CN117523939A - System for constructing practice teaching intelligent simulation factory based on Internet of things equipment - Google Patents
System for constructing practice teaching intelligent simulation factory based on Internet of things equipment Download PDFInfo
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Abstract
The system for constructing the practice teaching intelligent simulation factory based on the Internet of things equipment comprises a factory operation simulation mechanism, a factory control simulation mechanism, a simulation teaching integration mechanism and a data tag generation mechanism, wherein the factory operation simulation mechanism is used for simulating factory production operation and acquiring factory production operation data; the factory control simulation mechanism performs production operation control on the factory operation simulation mechanism to realize automation of the factory operation simulation mechanism; the simulation teaching integration mechanism realizes simulation teaching according to a cultivation scheme; the data tag generating mechanism marks the practice teaching training process data and the data generated by the simulation teaching integrating mechanism in the practice teaching training process of students, so that the data of different students in the simulation practice process are distinguished, and the target data are rapidly extracted when the data are used by the factory operation simulation mechanism, the factory control simulation mechanism and the simulation teaching integrating mechanism.
Description
Technical Field
The invention relates to the technical field of engineering education, in particular to a system for constructing a practical teaching intelligent simulation factory based on Internet of things equipment.
Background
With the continuous development and application of technology, internet of things (IOT) technology is becoming an important foundation for the development of modern industry. The internet of things technology combines various sensors, controllers, communication equipment and the Internet to realize real-time communication and data sharing between the equipment and the system. The application of the technology can improve the efficiency, quality and safety of industrial production, and simultaneously reduce the enterprise cost and the resource consumption.
In the field of engineering education, in order to cultivate the professional skills and practice ability of students (students), the application of intelligent simulation equipment is gradually becoming a mainstream practice teaching mode.
At present, a plurality of practical teaching intelligent simulation factory cases based on the Internet of things exist at home and abroad, for example, FANUC intelligent manufacturing college in Japan, intelligent manufacturing laboratory in Singapore, intelligent manufacturing laboratory in Miami Wheatstone university in America and the like. The laboratories all utilize the internet of things technology to construct an intelligent manufacturing system with high simulation degree and real-time property, and a large number of excellent talents are cultivated for the industrial automation field through practical teaching and innovative research.
However, related research and application in China are weak, most education institutions except for a few universities and scientific institutions do not have the field, and the corresponding teaching and learning forces and equipment conditions are lacked.
Existing intelligent simulation factory systems are typically closed systems developed by a single vendor or manufacturer, are difficult to meet the different needs of different schools and businesses, and have the following problems:
(1) Lack of a true production run environment: the existing intelligent simulation factories are biased to discrete manufacturing industry in a large number, and cannot provide an actual production running environment to carry out practical training teaching support, and cannot construct an intelligent production scene and a practical teaching activity scene based on production running, so that practical training teaching is digitalized, intelligent production atmosphere is insufficient, and the Internet of things equipment can monitor the process and the system of the factories in real time and collect related data;
(2) Multiplexing cost is high: virtual simulation and practice operation are high in cost, practice work cannot be quickly and repeatedly consolidated, an existing simulation device or a pre-construction simulation device cannot perform convenient operations such as one-key reset, one-key recovery and the like, and a great deal of teaching resource management cost is required to be consumed for aiming at the teaching requirements of repeated practice or consolidation practice in practice teaching, so that students (students) are supported to perform real practice operation. Meanwhile, the built simulation device can only localize off-line practical operation and cannot achieve 'co-building sharing';
(3) Lack of sophisticated teaching assistance and evaluation analysis: at present, hardware construction and software construction are only provided for a simulation device in China, statistics and evaluation analysis cannot be performed for practical teaching data and practical operation data of students, so that a teacher is helped to perform reasonable teaching guidance and personalized student (student) evaluation for practical teaching activities, and a powerful practical operation basis is provided.
Therefore, a general method for constructing practice teaching intelligent simulation factories based on the Internet of things equipment is developed, and the method has important theoretical and practical values.
Disclosure of Invention
(one) object of the invention: in order to solve the problems in the prior art, the invention aims to provide a system and a method for constructing an intelligent simulation factory for practical teaching by using Internet of things equipment for practical teaching of engineering education in the future, which are applied to related process industry, so that the continuous development is satisfied, and the practical teaching requirements of the modern and intelligent engineering education field are met.
(II) technical scheme: in order to solve the technical problems, the technical proposal provides a system for constructing a practical teaching intelligent simulation factory based on Internet of things equipment, which comprises a factory operation simulation mechanism, a factory control simulation mechanism, a simulation teaching integration mechanism and a data tag generation mechanism,
The factory operation simulation mechanism is used for simulating factory production operation and acquiring factory production operation data;
the factory control simulation mechanism performs production operation control on the factory operation simulation mechanism to realize automation of the factory operation simulation mechanism;
the simulation teaching integration mechanism realizes simulation teaching according to a cultivation scheme;
the data tag generating mechanism marks the practice teaching training process data and the data generated by the simulation teaching integrating mechanism in the practice teaching training process of students, so that the data of different students in the simulation practice process are distinguished, and the target data are rapidly extracted when the data are used by the factory operation simulation mechanism, the factory control simulation mechanism and the simulation teaching integrating mechanism.
The system for constructing the practice teaching intelligent simulation factory based on the Internet of things equipment comprises a simulation device, the Internet of things equipment and an intelligent robot, wherein the factory operation simulation mechanism comprises a simulation device, a simulation system and a simulation system;
the factory control simulation mechanism comprises a production control layer and a production execution layer, wherein the production control layer is used for controlling the production operation of the factory operation simulation mechanism, and the production execution layer is used for performing management of the production operation of the factory operation simulation mechanism;
The simulation teaching integration mechanism comprises a teaching task management unit, an artificial intelligent reasoning unit, a factory digital twin unit and a capability assessment and diagnosis unit.
The system for constructing the practice teaching intelligent simulation factory based on the Internet of things equipment comprises practice teaching training process data and data generated by the simulation teaching integration mechanism in the practice teaching training process of students, wherein the practice teaching training process data and the data generated by the simulation teaching integration mechanism are collectively called as process data; the data tag generating mechanism comprises a data pool, a tag contrast generating unit, a tag generating unit and a data extracting unit,
the data pool is used for storing different process data generated in simulation teaching, including practical data and analysis data; the mark comparison generating unit is used for generating marks corresponding to different tasks, different students and/or different times, and the mark comparison generating unit is used for comparing and storing the generated marks corresponding to the tasks, the students and/or the times; the mark generation unit marks the process data in the data pool to obtain tag data; and the data extraction unit performs data extraction in the data pool according to the data extraction command to obtain target data.
The system for constructing the practice teaching intelligent simulation factory based on the Internet of things equipment comprises a data pool, wherein data in the data pool are tag data, the data pool comprises a data base part and a tag part, the data base part is data original content, and the tag part is used for marking corresponding tasks, students and/or time;
The label comparison generating unit comprises a label comparison table, wherein the label comparison table is used for recording labels corresponding to different tasks, different students and different times; the tag comparison table comprises a header column, a member column and a mark column, wherein the header column is a specific description of the mark classification of tasks, students or time corresponding to the member column and the mark column, and the specific mark in the header column is classified as a header to be selected; the member column is a member division column for comparing task names, student names and time points which are different in tasks, students and/or time; the marking column is a label corresponding to a specific task, a student and/or time; the labels corresponding to different tasks, different students and/or different times in the label column are generated by the label contrast generating unit according to a preset first rule or the designated labels input by the input mechanism.
The system for constructing the practical teaching intelligent simulation factory based on the Internet of things equipment comprises a data tag generation unit, a data pool and a data storage unit, wherein when the data tag generation unit needs to mark new data, the new data is searched in the data pool by the tag generation unit: when the data pool does not have the same data base as the new data, the new data is the data to be marked, and the mark generating unit selects the label of the task, the student and/or the time corresponding to the current new data according to the label comparison table to be combined with the new data to obtain marked data; when the same data part exists in the data pool, the mark generating unit grabs the data with the same data as the new data in the data pool to obtain the data to be marked, and at the moment, the data with the same data as the new data in the data pool is the data to be marked; and the label generating unit selects labels of tasks, students and/or time corresponding to the current new data according to the label comparison table and combines the labels with the data to be labeled to obtain label data.
The system for constructing the practice teaching intelligent simulation factory based on the Internet of things equipment comprises a label generation unit, a data pool and a data storage unit, wherein the label generation unit is used for directly storing new label data into the data pool when the new label data are obtained by combining the new data with corresponding labels.
When the new tag data is obtained by combining the data of the same data as the new data in the data pool with the corresponding tag, the tag generating unit deletes the initial data of the same data as the new data in the data pool, and directly puts the new tag data into the data pool, namely, replaces the tag data of the same data as the new data in the data pool with the new tag data.
The system for constructing practice teaching intelligent simulation factories based on the Internet of things equipment comprises a target member to be selected corresponding to target data, a data extraction unit for obtaining the target data,
the data extraction unit reads target members to be selected in the extraction command;
the data extraction unit searches the target member to be selected in the tag comparison table, and reads the corresponding tag in the tag column corresponding to the target member to be selected to obtain a target tag;
The data extraction unit searches in the data pool, reads the tag data of the tag data in the data pool, wherein the tag data comprises the target tag, and obtains the target tag data;
the data extraction unit divides the data base part of the target tag data from the tag part, and then sorts all the data base parts in the target tag data to obtain the target data.
The system for constructing the practice teaching intelligent simulation factory based on the Internet of things equipment comprises a tag corresponding to target data, the data extraction unit obtains the target data by the following method,
the data extraction unit reads a label corresponding to target data in the extraction command, namely a target label;
the data extraction unit searches in the data pool, reads the tag data of the tag data in the data pool, wherein the tag data comprises the target tag, and obtains the target tag data;
the data extraction unit divides the data base part of the target tag data from the tag part, and then sorts all the data base parts in the target tag data to obtain the target data.
The system for constructing the practice teaching intelligent simulation factory based on the Internet of things equipment comprises a plurality of data sub-pools, tag data in each data sub-pool stores tag data corresponding to different header columns in the same data sub-pool according to different headers in the header columns, and the data extraction unit searches in the corresponding data sub-pool according to a target member to be selected or a tag corresponding to target data in an extraction command and the corresponding header column.
The system for constructing the practice teaching intelligent simulation factory based on the Internet of things equipment comprises a plurality of data sub-pools, tag data in each data sub-pool are stored according to different task names, different student names and/or different time point data in member columns, tag data of the same task names, the same student names and/or the same time point are stored in the same data sub-pool, and the data extraction unit searches in the corresponding data sub-pool according to a target member to be selected or a corresponding tag of target data in an extraction command and the corresponding member column.
(III) beneficial effects: the invention provides a system for constructing a practice teaching intelligent simulation factory based on Internet of things equipment, which adopts a brand-new Internet application mode to connect a physical world with a digital world, so as to realize the internet of things technology, thereby realizing practice teaching, realizing a more efficient and intelligent teaching mode, and improving the practice capability and innovation capability of students;
the intelligent production interactive teaching environment is safe, low in cost and efficient, better teaching resources are provided for practical teaching, students can perform real practical operation, meanwhile, the application of the Internet of things equipment and intelligent control technology is known, and the practical capability and innovation capability of the students are improved;
The method has the advantages that the teaching mode of dynamic update of process data is provided, which is close to real production data, meets the requirement of actual factory operation, and ensures the accuracy and the authenticity of practical teaching (training);
the teaching system which can occupy less storage space and store multi-process data is provided, and a mode of classifying data pools is adopted when data are read, so that the reading speed of the process data is improved, and the experience of the teaching system is improved.
Drawings
Fig. 1 is a schematic diagram of a process for marking data by a data tag generating mechanism of a system for constructing a practical teaching intelligent simulation factory based on internet of things equipment.
Detailed Description
The present invention will be described in further detail with reference to the preferred embodiments, and more details are set forth in the following description in order to provide a thorough understanding of the present invention, but it will be apparent that the present invention can be embodied in many other forms than described herein, and that those skilled in the art may make similar generalizations and deductions depending on the actual application without departing from the spirit of the present invention, and therefore should not be construed to limit the scope of the present invention in the context of this particular embodiment.
The drawings are schematic representations of embodiments of the invention, it being noted that the drawings are by way of example only and are not drawn to scale and should not be taken as limiting the true scope of the invention.
The traditional practice teaching method based on the simulation factory has many limitations in the engineering education field, such as lack of real production and operation data, high multiplexing cost, high virtual simulation and practice operation cost, incapability of rapidly and repeatedly consolidating practice operation, lack of perfect teaching assistance, evaluation analysis and the like.
For example:
in the first prior art, virtual simulation software is constructed by using a computer simulation technology, and students (students) practice operations by simulating production indexes and production processes. Students (students) can perform practical operation and optimization experiments in a virtual environment, perform practical operation and experiment simulation of process production through virtual simulation software, and then perform optimization. Disadvantages of virtual simulation software in practice teaching include practice and production disjointing: the virtual simulation software cannot restore the real factory environment and equipment, and the production data and production operation parameters in the virtual simulation software are different from the actual situation, so that the teaching purpose of cognition and simple operation can be achieved; lack of real data: the data in the virtual simulation software is often generated according to a model and an algorithm, and the quality of the model and the algorithm of various factories is uneven, so that students (students) lack the processing and analysis capability of real data in practical operation and optimization experiments, and the data acquisition and analysis technology in the actual production process cannot be really understood and applied.
In the second prior art, simulation of a simulation factory is performed by combining the simulation factory with matched virtual simulation software, students (students) perform practice operation and practice through a field simulation factory device, and communicate key equipment states through the matched virtual simulation software, and simulate related production process contents and indexes so as to develop booster practice teaching. The method has the advantages that only the digitization of the simulation device is finished, the simulation of the production process is primarily finished, and only the cognition and practical capability teaching in the practical teaching in the traditional engineering teaching field can be supported.
The traditional practice teaching method is used in the engineering education field related to the process industry, so that more and more intelligent process industry talents are gradually required to be separated.
In recent years, with the development of the internet of things technology, new opportunities are provided for the practical teaching of engineering education related to process industry. Therefore, by combining the Internet of things equipment and the intelligent technology and constructing an intelligent simulation factory meeting practice teaching application based on the simulation device, the invention provides a safer, flexible and extensible practice environment: a system for constructing a practical teaching intelligent simulation factory based on Internet of things equipment.
A system for constructing a practice teaching intelligent simulation factory based on Internet of things equipment is applied to auxiliary practices of engineering education in different industries.
A system for constructing a practical teaching intelligent simulation factory based on Internet of things equipment comprises a factory operation simulation mechanism, a factory control simulation mechanism and a simulation teaching integration mechanism. The factory operation simulation mechanism is respectively connected with the factory control simulation mechanism and the simulation teaching integration mechanism. The factory operation simulation mechanism is used for simulating factory production operation and acquiring factory production operation data. The factory control simulation mechanism controls the production operation of the factory operation simulation mechanism, and automation of the factory operation simulation mechanism is achieved. The simulation teaching integration mechanism realizes simulation teaching according to a cultivation scheme.
The factory operation simulation mechanism comprises a simulation device, internet of things equipment and an intelligent robot.
The simulation device is a newly-built intelligent simulation factory or a modified simulation factory. The simulation device can simulate different production conditions and changes, and can be a virtual production environment which simulates real production flow and equipment, and can learn the production flow and equipment built by a factory according to a target.
The simulation device comprises the following functions:
1. and (3) simulating a reaction process: the dynamic behavior during the chemical reaction process is simulated, including reaction rate, material flow, temperature and pressure changes, etc.
2. Simulation of a transmission process: simulating mass transfer, heat transfer, momentum transfer and other processes to understand the distribution, transfer and conversion of materials in the device.
3. And (3) simulating a control strategy: different control strategies were simulated and their impact on process performance was evaluated.
4. Unit operation simulation: various unit operations such as distillation, extraction, adsorption and the like are simulated to understand the separation and purification effects of the unit operations on the fluid components.
5. Security assessment and risk analysis: through simulation and analysis, the safety, stability and risk of the chemical process are evaluated, potential risks and risks are identified, and corresponding measures are taken to ensure the safety of staff and equipment.
The simulation device comprises reconstruction equipment for data acquisition and transmission, wherein the reconstruction equipment comprises an intelligent instrument, a trigger, an industrial inspection terminal, an industrial control terminal and a camera.
The intelligent instrument is arranged on a main process pipeline and an auxiliary process pipeline which are required by typical process production equipment meeting teaching requirements in the simulation device and is used for monitoring and controlling key process variables of key equipment in real time. The intelligent instrument comprises a control valve, an electromagnetic valve, a motor and the like, and the control valve can be a regulating valve or a cut-off valve.
The trigger is arranged in the PLC control cabinet of the simulation device and is used for simulating independent signals required by the intelligent instrument and an OTS process simulation production operation model, triggering and controlling abnormal events and states and controlling the intelligent instrument to cut, commission and reset.
The industrial inspection terminal is portable equipment and is used for carrying out equipment inspection and receiving and transmitting maintenance tasks in an industrial environment. The application of the industrial inspection terminal can improve inspection efficiency, reduce inspection cost and improve equipment reliability, so that workers can more conveniently and accurately inspect and maintain equipment, discover and solve problems in time, and ensure normal operation and production safety of the equipment.
The industrial inspection terminal has the following characteristics and functions: the durability and the adaptability are good, the characteristics of earthquake resistance, dust prevention, water resistance and the like are achieved, and the method is suitable for severe environmental conditions of industrial sites; monitoring and displaying in real time, and receiving and displaying monitoring signals of various sensors or devices, such as temperature, pressure, current and the like; the data recording and analyzing function is realized, and various data in the inspection process are recorded, including monitoring data, inspection time, operation records and the like; the image and video acquisition function is provided, and the condition of the equipment is shot through a camera and recorded; the remote communication is connected with the cloud platform, and the remote communication is connected with the cloud platform or a remote server in a wireless communication mode, so that functions of remote monitoring, data uploading, remote operation and the like are realized; and (3) managing the inspection task, providing a task management function, planning, distributing and recording the inspection task, and generating inspection reports and statistical data.
The industrial control terminal is used for displaying the contents related to production process indexes, such as key production parameters, reaction mechanisms and the like of different devices in the simulation device, displaying and operating the interactive task activity contents of the current device, and sending the interactive activity process to the factory control simulation mechanism and/or the simulation teaching integration mechanism.
The camera is used for collecting and monitoring the behavior data of the area where the simulation device is located, and the collected behavior data is transmitted to the factory control simulation mechanism and/or the simulation teaching integration mechanism through an RTSP transmission protocol.
The internet of things equipment comprises an M5, a GPS tag and other internet of things sensors, and the internet of things equipment is used for realizing equipment interconnection, data sharing and real-time monitoring and control of a factory operation simulation mechanism, a factory control simulation mechanism and a simulation teaching integration mechanism.
The internet of things device can be arranged on related devices for real-time monitoring and control, and the related devices comprise dynamic devices and static devices. The Internet of things equipment collects real data, presents abnormal condition simulation data, and simulates the positioning of a factory area inspection map and the interaction in practical teaching activities through the Internet of things. The internet of things device may be provided to a feed line centrifugal pump device of a typical installation for simulating an industrial centrifugal pump preventative maintenance scenario. The internet of things equipment can be arranged on a feeding pipeline valve or a discharging pipeline valve of a typical device and is used for simulating abnormal phenomena such as running, overflowing, dripping, leaking and the like, sound and the like.
The related device and the key device may be the same device or different devices.
The internet of things equipment performs data acquisition through a standard 0PC protocol, for example, acquires digital signals, trend signals, sensor signals and the like of key equipment in a factory operation simulation mechanism, acquires OTS simulation process data information and interaction information of the whole simulation device, and constructs an equipment layer which is the factory operation simulation mechanism and accords with the manufacturing industry international industrial SA-95 system model standard by matching with an intelligent robot technology and a 5G communication technology. And combining the specific interaction attribute design of the Internet of things equipment M5, supporting the specific interaction operation of key equipment, and completing the practical teaching task of the factory control simulation mechanism equipment.
The digital signal is a discrete digital sequence signal which is obtained by converting an analog signal into a discrete digital sequence signal through processes such as sampling, quantization, encoding and the like, and the value of the digital sequence signal can only take a limited number. In digital signals, signal values are represented by binary numbers. The digital signals can be processed, transmitted and stored by digital technologies such as a computer, and the like, and the digital signal processing device has the advantages of good anti-interference performance, long transmission distance and the like. In the process industry, digital signals are widely applied to control and monitoring of a production process, and signals in the production process are collected through a sensor and are converted into digital signals to be processed and analyzed, so that real-time monitoring and adjustment of the production process are realized. The digital signal can effectively solve the problems of the traditional analog signal technology in the aspects of signal transmission distance, interference resistance, signal quality and the like, and improves the production efficiency and the product quality. The digital signals may also be combined with other digital technologies such as digital image processing, digital signal processing, machine learning, etc., to implement higher level functions and solutions. In chemical enterprises, the digital signals can help the enterprises to realize intelligent manufacturing, and the production efficiency and the competitiveness of the enterprises are improved.
The trend signal is a signal which expresses the evolution trend of the system state and is obtained by counting and analyzing the monitoring data in a certain time range. It reflects the running state and the change trend of the system in a period of time. In the process industry, trend signals are often used to monitor and evaluate the stability and reliability of a production process. By monitoring and recording key parameters in the production process in real time, a large amount of data can be obtained. By analyzing the data, the change trend of the system state, such as the rising or falling trend of parameters of temperature, pressure, flow and the like, is obtained. The trend signal can help chemical enterprises to find potential problems or anomalies in time, and corresponding measures are taken to adjust and improve the potential problems or anomalies so as to ensure the stability and the product quality of the production process. In addition, the trend signal can also be used for prediction and optimization, and future system states and trend changes can be predicted through trend analysis of historical data, so that corresponding adjustment and decision can be made in advance. The application of trend signals in the process industry may be combined with other data analysis and control techniques, such as artificial intelligence, machine learning, etc., to achieve more accurate and intelligent analysis and decision-making. Through monitoring and analysis to trend signal, chemical industry enterprise can improve production efficiency, reduce manufacturing cost to promote enterprise's competitiveness.
The sensor signal is a signal that is converted into an electrical signal or a digital signal by a physical quantity measured by a sensor, and is transmitted and processed. A sensor is a device that converts one or more physical quantities into an electrical signal, such as a temperature sensor, a pressure sensor, a light sensor, etc. The sensor signal may provide information about the measured physical quantity, such as temperature, pressure, light intensity, etc. In the process industry, sensor signals are an important source of information for monitoring and controlling the production process in real time. Parameters such as temperature, pressure, flow and the like in the production process can be known through signals measured by the sensor, and real-time monitoring and adjustment of the production process are facilitated. The sensor signals can also be used for prediction and optimization, and future system states and trend changes can be predicted through analysis of historical data, so that corresponding adjustment and decision can be made in advance. The application of sensor signals can be combined with other data analysis and control techniques, such as internet of things, artificial intelligence, big data, etc., to achieve higher level functions and solutions. Through monitoring and analysis to sensor signal, chemical industry enterprise can improve production efficiency, reduce manufacturing cost to promote the competitiveness of enterprise.
The intelligent robot guides the inspection route aiming at the area where the simulation device is located, guides the practical teaching content of related key inspection posts and the extended knowledge surface, and consolidates and improves the key inspection post capability through cognition and exercise of the inspection post capability and repeated exercise and practical operation. The teacher (manager) issues a patrol practice teaching task through a teaching task management unit of the simulation teaching integration mechanism, the intelligent robot receives the patrol task, and guides the student (student) to a safety patrol area of the area where the simulation device is located by combining a patrol route selected by the student (student), so that target explanation, necessary knowledge, consolidation expansion, safety specification and other patrol notes of the patrol task are realized, and the student (student) is sequentially guided to carry out patrol point content explanation, interaction and filing. The intelligent robot can be an industrial inspection robot or an industrial inspection unmanned plane and the like.
The factory control simulation mechanism comprises a production control layer and a production execution layer, wherein the production control layer is used for controlling the production operation of the factory operation simulation mechanism, and the production execution layer is used for managing the execution of the production operation of the factory operation simulation mechanism.
After the production control layer collects, distributes and/or stores relevant data in the factory operation simulation mechanism, safety early warning management of the factory operation simulation mechanism, intelligent control and optimization of production steady state of the factory operation simulation mechanism, safety control of the factory operation simulation mechanism and simulation of a complete production process and a control system of the factory operation simulation mechanism are realized according to the collected relevant data in the factory operation simulation mechanism.
The production control layer comprises a data integration module, an early warning management module, a production control module, a safety control module and a simulation optimization module. And the data integration module collects, distributes and/or stores related data in the factory operation simulation mechanism to obtain simulation production data. And the early warning management module generates safety early warning data according to the simulation production data, so as to realize the safety early warning management of the factory control simulation mechanism. And the production control module realizes intelligent control and production optimization of the production steady state of the factory operation simulation mechanism according to the simulation production data. And the safety control module realizes the safety control of the factory operation simulation mechanism according to the safety early warning data of the early warning management module. And the simulation optimizing module simulates the complete production process and control system of the factory operation simulation mechanism according to the simulation production data, so as to realize real-time simulation operation.
The data integration module collects, distributes and/or stores relevant data of the factory operation simulation mechanism, wherein the relevant data comprises production operation data, inspection data, a 0TS process simulation model, behavior data, personnel positioning data, environment data, check-in data and the like.
The early warning control management module monitors personnel behaviors and production operations in the factory operation simulation mechanism according to the collected related data in the factory operation simulation mechanism, and achieves early warning and management of unsafe behaviors, including unsafe behaviors of personnel and unsafe behaviors of production operations. Specifically, the production operation condition, the typical equipment operation condition and the personnel safety condition of the regional practice teaching activity of the factory operation simulation mechanism are monitored in real time by monitoring the collected patrol data, production operation data, behavior data, personnel positioning data sets, check-in data and the like of relevant data in the factory operation simulation mechanism, so that the early warning and management of unsafe behaviors are realized, and the normal production operation and abnormal fault linkage simulation of the intelligent simulation factory are ensured.
For unsafe management and early warning of personnel behaviors, the method specifically can be based on a 5G+ monitoring and AI behavior analysis technology, a practical training monitoring center of a factory control simulation mechanism timely monitors various sudden emergency early warning behaviors in practical operation places such as a practical operation training area, a practical operation training peripheral area and the like of a practical training room, early warning and management are carried out on student behaviors of a teacher in a teaching process, so that unsafe behaviors of the student can be corrected and helped in time, the safety of the student is ensured, teaching safety guarantee is provided for practical playgrounds such as the practical operation training area, the practical operation training peripheral area and the like, and safety production consciousness is enhanced from practice practical training to moment. Various sudden emergency early warning behaviors are collected, including action behaviors of a learner, operation behaviors of the learner on a factory control simulation mechanism and the like.
For unsafe management and early warning of production operation, the industrial production process of the factory control simulation mechanism is comprehensively and timely subjected to data acquisition and analysis, accurate information support is provided for production decision, and safe production monitoring and early warning are realized. The unsafe management and early warning of the production operation comprises various functional modules such as production full-element monitoring, process key parameter monitoring, equipment monitoring, intelligent advanced control system monitoring, intelligent safety instrument system monitoring, AI intelligent safety monitoring, three-dimensional digital twin-factory display, three-dimensional pipeline flow display, key equipment operation monitoring and the like.
The production control module can adopt advanced process control, english: advanced process control, abbreviated as: APC, alias: advanced process control, advanced process control systems include all things ranging from data acquisition processing, mathematical model building, advanced control strategies to engineering implementations.
Advanced process control systems utilize modern mathematical modeling, optimization calculations, and control algorithms to control and optimize multiple variables for typical reaction devices in a plant operation simulation facility. The method is mainly applied to the production process of chemical industry factories, aims at improving the production efficiency and the product quality, can monitor and control a process system on line, and automatically adjusts production parameters so as to achieve the aim of improving the product quality and the production efficiency. The advanced process control system can monitor the running state, key parameters and key point values of a typical reaction device in real time, and display the energy consumption, the component state and the operation state of the production process in real time, so that operators can comprehensively know the production condition; the advanced process control system adopts a big data model matrix and an intuitive visual interface to display the trend and real-time data of key process variables such as temperature, components and the like in real time, and provides decision basis for optimizing production operation and maximizing production performance for operators. Compared with the traditional PID control system, the advanced process control system has higher control precision and stronger adaptability, and can effectively cope with various changes and anomalies in the process system, thereby improving the production efficiency and the product quality, reducing the production cost, and students (students) know the real-time monitoring and operation of the actual production process through the operator station of the advanced process control system, and cultivate the capability of the students in the actual operation environment. The engineer station of the advanced process control system provides deeper learning opportunities for students (students) to learn and understand the principles and methods of system configuration, parameter adjustment and optimization strategies, thereby improving practice skills.
The safety control module can adopt a safety instrument system, safety Instrumented System, SIS for short; also known as a safety interlock system (Safety interlocking System). The control system mainly comprises an alarm and interlocking part in a factory control system, and an alarm action or regulation or shutdown control is implemented on a detection result in the control system, so that the control system is an important component in automatic control of factory enterprises.
The safety instrument system monitors key process variables of the factory operation simulation mechanism in real time, so that process operation is ensured to be carried out within a safety range, potential hazards are monitored and treated, and the safe operation of the process is ensured. The safety instrumented system collects and analyzes parameters such as pressure, temperature, fluid level, etc., and compares with preset safety limits. If a certain parameter exceeds the safety limit, the safety instrumented system will immediately trigger an alarm and emergency stop function to avoid a potentially dangerous situation. When the safety instrument system is abnormal, the safety instrument system starts safety protection measures, such as automatic closing of a valve, shutdown and the like, so as to ensure the safety of the technological process.
The simulation optimization module can adopt a virtual simulation system, which is called DCS for short. The virtual simulation system is a virtual simulation system which is realized based on computer technology and simulates a real control process. The virtual simulation system can simulate a complete production process and a control system, can perform real-time simulation operation, can be used for industrial production, research and development experiments, training education and the like, and can optimize the process flow, improve the product quality and reduce the production cost.
The execution management of the production execution layer on the production operation of the factory operation simulation mechanism can be realized by a production control system, which is called EMS for short. The production control system is used for monitoring, managing and optimizing the manufacturing process, improving the production efficiency and quality and reducing the production cost. In the factory operation simulation mechanism, the production control system is used for production planning and scheduling, production instruction management, material tracking, quality control, production data acquisition, data analysis and the like. The production control system helps enterprises realize digitization, intellectualization, networking and standardization of the production process, improves production efficiency and quality, reduces production cost, strengthens control and management of the enterprise on the production process, and improves competitiveness and market share of the enterprise. Meanwhile, the production control system can help enterprises to realize real-time monitoring and data analysis of the production process, timely master production conditions and provide scientific basis for decisions of the enterprises.
The production control system can help practice teaching training scene simulation, provide more real process industrial production simulation scene and simulated typical post practice foundation, and deepen students' understanding and mastering the production process; the production environment can be simulated on a computer, the operation process of the production line is known, the production flow and the equipment operation are mastered, the understanding and mastering of the production process by students (students) are improved, and preparation is made for future professional development; control of the production process, such as adjusting process parameters, optimizing production lines, etc., can be practiced to help improve the practical ability of students (scholars) and enhance understanding of production control; a large amount of production data such as equipment running state, product quality and the like can be collected and stored, an analysis production data application scene is provided, students (students) can use the data to analyze, the problems in the production process are known, and an improvement scheme is provided to improve the production efficiency; the production control system can be used for optimizing production operation flows, students (students) can optimize the production flows by utilizing the production control system, for example, technological parameters are adjusted, the running state of equipment is improved, the product quality is improved, the innovation capacity of the students (students) is improved, and preparation is made for future professional development; the method can train staff aiming at the existing practice teaching resources co-building sharing and external service, train students (scholars) about to enter the job site, for example, enable the front scholars to know the running process of the production line and master the equipment operation through simulation operation. The production control system realizes the capability of making and implementing a production plan, the capability of controlling chemical production by operating the intelligent manufacturing system and equipment, the capability of managing the quality of a production process, and the capability of finding out an optimized process index and realizing high-quality low-consumption production by carrying out data analysis and excavation.
The production control system specifically comprises production plan management, production scheduling management, QHSE management, equipment management, supply chain and related management, and realizes the collection, organization, arrangement, analysis and integration of chemical production related data such as plan execution, production scheduling, product quality, model management, equipment management, preventive maintenance management, material management, personnel information management and the like, thereby providing plan execution, tracking and current state information of all resources for students (students) and teachers (management personnel). Acquiring the current state information of all resources comprises acquiring the current state information of the resources such as people, equipment, materials and the like.
The production plan management includes inspection maintenance planning, production planning, and procurement planning. Each plan includes contents of plan name, start time, end time, plan type, plan number, actual number, execution progress, plan priority, and the like. The production plan management supports an administrator to schedule or customize new plan tasks, and the plan tasks are managed.
The production schedule management comprises relevant information such as schedule name, type, execution content, execution state, association plan number and the like. The production schedule management supports the schedulers to participate in setting various production schedule schedules, so that students (students) can learn related operations and skills of production schedule management: students (students) can learn how to formulate reasonable production plans and schedules, flexibly cope with environmental changes, know and master new technologies, new methods and new tools, how to apply to production scheduling, improve scheduling efficiency and quality, master how to conduct team cooperation, better achieve production targets, and finally learn how to solve, communicate and process by simulating various problems and difficulties in the production environment, and improve strain capacity and team cooperation capacity of the students (students).
The QHSE management is used for ensuring that the management of enterprises in health, safety, environment and the like reaches the optimal level, and is based on the comprehensive risk management concept, and the QHSE performance of the enterprises is ensured to reach the expected level by identifying, evaluating and controlling potential risks and taking preventive measures. The QHSE management aims at environmental management and product quality management, and pays attention to production quality and environmental protection so as to achieve the purposes of controllable environmental hazard and better efficiency of produced products. The QHSE management is used for forming a product quality management system by monitoring and storing quality indexes of various raw material products, including the conditions of detection object names, detection items, detection standards, inspection time (selection), detection states (selection), inspection persons, detection results (selection), quality phenomenon description, inspection persons and the like.
The QHSE management enables students (students) to simulate the production flow of products in a simulation environment, so that key technologies and flows in the production process of the products are known more deeply, and the practical capability of the students is improved. Meanwhile, the quality of products can be effectively controlled, sudden problems can be handled, and the like under different production environments, so that the problem processing capacity of the students (students) is enhanced, the students (students) are promoted to have comprehensive quality in the aspects of team cooperation, communication and the like, the team cooperation and communication capacity of the students (students) is trained in practice, the comprehensive quality of the students (students) is improved, multi-level, omnibearing and three-dimensional teaching resources are provided, experimental teaching contents are widened, the technological content of experimental teaching is increased, and the students (students) can better apply teaching knowledge.
The equipment management comprises equipment accounts, equipment overhaul and maintenance records, equipment abnormality report treatment records and the like, so that students (students) can learn related operations and skills of equipment abnormality report treatment in a virtual environment, learn how to process equipment abnormality report, and know the coping methods required to be adopted by different equipment abnormalities. Meanwhile, the equipment abnormality report treatment record function can enable students (students) to know and master new technologies, new methods and new tools, how to apply the equipment abnormality report treatment, improve treatment efficiency and quality, and master how to conduct team cooperation, so that the production goal is better achieved. Finally, students (students) learn how to conduct equipment abnormality investigation and treatment through simulating equipment abnormality repair and treatment processes, and can master how to conduct preventive maintenance on equipment, so that the service life of the equipment is prolonged, and the production cost is reduced.
The supply chain and related management, including coordination and control of suppliers, manufacturers, logistics service providers, and vendors, are very important links in modern enterprise operations. The supply chain and related management enable enterprises to efficiently transfer raw materials, parts, finished goods, etc. from suppliers to end consumers while ensuring efficiency and cost control throughout the process, and involves only typical material management and personnel information management. The supply chain and the related management can enable students (students) to know the basic principle of product storage and the related management method, know the modern logistics management technology, help the students (students) to understand the importance of warehouse management, such as warehousing and ex-warehouse of products, and knowledge in aspects of quality control, inventory management and the like, and enable the students (students) to enhance understanding and mastering of the warehouse management through practical operation, and help the students (students) to cultivate the capability of team cooperation and solve problems. In the operation of simulation time, students (students) need to cooperate with other students to finish various tasks together, and find and solve problems in the operation process, so that the comprehensive quality of the students is improved.
The simulation teaching integration mechanism comprises a teaching task management unit, an artificial intelligent reasoning unit, a factory digital twin unit and a capability assessment and diagnosis unit. The teaching task management unit is used for completing issuing of teaching tasks, linkage simulation of a factory operation simulation mechanism, acquisition of task completion progress and acquisition of task achievements, and making practice reports corresponding to students. The artificial intelligence reasoning unit evaluates the capability evaluation of students (students) through a knowledge graph according to practical teaching training process data. The factory digital twin unit carries out synchronous operation of simulation production according to real-time data of a factory operation simulation mechanism, forms a precise digital copy, and realizes real-time monitoring, prediction and optimization of simulation production in a digital environment. The capability assessment and diagnosis unit summarizes course achievement conditions of the practice teaching conditions of the whole course according to individual capability conditions of students (students) in practice teaching, so that the teaching capability of a teacher team is improved.
The practical teaching training process data comprises process data corresponding to a factory operation simulation mechanism and a factory control simulation mechanism of students (students) in practical operation.
The teaching task management unit completes dynamic issuing of practical teaching tasks and restoration and simulation of working conditions of the factory operation simulation mechanism corresponding to the tasks through linkage simulation of the factory operation simulation mechanism and simulation of the teaching tasks, and practical teaching task management and simulation of typical practical teaching environments of the factory operation simulation mechanism are completed simultaneously. The teaching task management unit supports the acquisition management and control of issuing, progress and achievements of practical teaching tasks, and generates the acquisition of practical teaching training process data corresponding to students according to the teaching tasks, the completion progress and the achievements.
The artificial intelligence reasoning unit is used for reasoning, analyzing and summarizing the pre-cause, the post-event and the influence relation of the two-way reasoning of the knowledge graph on the basis of the practical teaching training process data, and carrying out personalized and post-oriented capability assessment on students (students), so that the analysis and guidance of the individual capability condition of the students are realized, and consolidated and promoted suggestions and directions are provided for the students (students).
The digital twin unit of the factory is synchronous with all valves, instrument data and the like of the running simulation mechanism of the factory in a cloud deployment mode on the basis of the running simulation mechanism of the factory, and the cloud service technology and the Internet of things technology are adopted to extend the field practice teaching training application scene to the Internet so as to achieve the co-building sharing of practice teaching resources. The factory digital twin unit not only can be linked with the production operation of the field factory operation simulation mechanism, but also can support the independent cloud operation. In addition, a 0TS process simulation model of the factory operation simulation mechanism is adopted, so that the production process, production index and production energy consumption data in the application scene of the twin practice teaching training are completely consistent with the factory operation simulation mechanism, and the purposes of facing society and regional service are achieved. Due to the consistency of the practical effect of the factory digital twin unit and the factory operation simulation mechanism, practical teaching training activities can be developed through the cloud operation mode of the factory digital twin unit, tasks are issued and task completion progress and task score are collected through the teaching task management unit, and the remote control practical teaching training scene is completed.
The capability assessment and diagnosis unit is combined with a practice teaching training course system, presents the operation capability condition of the practice process in big data visualization, and forms common practice teaching training mastering conditions according to the summary of course achievement conditions of the whole course practice teaching conditions, thereby helping the improvement of the teaching capability of a teacher team.
The system for constructing the practice teaching intelligent simulation factory based on the Internet of things equipment further comprises a data tag generation mechanism, wherein the data tag generation mechanism marks the practice teaching training process data and the data generated by the simulation teaching integration mechanism in the student practice teaching training process, so that the data of different students (students) in the simulation practice process are distinguished, and in addition, the factory operation simulation mechanism, the factory control simulation mechanism and the simulation teaching integration mechanism can rapidly extract target data when the data are used.
The practical teaching training process data and the data generated by the simulation teaching integration mechanism in the practical teaching training process of students are collectively called as process data.
The data tag generation mechanism comprises a data pool, a tag contrast generation unit, a tag generation unit and a data extraction unit. The data pool is used for storing different process data generated in simulation teaching, including practical data, analysis data and the like. The mark control generation unit is used for generating marks corresponding to different tasks, different students and/or different times and the like, and the mark control generation unit is used for carrying out control storage on the generated marks corresponding to the tasks, the students and/or the times and the like. The mark generating unit marks data (process data) generated by the process data factory operation simulation mechanism, the factory control simulation mechanism and the simulation teaching integration mechanism in the data pool to obtain tag data. And the data extraction unit performs data extraction in the data pool according to the data extraction command to obtain target data.
The data in the data pool is tag data and comprises a data base part and a tag part, wherein the data base part is the original content of the data, and the tag part is used for marking corresponding tasks, students and/or time and the like. The same data home part in the data pool only comprises one data home part, and the label part corresponding to each data home part can correspond to a plurality of tasks, students and/or time and the like. When a new data is generated, the data tag generating mechanism searches in the data pool, when the same data base does not exist in the data pool, the new data is to-be-marked data, and the new data is placed in the data pool after marking is completed. When the same data part exists in the data pool, the data label generating mechanism grabs the data with the same data as the new data in the data pool to obtain the data to be marked, and at the moment, the data with the same data as the new data in the data pool is the data to be marked.
The label comparison generating unit comprises a label comparison table, and the label comparison table is used for recording labels corresponding to different tasks, students, time and the like. The tag comparison table comprises a header column, a member column and a mark column, wherein the header column can be input by an administrator through an input mechanism, and is a specific description of the classification of the marks of tasks, students or time corresponding to the member column and the mark column, and the specific marks in the header column are classified as the header to be selected. The member column is a member division column for comparing task names, student names and time points which are different in tasks, students and/or time. The tag column is a tag corresponding to a specific task, a student and/or time, and the tag in the tag column can be binary data, an image or other data, and is not limited in particular.
An example structure of a tag lookup table is given here as follows:
the labels corresponding to different tasks, different students and/or different times in the label column can be generated by the first rule preset by the label contrast generating unit, and can also be designated labels input by the input mechanism.
When the data tag generation mechanism needs to tag new data (newly generated data), the specific steps are as follows,
the mark generating unit searches the new data in the data pool;
when the data pool does not have the same data base as the new data, the new data is the data to be marked, and the mark generating unit selects the label of the task, the student and/or the time corresponding to the current new data according to the label comparison table to be combined with the new data (the data to be marked) to obtain marked data;
when the same data part exists in the data pool, the mark generating unit grabs the data with the same data as the new data in the data pool to obtain the data to be marked, and at the moment, the data with the same data as the new data in the data pool is the data to be marked; and the label generating unit selects labels of tasks, students and/or time corresponding to the current new data according to the label comparison table and combines the labels with the data to be labeled to obtain label data.
The tag data includes current new data and identifies which task, student, and/or time the current new data was generated.
The label generating unit selects the label of the task, the learner and/or the time corresponding to the current new data according to the label comparison table, specifically in the following way,
the mark generating unit selects a corresponding header to be selected from the header column according to the task, the student and/or the time corresponding to the current new data;
the mark generation unit selects a corresponding task name, a student name and/or a time point in a member column corresponding to the selected list head to be selected, namely the member to be selected;
and the tag generating unit reads the corresponding tag in the tag column corresponding to the member to be selected.
The label generating unit selects the label of the task, the learner and/or the time corresponding to the current new data to be combined with the data to be labeled according to the label comparison table, specifically, the method can be as follows,
and the mark generating unit attaches a label corresponding to the task, the student and/or the time of the current new data to the tail end of the data to be marked to obtain new label data. When new tag data is obtained by combining new data with a corresponding tag, the tag generation unit directly puts the new tag data into the data pool. When new tag data is obtained by combining the data of the same data as the new data in the data pool with the corresponding tag, the tag generating unit deletes the initial data of the same data as the new data in the data pool, and directly puts the new tag data into the data pool, namely, replaces the tag data of the same data as the new data in the data pool with the new tag data.
The data extraction command may be a data extraction command generated by the plant operation simulation mechanism, the plant control simulation mechanism or the simulation teaching integration mechanism when using data, or may be a data extraction command input by an administrator through an input mechanism, and is not particularly limited herein.
The extraction command at least comprises a target member to be selected corresponding to the target data, namely a task name, a student name and/or a time point corresponding to the target data, and can also comprise a label corresponding to the target data.
When the extraction command includes a target member to be selected corresponding to the target data, the data extraction unit may obtain the target data by,
the data extraction unit reads target members to be selected in the extraction command, namely target task names, student names and/or time points;
the data extraction unit searches the target member to be selected in the tag comparison table, and reads the corresponding tag in the tag column corresponding to the target member to be selected to obtain a target tag;
the data extraction unit searches in the data pool, reads the tag data of the tag data in the data pool, wherein the tag data comprises the target tag, and obtains the target tag data;
The data extraction unit divides the data base part of the target tag data from the tag part, and then sorts all the data base parts in the target tag data to obtain the target data.
When the extraction command includes a tag corresponding to the target data, the data extraction unit may obtain the target data by,
the data extraction unit reads a label corresponding to target data in the extraction command, namely a target label;
the data extraction unit searches in the data pool, reads the tag data of the tag data in the data pool, wherein the tag data comprises the target tag, and obtains the target tag data;
the data extraction unit divides the data base part of the target tag data from the tag part, and then sorts all the data base parts in the target tag data to obtain the target data.
The data pool comprises a plurality of data sub-pools, tag data in each data sub-pool can store tag data corresponding to different header columns in the same data sub-pool according to different header columns in the header columns, and the data extraction unit searches in the corresponding data sub-pool according to the target member to be selected in the extraction command or the tag corresponding to the target data, so that the extraction speed is increased.
The label data in each data sub-pool can be stored according to different task names, different student names and/or different time point data in the member columns, the label data of the same task names, the same student names and/or the same time point are stored in the same data sub-pool, and the data extraction unit searches in the corresponding data sub-pool according to the corresponding label of the target member to be selected or the target data in the extraction command, so that the extraction speed is increased.
A system for constructing a practical teaching intelligent simulation factory based on Internet of things equipment is characterized in that an institution simulation device or simulation factory is used as a guide, a 0TS process production algorithm content is used as a basis, a practical teaching intelligent factory based on a real object is constructed, a virtual system is used as a guide, an intelligent digital factory with virtual reality and virtual reality combination is constructed, and a practical industrial production scene, simulated production data and simulated production indexes are simulated through a teaching task management unit, an artificial intelligent reasoning unit, a factory digital twin unit and a capability assessment and diagnosis unit of a simulation teaching integration mechanism and matched hardware equipment, an intelligent factory environment is comprehensively simulated, modern and intelligent industrial development trend is recognized from practice, an intelligent production environment and intelligent manufacturing talent cultivation are realized, so that the requirement of practical teaching (training) is met.
A system for constructing a practice teaching intelligent simulation factory based on Internet of things equipment is characterized in that the Internet of things equipment is adopted to collect simulation process production data of key equipment, unified standardized production parameters and production indexes are constructed, the simulation process production data are processed and analyzed, and the production data indexes are referred to perform post capability practice, process data optimization and the like required by intelligent production.
A system for constructing practice teaching intelligent simulation factories based on Internet of things equipment is closest to real production data to the greatest extent, meets the operation requirement of the real factories, can obtain dynamic update of process data, and ensures accuracy and authenticity of practice teaching (training).
The system for constructing the practice teaching intelligent simulation factory based on the Internet of things equipment reduces the memory occupation space in the process of storing the data, ensures the reading speed in the process of reading the data, and improves the overall use quality of the system for constructing the practice teaching intelligent simulation factory based on the Internet of things equipment.
The foregoing is a description of a preferred embodiment of the invention to assist those skilled in the art in more fully understanding the invention. However, these examples are merely illustrative, and the present invention is not to be construed as being limited to the descriptions of these examples. It should be understood that, to those skilled in the art to which the present invention pertains, several simple deductions and changes can be made without departing from the inventive concept, and these should be considered as falling within the scope of the present invention.
Claims (10)
1. A system for constructing a practical teaching intelligent simulation factory based on Internet of things equipment is characterized by comprising a factory operation simulation mechanism, a factory control simulation mechanism, a simulation teaching integration mechanism and a data tag generation mechanism,
the factory operation simulation mechanism is used for simulating factory production operation and acquiring factory production operation data;
the factory control simulation mechanism performs production operation control on the factory operation simulation mechanism to realize automation of the factory operation simulation mechanism;
the simulation teaching integration mechanism realizes simulation teaching according to a cultivation scheme;
the data tag generating mechanism marks the practice teaching training process data and the data generated by the simulation teaching integrating mechanism in the practice teaching training process of students, so that the data of different students in the simulation practice process are distinguished, and the target data are rapidly extracted when the data are used by the factory operation simulation mechanism, the factory control simulation mechanism and the simulation teaching integrating mechanism.
2. The system for constructing a practical teaching intelligent simulation factory based on the internet of things equipment according to claim 1, wherein the factory operation simulation mechanism comprises a simulation device, the internet of things equipment and an intelligent robot;
The factory control simulation mechanism comprises a production control layer and a production execution layer, wherein the production control layer is used for controlling the production operation of the factory operation simulation mechanism, and the production execution layer is used for performing management of the production operation of the factory operation simulation mechanism;
the simulation teaching integration mechanism comprises a teaching task management unit, an artificial intelligent reasoning unit, a factory digital twin unit and a capability assessment and diagnosis unit.
3. The system for constructing a practice teaching intelligent simulation factory based on the internet of things equipment according to claim 1, wherein practice teaching training process data and data generated by the simulation teaching integration mechanism in a student practice teaching training process are collectively called as process data; the data tag generating mechanism comprises a data pool, a tag contrast generating unit, a tag generating unit and a data extracting unit,
the data pool is used for storing different process data generated in simulation teaching, including practical data and analysis data; the mark comparison generating unit is used for generating marks corresponding to different tasks, different students and/or different times, and the mark comparison generating unit is used for comparing and storing the generated marks corresponding to the tasks, the students and/or the times; the mark generation unit marks the process data in the data pool to obtain tag data; and the data extraction unit performs data extraction in the data pool according to the data extraction command to obtain target data.
4. The system for constructing practice teaching intelligent simulation factories based on the Internet of things equipment according to claim 3, wherein the data in the data pool is tag data and comprises a data base part and a tag part, wherein the data base part is data original content, and the tag part is used for marking corresponding tasks, students and/or time;
the label comparison generating unit comprises a label comparison table, wherein the label comparison table is used for recording labels corresponding to different tasks, different students and different times; the tag comparison table comprises a header column, a member column and a mark column, wherein the header column is a specific description of the mark classification of tasks, students or time corresponding to the member column and the mark column, and the specific mark in the header column is classified as a header to be selected; the member column is a member division column for comparing task names, student names and time points which are different in tasks, students and/or time; the marking column is a label corresponding to a specific task, a student and/or time; the labels corresponding to different tasks, different students and/or different times in the label column are generated by the label contrast generating unit according to a preset first rule or the designated labels input by the input mechanism.
5. The system for constructing a practical teaching intelligent simulation factory based on the internet of things equipment according to claim 4, wherein when the data tag generating mechanism needs to tag new data, the tag generating unit retrieves the new data in the data pool: when the data pool does not have the same data base as the new data, the new data is the data to be marked, and the mark generating unit selects the label of the task, the student and/or the time corresponding to the current new data according to the label comparison table to be combined with the new data to obtain marked data; when the same data part exists in the data pool, the mark generating unit grabs the data with the same data as the new data in the data pool to obtain the data to be marked, and at the moment, the data with the same data as the new data in the data pool is the data to be marked; and the label generating unit selects labels of tasks, students and/or time corresponding to the current new data according to the label comparison table and combines the labels with the data to be labeled to obtain label data.
6. The system for constructing a practical teaching intelligent simulation factory based on the internet of things equipment according to claim 5, wherein when new tag data is obtained by combining new data with corresponding tags, the tag generation unit directly puts the new tag data into the data pool;
When new tag data is obtained by combining the data of the same data as the new data in the data pool with the corresponding tag, the tag generating unit deletes the initial data of the same data as the new data in the data pool, and directly puts the new tag data into the data pool, namely, replaces the tag data of the same data as the new data in the data pool with the new tag data.
7. The system for constructing a practical teaching intelligent simulation factory based on the Internet of things equipment according to claim 3, wherein the extraction command comprises target members to be selected corresponding to target data, the data extraction unit obtains the target data by the following method,
the data extraction unit reads target members to be selected in the extraction command;
the data extraction unit searches the target member to be selected in the tag comparison table, and reads the corresponding tag in the tag column corresponding to the target member to be selected to obtain a target tag;
the data extraction unit searches in the data pool, reads the tag data of the tag data in the data pool, wherein the tag data comprises the target tag, and obtains the target tag data;
the data extraction unit divides the data base part of the target tag data from the tag part, and then sorts all the data base parts in the target tag data to obtain the target data.
8. The system for constructing a practical teaching intelligent simulation factory based on the Internet of things equipment according to claim 3, wherein the extraction command comprises a label corresponding to target data, the data extraction unit obtains the target data by the following method,
the data extraction unit reads a label corresponding to target data in the extraction command, namely a target label;
the data extraction unit searches in the data pool, reads the tag data of the tag data in the data pool, wherein the tag data comprises the target tag, and obtains the target tag data;
the data extraction unit divides the data base part of the target tag data from the tag part, and then sorts all the data base parts in the target tag data to obtain the target data.
9. The system for constructing an intelligent simulation factory for practical teaching based on the internet of things equipment according to claim 7 or 8, wherein the data pool comprises a plurality of data sub-pools, tag data in each data sub-pool stores tag data corresponding to different header columns in the same data sub-pool according to different header columns, and the data extraction unit searches in the corresponding data sub-pool according to a target member to be selected or a tag corresponding to target data in an extraction command.
10. The system for constructing an intelligent simulation factory for practical teaching based on the internet of things equipment according to claim 7 or 8, wherein the data pool comprises a plurality of data sub-pools, tag data in each data sub-pool is stored according to different task names, different student names and/or different time point data in member columns, tag data of the same task names, the same student names and/or the same time point are stored in the same data sub-pool, the data extraction unit searches in the corresponding data sub-pool according to a target to be selected member or a target data corresponding tag in an extraction command, and the corresponding member column.
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