CN115936671A - Bulk cargo port self-learning operation and maintenance system based on 5G hybrid network - Google Patents
Bulk cargo port self-learning operation and maintenance system based on 5G hybrid network Download PDFInfo
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- CN115936671A CN115936671A CN202211534402.0A CN202211534402A CN115936671A CN 115936671 A CN115936671 A CN 115936671A CN 202211534402 A CN202211534402 A CN 202211534402A CN 115936671 A CN115936671 A CN 115936671A
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Abstract
The invention relates to a bulk cargo port self-learning operation and maintenance system based on a 5G hybrid network, which comprises the following steps: improving the field hardware facilities of the bulk cargo wharf and constructing a 5G network operation environment; building and deploying an intelligent operation and maintenance platform; building a data primary processing model and a fault analysis model on the intelligent operation and maintenance platform; comparing the data collected by the sensor with the base value of the database, reporting abnormal characteristics, locking faults and outputting an alarm; and generating a maintenance work order to guide maintenance by associating the fault analysis, the installation position of the equipment, the operation environment and the process requirements with a maintenance list and matching maintenance steps. The invention effectively avoids the condition of indefinite maintenance time or indefinite required spare parts or insufficient quantity due to indefinite equipment faults on the one hand, and can grasp the equipment loss condition in real time, replace and maintain in time and prolong the life cycle of production equipment on the other hand, thereby improving the production efficiency.
Description
Technical Field
The invention relates to the technical field of self-learning operation and maintenance systems, in particular to a bulk cargo port self-learning operation and maintenance system based on a 5G hybrid network.
Background
In recent years, due to the demand for the development of intellectualization of bulk ports, higher requirements are put on the safety monitoring of equipment. In order to improve the overhaul and maintenance level of the equipment in the bulk cargo port, a scheme of strictly monitoring the equipment in the bulk cargo port is needed. The traditional equipment maintenance mode is to perform periodic inspection and manually record data or measure temperature by an infrared monitoring device, however, the efficiency of the maintenance mode is low, because the types of equipment, monitoring items and monitoring data related to equipment state monitoring are gradually increased, the integration degree of an information management system and operation and maintenance services is not high, and the operation and maintenance services are lack of intercommunication, faults are difficult to find comprehensively, so a reliable equipment operation and maintenance system is needed; in addition, the fault alarm of the equipment only depends on a single data source, and if the quality is poor or other special conditions occur, the alarm accuracy is not high; the intelligent monitoring algorithm based on big data is relatively lack of application, timely and effective evaluation means for equipment monitoring results and state information are lack, and probability analysis for fault possibility of related electrical equipment is also lack, so that great challenges are brought to daily operation and maintenance tasks.
Disclosure of Invention
The invention aims to solve the problems mentioned in the background technology, and provides a bulk cargo port self-learning operation and maintenance system based on a 5G hybrid network, which can monitor equipment in real time, and can improve the fault discovery rate and accuracy of port equipment by surrounding three problems of equipment fault diagnosis, early warning and state evaluation, and can effectively evaluate the state by combining the operation characteristics of the equipment.
In order to achieve the purpose, the invention adopts the following technical scheme: a bulk cargo port self-learning operation and maintenance system based on a 5G hybrid network comprises the following steps:
(1) The method comprises the steps of improving field hardware facilities of the bulk cargo terminal, constructing a 5G network operation environment, carrying out fusion transformation on the existing communication modules of the bulk cargo terminal equipment, and realizing wireless communication under a 5G network;
(2) Building and deploying an intelligent operation and maintenance platform;
(3) Building a data primary processing model and a fault analysis model on the intelligent operation and maintenance platform;
(4) The sensor collects, marks and classifies the running state of the bulk cargo wharf equipment, uploads the data to the intelligent operation and maintenance platform, calculates the data base value through a data primary processing model, calculates the difference value of the data base value, and finely adjusts the deviation weight;
(5) Comparing the data collected by the sensor with a base value of a database, reporting abnormal characteristics, comparing the data fault characteristics of abnormal reporting equipment with a fault database in a fault analysis model, locking a fault and outputting an alarm;
(6) Associating the fault analysis and the installation position, the operation environment and the process requirements of the equipment with a maintenance list, matching the maintenance steps and generating a maintenance work order to guide maintenance;
(7) And after the maintenance is finished, feeding back and uploading the work order, and revising the algorithm model matched with the maintenance step.
Particularly, in the step (4), a section of data of the type is compared with a database base value through data classification, the data is preliminarily screened through preset deviation, the section of data is discarded when exceeding a deviation weight range, an average value is calculated when all the section of data accords with the deviation weight range, and the base value of the section of data in the database is updated.
In particular, the standard deviation preset for the first time is given by a person or an expert database.
In particular, the base value of the database is a reference value for normal operation of the bulk terminal equipment.
The beneficial effects of the invention are:
the system of the invention judges the current equipment state by monitoring, tracking and backtracking the installation environment, position, operation time length of the code head equipment, historical data monitored in operation and the like, automatically triggers, prompts and displays a maintenance work order alarm when the equipment operation data is abnormal or the system monitors that the equipment reaches the maintenance and replacement standard, and provides a corresponding maintenance or maintenance guide plan, equipment warehouse allowance, maintenance plan time length and the like according to the maintenance record, thereby providing data support for operation, maintenance and repair decisions, effectively avoiding the situations of indefinite maintenance time length or indefinite or insufficient quantity of required spare parts due to uncertain equipment faults on the one hand, and effectively mastering the equipment loss situation in real time, replacing and maintaining in time and prolonging the life cycle of production equipment on the other hand, thereby improving the production efficiency.
Drawings
FIG. 1 is a block diagram of the system flow of the present invention;
FIG. 2 is a block diagram of a data preliminary processing model of the present invention;
FIG. 3 is a block diagram of a fault analysis model of the present invention;
the following detailed description will be made in conjunction with embodiments of the present invention with reference to the accompanying drawings.
Detailed Description
The invention is further illustrated by the following examples:
as shown in fig. 1-3, a self-learning operation and maintenance system for bulk cargo port based on 5G hybrid network comprises the following steps:
(1) The method comprises the steps of improving field hardware facilities of the bulk cargo terminal, constructing a 5G network operation environment, fusing and transforming existing communication modules (such as WIFI, 3G, 4G, CAN and the like) of the bulk cargo terminal equipment, and realizing wireless communication under a 5G network;
(2) Building and deploying an intelligent operation and maintenance platform;
(3) Building a data primary processing model and a fault analysis model on the intelligent operation and maintenance platform;
(4) The sensor collects, marks and classifies the running state of the bulk cargo wharf equipment, uploads the data to the intelligent operation and maintenance platform, calculates the data base value through a data primary processing model, calculates the difference value of the data base value, and finely adjusts the deviation weight; through the classification of data, a section of data of the type is compared with a base value of a database, the data is preliminarily screened through a preset deviation, the section of data is discarded if exceeding a deviation weight range, an average value is calculated if all the section of data accords with the deviation weight range, and the base value of the section of data in the database is updated; the standard deviation preset for the first time is given by people or an expert database; the base value of the database is a reference value for normal operation of the bulk cargo wharf equipment.
(5) Comparing the data collected by the sensor with a base value of a database, reporting abnormal characteristics, comparing the data fault characteristics of abnormal reporting equipment with a fault database in a fault analysis model, locking a fault and outputting an alarm;
(6) Associating the fault analysis and the installation position, the operation environment and the process requirements of the equipment with a maintenance list, matching the maintenance steps and generating a maintenance work order to guide maintenance;
(7) And after the maintenance is finished, feeding back and uploading the work order, and revising the algorithm model matched with the maintenance step.
As shown in fig. 1, the system flow diagram has the following steps: the sensor acquisition module performs characteristic extraction after acquiring data, then performs fault characteristic extraction, and determines an abnormal value to be diagnosed;
comparing the fault type with an expert database; judging whether the fault is reserved or not, if not, establishing a new fault type, confirming on site, and finally filling the new fault type into a fault analysis model; if so, performing fault feature association through a fault analysis model, analyzing the position, the operating environment and the process of the equipment, and then associating with a maintenance list; and according to the fault characteristics, making a special maintenance scheme and generating a maintenance work order.
As shown in fig. 2, the data preliminary processing model:
the equipment data acquisition sensor acquires data A1;
determining whether A1 is present; if yes, classifying the data; if not, newly building a classification, and giving an initial base value (B1) and a deviation (C1);
comparing with the base value and the deviation; judging whether the current value is in a normal value interval, and if not, abandoning; if yes, judging whether the operation is continuous, and if not, abandoning; if yes, calculating an average value, and updating the base value of the data in the database.
As shown in fig. 3, the fault analysis model:
comparing input data from an input source with a base value and deviation to obtain an abnormal value, determining fault characteristics, and establishing a fault characteristic data set;
the fault characteristic data set is divided into a normal value C1, an upper limit abnormity C2 and a lower limit abnormity C3;
if the fault characteristic is a normal value C1, a normal result set (E) is directly obtained 1 、E 2 、E 3 、En-δ)；
If the fault characteristic is an upper limit abnormality C2, based on an upper limit deviation factor (D) 1 、D 2 、D 3 Dn) to analyze the upper limit deviation result, and screening and eliminating the delta term to obtain an upper limit result abnormal set (E) 1 ′、E 2 ′、E 3 ', en-delta'), and then feeding back the characteristic condition value to the upper limit exception C2 by the work order;
if the fault characteristic is a lower limit anomaly C3, based on a lower limit deviation factor (D) 1 ′、D 2 ′、D 3 ', dn') to analyze the lower limit deviation result, and screening and excluding the delta term to obtain a lower limit result abnormal set (E) 1 ″、E 2 ″、E 3 ", en- δ"), and then the work order feeds back the characteristic condition value to the lower limit anomaly C3.
The sensor monitors, tracks and backtracks the installation environment, position, running time, historical data monitored during running and the like of the wharf equipment, and judges the current equipment state;
when the equipment operation data is abnormal or the system monitors that the equipment reaches the maintenance and replacement standard, the system automatically triggers a prompt and displays a maintenance work order alarm, and provides a corresponding maintenance or maintenance guide plan, equipment warehouse allowance, maintenance plan duration and the like according to the maintenance record, so as to provide data support for operation, maintenance and repair decisions;
the invention effectively avoids the condition of indefinite maintenance time or indefinite required spare parts or insufficient quantity due to indefinite equipment faults on the one hand, and can grasp the equipment loss condition in real time, replace and maintain in time and prolong the life cycle of production equipment on the other hand, thereby improving the production efficiency.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise explicitly stated or limited, the terms "mounted," "connected," "fixed," and the like are to be construed broadly, e.g., as being permanently connected, detachably connected, or integral; may be mechanically coupled, may be electrically coupled or may be in communication with each other; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood according to specific situations by those of ordinary skill in the art.
The invention has been described in an illustrative manner, and it is to be understood that the invention is not limited to the specific embodiments described above, but is intended to cover various modifications, which may be made by the methods and technical solutions of the invention, or may be applied to other applications without modification.
Claims (4)
1. The self-learning operation and maintenance system for the bulk cargo port based on the 5G hybrid network is characterized by comprising the following steps of:
(1) The method comprises the steps of improving field hardware facilities of the bulk cargo terminal, constructing a 5G network operation environment, carrying out fusion transformation on the existing communication modules of the bulk cargo terminal equipment, and realizing wireless communication under a 5G network;
(2) Building and deploying an intelligent operation and maintenance platform;
(3) Building a data primary processing model and a fault analysis model on the intelligent operation and maintenance platform;
(4) The sensor collects, marks and classifies the running state of the bulk cargo wharf equipment, uploads the data to the intelligent operation and maintenance platform, calculates the data base value through a data primary processing model, calculates the difference value of the data base value, and finely adjusts the deviation weight;
(5) Comparing the data collected by the sensor with a base value of a database, reporting abnormal characteristics, comparing the data fault characteristics of abnormal reporting equipment with a fault database in a fault analysis model, locking a fault and outputting an alarm;
(6) Associating the fault analysis and the installation position, the operation environment and the process requirements of the equipment with a maintenance list, matching the maintenance steps and generating a maintenance work order to guide maintenance;
(7) And after the maintenance is finished, feeding back and uploading the work order, and revising the algorithm model matched with the maintenance step.
2. The self-learning operation and maintenance system for bulk cargo port based on 5G hybrid network as claimed in claim 1, wherein in step (4), a section of data of this type is compared with the base value of the database by classifying the data, the data is primarily screened by a preset deviation, the section of data is discarded if the section of data exceeds the deviation weight range, the average value is calculated if the section of data all meets the deviation weight range, and the base value of the section of data in the database is updated.
3. The self-learning operation and maintenance system for the bulk cargo port based on the 5G hybrid network as claimed in claim 2, wherein the standard deviation preset for the first time is given by a person or an expert database.
4. The self-learning operation and maintenance system for the bulk cargo port based on the 5G hybrid network as claimed in claim 3, wherein the database base value is a reference value for normal operation of the bulk cargo terminal equipment.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116258481A (en) * | 2023-05-15 | 2023-06-13 | 青建集团股份公司 | Control method and system for intelligent construction of building engineering |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116258481A (en) * | 2023-05-15 | 2023-06-13 | 青建集团股份公司 | Control method and system for intelligent construction of building engineering |
| CN116258481B (en) * | 2023-05-15 | 2023-08-15 | 青建集团股份公司 | Control method and system for intelligent construction of building engineering |
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