CN117123627A - Rolling mill roller state intelligent monitoring method and system based on Internet of things - Google Patents

Rolling mill roller state intelligent monitoring method and system based on Internet of things Download PDF

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CN117123627A
CN117123627A CN202310548440.XA CN202310548440A CN117123627A CN 117123627 A CN117123627 A CN 117123627A CN 202310548440 A CN202310548440 A CN 202310548440A CN 117123627 A CN117123627 A CN 117123627A
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rolling mill
internet
digital signals
things
data
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陈雄
房晓鑫
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Fudan University
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Fudan University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B38/00—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B38/00—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product
    • B21B38/10—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product for measuring roll-gap, e.g. pass indicators
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
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    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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Abstract

The embodiment of the application provides a rolling mill roller state intelligent monitoring method and system based on the Internet of things. The intelligent monitoring method for the roller state of the rolling mill based on the Internet of things comprises the following steps: temperature sensors, pressure sensors, displacement sensors and vibration sensors are arranged on each rolling mill roller device in a factory, and basic production environment and state information of the rolling mill roller device are collected; the acquired basic production environment and state information of the rolling mill roller equipment are converted into digital signals through an industrial sensor data acquisition terminal; transmitting the digital signals to an Internet of things cloud platform or a local server; analyzing digital signals collected by an Internet of things cloud platform or a local server based on machine learning and artificial intelligence methods, learning the behaviors of entities, carrying out anomaly monitoring on historical data, and predicting the future according to the historical data so as to early warn the abnormal state; and displaying the early warning information to management personnel and decision-making personnel in real time, so as to realize intelligent monitoring of the state of the rolling mill roller.

Description

Rolling mill roller state intelligent monitoring method and system based on Internet of things
Technical Field
The application relates to the technical field of computers, in particular to a rolling mill roller state intelligent monitoring method and system based on the Internet of things.
Background
With the increasing degree of informatization of industrial manufacturing and production, complex industrial manufacturing systems are gradually developed. However, in the industrial manufacturing process, even high-end equipment cannot avoid the problem of abnormal system operation caused by abnormal part of modules in the operation process, in the prior art, the state of a rolling mill roller is dynamically adjusted along with the operation of the rolling mill roller, and a traditional abnormality monitoring method cannot timely predict.
Disclosure of Invention
The embodiment of the application provides a rolling mill roller state intelligent monitoring method and system based on the Internet of things, which further realize data acquisition of rolling mill roller equipment through a plurality of different types of sensors at least to a certain extent, output basic production environment and state information of the rolling mill roller equipment so as to convert the basic production environment and state information of the rolling mill roller equipment into digital signals, perform abnormal monitoring on historical data according to the digital signals and behaviors of learning entities and predict the future according to the historical data, so as to perform early warning on the abnormal state, realize intelligent monitoring on the rolling mill roller state, timely perform early warning on the abnormal state, and ensure intelligent monitoring on the rolling mill roller state based on the Internet of things.
Other features and advantages of the application will be apparent from the following detailed description, or may be learned by the practice of the application.
According to one aspect of the embodiment of the application, an intelligent monitoring method for the roller state of a rolling mill based on the Internet of things is provided, which comprises the following steps:
temperature sensors, pressure sensors, displacement sensors and vibration sensors are arranged on each rolling mill roller device in a factory, and basic production environment and state information of the rolling mill roller device are collected;
the acquired basic production environment and state information of the rolling mill roller equipment are converted into digital signals through an industrial sensor data acquisition terminal;
transmitting the digital signals to an Internet of things cloud platform or a local server;
analyzing digital signals collected by an Internet of things cloud platform or a local server based on machine learning and artificial intelligence methods, learning the behaviors of entities, carrying out anomaly monitoring on historical data, and predicting the future according to the historical data so as to early warn the abnormal state;
and displaying the early warning information to management personnel and decision-making personnel in real time.
In some embodiments of the present application, the installation of temperature sensors, pressure sensors, displacement sensors, vibration sensors on each rolling mill roll device in the factory, and the collection of basic production environment and status information of the rolling mill roll device, comprises:
measuring temperature data of the roller based on the temperature sensor;
measuring oil and gas pipeline hydraulic supply data based on the pressure sensor;
measuring displacement data in the running process of a main shaft of the rolling mill based on a displacement sensor;
measuring vibration data of three axes of the rolling mill X, Y, Z based on the vibration sensor;
and monitoring the state of the rolling mill roll equipment according to the temperature data, the hydraulic supply data of the oil and gas pipeline, the displacement data and the vibration data, and collecting basic production environment and state information of the rolling mill roll equipment.
In some embodiments of the present application, the collected basic production environment and status information of the rolling mill roll device is converted into a digital signal by an industrial sensor data collection terminal, which includes:
signal conversion is carried out on the basic production environment and state information of the collected rolling mill roller equipment based on the industrial sensor data collection terminal;
and converting the basic production environment and state information into digital signals, and conveying the digital signals in the Internet of things.
In some embodiments of the present application, the transmitting the digital signal to the internet of things cloud platform or the local server includes:
acquiring a digital signal;
the Internet of things cloud platform or the local server is carried on the basis of the Ali cloud Internet of things platform;
and uploading the digital signals to an Arian cloud Internet of things platform or a local server based on the MQTT protocol/4G network.
In some embodiments of the present application, the analyzing the digital signals collected by the cloud platform or the local server of the internet of things based on the machine learning and artificial intelligence method, learning the behavior of the entity, performing anomaly monitoring on the historical data and predicting the future according to the historical data to perform early warning on the anomaly state includes:
digital signals collected by an Internet of things cloud platform or a local server;
traversing the corresponding behavior of the learning entity and historical data according to the digital signals;
analyzing digital signals, learning entity behaviors and historical data based on machine learning and artificial intelligence methods;
performing anomaly monitoring on the digital signals, the behaviors of the learning entities and the historical data;
and predicting the future according to the historical data so as to early warn the abnormal state.
In some embodiments of the application, the method further comprises:
the machine learning and artificial intelligence method can adopt a time sequence anomaly detection and prediction model based on a transducer network;
the Transformer network is capable of parallel computation using a distributed GPU.
In some embodiments of the application, the method further comprises:
based on an improved version of the auto-former network architecture, the time series is first input into a one-dimensional convolution, the information around each node is extracted using the convolution, and then the relationships between the nodes are learned using a multi-headed attention mechanism.
In some embodiments of the present application, the presenting the early warning information to the manager and the decision maker in real time includes:
displaying the abnormality and early warning information to management staff and decision making staff in real time;
and the manager and the decision maker gather, sort, classify and integrate the abnormal information into a final judgment basis in time, and display the early warning information to the manager and the decision maker in real time.
According to an aspect of the embodiment of the application, there is provided a rolling mill roller state intelligent monitoring system based on the internet of things, comprising:
the acquisition module is used for installing a temperature sensor, a pressure sensor, a displacement sensor and a vibration sensor on each rolling mill roller device in a factory and acquiring basic production environment and state information of the rolling mill roller device;
the acquisition module is used for converting the acquired basic production environment and state information of the rolling mill roller equipment into digital signals through the industrial-level sensor data acquisition terminal;
the transmission module is used for transmitting the digital signals to the Internet of things cloud platform or the local server;
the early warning module is used for analyzing digital signals collected by the cloud platform or the local server of the Internet of things based on machine learning and artificial intelligence methods, learning the behaviors of entities, carrying out abnormal monitoring on historical data, and predicting the future according to the historical data so as to early warn abnormal states;
and the display module is used for displaying the early warning information to the manager and the decision maker in real time.
According to an aspect of the embodiment of the present application, there is provided a computer readable medium having stored thereon a computer program, which when executed by a processor, implements the intelligent monitoring method for the mill roll state based on the internet of things as described in the above embodiment.
According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: one or more processors; and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are caused to realize the intelligent monitoring method for the roller state of the rolling mill based on the Internet of things.
According to an aspect of embodiments of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the intelligent monitoring method for the roller state of the rolling mill based on the internet of things, which is provided in the embodiment.
In the technical schemes provided by some embodiments of the application, temperature sensors, pressure sensors, displacement sensors and vibration sensors are arranged on each rolling mill roller device in a factory, and basic production environment and state information of the rolling mill roller device are collected; the acquired basic production environment and state information of the rolling mill roller equipment are converted into digital signals through an industrial sensor data acquisition terminal; transmitting the digital signals to an Internet of things cloud platform or a local server; analyzing digital signals collected by an Internet of things cloud platform or a local server based on machine learning and artificial intelligence methods, learning the behaviors of entities, carrying out anomaly monitoring on historical data, and predicting the future according to the historical data so as to early warn the abnormal state; the early warning information is displayed to management staff and decision-making staff in real time, wherein data acquisition is carried out on rolling mill roller equipment through a plurality of sensors of different types, and basic production environment and state information of the rolling mill roller equipment are output, so that the basic production environment and state information of the rolling mill roller equipment are converted into digital signals, abnormal monitoring is carried out on historical data according to the digital signals and behaviors of learning entities, and future prediction is carried out on the historical data, so that abnormal states are early warned, intelligent monitoring on the rolling mill roller states is achieved, the abnormal states are early warned in time, intelligent monitoring on the rolling mill roller states based on the Internet of things is guaranteed, and time and economic losses caused by incapability of timely monitoring and early warning on abnormal problems of an industrial manufacturing system are avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
fig. 1 shows a schematic flow chart of a rolling mill roll state intelligent monitoring method based on the internet of things according to one embodiment of the application;
FIG. 2 shows a schematic flow chart of S110 in FIG. 1;
FIG. 3 shows a schematic flow chart of S120 in FIG. 1;
FIG. 4 shows a schematic flow chart of S130 in FIG. 1;
fig. 5 shows a schematic flow chart of S140 in fig. 1;
FIG. 6 shows a schematic flow chart of S150 in FIG. 1;
FIG. 7 shows a schematic diagram of an intelligent monitoring method for the state of a rolling mill roller based on the Internet of things according to one embodiment of the application;
FIG. 8 illustrates a block diagram of an intelligent monitoring system for mill roll status based on the Internet of things, according to one embodiment of the application;
fig. 9 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Fig. 1 shows a flow diagram of a rolling mill roll state intelligent monitoring method based on the internet of things according to an embodiment of the application. The method can be applied to a rolling mill.
Referring to fig. 1 to 7, the intelligent monitoring method for the roller state of the rolling mill based on the internet of things at least comprises steps S110 to S150, and is described in detail as follows (the following description uses the method as an example for terminal equipment):
in step S110, temperature sensors, pressure sensors, displacement sensors, vibration sensors are installed on each rolling mill roll apparatus in the factory, and basic production environment and status information of the rolling mill roll apparatus are collected.
The specific steps are as follows:
step S111, measuring temperature data of the roller based on a temperature sensor;
step S112, measuring oil and gas pipeline hydraulic pressure supply data based on a pressure sensor;
step S113, measuring displacement data in the running process of the main shaft of the rolling mill based on a displacement sensor;
step S114, measuring vibration data of three shafts of the rolling mill X, Y, Z based on the vibration sensor;
step S115, monitoring the state of rolling mill roller equipment according to temperature data, oil and gas pipeline hydraulic supply data, displacement data and vibration data, and collecting basic production environment and state information of the rolling mill roller equipment;
the industrial sensor module comprises a temperature sensor, a pressure sensor, a displacement sensor and a vibration sensor, and the industrial sensor module is arranged at the corresponding position of the rolling mill equipment and is used for primarily collecting basic information of the equipment. The temperature sensor is used for measuring the temperature change range of the roller and is arranged on one side of the roller in a non-contact manner; the pressure sensor is used for measuring whether the hydraulic supply of the oil and gas pipeline is normal or not, and the installation mode is that the pressure sensor is connected to the oil pipeline and the gas pipeline in parallel; the displacement sensor adopts an ultrasonic distance meter to measure the displacement in the running process of the main shaft of the rolling mill, the installation position is vertical to the measured object, and the screw installation or the sticking installation is carried out; the vibration sensor is used for measuring vibration speed and vibration displacement data of three shafts of the rolling mill X, Y, Z, and is arranged on an accessory of a base of the rolling mill in a threaded manner;
at this time, temperature data of the roll is measured based on the temperature sensor; measuring oil and gas pipeline hydraulic supply data based on the pressure sensor; measuring displacement data in the running process of a main shaft of the rolling mill based on a displacement sensor; vibration data of three shafts of the rolling mill X, Y, Z are measured based on the vibration sensors so as to monitor states of rolling mill roller equipment according to temperature data, oil-gas pipeline hydraulic supply data, displacement data and vibration data, state monitoring is carried out on the rolling mill roller equipment in a factory through different types of data of the plurality of sensors, and the basic production environment and state information of the rolling mill roller equipment are collected in different application scenes suitable for the rolling mill roller equipment.
In step S120, the acquired basic production environment and status information of the rolling mill roll apparatus is converted into a digital signal through the industrial-level sensor data acquisition terminal.
The specific steps are as follows:
step S121, signal conversion is carried out on the basic production environment and state information of the collected rolling mill roller equipment based on the industrial sensor data collection terminal;
step S122, converting the basic production environment and the state information into digital signals, and conveying the digital signals in the Internet of things.
The system comprises a rolling mill roll equipment, a sensor data acquisition terminal, a cloud platform server, a sensor data uploading terminal, a data processing system and a data processing system, wherein the signal conversion is carried out on basic production environment and state information of the acquired rolling mill roll equipment based on the industrial sensor data acquisition terminal, the basic production environment and state information of the acquired rolling mill roll equipment are converted into digital signals, at the moment, the sensor data acquisition terminal is provided with an industrial sensor data acquisition device on each set of equipment and is used for acquiring information of a sensor and converting the information of the sensor into the digital signals, and the sensor data uploading terminal is provided with an industrial sensor data uploading device on each set of equipment and is responsible for uploading the information of the acquisition sensor to the cloud platform server so as to facilitate the transmission in the Internet of things according to the digital signals, thereby guaranteeing the stable transmission of the basic production environment and the state information and the subsequent specific analysis.
In step S130, the digital signal is transmitted to the internet of things cloud platform or the local server.
The specific steps are as follows:
step S131, acquiring a digital signal;
step S132, carrying on the basis of the Internet of things platform or the local server.
And step S133, uploading the digital signals to an Ali cloud Internet of things platform or a local server based on an MQTT protocol/4G network.
The intelligent data analysis module based on deep learning comprises a cloud platform server and a data analysis platform, wherein the cloud platform or the local server of the Internet of things is carried on the basis of the Internet of things of the Arian, and digital signals are conveyed so as to be uploaded to the Internet of things platform or the local server of the Arian on the basis of an MQTT protocol/4G network, so that stable conveying of the digital signals is ensured; the cloud platform server provides data storage based on an Arian Internet of things platform, and timely checks and reviews historical data. The sensor data of each device is uploaded to an Array cloud Internet of things platform through an MQTT protocol/4G network; the data analysis platform is built on an enterprise server to acquire data in real time and conduct algorithm analysis and prediction.
In step S140, based on the machine learning and artificial intelligence method, the digital signals collected by the cloud platform or the local server of the internet of things, the behavior of the learning entity, the anomaly monitoring of the historical data, and the prediction of the future according to the historical data are analyzed to early warn the anomaly state.
The specific steps are as follows:
step S141, digital signals collected by an Internet of things cloud platform or a local server;
step S142, traversing the corresponding behavior and history data of the learning entity according to the digital signals;
step S143, analyzing digital signals, behaviors of learning entities and historical data based on a machine learning and artificial intelligence method;
step S144, carrying out anomaly monitoring on the digital signals and the behavior and history data of the learning entity;
step S145, predicting the future according to the historical data so as to early warn the abnormal state;
traversing the corresponding behavior of the learning entity and the historical data according to the digital signals so as to analyze the behavior and the historical data of the learning entity according to the digital signals, and analyzing the behavior and the historical data of the learning entity based on the machine learning and artificial intelligence methods; performing anomaly monitoring on the digital signals, the behaviors of the learning entities and the historical data; and predicting the future according to the historical data so as to early warn the abnormal state.
In addition, the machine learning and artificial intelligence method can adopt a time sequence anomaly detection and prediction model based on a transducer network; the Transformer network is capable of parallel computation using a distributed GPU. The time sequence is firstly input into a one-dimensional convolution based on an improved version of an Autoformer network architecture of a transducer, information around each node is extracted by the convolution, and then the relation among the nodes is learned by using a multi-head attention mechanism.
Specifically, the machine learning and artificial intelligence method may employ a transducer network-based time series anomaly detection and prediction model. Compared to the LSTM and GRU models previously in the market, the transducer has two significant advantages: (1) The transducer can utilize the distributed GPU to perform parallel computation, so that the model training efficiency is improved; (2) The semantic association effect of longer capture intervals is better when analyzing and predicting longer text. The industrial time sequence has the characteristics of high coupling, small sampling time interval, dependence on physical characteristics of specific equipment and the like. Aiming at the characteristics of the industrial time series, the embodiment of the patent is based on an improved version of an Autoformer network architecture of a transducer, the time series is firstly input into a one-dimensional convolution, the information around each node is extracted by utilizing the convolution, and then the relation among the nodes is learned by using a multi-head attention mechanism. Therefore, the attribute can be used for not only considering the value of each point, but also considering the context information of each point, and the areas with similar shapes are linked, so that the performance of industrial time series anomaly detection and future prediction can be expected to be effectively improved.
In step S150, the early warning information is displayed to the manager and the decision maker in real time.
The specific steps are as follows:
step S151, displaying the abnormality and early warning information to a manager and a decision maker in real time;
and step S152, the manager and the decision maker gather, sort, classify and integrate the abnormal information into final judgment basis in time, and display the early warning information to the manager and the decision maker in real time.
At the moment, the abnormal information and the early warning information are displayed to the manager and the decision maker in real time, and the manager and the decision maker gather, sort, classify and integrate the abnormal information into final judgment basis in time so as to avoid the time and economic loss caused by the incapability of timely monitoring and early warning of the abnormal problem of the industrial manufacturing system.
In the technical schemes provided by some embodiments of the application, temperature sensors, pressure sensors, displacement sensors and vibration sensors are arranged on each rolling mill roller device in a factory, and basic production environment and state information of the rolling mill roller device are collected; the acquired basic production environment and state information of the rolling mill roller equipment are converted into digital signals through an industrial sensor data acquisition terminal; transmitting the digital signals to an Internet of things cloud platform or a local server; analyzing digital signals collected by an Internet of things cloud platform or a local server based on machine learning and artificial intelligence methods, learning the behaviors of entities, carrying out anomaly monitoring on historical data, and predicting the future according to the historical data so as to early warn the abnormal state; the early warning information is displayed to management staff and decision-making staff in real time, wherein data acquisition is carried out on rolling mill roller equipment through a plurality of sensors of different types, and basic production environment and state information of the rolling mill roller equipment are output, so that the basic production environment and state information of the rolling mill roller equipment are converted into digital signals, abnormal monitoring is carried out on historical data according to the digital signals and behaviors of learning entities, and future prediction is carried out on the historical data, so that abnormal states are early warned, intelligent monitoring on the rolling mill roller states is achieved, the abnormal states are early warned in time, intelligent monitoring on the rolling mill roller states based on the Internet of things is guaranteed, and time and economic losses caused by incapability of timely monitoring and early warning on abnormal problems of an industrial manufacturing system are avoided.
The device embodiment of the application is introduced below, and can be used for executing the intelligent monitoring method for the roller state of the rolling mill based on the Internet of things in the embodiment of the application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the intelligent monitoring method for the roller state of the rolling mill based on the internet of things.
Fig. 8 shows a block diagram of an intelligent monitoring system for the state of rolling mill rolls based on the internet of things according to one embodiment of the application.
Referring to fig. 8, a rolling mill roll state intelligent monitoring system based on internet of things according to an embodiment of the present application includes:
an acquisition module 210, configured to mount a temperature sensor, a pressure sensor, a displacement sensor, and a vibration sensor on each rolling mill roll device in a factory, and acquire basic production environment and status information of the rolling mill roll device;
the acquisition module 220 is used for converting the acquired basic production environment and state information of the rolling mill roller equipment into digital signals through the industrial-level sensor data acquisition terminal;
the transmission module 230 is configured to transmit the digital signal to an internet of things cloud platform or a local server;
the early warning module 240 is configured to analyze digital signals collected by the internet of things cloud platform or the local server, learn the behaviors of the entity, perform anomaly monitoring on historical data, and predict the future according to the historical data based on machine learning and artificial intelligence methods, so as to early warn about an abnormal state;
and the display module 250 is used for displaying the early warning information to the manager and the decision maker in real time.
In one embodiment of the present application, there is also provided an electronic device including:
one or more processors;
and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the intelligent monitoring method for the roller state of the rolling mill based on the Internet of things.
In one example, FIG. 9 illustrates a schematic diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the application.
It should be noted that, the computer system of the electronic device shown in fig. 9 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 4, the computer system includes a central processing unit (Central Processing Unit, CPU) 301 (i.e., a processor as described above) that can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 302 or a program loaded from a storage section 308 into a random access Memory (Random Access Memory, RAM) 303. It should be understood that RAM303 and ROM302 are just described as storage devices. In the RAM303, various programs and data required for the system operation are also stored. The CPU 301, ROM302, and RAM303 are connected to each other through a bus 304. An Input/Output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output portion 307 including a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and the like, a speaker, and the like; a storage section 308 including a hard disk or the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. The drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 309, and/or installed from the removable medium 311. When executed by a Central Processing Unit (CPU) 301, performs the various functions defined in the system of the present application.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the methods described in the above embodiments.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (10)

1. The intelligent monitoring method for the roller state of the rolling mill based on the Internet of things is characterized by comprising the following steps of:
temperature sensors, pressure sensors, displacement sensors and vibration sensors are arranged on each rolling mill roller device in a factory, and basic production environment and state information of the rolling mill roller device are collected;
the acquired basic production environment and state information of the rolling mill roller equipment are converted into digital signals through an industrial sensor data acquisition terminal;
transmitting the digital signals to an Internet of things cloud platform or a local server;
analyzing digital signals collected by an Internet of things cloud platform or a local server based on machine learning and artificial intelligence methods, learning the behaviors of entities, carrying out anomaly monitoring on historical data, and predicting the future according to the historical data so as to early warn the abnormal state;
and displaying the early warning information to management personnel and decision-making personnel in real time.
2. The method of claim 1, wherein the step of installing temperature sensors, pressure sensors, displacement sensors, vibration sensors on each rolling mill roll apparatus in the factory and collecting basic production environment and status information of the rolling mill roll apparatus comprises:
measuring temperature data of the roller based on the temperature sensor;
measuring oil and gas pipeline hydraulic supply data based on the pressure sensor;
measuring displacement data in the running process of a main shaft of the rolling mill based on a displacement sensor;
measuring vibration data of three axes of the rolling mill X, Y, Z based on the vibration sensor;
and monitoring the state of the rolling mill roll equipment according to the temperature data, the hydraulic supply data of the oil and gas pipeline, the displacement data and the vibration data, and collecting basic production environment and state information of the rolling mill roll equipment.
3. The method of claim 2, wherein the acquired basic production environment and status information of the mill roll equipment is converted into digital signals by an industrial-scale sensor data acquisition terminal, comprising:
signal conversion is carried out on the basic production environment and state information of the collected rolling mill roller equipment based on the industrial sensor data collection terminal;
and converting the basic production environment and state information into digital signals, and conveying the digital signals in the Internet of things.
4. The method of claim 3, wherein the transmitting the digital signal to the internet of things cloud platform or the local server comprises:
acquiring a digital signal;
the Internet of things cloud platform or the local server is carried on the basis of the Ali cloud Internet of things platform;
and uploading the digital signals to an Arian cloud Internet of things platform or a local server based on the MQTT protocol/4G network.
5. The method of claim 4, wherein analyzing the digital signals collected by the internet of things cloud platform or the local server, learning the behavior of the entity, monitoring the history data for anomalies, and predicting the future based on the history data based on the machine learning and artificial intelligence methods to pre-warn of anomalies comprises:
digital signals collected by an Internet of things cloud platform or a local server;
traversing the corresponding behavior of the learning entity and historical data according to the digital signals;
analyzing digital signals, learning entity behaviors and historical data based on machine learning and artificial intelligence methods;
performing anomaly monitoring on the digital signals, the behaviors of the learning entities and the historical data;
and predicting the future according to the historical data so as to early warn the abnormal state.
6. The method of claim 5, wherein the method further comprises:
the machine learning and artificial intelligence method can adopt a time sequence anomaly detection and prediction model based on a transducer network;
the Transformer network is capable of parallel computation using a distributed GPU.
7. The method of claim 6, wherein the method further comprises:
based on an improved version of the auto-former network architecture, the time series is first input into a one-dimensional convolution, the information around each node is extracted using the convolution, and then the relationships between the nodes are learned using a multi-headed attention mechanism.
8. The method of claim 7, wherein the presenting the pre-warning information to the manager and the decision maker in real time comprises:
displaying the abnormality and early warning information to management staff and decision making staff in real time;
and the manager and the decision maker gather, sort, classify and integrate the abnormal information into a final judgment basis in time, and display the early warning information to the manager and the decision maker in real time.
9. Rolling mill roll state intelligent monitoring system based on thing networking, its characterized in that includes:
the acquisition module is used for installing a temperature sensor, a pressure sensor, a displacement sensor and a vibration sensor on each rolling mill roller device in a factory and acquiring basic production environment and state information of the rolling mill roller device;
the acquisition module is used for converting the acquired basic production environment and state information of the rolling mill roller equipment into digital signals through the industrial-level sensor data acquisition terminal;
the transmission module is used for transmitting the digital signals to the Internet of things cloud platform or the local server;
the early warning module is used for analyzing digital signals collected by the cloud platform or the local server of the Internet of things based on machine learning and artificial intelligence methods, learning the behaviors of entities, carrying out abnormal monitoring on historical data, and predicting the future according to the historical data so as to early warn abnormal states;
and the display module is used for displaying the early warning information to the manager and the decision maker in real time.
10. A computer readable medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the intelligent monitoring method of the mill roll state based on the internet of things according to any one of claims 1 to 8.
CN202310548440.XA 2023-05-16 2023-05-16 Rolling mill roller state intelligent monitoring method and system based on Internet of things Pending CN117123627A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310548440.XA CN117123627A (en) 2023-05-16 2023-05-16 Rolling mill roller state intelligent monitoring method and system based on Internet of things

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310548440.XA CN117123627A (en) 2023-05-16 2023-05-16 Rolling mill roller state intelligent monitoring method and system based on Internet of things

Publications (1)

Publication Number Publication Date
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