CN114118678B - Iron works management system based on edge Internet of things and construction method thereof - Google Patents
Iron works management system based on edge Internet of things and construction method thereof Download PDFInfo
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Abstract
The invention provides an iron works management system based on the edge Internet of things and a construction method thereof. Each subsystem and each subsystem of the iron works production system and the character ring system are upgraded into functional edge nodes by building an edge server and an edge service platform, and the edge nodes can longitudinally control the subsystems which the edge nodes belong to and can also carry out transverse information interaction with other edge nodes in an edge network mode; and unifying the edge nodes with a cloud to construct an edge Internet of things service management system, so as to form a cloud-edge-end three-layer architecture. The traditional iron-making process and the newly added systems of 'people', 'objects' and 'rings' in a factory are respectively subjected to edge intelligent upgrading, and the systems are mutually coupled to form a new system by analyzing the relation among the systems, so that collaborative unified management is realized.
Description
Technical Field
The invention relates to the technical field of Internet of things and iron works management, in particular to an iron works management system based on the edge Internet of things and a construction method thereof.
Background
Reviewing industry development history, from industry 1.0 to industry 3.0, through mechanization, electrification and automation, the current industry 4.0 is intelligent, and the Internet of things is a core key for realizing the industry 4.0. For industries serving as national economy pulse and foundation, the application of the Internet of things technology to the traditional industrial field is a mainstream direction of industrial development at present, and is a revolution of the traditional industry. The advent of technologies represented by the internet of things, cloud computing and edge computing has pushed the traditional industry to advance towards informatization and intellectualization, and in this context, data interconnection, sharing and fusion gradually become the mainstream trend of industrial development.
Taking the iron and steel industry as an example, in the traditional iron and steel industry, daily production activities are carried out around a blast furnace, and besides a blast furnace body system, the iron and steel industry also relates to a feeding and feeding system, a furnace top system, an air supply system, an iron slag processing system, a gas dust removing system and other associated systems which are mutually independent and only are responsible for overall management of subsystems under the respective systems without mutual communication, meanwhile, management of an iron and steel factory and operators are free from the systems, and the overall safety management is difficult to form from the change of the operation change of the systems of the traditional iron and steel technology.
In addition, the traditional ironmaking enterprise is used as one of enterprises with highest industrial injury accident occurrence rate in the industrial field, and has the advantages of complex production process, long flow, great danger source points and multiple faces. And because the safety guarantee of the personnel of the iron and steel enterprises is closely related to the production process, the personnel of the iron and steel enterprises can face the risks of high kinetic energy, high potential energy and high heat energy, and can face other risks of toxic and harmful substances, inflammability, explosiveness and the like.
Therefore, the traditional iron and steel enterprises are modified by using the Internet of things and the edge computing technology, and a set of production and safety management system is constructed on the basis, so that the system is a good thing for the iron and steel enterprises.
Disclosure of Invention
In order to overcome the defects in the background technology, the invention provides an iron works management system based on the edge Internet of things and a construction method thereof, which are characterized in that the traditional iron works and newly added 'people', 'objects', 'rings' systems in factories are respectively subjected to edge intelligent upgrading, and the relationship among the systems is analyzed to couple the systems to form a new system, so that collaborative unified management is realized.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
An iron works management system based on the edge Internet of things comprises an iron works production system, a figure ring system and an edge server.
The iron works production system comprises a blast furnace body system, a feeding and loading system, a furnace top system, an air supply system, an iron slag treatment system and a gas dust removal subsystem; decomposing the iron works production systems into sub-systems which decompose them into respective sub-systems;
the subsystem of the figure ring system comprises a system of people, a system of objects and a system of rings, wherein the system of people comprises a subsystem of people identification, people monitoring and people positioning; the system of the object comprises a vehicle, materials and facilities subsystem; the ring system includes natural, production, and energy consumption subsystems.
Each subsystem and each subsystem of the iron works production system and the character ring system are upgraded into functional edge nodes by building an edge server and an edge service platform, and the edge nodes can longitudinally control the subsystems which the edge nodes belong to and can also carry out transverse information interaction with other edge nodes in an edge network mode; and unifying the edge nodes with a cloud to construct an edge Internet of things service management system, so as to form a cloud-edge-end three-layer architecture.
The system architecture method of the iron works management system based on the edge Internet of things comprises the following steps:
1) Each subsystem and each subsystem of the iron works production system and the character ring system are upgraded into functional edge nodes by building an edge server and an edge service platform, and the edge nodes can longitudinally control the subsystems which the edge nodes belong to and can also carry out transverse information interaction with other edge nodes in an edge network mode; and unifying the edge nodes with a cloud to construct an edge Internet of things service management system, so as to form a cloud-edge-end three-layer architecture.
2) Coupling an iron works production system and a character ring system, specifically: all data of the subsystems of the two systems are shared, so that coordinated linkage is achieved, when a certain system of the production system of the iron mill has a safety accident, the factory responds to the system of people, and people in a dangerous area are positioned to prompt the people to leave the dangerous area rapidly; simultaneously, the system related to the 'object' generates response to withdraw important materials, and related facilities are closed; isolating the hazardous area with respect to the "ring" system; the coordination and linkage of the systems minimizes the harm.
3) Further comprises: and separating the hardware of each subsystem from the original system, and uniformly incorporating the hardware of each subsystem into a resource pool of a factory to control and combine resources by the cloud server.
4) Further comprises: and connecting equipment based on different protocols in respective subsystems of the iron works production system and the character ring system to the Internet of things platform through an industrial Internet of things gateway unified protocol standard to perform centralized management, namely heterogeneous fusion. The heterogeneous fusion specifically comprises the following steps: bluetooth networks, zigbee networks, infrared networking networks, and other types of networks; the heterogeneous devices and the network form an independent whole based on the respective internal protocols, and the Internet of things gateway realizes the connection between the heterogeneous devices through the conversion of the internal protocols.
Compared with the prior art, the invention has the beneficial effects that:
1) The method provided by the invention can respectively carry out edge intelligent upgrading on the traditional iron-making process and the newly added systems of people, objects and rings in a factory, and the new systems are formed after the systems are mutually coupled by analyzing the relation among the systems, so that the collaborative unified management is realized.
2) After the traditional production process of the ironmaking plant area is coupled with the new object conjunct system, the method establishes a brand-new system, and by means of the edge nodes (edge Internet of things servers) and the edge gateways in different molecular systems, the information of the running state, potential system hidden danger, production safety hidden danger and the like of the system can be known in real time, the problems of collecting, fusing and uploading a large amount of basic data of each system in the plant area are solved, and the safety condition of the whole ironmaking plant area can be analyzed and evaluated through analysis of logs, alarms and the like of equipment in the network.
Drawings
FIG. 1 is a diagram of an iron works security situation awareness system architecture based on the edge Internet of things technology of the present invention;
FIG. 2 is an exploded view of the iron works production system of the present invention;
FIG. 3 is an exploded view of the human, object, ring system of the present invention;
FIG. 4 is a schematic diagram of the heterogeneous fusion of the present invention;
FIG. 5 is a gateway protocol conversion schematic of the present invention;
FIG. 6 is a diagram of the relationship between the data acquisition gateway and the Internet of things gateway of the present invention;
FIG. 7 is a data acquisition gateway data flow diagram of the present invention;
FIG. 8 is a flow chart of the gateway arithmetic module data processing of the present invention;
FIG. 9 is a data acquisition gateway software flow diagram of the present invention;
FIG. 10 is an edge node design of the present invention;
FIG. 11 is a motor data acquisition flow chart of an embodiment of the present invention;
FIG. 12 is a graph of analysis of overall data of a motor in a dangerous mode of operation by a classifier in accordance with an embodiment of the present invention;
FIG. 13 is an intrinsic safety level assessment graph of the present invention;
fig. 14 is a diagram of a classical three-layer neural network.
Detailed Description
The following detailed description of the embodiments of the invention is provided with reference to the accompanying drawings.
The iron-making production in China is almost all blast furnace production flow, and although various direct reduction and melt reduction processes are being developed, the blast furnace process is kept monopoly due to the advantages of maturity and high efficiency. It can be seen from the figure that the blast furnace ironmaking has huge main body and auxiliary systems which are mutually connected together, and the systems mutually cooperate and restrict each other to form huge productivity.
The whole process of blast furnace ironmaking can be summarized as follows: under the condition of low energy consumption, the liquid metal product with ideal chemical composition and temperature is obtained through the controlled forward sinking of the furnace burden and the reverse movement of the gas flow to efficiently finish the processes of reduction, slag formation, heat transfer, slag iron reaction and the like. The process of each part is very complex, involves a lot of technology, and the production process is accompanied by a lot of physical and chemical changes.
The blast furnace production needs to be carried out in a sealed blast furnace, and people cannot directly observe the changes in the furnace and can only indirectly observe and know through instruments and meters. Therefore, a computer control system for a specific production process of the blast furnace body system and each auxiliary system is designed. Taking a hot blast stove system as an example, in order to determine an optimal combustion system, an expert collects data such as combustion exhaust gas components, exhaust gas temperature, furnace top combustion temperature and the like through terminal equipment of the Internet of things, and realizes the functions of automatically adjusting combustion air and gas quantity, determining optimal furnace changing time, automatically prompting and printing a report form through computer analysis according to the data. Compared with the traditional production process, the computer control system has obvious advantages in the aspects of energy conservation, air supply maintenance, wind pressure maintenance and wind furnace efficiency improvement. While computer-controlled production systems have significant advantages, based on the nature of conventional ironmaking processes, these systems are relatively decentralized, without interaction of information with each other or behavioral effects between each other, and are only responsible for performing their own specific tasks.
In order to solve the problems, we propose an iron works management system based on the edge internet of things, and a brand new system is established after the traditional production process of the iron works area is coupled with a new object system.
As shown in FIG. 1, the iron works management system based on the edge Internet of things comprises an iron works production system, a figure ring system and an edge server. The box numbered 1 in the figure is an edge gateway.
As shown in fig. 2, the iron works production system comprises a blast furnace body system, a feeding and loading system, a furnace top system, an air supply system, an iron slag treatment system and a gas dust removal subsystem; decomposing the iron works production systems into sub-systems which decompose them into respective sub-systems; in fig. 2, there is shown an example of a subsystem, for example, a blast furnace body system including monitoring, electric, cooling, etc. subsystems, a feeding and loading system including screening, weighing, conveying, etc. subsystems, a furnace top system including a lance for pulverized coal injection, a cabin pump, a coal grinding, etc. subsystem, an air supply system including an air furnace, a fan, combustion supporting, etc. subsystem, a slag treatment system including granulating, dewatering, slag flushing, etc. subsystem, and a gas dust removal subsystem including gravity, venturi, cloth bag, etc. subsystem.
As shown in fig. 3, the subsystem of the character ring system comprises a human system, an object system and a ring system, wherein the human system comprises a human identification subsystem, a human monitoring subsystem and a human positioning subsystem; the system of the object comprises a vehicle, materials and facilities subsystem; the ring system includes natural, production, and energy consumption subsystems.
As shown in fig. 1, each subsystem and each subsystem of an iron-mill production system and a character ring system are upgraded into functional edge nodes by building an edge server and an edge service platform, wherein a box numbered 1 in the figure is an edge gateway, and the edge nodes not only can longitudinally control each subsystem directly belonged to the edge gateway, but also can carry out transverse information interaction with other edge nodes in the form of an edge network; and unifying the edge nodes with a cloud to construct an edge Internet of things service management system, so as to form a cloud-edge-end three-layer architecture.
The system architecture method of the iron works management system based on the edge Internet of things comprises the following steps:
1) Cloud-edge-end three-layer architecture: each subsystem and each subsystem of the iron works production system and the character ring system are upgraded into functional edge nodes by building an edge server and an edge service platform, and the edge nodes can longitudinally control the subsystems which the edge nodes belong to and can also carry out transverse information interaction with other edge nodes in an edge network mode; and unifying the edge nodes with a cloud to construct an edge Internet of things service management system, so as to form a cloud-edge-end three-layer architecture.
2) And (3) system coupling: coupling an iron works production system and a character ring system, specifically: all data of the subsystems of the two systems are shared, so that coordinated linkage is achieved, when a certain system of the production system of the iron mill has a safety accident, the factory responds to the system of people, and people in a dangerous area are positioned to prompt the people to leave the dangerous area rapidly; simultaneously, the system related to the 'object' generates response to withdraw important materials, and related facilities are closed; isolating the hazardous area with respect to the "ring" system; the coordination and linkage of the systems minimizes the harm.
3) Pooling equipment: and separating the hardware of each subsystem from the original system, and uniformly incorporating the hardware of each subsystem into a resource pool of a factory to control and combine resources by the cloud server.
4) Heterogeneous fusion: and connecting equipment based on different protocols in respective subsystems of the iron works production system and the character ring system to the Internet of things platform through an industrial Internet of things gateway unified protocol standard to perform centralized management, namely heterogeneous fusion. The heterogeneous fusion specifically comprises the following steps: bluetooth networks, zigbee networks, infrared networking networks, and other types of networks; the heterogeneous devices and the network form an independent whole based on the respective internal protocols, and the Internet of things gateway realizes the connection between the heterogeneous devices through the conversion of the internal protocols.
The method comprises the following steps:
1. system decomposition
The system decomposition is to divide the original production process more carefully, so as to make the iron-making production process modularized, unified, concrete and convenient for centralized management. As shown in fig. 2, which is an exploded view of a production system of an iron mill, the subsystems of the production system of the iron mill comprise a blast furnace body system, a feeding and loading system, a furnace top system, an air supply system, an iron slag treatment system and a gas dust removal system; fig. 2 is an exploded view of the sub-systems of the iron works production system as they are broken down into their respective sub-systems. For example: the blast furnace body system is decomposed into: monitoring, electrical, cooling, etc. The furnace roof system is decomposed into: distributing, charging, bell and other systems.
FIG. 3 is an exploded view of a "person," "thing," and "ring" system, with subsystems of the person ring system including a person's system, a thing's system, and a ring's system, with the person's system including a person identification, person monitoring, and person positioning subsystem; the system of the object comprises a vehicle, materials and facilities subsystem; the ring system includes natural, production, and energy consumption subsystems.
The traditional iron-making production process system only considers the operation condition of each system in the production process, and takes less consideration on peripheral factors such as 'people', 'objects', 'rings', and the like, but in order to achieve the overall security situation perception, we need to take multiple directions into consideration, wherein an Internet of things monitoring system taking 'people', 'objects', 'rings' as main factors is also a part of important consideration. We decompose the "man", "thing", "ring" system into relatively independent vertical cell subsystems in the same way by analyzing the surrounding environmental factors. Taking a system related to a factory and a person as an example, in order to ensure the safety of the person in the factory, the identity of the person is generally required to be identified, so that the person is ensured to have the authority to enter a certain production area; secondly, the dynamic track of the person needs to be tracked and positioned, and the real-time information of the person is mastered.
2. System coupling
The system coupling is that two systems which are similar in communication and different in phase difference take some measures to guide and strengthen the systems, promote benign and forward interaction of the two systems and influence each other, excite intrinsic potential of the two systems, and therefore realize complementary advantages and common promotion of the two systems. After the system is decomposed, two kinds of systems are coupled. And (3) establishing a correlation between the original ironmaking system and the newly-built man, object and ring system.
As shown in fig. 1, the two systems with the association can share data, so as to achieve coordinated linkage. When a safety accident occurs in a certain system in the factory, the factory responds to the system about 'people', positions personnel in the dangerous area and prompts the personnel to leave the dangerous area quickly; simultaneously, the system related to the 'object' generates response to withdraw important materials, and related facilities are closed; the system for "rings" isolates the hazardous area. The coordination and linkage of the systems minimizes the harm.
3. Heterogeneous fusion
Heterogeneous integration is to connect devices based on different protocols to an internet of things platform through a unified protocol standard for centralized management. Because the subsystems related to the production process of the iron works are numerous, the area equipment contained in each subsystem is also different, so that the equipment connection modes are different, and the data transmission mode is complicated, a brand new Internet of things system is constructed through the Internet of things technology, and a plurality of subsystems which are relatively dispersed are connected. After connection, the new Internet of things system has hundreds of millions of connection points and hundreds of billions of information points, so that the functions of 'everything' interconnection, coordination and control of iron works are realized.
The internet of things technology is a core key for realizing the interconnection of everything in a factory, however, in the field of information perception of the internet of things, the wireless sensor network technology application scene has specificity, is generally applied to a local area, cannot be communicated with each other, is not suitable for remote data transmission, and enables each system to form an information island, and cannot form real and comprehensive interconnection. The gateway is a key for solving the problem, and is a tie for connecting the wireless sensor network and the traditional communication network to finish the conversion of protocols among the wireless sensor network, the traditional communication network and different types of networks. The key point of realizing heterogeneous fusion is that the wireless networking is realized through the gateway and the connection with the platform is realized.
The gateway is not a constant individual, but rather has a plurality of kinds, and the functions of the gateway can be correspondingly expanded according to different production requirements so as to meet the daily production requirements. For example, there are humiture gateways dedicated to collecting temperature and humidity information, internet of things connection gateways dedicated to negative protocol conversion, and the like.
In the field of iron works, a proper gateway is selected according to the equipment and the operation characteristics of each subsystem and is paved on each subsystem so as to form physical connection with the subsystem. After the physical connection is established, all subsystems are connected through a networking module of the gateway by using the same domain name, and a wireless sensor network is established. Then the wireless sensor network is connected with the network of the factory through the protocol conversion function of the gateway, so that all the gateways connected with the subsystem are connected to the cloud platform of the factory
The specific principle of the gateway connection is shown in fig. 4. The Bluetooth network is a network set formed by devices which are connected through an embedded Bluetooth module to finish data receiving, transmitting and transmitting; the zigbee network is an information network composed of various sensing devices connected through a zigbee protocol; and an infrared networking network is a collection of devices controlled by infrared remote sensing technology. The heterogeneous devices and the networks form an independent whole based on the protocols in the heterogeneous devices and the networks, data transmission and exchange are only carried out in the networks of the heterogeneous devices and the networks, and communication cannot be carried out between the networks due to the difference of the protocols. The internet of things gateway can realize the connection between heterogeneous devices through the conversion of an internal protocol. As shown in fig. 5.
4. Pooling of devices
The device pooling is to separate the hardware of each subsystem from the original system, and uniformly incorporate the hardware into the resource pool of the factory to be controlled and combined by the cloud server. In the traditional iron-making process, because the fixity of the hardware resources causes that each system can form a fixed production mode once being built, it is difficult to build a new system to meet different production demands by mobilizing the hardware resources, and only slight changes can be made on the original system, or a new system can be built, which wastes a great deal of hardware resources. Whereas device pooling provides the possibility for flexible mobilization of hardware. In the previous design, the integration of factory heterogeneous equipment is realized through the gateway of the Internet of things, the hardware resources of each subsystem and each sub-system can be separated from the original fixed system through the combination of the Internet of things and the equipment, and the hardware resources are integrated into the cloud-end internet-of-things heterogeneous integration monitoring platform, so that a so-called 'hardware resource pool' can be formed through integration, and all the architectures can be built on the resource pool, so that the hardware resource dynamics is realized. According to different requirements, relevant hardware resources are mobilized through the cloud to control and integrate, and new systems and applications are built on the integrated resources, so that the resources are delivered in a service mode. Compared with the traditional mode, the pooled hardware resources are more flexible, the characteristic of hardware solidification of the traditional ironmaking system is broken, and infinite possibility can be created.
The iron works management system of the invention is used for realizing the concrete embodiment of the safety management and evaluation of the iron works
1. Data acquisition
The original purpose that the concept of the internet of things put forward is to enable novel sensing equipment with RFID radio frequency technology, infrared sensing technology and other sensing capability to be seamlessly connected into the internet of things, so that information collection and transmission are realized, and intelligent identification of a production process and monitoring management of personnel and equipment are further achieved. The internet of things is not applied to the purpose of iron and steel enterprises. For the construction of a security situation awareness system, data acquisition is an important link, and the security situation awareness three-layer architecture defines the data acquisition as follows: the method is characterized in that different systems, devices and products are accessed through the existing communication means, and the data foundation required by the sensing platform is constructed according to the unique edge processing and protocol conversion of the heterogeneous data on the premise that the acquired data meets deep large-scale conditions. The access to different systems, devices and products by the existing communication means is realized through the Internet of things heterogeneous fusion link, and conditions are created for data acquisition. For the data acquisition link, taking a zigbee sensing network as an example, the data collection is realized through a zigbee data acquisition gateway paved on the device. The gateway has a plurality of types, the modules can be added and deleted according to different requirements, and the gateway is different from the mentioned gateway of the Internet of things, so that the conversion of the gateway of the Internet of things and the protocol is realized, the fusion between heterogeneous network devices is realized, the zigbee data acquisition gateway is used for acquiring the data, and the gateway of the Internet of things can be connected with hundreds of zigbee data acquisition gateways. The relationship between the two is shown in fig. 6.
The gateway is used as a data acquisition terminal device and can acquire basic data of subsystem devices which are in physical connection with the gateway, wherein the basic data comprise measurement data such as temperature, humidity, gas concentration and the like which need to be measured; operation and maintenance data such as equipment operation time, start-stop times and real-time data such as equipment state. And the edge gateway periodically reads data according to the set time, processes the data, stores the data and displays the data.
As shown in fig. 7, the sensing module on the data acquisition gateway uploads the acquired related data to the local storage module of the gateway for storage according to the set time through the RTDB timing service, uploads the data to the operation processing module for operation, and is connected to the local upper computer for display through the web interface. The Web interface and the operation processing module can also call related data from the storage module, and the data after local operation processing is uploaded to the gateway of the Internet of things through wireless transmission modes such as Bluetooth, wiFi, zigbee, lora and the like, and is uploaded to the cloud after protocol conversion.
Not all the data collected is uploaded to the cloud platform inside the gateway, and the core processor of the computing center inside the gateway has some simple data processing capabilities, as shown in fig. 8.
The three types of data (equipment state, measurement data and operation and maintenance state) collected by the sensor are all required to be processed correspondingly in the gateway, and firstly analog-to-digital conversion is carried out to convert an analog signal into a digital signal which can be processed by a machine; unpacking the data, removing the data head and the data tail, and only leaving the data which can be directly processed; three types of data reflecting the visual state of the equipment are stored through the storage module on one hand, and on the other hand, the required information is obtained through calculation through an algorithm. Taking a motor as an example, the method collects measurement data such as current, voltage, temperature and the like, operation and maintenance data such as motor running time, start-stop times and the like, and real-time data such as motor state and the like. Evaluating a score according to a fuzzy algorithm or expert learning method and the like to reflect the safety condition of equipment, and then outputting only one data reflecting the overall safety of the motor; and finally, packaging and uploading the obtained final data to a cloud platform. Therefore, the original data such as voltage, current, start-stop times and operation and maintenance states are obtained on the cloud platform side, and only data of the safety state of the equipment are directly displayed. Fig. 9 is a data acquisition gateway software flow diagram.
The safety state data of all the devices uploaded through the gateway are stored in a cloud background storage server to form a huge data pool, and the data in the data pool are displayed on the cloud on one hand and provided for local calling through a resource access interface on the other hand so as to carry out subsequent further processing.
2. Construction of edge platforms
The construction of the edge platform is characterized in that the original iron works subsystems (such as a blast furnace body system, a pulverized coal injection system and the like) are upgraded into functional edge nodes by constructing an edge server and an edge service platform, and the edge nodes can longitudinally control all subsystems which the edge nodes belong to and can also carry out transverse information interaction with other edge nodes in an edge network mode. And unifying the edge nodes with a cloud to construct an edge Internet of things service management system, so as to form a cloud-edge-end three-layer architecture.
Edge computing techniques are understood to mean computing that occurs at the edge of a network. The traditional computing mode is that terminal equipment such as a gateway and the like are directly connected with a cloud server, the terminal equipment directly transmits data to a data center or cloud at a central position, the cloud server uniformly processes the data and sends out instructions, and the mode can process a large amount of complex data. However, as the number of terminal devices increases, the burden of the cloud server gradually increases, and the data processing speed is also reduced due to the increase of the data volume, so that higher time delay is generated, and the requirements of some industrial production links on the time delay cannot be met. In normal industrial production processes, data which need to be processed in time are always accompanied, and if the time delay is large, irreversible losses can be caused. The above problems can be well solved by putting down some simple data analysis processing functions to the edge server closer to the terminal.
For iron works, after the internet of things system is established, more and more devices are connected into the network, and the cloud server faces significant pressure. In addition, according to the basic requirements of factory production safety protection, in order to protect the production process and the safety of workers, the server is required to be high in calculation speed, accurate in data processing and decision to send out a break. It is necessary to construct an edge internet of things service management system in an iron works.
The production process of the iron works has the characteristics of one center and multiple systems, is matched with an edge computing framework, and is very suitable for the deployment of edge computing. The iron-making process consists of a blast furnace body system and a series of auxiliary systems, wherein in the system decomposition design, the systems are divided into nine types of sub-systems, and in the deployment link of edge calculation, the nine types of sub-systems are taken as basic units, and the edge system is built on the basis of the nine types of sub-systems. An original subsystem is taken as an edge node, an edge server is added, and an edge platform is built through a system environment formed by designing a network, calculation, storage, application and the like at the edge node, so that near-end service is provided for the subsystem. The construction of the edge platform distributes large-scale services which are completely processed by cloud or central nodes to the edge service nodes, integrates the functions of the core control parts of the original systems of the iron works into the edge Internet of things server, and realizes the nearby processing of internal transactions of the system at the position closest to the terminal. As shown in fig. 11.
After the edge platform is designed, all edge nodes are connected to the cloud platform according to the heterogeneous fusion principle, and an edge Internet of things service management system is built at the cloud end, so that overall management of edge services at the cloud end is realized. Thereby realizing the design of a cloud-edge-end three-layer architecture.
Firstly, the construction of the edge platform realizes the longitudinal 'penetrating' deep penetration from the end to the edge node to the cloud, and the edge node serves as an intermediate link of the end and the cloud, so that the longitudinal intelligent control of the subsystem can be realized, and the data can be uploaded to the cloud platform for overall decision. And secondly, barriers for information interaction between edge nodes are opened, and the edge nodes are connected in pairs to form an edge information interaction network, so that a closed loop for data flow and control is ensured. The whole system is designed in a "+" shape, and through interconnection and intercommunication of two dimensions, the forwarding and processing time in data transmission is shortened, the end-to-end time delay is reduced, the network bandwidth pressure is relieved, the service response capability is enhanced, and a powerful support is provided for the construction of the overall safety situation awareness system of the ironworks.
3. Construction of factory security situation awareness system
The construction of the factory security situation awareness system not only comprises the construction of a hardware system, but also comprises the construction of a security situation awareness model and the prediction of two parts of contents by a neural network.
(1) Security posture assessment
And for the safety situation assessment link, comprehensively utilizing a safety check list method, a forenotice danger analysis method, a fault type, influence analysis and other methods to carry out overall situation assessment of the iron works. Firstly, constructing a safety evaluation index system, namely determining an object to be evaluated and specific content of the object to be evaluated; then, extracting data about the safety conditions of each evaluation object stored in the constructed data pool through a data interface, analyzing information affecting the safety of the system such as a dangerous source, a dangerous type and the like from the data, and defining the conditions of the system to form a safety analysis table; and then, calculating the security analysis table based on a certain rule through an evaluation algorithm, so as to obtain an evaluation value reflecting the security situation of the system, and achieve the purpose of security situation evaluation. Finally, through the learning training of the neural network, the defects of manual prediction are simplified, and the automatic assessment prediction of the security situation is realized.
The method used in the industrial enterprise dynamic security risk assessment model is adopted, and a new system constructed by the method is used as a framework for proper modification, so that the security situation assessment problem of the iron works designed by the method is realized. For the selection of the evaluation object, the design of the system is based on the architecture of the sub-system, so that the sub-system is used as one type of index, and the sub-system is used as two types of index as the evaluation object of the design. The evaluation content is considered from four aspects, namely:
(1) The first index is the "number of dangerous modes", i.e. the total number of times the system is operated in dangerous mode.
(2) The second index is "number of equipment and facility defects".
(3) The third index is "the number of work environment defects".
(4) The fourth index is "hazard exposure duration".
After the index system is established, data of the indexes need to be acquired. And S2, retrieving the safety data of each sub-system and each subsystem in one year from a data pool designed in the step S2 through a data interface, analyzing the safety data according to a statistical method, counting the types of safety accidents of each system in one year, determining the defects of each system device and facility through a device defect analysis method, counting the number of operation environment defects in one year through the environment data collected by the environment systems in the human, object and environment systems, and forming the dangerous exposure duration through extracting the device log information. The index system and the data are acquired to form a safety analysis table, as follows:
TABLE 1 safety analysis Table
Six types of indexes of the neural network input matrix are described by taking one motor device in an electric subsystem under a blast furnace body subsystem as an example. The three types of data of the motor are respectively operation and maintenance states, measurement data and environmental data, and are specifically divided into the following aspects of motor temperature, input voltage, input current, motor rotating speed, motor operation time length, start-stop times, operation stop states, environmental temperature, environmental humidity, noise, corrosive gas, dust, air pressure, safety essence level scores and the like, wherein the data are collected through various sensors connected to the data collection gateway, and the safety essence level scores are collected by adopting an RFID technology. According to the design of the data acquisition part in the step S2, the data are locally calculated, locally stored and locally displayed on the data acquisition gateway side, and the operation mode of the motor is uploaded to the cloud platform as a result of the local calculation, and the data are stored, displayed and analyzed on the cloud platform side. The operation mode is divided into safety and danger, the gateway automatically uploads the information of the motor once every certain time, and the data of the motor obtained by the cloud platform side are as follows:
As shown above, the number of dangerous occurrence times in one month is the dangerous operation mode number of the motor, and according to the formula:
dangerous mode number of electric subsystem = dangerous mode number of motor + dangerous mode number of other equipment
The number of dangerous modes of the whole electrical subsystem, namely the first type of index data input by the neural network, can be calculated.
The index data of the defect number of the equipment and the index data of the environmental defect number are obtained through the following design, and the one-time dangerous operation mode of the motor equipment is taken as an example for explanation, and when the motor is in the dangerous operation mode, the designed whole system data flow diagram 12 is shown. The main index data analysis is performed by a classifier, which is a simple judgment program based on a certain logic, similar to a NAND gate, and can judge and classify input data. The classifier adopts a layered design, and each layer carries out logic judgment of different functions, so that data classification is realized. The first layer classifies data indexes and judges whether the data is 13 types of data; the second layer is required to classify the data types into two major categories, namely equipment data and environment data, so as to carry out further judgment; the third layer needs to perform interval judgment, sets a reasonable interval of each index data and judges whether the input index is in the reasonable interval; and finally judging the defect type through the fourth layer.
As shown in fig. 13, by analyzing the overall data of the motor in the dangerous operation mode by the classifier, it is possible to obtain whether the motor is in dangerous operation due to a device defect or an environmental defect. And counting the dangerous defect number and the environment defect number of all equipment in the electrical subsystem to obtain the second type and the third type of indexes input by the neural network.
And in the fourth category, the dangerous exposure duration is simple, the data record is not too much designed, the time interval from the occurrence of the danger to the solving of the danger is the dangerous exposure duration of the equipment, and the dangerous exposure duration can be intuitively acquired through the common equipment log.
After the safety analysis table is constructed, the evaluation value is calculated through an evaluation algorithm. The method comprises the following steps:
(1) Calculating a risk assessment index according to the safety analysis table data;
(1) calculating the level of security materialization h s . The level of intrinsic safety is broadly defined as the capability of effectively preventing accidents from occurring from the source through various means, and we evaluate the level of intrinsic safety from three aspects, firstly, people are key to realizing the intrinsic safety of enterprises; secondly, the equipment is used for guaranteeing the intrinsic safety of enterprises; finally, the environment is a push to achieve enterprise intrinsic safety. We consider the level of security intrinsically from the three aspects of man, equipment, environment. Fig. 14 is an intrinsic safety level assessment graph.
(2) Calculating the risk coefficient k of the equipment 1 . The risk coefficient of the equipment is calculated by the formula
k 1 =device defect count 5%. And (5) performing calculation.
(3) Calculating the environmental risk coefficient k 2 . The environmental risk coefficient is calculated by the formula
k 2 =number of environmental defects 5%. And (5) performing calculation.
(4) Judging the dangerous coefficient k of the substance 3 The evaluation criteria are as follows:
TABLE 2 evaluation of risk factors for substances
(5) According to the formula h=h s (1+k 1 )(1+k 2 )(1+k 3 ) And calculating the risk index h of each subsystem.
The risk assessment index of each system calculated in the step (1) is shown in the following table:
TABLE 3 Risk assessment index Table for systems
(2) Prediction of risk index h by artificial neural network
Artificial neural network structure fig. 14, the artificial neural network prediction process is divided into two parts, one is a forward propagation process of data and the other is a reverse propagation process of errors. Firstly, an input matrix and an expected output are given, the input matrix is imported into a neural network, an actual value is generated according to a calculation rule in the neural network, an error between the actual value and the expected value is calculated, the error is counter-propagated, the neural network is enabled to continuously improve the weight and the threshold in the optimization, and therefore the actual output is enabled to be closer to the expected output. The process of continuously modifying the weight threshold value is a training process of the neural network, relatively optimal weight value and threshold value are obtained in the neural network through a large amount of training, at the moment, an input matrix is given to the neural network again, and the neural network can generate an actual value with small error between the actual value and the expected value according to a trained rule to serve as a prediction result of the neural network. Considering comprehensively, the invention predicts by taking the BP neural network optimized by the particle swarm optimization as an artificial neural network model of the experiment. The BP neural network is an algorithm which continuously adjusts the weight and the threshold value of the network through an error back transmission principle so as to enable the next output of the network to be closer to the expected output, is an artificial neural network model which can be really used, belongs to a feedback type neural network, is mature in theory and performance, and has a strong nonlinear mapping capability and a flexible network structure. The particle swarm algorithm is derived from research on the prey behavior of the bird swarm, and the particles follow the optimal particles in the solution space to perform global search through swarm iteration. The BP neural network is optimized by utilizing the global searching capability of the particle swarm algorithm, and the good global optimizing capability of the particle swarm algorithm and the good local optimizing capability of the BP algorithm can be combined to improve the generalization capability and learning performance of the neural network, so that the prediction accuracy is improved. And taking the error calculation formula as an optimization function of a particle swarm algorithm, calculating the minimum error through group iteration, substituting the minimum error into the BP neural network to serve as the minimum error of the BP neural network to carry out error back propagation, and training weights and thresholds among layers of the neural network through error back propagation so as to achieve the purpose of optimization.
The artificial neural network (PSO-BP neural network) is specifically designed:
input layer: the input layer considers six indexes of the dangerous mode number, the equipment defect number, the environment defect number, the dangerous exposure time, the intrinsic safety level and the material dangerous coefficient, so that the number of the input layer nodes is 6. Six types of index data of 24 subsystems are intercepted once by taking one month as a time node, and the total intercepted data of 1-11 months form 6 rows and 264 columns of input matrixes to form a neural network training set for training the neural network. The training set is as follows:
TABLE 4 neural network training set input
And (3) converting the actual value of the risk index h calculated in the step (1) into a numerical matrix of 1 row and 264 columns to be used as the expected output of the neural network training set.
TABLE 5 expected output of neural network training set
Six types of index data of 24 subsystems in month 12 are intercepted and used as a test set for testing. The following is shown:
TABLE 6 neural network test set input
The sizes of the training set and the testing set constructed by the method are different, and the direct input of the training set and the testing set into the neural network can form larger errors, so that normalization processing is needed before the neural network is input, and the sizes of all index data are unified within the range of (0, 1). Normalization processing The formula is:where x is the original data, x min ，x max Is the minimum and maximum of the data. The training set and the test set which are subjected to normalization processing are data sets which can be directly processed by the neural network.
Hidden layer: in the neural network model construction, the number of hidden layer neurons is not specifically defined, and is generally larger than that of input layer neurons, so that the number of hidden layer neurons is 12 in order to ensure the training accuracy and shorten the training time. The activation function between neurons selects the s-function. The activation function is a function that maps the net activation amount with the output, which is needed because the input data may not be of the same order of magnitude as the expected value. The S function has an output between (0, 1), a limited output range, is optimized and stable, and is a continuous function, is convenient to derive, and can be used as an activation function of the neural network.Training times are designed 1000 times, learning rate is 0.01, and target minimum error is 0.000001.
Output layer: the output result of the neural network is the risk index h of 24 subsystems, which is a matrix of 1 row and 24 column values, and the dimension is 1, so that the number of neurons of the output layer is 1, the dimension of the output matrix of the output layer is between (0 and 1), and the data of the risk index h of the visual reaction system can be obtained by inverse normalization.
Through neural network training and testing, the risk indexes h of all subsystems can be obtained in real time, and although the obtained risk indexes h can reflect the overall safety condition of the system through further calculation, a large amount of calculation processes are saved through the application of the neural network, a large amount of collected index data are processed into data reflecting the risk indexes of the subsystems, and convenience is provided for the subsequent safety situation perception of the overall system.
The 24 subsystem risk indices calculated from the index data at month 12 in the above example were calculated algorithmically as follows:
TABLE 7 test set 24 subsystem risk index Table
(3) Iron works integral safety situation awareness realization
(1) And calculating the overall system risk index H. The risk index H of each subsystem predicted by the neural network cannot be completely used as a basis for evaluating the safety level of the system, and further processing is needed, namely, the risk index H is calculated. The calculation formula is that
h i N is the subsystem hazard index i For the number of subsystems, E i The risk index of the present demonstration example is calculated for the exposure time of the risk point
(2) An enterprise initial security risk level S (K-1) is calculated. The calculation is carried out according to the following formula: Wherein G is a constant of 10, and Y is a thousand person negative injury rate. Assuming that the thousand persons of the iron works have a negative injury rate of 3/1000, the initial safety risk level of the iron works is 58.09. The greater the thousands of person negative injuries the lower the initial security risk level.
(3) And judging the enterprise risk management and control capability C. And on the basis of the percentile, each system of the enterprise is examined according to the items on the enterprise risk management and control scoring table, so that the enterprise risk management and control capability is scored. This experiment assumes that the iron works risk management capacity c=66.
(4) And calculating the management and control capacity index of the iron works. The specific calculation formula is as follows:
b (k) =αc- βhα and β are constants, α=0.068, β=0.55
The management and control index is used for representing dynamic change of the enterprise risk level, and when B (K) is larger than 0, the system risk level is rising, and when B (K) is smaller than 0, the system risk level is falling. Based on the experimental data, the iron works management ability index B (k) =1.40.
(5) And calculating the comprehensive security risk level S (k) of the enterprise. The specific calculation formula is as follows:
S(K)＝S(K-1)+B(K)
the comprehensive security risk level S (K) = 59.49 after the calculation of the experiment
Through the five steps of calculation, the calculation result of the security risk level of the system is shown in table 8:
Table 8, system risk level calculation results
(6) And (3) carrying out security risk level classification according to the enterprise comprehensive security risk level S (k). The security risk level is divided as shown in the following table
TABLE 9 safety risk rating
S1 and S2 are interval critical values, and the calculation formula is as follows:
in the formula, M is the actual number of staff, P is the expected thousand person injury rate, let us assume that the company has 3500 persons, the expected thousand person injury rate is 0.002, and through calculation, s1=67.912, s2= 63.725, and the comprehensive risk level of the system is s= 59.49, so that the system is not qualified.
And realizing the security situation awareness of the iron works. Through the comprehensive risk level of the system, the security level of the iron works can be judged, when the security level of the iron works is low, the factors which cause the low security level of the system are traced by adopting a reverse reasoning method, and the source of dangerous accidents is positioned, so that corresponding measures are adopted to realize the accurate treatment of the accidents. Firstly, judging the safety level of an iron mill according to the comprehensive risk level; secondly, if the safety level is lower, an established safety analysis table is exported, index data with lower safety risk level caused by the occurrence of abnormality in the safety analysis table is checked, and taking the defect number of the equipment of the cooling subsystem of the blast furnace body as an example, the defect number of the equipment is checked through the safety analysis table, and then an instruction is sent out to call all the original equipment data of the cooling system forming the defect number of the equipment in the safety analysis table; finally, specific equipment can be precisely positioned through the investigation of the original data, so that fault analysis can be carried out on the equipment in a targeted manner, after a result is analyzed, a maintenance scheme is formulated according to a fault reason, equipment maintenance can be carried out in a local maintenance mode according to human resources and operation danger degree, and maintenance can be carried out by remotely controlling maintenance equipment through a constructed edge Internet of things service management system. The cloud issues an instruction, and the instruction is issued to a subsystem to which the accident belongs through an edge service management platform, and the subsystem instruction is converted into an instruction which can be identified by a terminal through a protocol of an Internet of things gateway so as to control industrial control equipment or relay equipment on the data acquisition gateway to perform corresponding actions. The final safety problem of the iron works is solved through the tracing of dangerous accidents.
The deep combination of the traditional industry and the Internet of things is the core of realizing 4.0 of industry in the early days, is the core layout of a national industrial system, takes an iron mill as an example, combines the Internet of things technology and the edge computing technology to collect and preprocess the data of the traditional iron-making process flow, carries out integral assessment through a safety risk assessment model, predicts through an artificial neural network, and realizes safety regulation and control through analysis of a prediction result. The method is a reasonable construction scheme of the security situation awareness system of the iron works.
The above examples are implemented on the premise of the technical scheme of the present invention, and detailed implementation manners and specific operation processes are given, but the protection scope of the present invention is not limited to the above examples. The methods used in the above examples are conventional methods unless otherwise specified.
Claims (1)
1. A system architecture method of an iron works management system based on the edge Internet of things, wherein the system comprises an iron works production system, a figure ring system, an edge server and a security situation assessment system;
the iron works production system comprises a blast furnace body system, a feeding and loading system, a furnace top system, an air supply system, an iron slag treatment system and a gas dust removal subsystem; decomposing the production system of the iron works into respective subsystems;
The subsystem of the figure ring system comprises a system of people, a system of objects and a system of rings, wherein the system of people comprises a subsystem of people identification, people monitoring and people positioning; the system of the object comprises a vehicle, materials and facilities subsystem; the ring system comprises a natural, production and energy consumption subsystem;
the method is characterized by comprising the following steps:
1) Each subsystem and each subsystem of the iron works production system and the character ring system are upgraded into functional edge nodes by building an edge server and an edge service platform, and the edge nodes can longitudinally control the subsystems which the edge nodes belong to and can also carry out transverse information interaction with other edge nodes in an edge network mode; unifying the edge nodes with a cloud to construct an edge Internet of things service management system, so as to form a cloud-edge-end three-layer architecture;
2) Coupling an iron works production system and a character ring system, specifically: all data of the subsystems of the two systems are shared, so that coordinated linkage is achieved, when a certain system of the production system of the iron mill has a safety accident, the factory responds to the system of people, and people in a dangerous area are positioned to prompt the people to leave the dangerous area rapidly; simultaneously, the system related to the 'object' generates response to withdraw important materials, and related facilities are closed; isolating the hazardous area with respect to the "ring" system; the harm is reduced to the minimum by coordinating and linking the systems;
Further comprises: separating the hardware of each subsystem from the original system, and uniformly incorporating the hardware of each subsystem into a resource pool of a factory to be controlled and combined by a cloud server;
the method comprises the steps that equipment based on different protocols in each subsystem of an iron mill production system and a character ring system is connected to an Internet of things platform through an industrial Internet of things gateway unified protocol standard to perform centralized management, namely heterogeneous fusion;
the security situation assessment system comprises the following steps:
(1) And (3) data acquisition: the method is characterized in that different systems, devices and products are accessed through the existing communication means, and under the premise of ensuring that the acquired data meet deep large-range conditions, the data foundation which is required by a perception platform and related to the safety of an evaluation object is constructed according to the unique edge processing and protocol conversion of heterogeneous data; the data collection is realized through a data acquisition gateway paved on the equipment, and the gateway of the Internet of things is connected with a plurality of data acquisition gateways;
the gateway is used as a data acquisition terminal device and can acquire basic data of subsystem devices which are in physical connection with the gateway, wherein the basic data comprise, but are not limited to, measurement data of temperature, humidity and gas concentration which need to be measured; operation and maintenance data of equipment operation time and start-stop times and real-time data of safety of an evaluation object related to equipment states; the sensing module on the data acquisition gateway uploads the acquired related data to the gateway of the Internet of things, and the data are uploaded to the cloud end after protocol conversion;
In the gateway, not all collected data are uploaded to the cloud, three types of data including equipment state, measurement data and operation and maintenance state collected through a sensor are correspondingly processed in the gateway, analog-to-digital conversion is firstly carried out, analog signals are converted into digital signals which can be processed through a machine, three types of data reflecting the visual state of the equipment are evaluated into a score according to a fuzzy algorithm or an expert learning method and used for reflecting the safety condition of the equipment, and then only one data reflecting the overall safety of the equipment is output; finally, packaging and uploading the obtained final data to a cloud platform;
(2) Security situation assessment: firstly, a security evaluation index system is constructed, namely, an object needing to be evaluated and specific contents of the object needing to be evaluated are determined, wherein the specific contents are as follows: (1) "number of dangerous modes", i.e., the total number of times the system is operated in dangerous mode; (2) "number of equipment and facility defects"; (3) "number of operating environment defects"; (4) "duration of danger exposure"; then, extracting data about the safety condition of each evaluation object stored in the constructed data pool through a data interface, analyzing information affecting the system safety from the data, including a dangerous source and a dangerous type, and defining the condition of the system to form a safety analysis table; then, calculating a security analysis table based on a preset rule by a BP neural network evaluation algorithm optimized by a particle swarm algorithm, so as to obtain an evaluation value reflecting the security situation of the system, and achieve the purpose of evaluating the security situation; finally, through the learning training of the neural network, the defects of manual prediction are simplified, and the automatic assessment prediction of the security situation is realized.
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