Digital twinborn monitoring method for coal storage, transportation and loading

Abstract

The invention relates to a digital twinborn monitoring method for coal storage, transportation and loading. A physical entity layer; a physical fusion layer; a 3D model layer; a data interaction layer; an application layer; the method comprises the following steps: establishing and implementing a digital twin model; collecting data; processing data; and application service. A data integration monitoring and visualization platform is constructed and is fused with a traditional quantitative automatic loading station platform to form an automatic coal loading digital twinning system, namely, a physical entity layer is constructed, sensors and controllers of original subsystems of an automatic loading station are integrated, and the real-time analysis capability of data of various sensors is enhanced; a coal loading information physical fusion layer is formed, a loading digital twin 3D model layer, a loading storage and transportation data interaction layer and an intelligent application service layer are constructed, and intelligent training is performed through a big data platform. Compared with an existing automatic quantitative loading station, the automatic loading measurement and control operation is simpler, the automation degree is higher, and the loading efficiency is higher.

Description

Technical Field

[0001] The present invention relates to a digital twin monitoring method for coal storage, transportation and loading, which is a method for organically combining computer network space with physical space, and a measurement and control method for combining coal transportation machinery with computer network space. Background Art

[0002] Digital twins make full use of physical models, sensor updates, historical record queries and other technologies, integrate multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes, complete the mapping of coal logistics storage and transportation equipment in virtual space, and correspond to the process of the life cycle of physical equipment in reality. At present, digital twin technology has integrated important research and development directions in the field of computer simulation and integration, and has been widely studied and paid attention to in the fields of intelligent manufacturing and energy development and utilization. In the early stages of development, digital twin technology was mainly used in the military and aerospace fields, and then expanded to ships, power plants, stereoscopic warehouses, medical treatment, workshops, etc. Due to the implementation of special measures for the development of networked collaborative manufacturing, intelligent factories and coal mines, data-driven intelligent services are the future development trend of science and technology frontiers and application intelligence. How to provide technical support for the intelligent control of coal storage and transportation and make the storage and transportation process of the transfer yard more intelligent is a problem that needs to be solved. Summary of the invention

[0003] In order to overcome the problems of the prior art, the present invention proposes a digital twin monitoring method for coal storage and transportation loading. The method constructs a data integration monitoring and visualization platform to form an information-physical fusion of coal loading and builds a loading digital twin 3D model, thereby improving the degree of automation and response speed. The automatic loading described in the present invention is simpler to operate, more automated, and more efficient in loading.

[0004] The object of the present invention is achieved as follows: a digital twin monitoring method for coal storage and transportation loading, the digital twin monitoring system for coal storage and transportation loading used in the digital twin monitoring method comprises: a data integration monitoring and visualization platform installed on a coal storage and transportation rapid quantitative loading station, the data integration monitoring and visualization platform is provided with: a multi-level monitoring subsystem, a three-dimensional visualization monitoring subsystem, a data communication and storage subsystem, and a unified data interface; The multi-level monitoring subsystem includes: upper warehouse belt monitoring, lower warehouse feeder monitoring, hydraulic system monitoring, and environmental status monitoring; The three-dimensional visual monitoring subsystem includes: virtual scene digital twin, entity data synchronous feedback, and real-time status monitoring; The data communication and storage subsystem includes: OPC communication, TCP / UDP communication protocol, WebServer interface, database communication interface; Unified data interface: production subsystem MES, visual terminal, transportation equipment, loading subsystem; Characterized in that the system architecture of the digital twin monitoring system for coal storage, transportation and loading includes: Perception layer: used for digital twin information collection for rapid quantitative loading of coal storage and transportation; the main collected signals include oil temperature, oil pressure, liquid level value, various alarm signals of hydraulic equipment, and the position and opening status of each gate of the precision distribution bin and silo, as well as different set values ​​of silo material height parameters. At the same time, the information of vehicle batch, train number, material type, receiving company, vehicle number, rated load, and actual loading tonnage in the loading process needs to be integrated and packaged for storage. These data are collected for the later control and perception function improvement of the digital twin system; the perception layer uses PLC equipment, voltage sensors, temperature sensors, and pressure sensors to collect on-site operating parameters in real time, collects physical equipment production factors of hydraulic system pump stations, filter pumps, fans, and heating devices, and obtains multiple heterogeneous data of people, machines, objects, and the environment; Physical entity layer: used to provide reference objects for production operation planning and data interface modeling for subsequent layers, and provide service support for subsequent platform resource integration, virtual model simulation, logic verification and data analysis; Physical fusion layer: Links the interactive mapping between virtual twins and physical entities, and realizes synchronous state feedback through the control system; 3D model layer: It realizes core components such as the periodic planning and design of coal storage and transportation loading system, production process control management, hydraulic machinery equipment operation maintenance and fault prediction. It is integrated by coupling of physical model, simulation model, logic model and data model. It realizes the digital twin of the object, process twin and performance twin driven by a large amount of information data of the loading tower: Data interaction layer: Through data interaction and iterative optimization between the structure and the model, the physical model, virtual model and data layer form a whole through the interaction layer, realizing the interconnection between various levels, effectively avoiding the generation of information islands, and effectively integrating various businesses. Materials are transported from the upper production belt to the loading tower, and each process achieves feedback guidance and improves engineering efficiency; Application layer: used to control physical actions; the application layer realizes unified access to the status data and process data of physical devices through OPC UA client connection, and based on the data support of the transport layer, enables the digital twin service platform to realize the functions of fault diagnosis, remaining life prediction, and intelligent management and control; The digital twin monitoring method comprises the following steps: Step 1: Establish and implement the digital twin model: Build a loading digital twin, establish a digital platform information sharing based on the integration of digital twin and 5G communication technology, and use the advantages of 5G communication gateway technology, edge computing, high bandwidth and low latency to provide large-scale edge computing and high-speed transmission for rapid quantitative loading of coal storage and transportation; establish digital twin monitoring, and use digital twins to monitor the production scene of coal storage belts in real time, and realize intelligent loading precision monitoring through digital twins, data fusion, deep learning and iterative optimization, and improve the recognition of dangerous sources and personnel; build a digital twin model for rapid quantitative loading of coal storage and transportation; Step 2, data collection: The perception layer collects safety monitoring data, industrial site data, monitoring and detection data, oil temperature and oil pressure detection data, operation control data, risk data, and personnel safety positioning; Step 3, data processing: through expert experience database, relational database, time series database, data analysis model, data streaming computing, data batch processing, applying artificial intelligence algorithms, tools such as big data analysis, machine learning and deep learning, and intelligent group theory, finally completing the information integration of the service layer; Step 4, application services: Application services include: intelligent loading and operation management, risk prediction and warning, information query, and three-dimensional digital twin display.

[0005] The advantages and beneficial effects of the present invention are as follows: the present invention constructs a data integration monitoring and visualization platform, and integrates it with the traditional quantitative automatic loading station platform to form a coal automatic loading digital twin system, that is, constructs a physical entity layer, integrates the sensors and controllers of the original subsystems of the automatic loading station, and strengthens the real-time analysis capabilities of various sensor data, forms a coal loading information physical fusion layer, constructs a loading digital twin 3D model layer, a loading storage and transportation data interaction layer, and an intelligent application service layer. Through the big data platform and intelligent training, the existing coal storage and transportation collaborative management algorithms and models are not mature, and the loading process needs to adapt to the complex and changeable loading environment and vehicle changes, thereby improving the degree of automation and response speed, and upgrading the traditional automatic control system to a loading method of a coal loading integrated system based on digital twins. Compared with the existing automated quantitative loading station, the automatic loading measurement and control described in the present invention is simpler to operate, has a higher degree of automation, and has a higher efficiency in loading. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0007] Figure 1 is a schematic diagram of the system structure used by the method described in the embodiment of the present invention; Figure 2 It is a schematic diagram of the system architecture of the method according to an embodiment of the present invention; Figure 3is a flow chart of the method described in an embodiment of the present invention. DETAILED DESCRIPTION

[0008] Embodiment 1: This embodiment is a digital twin monitoring method for coal storage and transportation loading. The digital twin monitoring system for coal storage and transportation loading used in the digital twin monitoring method includes: a data integration monitoring and visualization platform installed on a coal storage and transportation rapid quantitative loading station, and the data integration monitoring and visualization platform is provided with: a multi-level monitoring subsystem, a three-dimensional visualization monitoring subsystem, a data communication and storage subsystem, and a unified data interface, such as Figure 1 shown.

[0009] The multi-level monitoring subsystem includes: upper warehouse belt monitoring, lower warehouse feeder monitoring, hydraulic system monitoring, and environmental status monitoring; The three-dimensional visual monitoring subsystem includes: virtual scene digital twin, entity data synchronous feedback, and real-time status monitoring; The data communication and storage subsystem includes: OPC communication, TCP / UDP communication protocol, WebServer interface, database communication interface; Unified data interface: production subsystem MES, visual terminal, transportation equipment, loading subsystem; The system architecture of the digital twin monitoring system for coal storage, transportation and loading includes: Perception layer: used for digital twin information collection for rapid quantitative loading of coal storage and transportation; the main collected signals include oil temperature, oil pressure, liquid level value, various alarm signals of hydraulic equipment, as well as the position and opening status of each gate of the precision distribution bin and silo, and different set values ​​of the material height parameters of the silo. At the same time, the information of vehicle batch, train number, material type, receiving company, vehicle number, rated load, and actual loaded tonnage in the loading process needs to be integrated and packaged for storage. These data are collected for the later control and perception function improvement of the digital twin system; the perception layer uses PLC equipment, voltage sensors, temperature sensors, and pressure sensors to collect on-site operating parameters in real time, collects physical equipment production factors of hydraulic system pump stations, filter pumps, fans, and heating devices, obtains multiple heterogeneous data of people, machines, objects, and the environment, and uses different communication protocols such as RS485, PROFIBUS, and ETHERNET to process data standardization and realize semantic interoperability. Data is collected through the OPC UA client digital twin service platform, a communication architecture is established, and Unity 3D modeling is used.

[0010] The digital twin model architecture includes the physical entity layer, the coal loading information-physical fusion layer, the loading digital twin 3D model layer, the loading storage and transportation data interaction layer, and the intelligent application service layer. It provides scenario analysis through intelligent perception, collaborative control, intelligent decision-making and optimization of the loading system.

[0011] Physical entity layer: The digital twin model body infrastructure, namely the physical layer, is used to provide reference objects for production operation planning and data interface modeling for subsequent layers, and to provide service support for the subsequent platform resource integration, virtual model simulation, logic verification and data analysis; Physical fusion layer: Links the interactive mapping between virtual twins and physical entities, and realizes synchronous state feedback through the control system; The fusion layer runs through the entire life cycle of the intelligent loading system, providing comprehensive information and data support for intelligent perception of physical entity elements, model building, data interaction and intelligent services.

[0012] 3D model layer: It realizes core components such as the periodic planning and design of coal storage and transportation loading system, production process control management, hydraulic machinery equipment operation maintenance and fault prediction. It is integrated by coupling of physical model, simulation model, logic model and data model. It realizes the digital twin of the object, process twin and performance twin driven by a large amount of information data of the loading tower: VR / AR technology is used to visualize virtual reality, enhance three-dimensional reconstruction and digital drive capabilities, and realize the deep integration of digital twin, intelligent control, implementation feedback and interactive mapping technology for the loading of coal materials, to realize a digital twin model of coal storage and transportation with all-round, all-time and space, and intelligent monitoring.

[0013] Data interaction layer: Through data interaction and iterative optimization between the structure and the model, the physical model, virtual model and data layer form a whole through the interaction layer, realizing the interconnection between various levels, effectively avoiding the generation of information islands, and effectively integrating various businesses. Materials are transported from the upper production belt to the loading tower, and each process achieves feedback guidance and improves engineering efficiency; The data interaction layer solves the information island problems of low information completeness and delayed information interaction, unifies and standardizes the multi-source heterogeneous data of physical equipment, and provides a unified communication architecture for digital twins and services; reconstructs the address space of the coal storage and transportation rapid quantitative loading system, realizes information fusion through the OPC UA server and client, and provides data support for the upper application layer.

[0014] Application layer: used to control physical actions; the application layer realizes unified access to the status data and process data of physical devices through OPC UA client connection, and based on the data support of the transport layer, enables the digital twin service platform to realize the functions of fault diagnosis, remaining life prediction, and intelligent management and control; The digital twin model of coal storage, transportation and loading extracts the sensor data of the digital twin model of the coal quick loading station through the application service platform and the unified interface of the sensor, builds the model using data, algorithms and simulation, and uses tool components to realize the application service functions, including equipment control, real-time material monitoring, job scheduling, data collection and other functions. The application service layer includes service functions for operators and models, including data and model management. Services for data types include signal acquisition and storage, data management and transmission, and construct data interaction. Model management services include model simulation and model coupling to realize convenient demand and operator-oriented services.

[0015] The digital twin monitoring method comprises the following steps: Step 1: Establish and implement the digital twin model: Build a loading digital twin, establish a digital platform information sharing based on the integration of digital twin and 5G communication technology, and use the advantages of 5G communication gateway technology, edge computing, high bandwidth and low latency to provide large-scale edge computing and high-speed transmission for rapid quantitative loading of coal storage and transportation; establish digital twin monitoring, and use digital twins to monitor the production scene of coal storage belts in real time, and realize intelligent loading precision monitoring through digital twins, data fusion, deep learning and iterative optimization, and improve the recognition of dangerous sources and personnel; build a digital twin model for rapid quantitative loading of coal storage and transportation; Establish a smart service system of digital loading station holographic perception system, business collaborative control, and operation and maintenance digital twins. Based on digital twins, the Internet of Things, big data and AI technologies, as well as smart service models such as data visualization APP, realize intelligent coal flow transportation, intelligent safety supervision, and intelligent operation and maintenance management and scheduling for bulk coal loading. Through the Internet of Things and big data platforms, realize the digital twins, process twins, and performance twins of physical scenes in virtual scenes based on digital twins, realize three-dimensional visual intelligent operation and maintenance, and use the digital digital twin system of the loading station to realize multi-source integration, deep learning, iterative optimization, and autonomous decision-making of coal storage and transportation, realize the intelligence of the entire life cycle of operation and maintenance services, and the intelligent management and control system of production safety, emergency response, and green mining.

[0016] To realize intelligent loading and unattended digital loading, we need to build holographic perception, multi-source fusion, process control and data interaction, and apply intelligent digital twin monitoring to meet the common needs of smart construction. Intelligent loading in coal mines uses intelligent sensing technology to collect equipment status parameters, silo material height, belt running frequency, feeder starting power, hydraulic station system parameters, etc. in real time. The coal storage and loading rapid quantitative loading fault and health detection system (MEFHP mine equipments fault and helath prediciton) uses multi-source sensors and data fusion to diagnose loading system faults and predict performance.

[0017] For the digital twin drive system of coal storage and transportation loading, it is necessary to study the physical layer and virtual interaction inhibition mechanism of the equipment, equipment fault feature extraction, fault process modeling, and disturbance factor analysis. The human-machine interactive collaborative intelligent loading system is a typical application scenario of smart mines, which realizes remote control, real-time monitoring and precise positioning, and health prediction through human-machine interaction. The intelligent coordination of man-machine-environment control needs to be studied: ① VR / AR high-precision image and precise pattern recognition technology based on digital twin digital body; ② Three-dimensional graphics reconstruction mapping, derivation and reconstruction, and holographic projection technology based on digital twin; ③ Multi-dimensional precise characterization and low-latency, high-speed communication technology based on 5G edge computing and virtual reality fusion.

[0018] The digital twin model consists of physical entities, virtual entities, the connection and interaction between the two, data service functions, etc. It concentrates on the virtual entity and functional service dimensions, and is presented in a three-dimensional visualization model, combined with big data artificial intelligence algorithms. The physical entity is the foundation of the digital twin and the terminal service object, and the connection and interaction guarantee the data foundation and terminal service. The maturity of the digital twin is divided into several levels: virtual imitation of reality, virtual reflection of reality, virtual control of reality, virtual prediction of reality, virtual optimization of reality, and virtual and real symbiosis. At present, the field of coal storage, transportation and loading is controlled by virtual.

[0019] Step 2, data collection: The perception layer collects safety monitoring data, industrial site data, monitoring and detection data, oil temperature and oil pressure detection data, operation control data, risk data, and personnel safety positioning; The digital twin of the coal storage, transportation and loading system mainly collects big data collected during the coal loading process and conducts in-depth mining, including the height of the vehicle side, the height of the vehicle floor from the ground, the length of the carriage, and the positioning of each carriage, including the rated load, actual loaded weight, deviation, material overload analysis, etc. Through real-time perception and application of basic data, fusion of virtual and real data, combination of online judgment of equipment status and data mining, and comprehensive application of information perception and edge technology, it ultimately realizes virtual simulation of multiple scenarios, dynamic modeling of the intelligent coal loading, storage and transportation platform, and interactive online fusion of virtual and real, and establishes intervention prediction models for intelligent decision-making and management, online perception and equipment status analysis, safety alarm and early warning, and interactive reproduction.

[0020] Applied sensing technology, including intelligent loading system, proximity switch, sensor, radar material level, positioning grating beacon and on-site environment data collection, establish twin model and lightweight computing model through network layer edge gateway, and optimize physical model, twin model and digital model through big data platform.

[0021] Step 3, data processing: Through expert experience database, relational database, time series database, data analysis model, data streaming calculation, data batch processing, apply artificial intelligence algorithms, tools such as big data analysis, machine learning and deep learning, and intelligent group theory, and finally complete the information integration of the service layer.

[0022] Coal storage and transportation loading and unloading simulation software supports real-time simulation of material dropping, reflecting the impact of various factors on system performance and safety. Digital twins need to adapt to complex and changeable loading environments, improve the degree of automation and response speed, and real-time analysis of various sensor data. Data packaging and control, coal storage and transportation loading needs to obtain and integrate data information, display it to users in a visual way and help users better understand the storage and transportation coal loading model.

[0023] Step 4, application services: Application services include: intelligent loading and operation management, risk prediction and warning, information query, and three-dimensional digital twin display.

[0024] The main applications are: online equipment monitoring, intelligent dispatching services, safety situation analysis and collaborative management and control platforms, including monitoring of bulk material specifications, attributes, status and other parameters, data integration, fusion processing and derived data, solving the problems of integrity, standardization, integrity and flexibility of twin data. The standardization promotes the formulation of unified data standards to provide corresponding data analysis and conversion tools, and effectively integrates the data feature information of each sub-model in an integrated manner, while adapting to the ever-changing coal storage and transportation loading environment.

[0025] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred arrangement scheme, a person skilled in the art should understand that the technical solution of the present invention (such as the form and structure of the loading station, the form and structure of various electronic equipment, the sequence of steps, etc.) can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A digital twin monitoring method for coal storage, transportation and loading, wherein the digital twin monitoring system for coal storage, transportation and loading used in the digital twin monitoring method comprises: A data integration monitoring and visualization platform installed at a coal storage and transportation rapid quantitative loading station, wherein the data integration monitoring and visualization platform is provided with: a multi-level monitoring subsystem, a three-dimensional visualization monitoring subsystem, a data communication and storage subsystem, and a unified data interface; The multi-level monitoring subsystem includes: upper warehouse belt monitoring, lower warehouse feeder monitoring, hydraulic system monitoring, and environmental status monitoring; The three-dimensional visual monitoring subsystem includes: virtual scene digital twin, entity data synchronous feedback, and real-time status monitoring; The data communication and storage subsystem includes: OPC communication, TCP / UDP communication protocol, WebServer interface, database communication interface; Unified data interface: production subsystem MES, visual terminal, transportation equipment, loading subsystem; Characterized in that the system architecture of the digital twin monitoring system for coal storage, transportation and loading includes: Perception layer: used for digital twin information collection for rapid quantitative loading of coal storage and transportation; the main collected signals include oil temperature, oil pressure, liquid level value, various alarm signals of hydraulic equipment, and the position and opening status of each gate of the precision distribution bin and silo, as well as different set values ​​of silo material height parameters. At the same time, the information of vehicle batch, train number, material type, receiving company, vehicle number, rated load, and actual loading tonnage in the loading process needs to be integrated and packaged for storage. These data are collected for the later control and perception function improvement of the digital twin system; the perception layer uses PLC equipment, voltage sensors, temperature sensors, and pressure sensors to collect on-site operating parameters in real time, collects physical equipment production factors of hydraulic system pump stations, filter pumps, fans, and heating devices, and obtains multiple heterogeneous data of people, machines, objects, and the environment; Physical entity layer: used to provide reference objects for production operation planning and data interface modeling for subsequent layers, and provide service support for subsequent platform resource integration, virtual model simulation, logic verification and data analysis; Physical fusion layer: Links the interactive mapping between virtual twins and physical entities, and realizes synchronous state feedback through the control system; 3D model layer: It realizes core components such as the periodic planning and design of coal storage and transportation loading system, production process control management, hydraulic machinery equipment operation maintenance and fault prediction. It is integrated by coupling of physical model, simulation model, logic model and data model. It realizes the digital twin of the object, process twin and performance twin driven by a large amount of information data of the loading tower: Data interaction layer: Through data interaction and iterative optimization between the structure and the model, the physical model, virtual model and data layer form a whole through the interaction layer, realizing the interconnection between various levels, effectively avoiding the generation of information islands, and effectively integrating various businesses. Materials are transported from the upper production belt to the loading tower, and each process achieves feedback guidance and improves engineering efficiency; Application layer: used to control physical actions; the application layer realizes unified access to the status data and process data of physical devices through OPC UA client connection, and based on the data support of the transport layer, enables the digital twin service platform to realize the functions of fault diagnosis, remaining life prediction, and intelligent management and control; The digital twin monitoring method comprises the following steps: Step 1: Establish and implement the digital twin model: Build a loading digital twin, establish a digital platform information sharing based on the integration of digital twin and 5G communication technology, and use the advantages of 5G communication gateway technology, edge computing, high bandwidth and low latency to provide large-scale edge computing and high-speed transmission for rapid quantitative loading of coal storage and transportation; establish digital twin monitoring, and use digital twins to monitor the production scene of coal storage belts in real time, and realize intelligent loading precision monitoring through digital twins, data fusion, deep learning and iterative optimization, and improve the recognition of dangerous sources and personnel; build a digital twin model for rapid quantitative loading of coal storage and transportation; Step 2, data collection: The perception layer collects safety monitoring data, industrial site data, monitoring and detection data, oil temperature and oil pressure detection data, operation control data, risk data, and personnel safety positioning; Step 3, data processing: through expert experience database, relational database, time series database, data analysis model, data streaming computing, data batch processing, applying artificial intelligence algorithms, tools such as big data analysis, machine learning and deep learning, and intelligent group theory, finally completing the information integration of the service layer; Step 4, application services: Application services include: intelligent loading and operation management, risk prediction and warning, information query, and three-dimensional digital twin display.

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