to obtain the label data of the production control instance data. The method can effectively detect and prevent various abnormal conditions in the pipe pile production process, improves the production efficiency and ensures the product quality.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an optimization method and system applied to a tubular pile production control system.
Background
Tubular pile production is a complex engineering task, and its production control system needs to process a large amount of instance data and predict and manage possible anomalies. Traditional anomaly detection methods rely mainly on empirical rules or simple algorithms based on threshold values, but such methods are difficult to cope with complex and variable production environments and potential anomalies, and often require manual definition and updating of rules, resulting in inefficiency and inflexibility.
Disclosure of Invention
In view of the above, an object of the embodiments of the present invention is to provide an optimization method and system applied to a tubular pile production control system, wherein first, a plurality of production control behaviors in production control instance data of the tubular pile production control system are loaded to a first neural network, and tag data of different abnormal nodes corresponding to each production control behavior are generated, and the first neural network is an auxiliary neural network generated by learning knowledge according to training labeling requests of different abnormal nodes. Then, a corresponding fusion embedded vector is generated based on the tag data of each production control action. Finally, the fusion embedded vector is loaded to a second neural network to obtain the label data of the production control instance data. The method can effectively detect and prevent various abnormal conditions in the pipe pile production process, improves the production efficiency and ensures the product quality.
According to an aspect of the embodiment of the present invention, there is provided an optimization method and system applied to a pipe pile production control system, the method including:
loading a plurality of production control behaviors in production control instance data of a tubular pile production control system to a first neural network, and generating label data of different abnormal nodes corresponding to each production control behavior, wherein the first neural network is an auxiliary neural network generated by knowledge learning according to training marking requests of the different abnormal nodes;
generating a corresponding fusion embedded vector based on the label data of each production control behavior;
and loading the fusion embedded vector to a second neural network to obtain tag data of the production control instance data.
In an alternative embodiment, before loading the plurality of production control actions in the production control instance data into the first neural network, further comprising:
determining sample production control data of training marking requests of different abnormal nodes;
and carrying out knowledge learning on the first neural network according to the sample production control data, wherein the first neural network comprises a basic function parameter layer, encoders and classification units with different depths, and the encoders and the classification units with different depths respectively correspond to the knowledge learning of a training marking request.
In an alternative embodiment, the training labeling requests of the different abnormal nodes include training labeling requests of a first labeling abnormal node, a second labeling abnormal node and a third labeling abnormal node, and the learning of knowledge of the first neural network according to the sample production control data includes:
and carrying out knowledge learning on the first neural network according to the sample production control data, the KL divergence cost function of the first marked abnormal node, the logarithmic error function of the second marked abnormal node and the range cost function of the third marked abnormal node.
In an alternative embodiment, the determining the sample production control data of the training annotation request of the different abnormal nodes includes:
and acquiring corresponding initial production control events based on training marking requests of different abnormal nodes, embedding abnormal characteristics of the initial production control events, and fusing abnormal characteristic embedded data with the initial production control events to obtain sample production control data.
In an alternative embodiment, the tag data of each production control action includes confidence tag data and classification tag data, and the generating a corresponding fusion embedded vector based on the tag data of each production control action includes:
and integrating the confidence label data and the classification label data to obtain a target fusion embedded vector.
In an alternative embodiment, before loading the fused embedded vector into a second neural network to obtain tag data of the production control instance data, further comprising:
randomly extracting sample production control behaviors of target quantity in the sample production control data;
and carrying out abnormal classification on the sample production control behaviors according to different dimensions, integrating abnormal classification data into sample production control data of a second neural network, and carrying out knowledge learning on the second neural network.
In an alternative embodiment, the training annotation requests of the different abnormal nodes include at least two of a training annotation request of a data abnormal node, a training annotation request of a behavior abnormal node, a training annotation request of a performance abnormal node and a training annotation request of a safety abnormal node.
According to another aspect of the embodiments of the present invention, there is provided an optimizing method and system applied to a pipe pile production control system, the system including:
the first generation module is used for loading a plurality of production control behaviors in the production control instance data to a first neural network to generate label data of different abnormal nodes corresponding to each production control behavior, wherein the first neural network is an auxiliary neural network generated by knowledge learning according to training marking requests of the different abnormal nodes;
the second generation module is used for generating a corresponding fusion embedded vector based on the label data of each production control behavior;
and the loading module is used for loading the fusion embedded vector to a second neural network to obtain the label data of the production control instance data.
According to another aspect of an embodiment of the present invention, there is provided a server including: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus; the memory is used for storing a computer program; the processor is configured to implement the steps of the optimization method applied to the tubular pile production control system described in any one of the above when executing the computer program.
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, performs the above-described steps of the optimizing method applied to a pipe pile production control system.
The foregoing objects, features and advantages of embodiments of the invention will be more readily apparent from the following detailed description of the embodiments taken in conjunction with the accompanying drawings.
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 a server provided by an embodiment of the present invention;
fig. 2 is a schematic flow chart of an optimization method applied to a pipe pile production control system according to an embodiment of the present invention;
fig. 3 shows a functional block diagram of an optimization system according to an embodiment of the present invention applied to a pipe pile production control system.
Detailed Description
In order to enable those skilled in the art to better understand the present invention, a technical solution of the present embodiment of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiment of the present invention, and it is apparent that the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. All other embodiments, which can be made by those skilled in the art without the benefit of the teachings of this invention, are intended to fall 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 schematic flow chart of an optimization method and system for a tubular pile production control system 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 optimization method for a tubular pile production control system are described below.
Step S110, loading a plurality of production control behaviors in production control instance data of a tubular pile production control system to a first neural network, and generating label data of different abnormal nodes corresponding to each production control behavior, wherein the first neural network is an auxiliary neural network generated by knowledge learning according to training marking requests of the different abnormal nodes;
step S120, generating corresponding fusion embedded vectors based on the label data of each production control behavior;
step S130, loading the fusion embedded vector to a second neural network to obtain the label data of the production control instance data.
Based on the above steps, in this embodiment, first, a plurality of production control behaviors in production control instance data of a tubular pile production control system are loaded to a first neural network, and tag data of different abnormal nodes corresponding to each production control behavior are generated, where the first neural network is an auxiliary neural network generated by performing knowledge learning according to training labeling requests of different abnormal nodes. Then, a corresponding fusion embedded vector is generated based on the tag data of each production control action. Finally, the fusion embedded vector is loaded to a second neural network to obtain the label data of the production control instance data. The method can effectively detect and prevent various abnormal conditions in the pipe pile production process, improves the production efficiency and ensures the product quality.
In an alternative embodiment, before loading the plurality of production control actions in the production control instance data into the first neural network, further comprising:
determining sample production control data of training marking requests of different abnormal nodes;
and carrying out knowledge learning on the first neural network according to the sample production control data, wherein the first neural network comprises a basic function parameter layer, encoders and classification units with different depths, and the encoders and the classification units with different depths respectively correspond to the knowledge learning of a training marking request.
In an alternative embodiment, the training labeling requests of the different abnormal nodes include training labeling requests of a first labeling abnormal node, a second labeling abnormal node and a third labeling abnormal node, and the learning of knowledge of the first neural network according to the sample production control data includes:
and carrying out knowledge learning on the first neural network according to the sample production control data, the KL divergence cost function of the first marked abnormal node, the logarithmic error function of the second marked abnormal node and the range cost function of the third marked abnormal node.
In an alternative embodiment, the determining the sample production control data of the training annotation request of the different abnormal nodes includes:
and acquiring corresponding initial production control events based on training marking requests of different abnormal nodes, embedding abnormal characteristics of the initial production control events, and fusing abnormal characteristic embedded data with the initial production control events to obtain sample production control data.
In an alternative embodiment, the tag data of each production control action includes confidence tag data and classification tag data, and the generating a corresponding fusion embedded vector based on the tag data of each production control action includes:
and integrating the confidence label data and the classification label data to obtain a target fusion embedded vector.
In an alternative embodiment, before loading the fused embedded vector into a second neural network to obtain tag data of the production control instance data, further comprising:
randomly extracting sample production control behaviors of target quantity in the sample production control data;
and carrying out abnormal classification on the sample production control behaviors according to different dimensions, integrating abnormal classification data into sample production control data of a second neural network, and carrying out knowledge learning on the second neural network.
In an alternative embodiment, the training annotation requests of the different abnormal nodes include at least two of a training annotation request of a data abnormal node, a training annotation request of a behavior abnormal node, a training annotation request of a performance abnormal node and a training annotation request of a safety abnormal node.
Fig. 3 shows a functional block diagram of an optimizing system 200 applied to a pipe pile production control system according to an embodiment of the present invention, where the functions performed by the optimizing system 200 applied to a pipe pile production control system may correspond to the steps performed by the above-described method. The optimizing system 200 according to the present invention applied to the pipe pile production control system may be understood as the server 100, or the processor of the server 100, or may be understood as a component which is independent from the server 100 or the processor and performs the function of the present invention under the control of the server 100, as shown in fig. 3, and the functions of the respective functional modules of the optimizing system 200 according to the present invention applied to the pipe pile production control system will be described in detail.
The first generation module 210 is configured to load a plurality of production control behaviors in the production control instance data to a first neural network, and generate tag data of different abnormal nodes corresponding to each production control behavior, where the first neural network is an auxiliary neural network generated by learning knowledge according to training labeling requests of the different abnormal nodes;
a second generating module 220, configured to generate a corresponding fusion embedded vector based on the tag data of each production control action;
and a loading module 230, configured to load the fusion embedded vector into a second neural network to obtain tag data of the production control instance data.
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.
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.
Claims (10)
1. An optimization method applied to a tubular pile production control system, the method comprising:
loading a plurality of production control behaviors in production control instance data of a tubular pile production control system to a first neural network, and generating label data of different abnormal nodes corresponding to each production control behavior, wherein the first neural network is an auxiliary neural network generated by knowledge learning according to training marking requests of the different abnormal nodes;
generating a corresponding fusion embedded vector based on the label data of each production control behavior;
and loading the fusion embedded vector to a second neural network to obtain tag data of the production control instance data.
2. The optimization method applied to the pipe pile production control system according to claim 1, further comprising, before loading the plurality of production control actions in the production control instance data into the first neural network:
determining sample production control data of training marking requests of different abnormal nodes;
and carrying out knowledge learning on the first neural network according to the sample production control data, wherein the first neural network comprises a basic function parameter layer, encoders and classification units with different depths, and the encoders and the classification units with different depths respectively correspond to the knowledge learning of a training marking request.
3. The optimization method applied to the tubular pile production control system according to claim 2, wherein the training labeling requests of the different abnormal nodes include training labeling requests of a first labeling abnormal node, a second labeling abnormal node and a third labeling abnormal node, and the learning of knowledge of the first neural network according to the sample production control data includes:
and carrying out knowledge learning on the first neural network according to the sample production control data, the KL divergence cost function of the first marked abnormal node, the logarithmic error function of the second marked abnormal node and the range cost function of the third marked abnormal node.
4. The optimization method applied to the tubular pile production control system according to claim 2, wherein the determining the sample production control data of the training annotation request of the different abnormal nodes comprises:
and acquiring corresponding initial production control events based on training marking requests of different abnormal nodes, embedding abnormal characteristics of the initial production control events, and fusing abnormal characteristic embedded data with the initial production control events to obtain sample production control data.
5. The optimization method applied to the tubular pile production control system according to claim 1, wherein the label data of each production control action includes confidence label data and classification label data, and the generating the corresponding fusion embedding vector based on the label data of each production control action includes:
and integrating the confidence label data and the classification label data to obtain a target fusion embedded vector.
6. The optimization method applied to a pipe pile production control system according to claim 1, further comprising, before loading the fusion embedding vector into a second neural network to obtain tag data of the production control instance data:
randomly extracting sample production control behaviors of target quantity in the sample production control data;
and carrying out abnormal classification on the sample production control behaviors according to different dimensions, integrating abnormal classification data into sample production control data of a second neural network, and carrying out knowledge learning on the second neural network.
7. An optimisation method for a tubular pile production control system according to any of claims 1 to 6, wherein the training annotation requests for different anomaly nodes comprise at least two of training annotation requests for data anomaly nodes, training annotation requests for behavioural anomaly nodes, training annotation requests for performance anomaly nodes, and training annotation requests for security anomaly nodes.
8. An optimization system for a tubular pile production control system, comprising:
the first generation module is used for loading a plurality of production control behaviors in the production control instance data to a first neural network to generate label data of different abnormal nodes corresponding to each production control behavior, wherein the first neural network is an auxiliary neural network generated by knowledge learning according to training marking requests of the different abnormal nodes;
the second generation module is used for generating a corresponding fusion embedded vector based on the label data of each production control behavior;
and the loading module is used for loading the fusion embedded vector to a second neural network to obtain the label data of the production control instance data.
9. A server, comprising: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus; the memory is used for storing a computer program; the processor, when configured to execute the computer program, implements the steps of the optimization method of any one of claims 1-7 applied to a tubular pile production control system.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the optimization method of any one of claims 1-7 applied to a pipe pile production control system.
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| CN202311577754.9A CN117519035A (en) | 2023-11-23 | 2023-11-23 | Optimization method and system applied to tubular pile production control system |
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| CN202311577754.9A CN117519035A (en) | 2023-11-23 | 2023-11-23 | Optimization method and system applied to tubular pile production control system |
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