CN115099738A - Digital twin system of intelligent monitoring platform for dry bulk cargo wharf safety - Google Patents
Digital twin system of intelligent monitoring platform for dry bulk cargo wharf safety Download PDFInfo
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Abstract
The invention relates to the technical field of dry and bulk material transportation monitoring systems, in particular to a digital twin system of a dry and bulk wharf safety intelligent monitoring platform, wherein a digital entity model based on a high-precision physical model, factory data, historical data and sensor data is constructed in a digital space and respectively comprises an equipment layer, a communication layer, an information layer, a platform layer and an application layer from bottom to top, and the information layer and the platform layer jointly construct a digital twin body. The running safety and the intelligent level of the dry bulk cargo wharf equipment group are improved.
Description
Technical Field
The invention relates to the technical field of dry bulk material transportation monitoring systems, in particular to a digital twin system of a dry bulk wharf safety intelligent monitoring platform.
Background
The dry bulk cargo wharf is an important component of a port wharf system, and has the main function of providing services such as loading, unloading, transportation, stockpiling and the like for the bulk cargo such as coal, ore, bulk grain and the like to enter and exit a port. The operation of the dry bulk cargo wharf is a continuous operation process, which mainly comprises the steps of loading and unloading a ship, conveying, stacking and taking materials, overturning and the like, and the operation stop of any step can cause the interruption and even paralysis of the operation of the whole bulk cargo wharf.
The typical dry bulk cargo wharf loading and unloading equipment such as a bridge type grab ship unloader, a ship loader, a bucket wheel stacker-reclaimer, a gantry crane and the like is taken as a representative, and in order to enable the bulk cargo wharf to realize high-strength and all-weather continuous operation and high standard and high requirements of national policies and markets on safe production operation, the intelligent monitoring of the bulk cargo wharf loading and unloading equipment is urgently required to be realized in the industry at present. More and more bulk cargo wharfs adopt an automatic and intelligent information technology as an important means for port development, and certain effects are achieved in the aspects of enterprise business process optimization, improvement of operation efficiency of transportation tools, cooperation of all departments and the like.
The safe intelligent monitoring platform is used for evaluating and monitoring dry bulk cargo wharf loading and unloading equipment such as a bridge grab ship unloader, a ship loader, a bucket wheel stacker-reclaimer, a gantry crane and the like by utilizing an online monitoring technology, an internet of things technology, a cloud technology, an AI deep learning technology and the like, can monitor the use condition of the structural stress performance and the running condition of a mechanism in normal use (aiming at different loads and different wind speeds) and can also predict the performance trend of analysis equipment in real time, can give an alarm in time once a problem occurs or predict the safety condition of the equipment in advance to remind a user, well performs maintenance work, and provides good technical support for port safe production.
At present, the equipment structure health monitoring and management system based on the internet of things carries out real-time monitoring and data remote transmission on crane operation parameters and structure monitoring, and realizes functions of data storage, remote query and the like. And (3) establishing a simplified calculation model of the equipment in a targeted manner according to the large-scale equipment in service on site, and searching the position of the dangerous point of the equipment from the site angle for long-term online strain monitoring by utilizing elastic mechanical calculation and finite element ANSYS software analysis. A whole set of Internet of things framework is built in an actual scene, hardware facilities are installed on active equipment, the local area network function is realized, and functions of data storage, remote user query and the like are supported.
However, the difference of operation mechanisms among various devices of the dry bulk cargo wharf is large, the work flows are related and different, the health monitoring effect for different single devices is very limited, the operation condition of the whole dry bulk cargo wharf cannot be reflected, the technical requirements for building monitoring platforms for different single devices are different, the various devices are not uniform, and the field management personnel and the operators are not easy to master.
The digital twin is a simulation process integrating multidisciplinary, multi-physical quantity, multi-scale and multi-probability by fully utilizing data such as a physical model, sensor updating, operation history and the like, and mapping is completed in a virtual space, so that the full life cycle process of corresponding entity equipment is reflected.
Therefore, in order to solve the problems, the application provides a digital twin-organism system of a dry bulk cargo wharf safety intelligent monitoring platform, provides an integrated monitoring platform among various typical devices of the existing dry bulk cargo wharf, is convenient for field personnel to control and learn in a digital mode, and further provides guidance analysis and fault early warning.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a digital twin system of a dry and bulk cargo wharf safety intelligent monitoring platform, provides a monitoring platform integrated among various typical devices of the prior dry and bulk cargo wharf, is convenient for field personnel to control and learn in a digital mode, and further provides guidance analysis and fault early warning.
In order to achieve the purpose, the invention provides a digital twin body system of a dry bulk cargo wharf safety intelligent monitoring platform, wherein a digital entity model based on a high-precision physical model, factory data, historical data and sensor data is constructed in a digital space, the digital entity model respectively comprises an equipment layer, a communication layer, an information layer, a platform layer and an application layer from bottom to top, and the information layer and the platform layer jointly construct a digital twin body;
the equipment layer is composed of main loading and unloading equipment groups of a dry bulk cargo wharf, such as a bridge type grab bucket ship unloader, a ship loader, a bucket-wheel stacker-reclaimer, a portal crane and the like, and is a physical entity of the equipment in reality;
the communication layer is composed of various sensors, drivers and PLC controllers which are arranged on a detachable equipment group in the equipment layer, collects and summarizes various monitoring parameters of a physical entity, and is a link connecting the equipment layer in a physical space and a digital twin body in a digital space;
the information layer comprises a basic software environment, a basic framework consisting of a development platform, an equipment digital model established on the basic framework, a transmission framework consisting of the Internet of things and an interface, and data acquired through the communication layer and screened by the transmission framework;
the platform layer consists of an equipment monitoring system, an equipment management system and an operation management system, the platform layer is provided with monitoring and management of single equipment of a dry and bulk cargo wharf and an integrated 'many-to-many' management system of the whole equipment group, and the platform layer is used for enabling field management personnel and operating personnel to be in direct contact and carrying out intelligent operation and maintenance;
the application layer is arranged on the uppermost layer of the model and is used for realizing the functions of operation guidance analysis, fault early warning and diagnosis, safety and guarantee of various typical equipment groups of the dry and bulk cargo wharf through application feedback of the digital twin body and AI deep learning based on big data.
The digital twin body is formed by cross-correlation interaction of each parameter of the information layer and each system of the platform layer, equipment parameter digitization logic is realized through an information layer algorithm, equipment parameter digitization framework and information communication are realized through software and hardware of the information layer, and man-machine interaction, movement management and monitoring management are realized through butt joint of a platform layer tool and the information layer digitization framework.
The digital twin system runs on a computer end, a mobile phone end and a tablet computer platform.
Compared with the prior art, the method has the advantages that a digital entity model based on a high-precision physical model, factory data, historical data and sensor data is constructed in a digital space, the model can reflect physical characteristics of a system and changeable characteristics of coping environments, and particularly in the operation and maintenance stage of the dry and bulk cargo wharf equipment, the operation environment of the dry and bulk cargo wharf equipment and the entity of the dry and bulk cargo wharf equipment can be modeled to form a digital operation environment and a digital operation body.
Drawings
FIG. 1 is a diagram illustrating the hierarchical relationship and details in the architecture of the present invention.
Detailed Description
The invention will now be further described with reference to the accompanying drawings.
Referring to fig. 1, the invention provides a digital twin body system of a dry bulk cargo wharf safety intelligent monitoring platform, wherein a digital entity model based on a high-precision physical model, factory data, historical data and sensor data is constructed in a digital space, the digital entity model respectively comprises an equipment layer, a communication layer, an information layer, a platform layer and an application layer from bottom to top, and the information layer and the platform layer jointly construct a digital twin body;
the equipment layer is composed of main loading and unloading equipment groups of a dry bulk cargo wharf, such as a bridge type grab bucket ship unloader, a ship loader, a bucket-wheel stacker-reclaimer, a portal crane and the like, and is a physical entity of the equipment in reality;
the communication layer is composed of various sensors, drivers and PLC controllers which are arranged on a detachable equipment group in the equipment layer, collects and summarizes various monitoring parameters of a physical entity, and is a link connecting the equipment layer in a physical space and a digital twin body in a digital space;
the information layer comprises a basic software environment, a basic framework consisting of a development platform, an equipment digital model established on the basic framework, a transmission framework consisting of the Internet of things and an interface, and data acquired through the communication layer and screened by the transmission framework;
the platform layer consists of an equipment monitoring system, an equipment management system and an operation management system, the platform layer is provided with monitoring and management of single equipment of a dry and bulk cargo wharf and an integrated 'many-to-many' management system of the whole equipment group, and the platform layer is used for enabling field management personnel and operating personnel to be in direct contact and carrying out intelligent operation and maintenance;
the application layer is arranged on the uppermost layer of the model and is used for realizing the functions of operation guidance analysis, fault early warning and diagnosis, safety and guarantee of various typical equipment groups of the dry and bulk cargo wharf through application feedback of the digital twin body and AI deep learning based on big data.
The digital twin body is formed by cross-correlation interaction of each parameter of the information layer and each system of the platform layer, equipment parameter digitization logic is realized through an information layer algorithm, equipment parameter digitization framework and information communication are realized through software and hardware of the information layer, and man-machine interaction, movement management and monitoring management are realized through butt joint of a platform layer tool and the information layer digitization framework.
The digital twin system runs on a computer end, a mobile phone end and a tablet computer platform.
The above are only preferred embodiments of the present invention, and are only used to help understanding the method and the core idea of the present application, the scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the scope of the present invention. It should be noted that modifications and adaptations to those skilled in the art may occur to persons skilled in the art without departing from the principles of the invention and should be considered as within the scope of the invention.
The invention integrally solves the technical problems that the difference of operation mechanisms among various devices of the dry bulk cargo wharf is large and the work flows are related and different in the prior art, builds a safe intelligent monitoring platform aiming at different device groups of a bridge type grab bucket ship unloader, a ship loader, a bucket wheel stacker-reclaimer, a gantry crane and the like of the dry bulk cargo wharf, comprehensively monitors and evaluates various performances of the device group of the dry bulk cargo wharf, optimizes device group control by combining operation environment information, performs operation guidance analysis, realizes early fault early warning and performance degradation prediction, provides reliable guarantee for safe production, and improves the operation safety and intelligent level of the device group of the dry bulk cargo wharf.
Claims (2)
1. A digital twin body system of a dry and bulk cargo wharf safety intelligent monitoring platform is characterized by comprising an equipment layer, a communication layer, an information layer, a platform layer and an application layer from bottom to top, wherein the information layer and the platform layer jointly form a digital twin body;
the equipment layer is composed of a bridge type grab bucket ship unloader, a ship loader, a bucket wheel stacker-reclaimer and a main loading and unloading equipment group of a dry bulk cargo wharf of a portal crane, and the equipment layer is a physical entity of the equipment in reality; the communication layer is composed of various sensors, drivers and PLC controllers which are arranged on a detachable equipment group in the equipment layer, and is used for collecting and summarizing various monitoring parameters of a physical entity, and the communication layer is a link connecting the equipment layer in a physical space and a digital twin in a digital space; the information layer comprises a basic software environment, a basic framework consisting of a development platform, an equipment digital model established on the basic framework, a transmission framework consisting of the Internet of things and an interface, and data acquired through the communication layer and screened by the transmission framework;
the platform layer consists of an equipment monitoring system, an equipment management system and an operation management system, the platform layer is provided with monitoring and management of single equipment of a dry bulk cargo wharf and an integrated 'many-to-many' management system of the whole equipment group, and the platform layer is used for enabling field managers and operators to directly contact and carry out intelligent operation and maintenance;
the application layer is arranged on the uppermost layer of the model and is used for realizing the functions of operation guidance analysis, fault early warning and diagnosis, safety and guarantee of various typical equipment groups of the dry and bulk cargo wharf through application feedback of the digital twin body and AI deep learning based on big data.
The digital twin body is formed by cross-correlation interaction of each parameter of the information layer and each system of the platform layer, equipment parameter digitalization logic is realized through an information layer algorithm, equipment parameter digitalization framework and information communication are realized through software and hardware of the information layer, and man-machine interaction, movement management and monitoring management are realized through butt joint of a platform layer tool and the information layer digitalization framework.
2. The digital twin system of dry bulk terminal security intelligent monitoring platform of claim 1, wherein the digital twin system runs on a computer side, a mobile phone side and a tablet computer platform.
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| CN202210653104.7A CN115099738A (en) | 2022-06-09 | 2022-06-09 | Digital twin system of intelligent monitoring platform for dry bulk cargo wharf safety |
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| CN202210653104.7A CN115099738A (en) | 2022-06-09 | 2022-06-09 | Digital twin system of intelligent monitoring platform for dry bulk cargo wharf safety |
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2022
- 2022-06-09 CN CN202210653104.7A patent/CN115099738A/en active Pending
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| CN204198036U (en) * | 2014-10-31 | 2015-03-11 | 交通运输部水运科学研究所 | A kind of bulk cargo terminals dust on-line monitoring and real-time control system |
| CN110147069A (en) * | 2019-04-28 | 2019-08-20 | 武汉理工大学 | A kind of the harbour real-time monitoring system and its working method twin based on number |
| CN112731887A (en) * | 2020-12-31 | 2021-04-30 | 南京理工大学 | Digital twin intelligent monitoring system and method for petrochemical unattended loading and unloading line |
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Application publication date: 20220923 |