Communication control system and method for shaft furnace feeding device

Abstract

The invention relates to the technical field of communication control, in particular to a communication control system and method for a shaft furnace feeding device, and the system comprises a feeding end which is composed of a raw material characteristic sensing layer, a feeding state sensing layer and a control response layer, the feeding state sensing layer is used for collecting data of the feeding state, the raw material characteristic sensing layer and the feeding state sensing layer both support multi-mode data collection, and the control response layer supports high-frequency control instruction response and controls the feeding state of raw materials; the system can actively adjust a feeding strategy under a dynamic working condition, reduce raw material waste and energy consumption and improve product quality; the self-learning capability continuously optimizes the control effect along with the prolonging of the operation time, a forward feedback cycle is formed, and the production efficiency and the equipment utilization rate are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of communication control, and in particular to a communication control system and method for a vertical furnace charging device. Background Art

[0002] As an important industrial equipment, vertical furnace is widely used in metallurgy, chemical industry, building materials and other industries for heating, reducing or melting raw materials. The vertical furnace charging device is the core component of the vertical furnace system. The precise control and efficient operation of its charging process directly affect product quality, energy consumption and production efficiency. However, the traditional vertical furnace charging control system usually relies on single sensor data and manual experience for operation, and has problems such as insufficient data collection dimension, poor real-time performance and low control accuracy. These problems not only limit the optimization potential of the vertical furnace process, but may also lead to waste of raw materials, increased energy consumption and low production efficiency. Summary of the invention

[0003] The present invention provides a communication control system and method for a vertical furnace charging device, which can effectively solve the problems in the background technology.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is: In one aspect, the present application provides a communication control system for a vertical furnace charging device, comprising: The feeding end is composed of a raw material characteristic sensing layer, a feeding state sensing layer and a control response layer. The raw material characteristic sensing layer is used to collect characteristic data of the added raw materials, and the feeding state sensing layer is used to collect data of the feeding state. Both the raw material characteristic sensing layer and the feeding state sensing layer support multi-modal data collection, and the control response layer supports high-frequency control instruction response and controls the raw material feeding state. The vertical furnace end is composed of a raw material state sensing layer and an environment sensing layer. The raw material state sensing layer is used to collect raw material state data in the vertical furnace, and the environment sensing layer is used to collect environment state data in the vertical furnace. Both the raw material state sensing layer and the environment sensing layer use high temperature resistant sensor arrays and multi-spectral imaging technology to collect data. The communication end integrates industrial Ethernet, 5G-U and edge computing modules to achieve multi-protocol data fusion and low-latency transmission of the data collected at the charging end and the data collected at the vertical furnace end; The controller receives the data transmitted by the communication terminal and generates a control strategy based on reinforcement learning and digital twin technology, and issues instructions to the control response layer through the OPC UA Pub / Sub model.

[0005] Based on the above embodiment, the data collected by the raw material characteristic perception layer includes physical characteristic data and chemical characteristic data. The physical characteristic data includes the particle size, humidity and density of the raw material, and the chemical characteristic data includes the composition and calorific value of the raw material.

[0006] Based on the above embodiments, the feeding status includes the feeding speed, feeding position and feeding amount of the raw material.

[0007] Based on the above embodiment, the data collected by the raw material status sensing layer in the furnace includes the material level and material quantity of the raw materials in the furnace.

[0008] Based on the above embodiment, the data collected by the environmental sensing layer includes temperature distribution, pressure and airflow distribution in the furnace.

[0009] Based on the above embodiment, the communication end also includes an edge gateway, and the edge gateway filters the communication data through at least one algorithm selected from low-pass filtering, moving average filtering, median filtering or Kalman filtering.

[0010] Based on the above embodiment, a redundant communication link is deployed between the communication end and the controller, and the redundant communication link includes at least one of a dual network card, a dual industrial Ethernet, a ring network and a time-sensitive network.

[0011] Based on the above embodiments, the control strategy is a multi-mode control method, and the multi-mode control method includes a continuous feeding mode, a batch feeding mode, a pulse feeding mode, a variable speed feeding mode, an intermittent feeding mode and a gradient feeding mode.

[0012] Based on the above embodiment, the system further includes a monitoring terminal, which is used to monitor the data received by the controller and manually adjust the control strategy.

[0013] On the other hand, the present application also provides a communication control method for a vertical furnace charging device, the method comprising the following steps: The characteristic data of the raw materials in the charging device and the charging state data when the raw materials are added to the vertical furnace are collected by using a multi-modal data collection method; Use high temperature resistant sensor array and multi-spectral imaging technology to collect raw material status data and environmental status data in the shaft furnace; Based on industrial Ethernet, 5G-U and edge computing modules, multi-protocol data fusion and low-latency transmission are performed on the collected raw material characteristic data, feeding status data, raw material status data and environmental status data; The transmitted data is processed based on reinforcement learning and digital twin technology, and a control strategy is generated. The control strategy controls and adjusts the charging state of raw materials when they are added to the vertical furnace through an OPC UA Pub / Sub model.

[0014] The technical solution of the present invention can achieve the following technical effects: The present invention breaks through the limitations of the traditional vertical furnace charging control system through the collaborative design of the charging end, vertical furnace end, communication end and controller, realizes multimodal perception of raw material characteristics and charging status, real-time and accurate monitoring of the furnace environment and raw material status, low-latency fusion transmission of data, and intelligent decision-making based on reinforcement learning and digital twins, thereby significantly improving the overall efficiency and stability of the vertical furnace process.

[0015] First, multimodal data acquisition combined with high-temperature resistant sensor arrays and multispectral imaging technology not only enriches the data dimension, but also overcomes the difficulty of data collection in high-temperature environments, providing high-quality input for accurate modeling.

[0016] Secondly, the integration of industrial Ethernet and 5G-U ensures the real-time transmission of massive heterogeneous data, while the edge computing module effectively reduces latency and reduces the central computing load, enabling the control system to respond quickly to changes in complex working conditions.

[0017] On this basis, the reinforcement learning algorithm generates a highly adaptable and robust control strategy by learning historical data and optimizing through trial and error in a simulation environment. At the same time, the virtual model constructed by digital twin technology can map the operating status of the vertical furnace in real time, realizing predictive maintenance and process optimization.

[0018] This multi-level, cross-domain deep integration brings unexpected results: on the one hand, the system can actively adjust the charging strategy under dynamic conditions, reduce raw material waste and energy consumption, and improve product quality; on the other hand, its self-learning ability continuously optimizes the control effect as the operating time increases, forming a positive feedback loop, greatly improving production efficiency and equipment utilization; ultimately, this overall logic not only solves the technical bottleneck of a single link, but also creates a new model of intelligent and efficient vertical furnace charging control, laying a solid foundation for the digital transformation of vertical furnace processes in the industrial field. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the present invention; DETAILED DESCRIPTION

[0020] In the description of this application, those skilled in the art should know that this application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, this application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, this application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0021] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0022] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0023] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0024] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0025] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0026] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0027] The present application is described in detail below in conjunction with the drawings in the present application.

[0028] Embodiment 1: like Figure 1 As shown, the communication control system for a vertical furnace charging device of the present invention comprises: The feeding end is composed of a raw material characteristic perception layer, a feeding state perception layer and a control response layer. The raw material characteristic perception layer is used to collect characteristic data of the added raw materials, and the feeding state perception layer is used to collect data on the feeding state. Both the raw material characteristic perception layer and the feeding state perception layer support multi-modal data collection, and the control response layer supports high-frequency control command response and controls the raw material feeding state. The vertical furnace end is composed of a raw material state sensing layer and an environment sensing layer. The raw material state sensing layer is used to collect data on the raw material state in the vertical furnace, and the environment sensing layer is used to collect data on the environment state in the vertical furnace. Both the raw material state sensing layer and the environment sensing layer use high temperature resistant sensor arrays and multi-spectral imaging technology to collect data. The communication end integrates industrial Ethernet, 5G-U and edge computing modules to achieve multi-protocol data fusion and low-latency transmission of data collected at the charging end and the vertical furnace end; The controller, based on reinforcement learning and digital twin technology, receives data transmitted by the communication end and generates control strategies, and issues instructions to the control response layer through the OPC UA Pub / Sub model.

[0029] In the present invention, the feeding end is installed on the feeding device, which is responsible for the collection of raw material characteristic data, the monitoring of the feeding status and the real-time control of the feeding process. Among them, the raw material characteristic perception layer uses multimodal sensor technology, including a spectrometer (for non-contact detection of raw material components), a microwave humidity sensor (real-time measurement of raw material humidity), a laser particle size analyzer (detection of raw material particle size distribution) and a densitometer (measurement of raw material density) to realize the collection of the characteristic data of the raw material itself; the feeding status perception layer uses multimodal sensing technology, including an encoder (real-time monitoring of feeding speed), a laser radar (detection of feeding position and material level distribution) and a flow sensor (measurement of feeding amount) to realize the collection of feeding status data; the control cycle of the high-frequency control instruction response adopted in the control response layer is ≤10ms, which can ensure the real-time and stability of the feeding process; the raw material characteristic perception layer and the feeding status perception layer both support high-frequency data collection (sampling frequency ≥1kHz) to ensure the real-time and accuracy of the data; The vertical furnace end is mainly installed on the vertical furnace. The raw material status sensing layer in the vertical furnace end can use sensors that can withstand high temperature environments (such as thermocouples, optical fiber temperature sensors or ceramic-based sensors) to monitor the temperature distribution of the raw materials. These sensors are usually arranged in an array to cover different areas inside the vertical furnace. The design of the sensor array needs to consider the stability of signal transmission. In extreme high temperature environments, wireless communication modules (such as LoRa, Zigbee or 5G private networks) can be used to send the collected data to the communication end; multispectral imaging technology can also be used, in which multispectral imaging technology uses light of different wavelengths to scan the raw materials to obtain information on their surface and internal physical and chemical properties. For example, infrared spectroscopy can be used to detect changes in temperature and chemical composition of raw materials, while visible light and ultraviolet spectra can be used to analyze the color, shape and distribution of raw materials. When in use, the imaging equipment is usually equipped with a cooling device (such as water cooling or air cooling) to protect the optical components from high temperature damage, and transmits images and data through a high-speed interface (such as Gigabit Ethernet or fiber optic link); the environmental perception layer is similar to the raw material state perception layer. The environmental perception layer also uses a high-temperature resistant sensor array, but its focus is on monitoring environmental parameters, such as: gas sensors (such as electrochemical sensors, infrared absorption sensors) are used to detect the gas composition in the furnace, pressure sensors (such as piezoresistive or capacitive sensors) are used to measure the pressure change in the furnace, and humidity sensors are used to monitor the moisture content in the furnace; and multispectral imaging technology can be used in the environmental perception layer to analyze the flame characteristics of the gas flow and combustion process in the furnace, for example, by capturing the temperature field distribution in the furnace through infrared imaging, or by studying the gas phase reaction kinetics through laser induced fluorescence (LIF) technology; On the communication side, Industrial Ethernet ensures the real-time and deterministic data transmission through TSN technology. TSN can allocate bandwidth and time windows for data streams of different priorities to ensure low-latency transmission of key control data. In addition, Industrial Ethernet supports a variety of industrial communication protocols (such as PROFINET, EtherCAT, Modbus TCP, etc.), which can seamlessly integrate various sensors and controllers at the charging end and the vertical furnace end to achieve unified transmission of multimodal data. The air interface delay of 5G-U can be as low as less than 1 millisecond, which is very suitable for real-time control applications. For example, during the charging process of the vertical furnace, 5G-U can quickly transmit high-frequency control instructions between the charging state perception layer and the control response layer. 5G-U ensures that the reliability of data transmission reaches more than 99.999% through enhanced physical layer design and network slicing technology, meeting the strict requirements of industrial control for data transmission quality. At the same time, 5G-U supports flexible spectrum configuration and dynamic resource allocation, and can adapt to different industrial application scenarios. For example, independent network slices can be allocated to different devices at the charging end and the vertical furnace end to avoid mutual interference. The edge computing module supports a variety of industrial protocols (such as OPC UA, MQTT, HTTP, etc.), can unify the heterogeneous data from the charging end and the vertical furnace end into a standardized format. The edge computing module has a built-in high-performance processor and AI algorithm, which can analyze the collected data in real time, extract key features and generate intermediate results. For example, the edge computing module can be used to correlate the data of the raw material characteristic perception layer and the charging status perception layer to predict potential problems in the charging process. At the same time, the edge computing module is equipped with a large-capacity storage device, which can temporarily save data when the network is interrupted and upload it to the central controller after the network is restored; Industrial Ethernet and 5G-U together constitute the communication end The backbone network is responsible for transmitting data from the charging end and the vertical furnace end to the edge computing module. The edge computing module performs multi-protocol analysis, format conversion and feature extraction on these data to generate a unified data stream for subsequent processing. At the same time, the combination of Industrial Ethernet and 5G-U achieves low latency and high reliability in data transmission. For example, Industrial Ethernet is responsible for stable communication between fixed devices, while 5G-U is used for the transmission of mobile devices or high-frequency control instructions. The edge computing module not only undertakes the task of data fusion, but also reduces the pressure on the central controller through localized processing. This "edge + center" architecture improves the overall efficiency and response speed of the system; Reinforcement learning in the controller is a machine learning method that continuously optimizes the decision-making strategy by interacting with the environment. In the present invention, reinforcement learning is used to generate the optimal control strategy based on the data collected from the charging end and the vertical furnace end; the controller uses the data transmitted by the communication end (such as raw material characteristics, charging status, raw material status in the furnace, and environmental status) as the state input of reinforcement learning. After preprocessing, these data are converted into standardized feature vectors to describe the current state of the system; the action space of reinforcement learning corresponds to the parameters that can be adjusted by the controller, such as charging rate, combustion air flow, exhaust valve opening, etc. The execution of each action will affect the working state of the vertical furnace. The reward function is the core part of reinforcement learning and is used to measure the contribution of a certain action to the system goal. For example, the reward function can be designed based on the production efficiency, energy consumption level or emission index of the vertical furnace. The controller optimizes the control strategy by maximizing the cumulative reward; the reinforcement learning model can also be continuously updated during operation, and the control strategy is gradually improved by learning new data. This adaptive ability enables the controller to cope with complex dynamic working conditions; digital twin technology is a virtual building Modeling technology, by building a digital copy of the physical system, to achieve real-time monitoring and predictive analysis of actual equipment. In the present invention, digital twin technology is used to simulate the operating status of the vertical furnace and the charging device, and to verify the effectiveness of the control strategy; the digital twin model receives real-time data from the charging end and the vertical furnace end through the communication end to ensure that the virtual model is always consistent with the physical system, and the digital twin model can simulate the physical and chemical reaction process inside the vertical furnace and the operating behavior of the charging device. For example, the digital twin can predict the impact of different charging rates on the temperature distribution in the furnace. Before applying the control strategy to the actual system, a simulation test is first performed in the digital twin model. If potential problems are found, the strategy can be adjusted in time to avoid damage to the actual equipment; OPC UA is an industrial communication standard that supports cross-platform and cross-vendor data exchange. The Pub / Sub model is an extension of OPC UA, designed for real-time data transmission. In the present invention, the controller sends the generated control instructions to the control response layer through the OPC UA Pub / Sub model. The control response layer receives and parses the instructions issued by the controller, and then performs corresponding actions, such as adjusting the motor speed of the charging device, the angle of charging, etc. Based on the above implementation, this multi-level, cross-domain deep integration has brought unexpected results: on the one hand, the system can actively adjust the charging strategy under dynamic conditions, reduce raw material waste and energy consumption, and improve product quality; on the other hand, its self-learning ability continuously optimizes the control effect as the operating time increases, forming a positive feedback loop, greatly improving production efficiency and equipment utilization; ultimately, this overall logic not only solves the technical bottleneck of a single link, but also creates a new intelligent and efficient vertical furnace charging control mode, laying a solid foundation for the digital transformation of vertical furnace processes in the industrial field; Of course, to further optimize the system, industrial-grade encryption protocols (such as TLS / SSL) can be used to encrypt communication data during transmission to ensure that the data is not stolen or tampered with during transmission; use distributed storage technology (such as local storage of edge computing nodes combined with cloud backup) to back up key data; deploy network diagnostic tools (such as SNMP protocol) at the communication end to monitor the network status in real time and detect communication delays, packet loss and other problems; integrate fault diagnosis algorithms (such as rule engines or machine learning models) in the controller to analyze sensor data in real time and identify abnormal conditions; use software-defined network (SDN) technology to dynamically adjust communication paths and bypass faulty nodes; implement predictive maintenance functions based on digital twins in the controller to detect potential faults in advance and automatically adjust control strategies; deploy smart meters and energy consumption sensors at the charging end and vertical furnace end to collect energy consumption data in real time, analyze energy consumption data through edge computing modules, and identify high-energy consumption links; optimize charging control strategies based on reinforcement learning algorithms to reduce unnecessary energy consumption, use digital twin technology to simulate energy consumption under different working conditions, and select optimal operating parameters; use industrial communication standard protocols (such as OPC UA, MQTT, Modbus) to achieve multi-device, multi-protocol data interoperability; by solving the system's deficiencies in data security, fault diagnosis, energy efficiency optimization and compatibility, it can not only improve the system's reliability and practicality, but also lay the foundation for future functional expansion and technology upgrades.

[0030] In some embodiments of the present invention, the data collected by the raw material property sensing layer includes physical property data and chemical property data, the physical property data includes the particle size, humidity and density of the raw material, and the chemical property includes the composition and calorific value of the raw material; In the present invention, physical property data is usually collected at a high frequency, so an efficient communication protocol (such as OPC UA or MQTT) is required to ensure real-time performance. At the same time, in order to reduce network load, edge computing technology can be used to pre-process the raw data and extract key features; the particle size detection of raw materials usually uses optical sensors or laser scattering technology, and the raw material particles are scanned by multi-spectral imaging equipment to obtain their size distribution information. After data collection, it is transmitted to the communication end through a high-speed interface (such as Gigabit Ethernet); humidity detection can use capacitive or resistive humidity sensors, which can measure the moisture content in the raw materials. In order to adapt to high temperature environments, humidity sensors may need to be equipped with cooling devices and upload data through wireless communication modules (such as LoRa or Zigbee); density detection can be achieved through weighing sensors or ultrasonic sensors. For example, the volume and mass of the raw material pile are measured using the ultrasonic reflection principle, and then the density is calculated. Data is transmitted to the communication end through an industrial bus (such as CAN bus or PROFIBUS); Chemical property data usually involves complex spectral signal processing, so high-performance computing resources are required. The edge computing module can deploy machine learning algorithms (such as deep neural networks) to quickly analyze spectral data. At the same time, to ensure the reliability of data transmission, redundant communication links (such as dual-link backup) and data verification mechanisms can be used; chemical property data (such as composition and calorific value) are used to analyze the chemical composition of raw materials and their combustion performance. These data directly affect the operating efficiency and emission level of the shaft furnace; component analysis usually uses near-infrared spectroscopy (NIRS) or X-ray fluorescence spectroscopy (XRF) technology, which determines the chemical composition by analyzing the spectral signals emitted or absorbed by the raw materials. The spectral data is transmitted to the edge computing module through a high-speed optical fiber link for analysis; calorific value measurement can be achieved through indirect methods, such as calculating the calorific value based on a mathematical model of raw material composition and physical properties. In addition, a special calorimeter can be used to directly measure the combustion calorific value of the raw material, and the data is transmitted to the communication end through industrial Ethernet; Of course, in addition to the above characteristics, the shape of the raw material, the metal content, volatility and other aspects can also be tested, which are all within the scope of protection of this case; By conducting multi-faceted tests on raw material characteristics, the performance of the vertical furnace charging device can be improved from multiple dimensions, such as optimizing the vertical furnace operating efficiency, improving combustion efficiency, improving airflow distribution, and reducing energy loss; improving product quality, controlling reaction conditions, and reducing slagging risks; enhancing equipment reliability, extending equipment life, and reducing mechanical failures; reducing environmental pollution, reducing harmful gas emissions, and improving resource utilization; through comprehensive testing of raw material characteristics, the vertical furnace charging device can achieve intelligent operation on the basis of high efficiency, stability, and environmental protection, creating greater value for the enterprise.

[0031] In some embodiments of the present invention, the feeding status includes the feeding speed, feeding position and feeding amount of the raw material; In the present invention, the feeding speed refers to the amount or volume of raw materials added to the vertical furnace per unit time, which can be measured by a flow meter sensor (such as a propeller flow meter or an electromagnetic flow meter). Monitoring the feeding speed can ensure that the raw materials enter the vertical furnace in a stable and controllable manner; the feeding position refers to the specific placement position of the raw materials in the vertical furnace, which can be determined by a high-precision position sensor (such as a laser rangefinder, an ultrasonic sensor or a visual recognition system) The current position of the feeding device can be determined. For mobile feeding equipment, dynamic positioning can be achieved in combination with GPS or an inertial navigation system (INS). Accurate control of the feeding position can optimize the material distribution and reaction conditions in the vertical furnace; the feeding amount refers to the total amount of raw materials put into the vertical furnace during each feeding process. It can be measured by a weighing sensor or a volumetric metering device (such as a screw pump or a belt scale). For batch feeding, the number of times each feeding and the single feeding amount can be counted by a counting sensor. Accurate control of the feeding amount can avoid excessive or insufficient conditions and ensure stable operation of the vertical furnace; The charging state perception layer provides a comprehensive state description for the vertical furnace charging device by collecting data on the charging speed, charging position and charging amount of raw materials. Combined with advanced technologies in the field of communication control (such as industrial Ethernet, 5G-U, edge computing and intelligent control algorithms), the system can not only efficiently collect and transmit data, but also generate optimized control strategies through intelligent processing, thereby improving the efficiency, stability and intelligence level of the vertical furnace charging process.

[0032] In some embodiments of the present invention, the data collected by the furnace raw material status sensing layer includes the material level and material quantity of the raw material in the furnace; In the present invention, the data collected by the raw material state sensing layer in the furnace include the material level and material quantity of the raw materials in the furnace, which are crucial for real-time monitoring of the raw material distribution state inside the vertical furnace, optimizing the charging process, and ensuring the operating efficiency of the vertical furnace; the material level refers to the height and position of the raw material accumulation in the vertical furnace. Monitoring the material level can ensure that the raw materials are evenly distributed in the vertical furnace, avoiding problems such as insufficient combustion or airflow short circuit caused by too high or too low material level. The material level detection can use a non-contact material level sensor (such as a radar material level meter, an ultrasonic material level meter) to measure the material level height. These sensors calculate the material level by emitting electromagnetic waves or sound waves and receiving reflected signals; for high-temperature environments, a high-temperature resistant radar material level meter or an ultrasonic sensor equipped with a cooling device can be selected; the material quantity refers to the total mass or volume of the raw materials in the vertical furnace. Monitoring the material quantity can evaluate the material reserve in the vertical furnace and provide a basis for the production plan. The material quantity monitoring can use a weighing sensor (such as a strain gauge weighing sensor) to directly measure the pressure change at the bottom of the vertical furnace, thereby inferring the material quantity. For large vertical furnaces, the material quantity can be calculated by combining the material level data and the cross-sectional area of ​​the furnace body; The raw material status perception layer in the furnace provides comprehensive status monitoring capabilities for the operation of the vertical furnace by collecting the material level and quantity data of the raw materials in the furnace, thereby improving the efficiency, stability and intelligence level of the vertical furnace operation.

[0033] In some embodiments of the present invention, the data collected by the environmental sensing layer includes temperature distribution, pressure and airflow distribution in the furnace; In the present invention, the data collected by the environmental sensing layer is crucial for real-time monitoring of the operating environment inside the vertical furnace, optimizing the combustion process, and ensuring equipment safety; the temperature distribution in the furnace reflects the temperature changes in different areas inside the vertical furnace. Accurate monitoring of the temperature distribution helps to optimize the combustion efficiency and prevent local overheating or slagging. It can use high-temperature resistant thermocouples or infrared thermometers to measure the temperature distribution in the furnace. Thermocouples are suitable for contact measurement, while infrared thermometers are suitable for non-contact measurement. At the same time, a multi-point thermocouple array can cover different areas inside the vertical furnace to obtain comprehensive temperature distribution data; the pressure in the furnace reflects the pressure state of the gas inside the vertical furnace. Monitoring the pressure can prevent safety problems or decreased combustion efficiency caused by excessive or low pressure. It can use piezoresistive pressure sensors or capacitive pressure sensors to measure the absolute pressure or differential pressure in the furnace. For high-temperature environments, high-temperature resistant pressure sensors or sensors equipped with cooling devices can be selected. The pressure sensor is connected via PROFIBUS, Modbus or OPC The UA protocol uploads data to the communication end; the airflow distribution reflects the direction and speed of the gas flow inside the vertical furnace. Monitoring the airflow distribution can optimize the heat and mass transfer efficiency during the combustion process and avoid airflow short circuit or local hypoxia. It can use a hot film anemometer or an ultrasonic flow meter to measure the airflow speed and direction. For complex flow fields, it can combine a multi-point sensor array and numerical simulation technology to obtain comprehensive airflow distribution information. The airflow sensor transmits data through industrial Ethernet or wireless communication modules (such as Wi-Fi 6, 5G).

[0034] In some embodiments of the present invention, the communication end further includes an edge gateway, and the edge gateway performs filtering processing on the communication data by using at least one algorithm selected from low-pass filtering, moving average filtering, median filtering or Kalman filtering; In the present invention, the edge gateway filters the communication data to eliminate noise interference and improve data quality. The edge gateway is usually equipped with a high-performance processor (such as an ARM architecture chip or FPGA) and a large-capacity memory, which can run complex filtering algorithms, and supports a variety of industrial communication protocols (such as OPC UA, Modbus, EtherCAT) to achieve data intercommunication between different devices; The algorithms in the edge gateway are introduced as follows: Low-pass filtering is a frequency-domain filtering method used to remove high-frequency noise and retain the low-frequency components in the signal. The edge gateway uses digital signal processing (DSP) technology to implement low-pass filtering. Common implementation methods include finite impulse response (FIR) filters and infinite impulse response (IIR) filters. Moving average filtering smoothes short-term fluctuations and retains the overall trend by averaging the data within a certain time window. The edge gateway records the data stream in real time and calculates the average value within a fixed window. The window size can be adjusted according to demand. Median filtering effectively suppresses impulse noise by sorting the data within a certain time window and taking the middle value. The edge gateway sorts the data stream and extracts the median as the output result. Median filtering is sensitive to extreme values ​​and can effectively remove spike noise. Kalman filtering is a recursive filtering algorithm based on a state space model that can simultaneously estimate the state and noise characteristics of the system. The edge gateway runs the Kalman filtering algorithm to update the state estimate and error covariance matrix in real time. Kalman filtering can process multi-dimensional data and support the modeling of dynamic noise characteristics. By using one or a combination of the above-mentioned filtering methods, noise interference in the sensor collected data can be effectively removed, the accuracy and reliability of the data can be improved, and the controller can be helped to generate a better control strategy, thereby improving the overall performance of the vertical furnace charging device. At the same time, the filtered data is more compact, reducing unnecessary redundant information and reducing the network load on the communication end. In industrial Ethernet or 5G-U networks, this helps to improve data transmission efficiency and real-time performance.

[0035] In some embodiments of the present invention, a redundant communication link is deployed between the communication terminal and the controller, and the redundant communication link includes at least one of a dual network card, a dual industrial Ethernet, a ring network, and a time-sensitive network; In the present invention, a redundant communication link is deployed between the communication end and the controller, which can automatically switch to the backup link when the main link fails, avoiding the whole system from being paralyzed due to a single point failure. This design significantly improves the operation stability of the vertical furnace charging device, especially in industrial environments with high temperature, high humidity or strong electromagnetic interference. At the same time, the redundant communication link is combined with the time-sensitive network (TSN) technology to ensure low-latency transmission of key control data and enhance real-time performance, which is particularly important for high-frequency control instructions in the vertical furnace charging device (such as charging speed adjustment or burner power adjustment), and can achieve millisecond-level response; the redundant design allows the maintenance or replacement of the faulty link without stopping the machine, reducing downtime and maintenance costs; The features of each redundant communication link in this embodiment are as follows: Dual network cards: By configuring two independent network cards on the communication end and the controller, two physically separated communication paths are formed. Each network card is connected to a different switch or router to ensure that even if one link fails, the other link can still work normally. Dual Industrial Ethernet: build two independent Industrial Ethernet links for redundant data transmission. Each link uses independent optical fiber or copper cable media and is equipped with redundant switches. Industrial Ethernet protocols (such as PROFINET IO or EtherNet / IP) support link status monitoring and fast switching. Ring network, which provides link redundancy and self-healing capabilities by forming a ring topology with multiple nodes. Each node in the ring network is connected to other nodes through two links to form a closed loop. Rapid Spanning Tree Protocol (RSTP) or ring network redundancy protocols (such as Hirschmann HiPER-Ring) are used to detect and isolate faulty links. Time-Sensitive Networking (TSN), TSN is a deterministic network technology based on the IEEE 802.1 standard, which is used to ensure low-latency and high-reliability transmission of critical data; TSN allocates bandwidth and time windows to data streams of different priorities through mechanisms such as time-aware shaper (TAS), frame preemption and traffic scheduling; in redundant design, TSN can be combined with dual industrial Ethernet or ring networks to further improve system reliability.

[0036] In some embodiments of the present invention, the control strategy is a multi-mode control mode, and the multi-mode control mode includes a continuous feeding mode, a batch feeding mode, a pulse feeding mode, a variable speed feeding mode, an intermittent feeding mode and a gradient feeding mode; In the present invention, the control strategy adopts a multi-mode control method, which can select the most suitable charging mode according to different working conditions of the vertical furnace, improve the adaptability of the system, for example, the continuous charging mode can be selected in the stable operation stage, and the intermittent charging mode can be switched to in the startup or shutdown stage; optimize the performance of the vertical furnace, each charging mode is optimized for specific process requirements, and can significantly improve the combustion efficiency, material distribution uniformity and production stability of the vertical furnace, for example, the pulse charging mode can improve the airflow distribution, and the gradient charging mode can reduce thermal shock; enhance flexibility, the multi-mode control method supports flexible switching to meet the needs of different production tasks; reduce energy consumption, by accurately controlling the charging process to avoid excessive or insufficient conditions, thereby reducing energy consumption; For ease of understanding, the following is a detailed explanation of each mode control method: In continuous feeding mode, a frequency converter is used to drive a feeding device (such as a screw conveyor or a belt conveyor) to maintain a stable feeding speed. The controller adjusts the motor speed by real-time monitoring of the feeding status (such as feeding speed and material level) to ensure a smooth feeding process. At the same time, based on industrial Ethernet or 5G-U technology, low-latency communication is achieved between the controller and the feeding device. Batch feeding mode uses a weighing sensor or volumetric metering device to measure the amount of each feeding. The controller adjusts the valve opening or motor running time according to the set target value to ensure that the amount of each batch of feeding is consistent. The OPC UA Pub / Sub model is used to issue batch feeding instructions to support efficient data transmission; Pulse charging mode uses a servo control system to drive the charging device to achieve precise time and flow control. The controller dynamically adjusts the pulse period and flow according to the internal state of the vertical furnace (such as airflow distribution and temperature distribution). At the same time, the time-sensitive network (TSN) ensures the precise synchronization and low-latency transmission of the pulse signal; Variable speed feeding mode, using PID controller or fuzzy control algorithm, adjusts the feeding speed based on the real-time collected feeding status data (such as material level and material quantity), the frequency converter adjusts the motor speed to achieve smooth speed change, the edge computing module analyzes the feeding status data in real time and generates speed control instructions, and industrial Ethernet or 5G-U technology supports the transmission of high-frequency control instructions; Intermittent charging mode uses a timer or event trigger mechanism to control the start and stop of the charging device. The controller determines the intermittent time according to the internal state of the shaft furnace (such as pressure and temperature). The OPC UA protocol supports remote configuration and management of scheduled tasks. Digital twin technology simulates the impact of intermittent charging on the operation of the shaft furnace and optimizes parameter settings. The gradient feeding mode uses a nonlinear control algorithm to generate a gradient curve and converts it into specific feeding speed instructions. The controller dynamically adjusts the gradient parameters according to the internal state of the vertical furnace. The edge computing module optimizes the gradient curve in real time to ensure that the feeding process meets the process requirements. The reinforcement learning algorithm predicts the impact of gradient feeding on the performance of the vertical furnace and improves control accuracy.

[0037] In some embodiments of the present invention, the system further comprises a monitoring terminal, which is used to monitor the data received by the controller and manually adjust the control strategy; In the present invention, the monitoring end obtains the data received by the controller (such as feeding status, raw material status in the furnace, environmental status, etc.) in real time, and uses the industrial SCADA (Supervisory Control and Data Acquisition) system or HMI (Human-Machine Interface) interface to display the data to the operator; the data is transmitted from the controller to the monitoring end via industrial Ethernet, OPC UA protocol or 5G-U network.

[0038] When the automatic control strategy cannot meet specific needs, or when the feeding status needs to be adjusted according to production needs and equipment requirements, the operator can manually adjust the control strategy through the monitoring end; the manual adjustment instructions are transmitted to the controller through the OPC UA Pub / Sub model or industrial Ethernet, overwriting or partially modifying the current automatic control strategy. The controller regenerates the control strategy according to the new instructions and sends it to the control response layer.

[0039] Embodiment 2: The communication control method for a vertical furnace charging device of the present invention comprises the following steps: The characteristic data of the raw materials in the charging device and the charging state data when the raw materials are added to the vertical furnace are collected by using a multi-modal data collection method; Use high temperature resistant sensor array and multi-spectral imaging technology to collect raw material status data and environmental status data in the shaft furnace; Based on industrial Ethernet, 5G-U and edge computing modules, multi-protocol data fusion and low-latency transmission are performed on the collected raw material characteristic data, feeding status data, raw material status data and environmental status data; The transmitted data is processed based on reinforcement learning and digital twin technology, and a control strategy is generated. The control strategy controls and adjusts the feeding status of raw materials when they are added to the vertical furnace through the OPC UA Pub / Sub model.

[0040] The various variations and specific embodiments of the communication control system for a vertical furnace charging device in the aforementioned embodiment 1 are also applicable to the communication control method for a vertical furnace charging device in this embodiment. Through the aforementioned detailed description of the communication control system for a vertical furnace charging device, those skilled in the art can clearly understand the implementation method of the communication control method for a vertical furnace charging device in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0041] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0042] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A communication control system for a vertical furnace charging device, characterized in that: include: The feeding end is composed of a raw material characteristic sensing layer, a feeding state sensing layer and a control response layer. The raw material characteristic sensing layer is used to collect characteristic data of the added raw materials, and the feeding state sensing layer is used to collect data of the feeding state. Both the raw material characteristic sensing layer and the feeding state sensing layer support multi-modal data collection, and the control response layer supports high-frequency control instruction response and controls the raw material feeding state. The vertical furnace end is composed of a raw material state sensing layer and an environment sensing layer. The raw material state sensing layer is used to collect raw material state data in the vertical furnace, and the environment sensing layer is used to collect environment state data in the vertical furnace. Both the raw material state sensing layer and the environment sensing layer use high temperature resistant sensor arrays and multi-spectral imaging technology to collect data. The communication end integrates industrial Ethernet, 5G-U and edge computing modules to achieve multi-protocol data fusion and low-latency transmission of the data collected at the charging end and the data collected at the vertical furnace end; The controller receives the data transmitted by the communication terminal and generates a control strategy based on reinforcement learning and digital twin technology, and issues instructions to the control response layer through the OPC UA Pub / Sub model.

2. The communication control system for a vertical furnace charging device according to claim 1, characterized in that: The data collected by the raw material characteristic perception layer includes physical characteristic data and chemical characteristic data. The physical characteristic data includes the particle size, humidity and density of the raw material, and the chemical characteristic data includes the composition and calorific value of the raw material.

3. The communication control system for a vertical furnace charging device according to claim 2, characterized in that: The feeding status includes the feeding speed, feeding position and feeding amount of the raw material.

4. The communication control system for a vertical furnace charging device according to claim 3, characterized in that: The data collected by the raw material status sensing layer in the furnace includes the material level and material quantity of the raw materials in the furnace.

5. The communication control system for a vertical furnace charging device according to claim 4, characterized in that: The data collected by the environmental sensing layer include temperature distribution, pressure and airflow distribution in the furnace.

6. The communication control system for a vertical furnace charging device according to claim 1, characterized in that: The communication end also includes an edge gateway, and the edge gateway performs filtering processing on the communication data through at least one algorithm selected from low-pass filtering, moving average filtering, median filtering or Kalman filtering.

7. The communication control system for a vertical furnace charging device according to claim 1 or 6, characterized in that: A redundant communication link is deployed between the communication terminal and the controller, and the redundant communication link includes at least one of a dual network card, a dual industrial Ethernet, a ring network and a time-sensitive network.

8. The communication control system for a vertical furnace charging device according to claim 1, characterized in that: The control strategy is a multi-mode control method, which includes a continuous feeding mode, a batch feeding mode, a pulse feeding mode, a variable speed feeding mode, an intermittent feeding mode and a gradient feeding mode.

9. The communication control system for a vertical furnace charging device according to claim 1, characterized in that: The system further comprises a monitoring terminal, which is used to monitor the data received by the controller and to manually adjust the control strategy.

10. A communication control method for a vertical furnace charging device, applicable to the communication control system for a vertical furnace charging device according to any one of claims 1 to 9, characterized in that: The steps include: The characteristic data of the raw materials in the charging device and the charging state data when the raw materials are added to the vertical furnace are collected by using a multi-modal data collection method; Use high temperature resistant sensor array and multi-spectral imaging technology to collect raw material status data and environmental status data in the shaft furnace; Based on industrial Ethernet, 5G-U and edge computing modules, multi-protocol data fusion and low-latency transmission are performed on the collected raw material characteristic data, feeding status data, raw material status data and environmental status data; The transmitted data is processed based on reinforcement learning and digital twin technology, and a control strategy is generated. The control strategy controls and adjusts the charging state of raw materials when they are added to the vertical furnace through the OPC UA Pub / Sub model.

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