Intelligent transformation system for steelmaking hopper batching scale based on Internet of Things

Abstract

The invention provides a steelmaking hopper batching scale intelligent reconstruction system based on the Internet of Things, and aims to improve the precision, stability and intelligent level of a batching scale in steelmaking production. A traditional hopper batching scale adopts an analog sensor, an analog junction box and an analog instrument, and has the problems of being susceptible to interference, low in precision, unstable in data, large in loss and the like. By introducing the Internet of Things, digitization and intelligent technologies, adopting the digital sensor, the Y-shaped connector, the terminator and the Internet of Things digital instrument and combining an intelligent self-learning algorithm, dynamic fall adjustment and intelligent self-learning deviation correction are achieved, the accuracy of the feeding amount is ensured, raw material loss is reduced, and the product quality and the production efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of batching in the steelmaking industry. Specifically, it particularly relates to an intelligent transformation system for a steelmaking hopper batching scale based on the Internet of Things, aiming to improve the accuracy, stability, and intelligence level of the batching scale in steelmaking production. Background Art

[0002] In modern steelmaking industry, the batching scale is one of the key equipment in steelmaking production, and its accuracy and stability directly affect the quality and production efficiency of steel. However, most of the existing hopper batching scales in steel mills adopt a traditional structure composed of analog sensors, analog junction boxes, and analog meters. This structure has the following problems:

[0003] Prone to interference: Analog sensors are easily affected by environmental interference during signal acquisition and transmission, resulting in the accuracy and stability of the signals being affected;

[0004] Low accuracy: The accuracy of analog meters in data processing and display is relatively low, making it difficult to meet the requirements of high-precision weighing;

[0005] Unstable data: When connecting multiple sensors, the analog junction box may have problems such as poor contact and wire aging, which will affect the signal transmission quality;

[0006] Large data loss: The compatibility and coordination between the components of the traditional system have limitations, increasing the complexity and maintenance difficulty of the system, resulting in large data loss;

[0007] Serious waste: Due to the lack of effective integration and collaboration between the components of the batching system, alloys and auxiliary materials are seriously wasted in actual production, and the product quality fluctuates greatly;

[0008] Therefore, the existing technology urgently needs a transformation solution that can improve the accuracy, stability, and intelligence level of the batching scale to meet the requirements of high precision and high stability in modern steelmaking production. Summary of the Invention

[0009] The purpose of the present invention is to provide an intelligent transformation system and method for a steelmaking hopper batching scale based on the Internet of Things. By introducing the Internet of Things, digital, and intelligent technologies, a comprehensive transformation and upgrade of the hopper batching scale is realized, improving the batching accuracy and stability, reducing raw material losses, and enhancing product quality and production efficiency.

[0010] The technical means adopted by the present invention are as follows:

[0011] An intelligent transformation system for a steelmaking hopper batching scale based on the Internet of Things includes: digital sensors, Y-type connectors, terminators, Internet of Things digital meters, and PLC core analysis units, where:

[0012] A plurality of digital sensors are provided and are respectively installed at the bottom or side of the hopper for accurately measuring the weight of the material in the hopper.

[0013] The number of Y - type connectors provided is the same as the number of digital sensors, and each digital sensor is connected to the Y - type connector through a cable; (This connector design can optimize the connection method, reduce signal loss, and improve the integrity and stability of signal transmission)

[0014] The terminator is connected to the cable coming out of the Y - type connector. The function of the terminator is to further ensure the integrity and stability of signal transmission and prevent signal attenuation or interference during transmission.

[0015] The Internet of Things digital instrument is connected to the terminator and is used to display and process data from the sensors; The digital touch - screen instrument can provide a user interface, enabling operators to monitor and control the batching process.

[0016] The PLC core analysis unit is integrated with an intelligent self - learning algorithm module for dynamically adjusting the drop value and implementing intelligent self - learning deviation correction.

[0017] Furthermore, the digital sensor transmits the collected signal to the Internet of Things digital instrument through the serial port 485 communication method. The Internet of Things digital instrument is connected to the switch through an RJ45 network cable, and the switch transmits the data to the PLC core analysis unit. The PLC core analysis unit analyzes the received data and applies the analyzed data to the actual weighing control link.

[0018] Furthermore, the intelligent self - learning algorithm module includes: a dynamic drop adjustment module and an intelligent self - learning deviation correction module, where:

[0019] The dynamic drop adjustment module is used to dynamically adjust the drop value according to the actual particle sizes of the auxiliary raw materials and alloy materials, the angle of the vibrating hopper, and the tightness of the vibrating screen pull rod spring to ensure the accuracy of the feeding amount.

[0020] The intelligent self - learning deviation correction module is used to receive weighing data through the self - learning control module, compare it with the preset target value, analyze the data reliability, recalculate the frequency conversion point and the drop closing point, implement fuzzy control, and ensure that the deviation between the actual feeding amount and the set value is controlled within ±3 kg.

[0021] Furthermore, the dynamic drop adjustment module can obtain the change law of the drop and dynamically adjust the drop value according to the change law.

[0022] Furthermore, the intelligent self - learning deviation correction module realizes deviation correction through the following steps:

[0023] Receive the weighing data, the corresponding bin positions, frequency conversion points, drop shutdown points in the PLC core analysis unit, and the target values preset by the terminal machine;

[0024] Compare the actual feeding value with the target value, and analyze the reliability of the data according to certain instructions;

[0025] Recalculate the new frequency conversion points and drop shutdown points, and replace the original frequency conversion points and drop shutdown points;

[0026] When feeding again, the PLC core analysis unit controls the feeding according to the newly set frequency conversion points and drop shutdown points;

[0027] The self-learning control module collects and analyzes the deviation information again, performs loop calculations, and realizes the fuzzy control of the self-learning control module to meet the expected batching control requirements.

[0028] Compared with the prior art, the present invention has the following advantages:

[0029] 1. An intelligent transformation system for a steelmaking hopper batching scale based on the Internet of Things provided by the present invention can achieve high-precision weighing through digital sensors and intelligent algorithms, ensuring the accuracy of the feeding amount.

[0030] 2. An intelligent transformation system for a steelmaking hopper batching scale based on the Internet of Things provided by the present invention has an optimized connection method and signal transmission module, significantly improving the stability of the system and reducing data loss.

[0031] 3. An intelligent transformation system for a steelmaking hopper batching scale based on the Internet of Things provided by the present invention can dynamically adjust the drop value and frequency conversion points according to the actual working conditions through a self-learning algorithm, realizing intelligent control.

[0032] 4. An intelligent transformation system for a steelmaking hopper batching scale based on the Internet of Things provided by the present invention has precise feeding amount control, reducing the waste of alloys and auxiliary materials, lowering the production cost, and improving the product quality at the same time.

[0033] Based on the above reasons, the present invention can be widely promoted in the fields of batching in the steelmaking industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1This is a schematic diagram of the sensing terminal structure of the hopper batching scale of the present invention.

[0036] Figure 2 This is a detailed view of the sensing terminal structure of the hopper batching scale of the present invention.

[0037] Figure 3 This is the change in the fall of the intelligent weighing system of the batching hopper scale provided by the embodiment of the present invention.

[0038] Figure 4 This is a record form of the discharging data of the No. 4 of the 2# auxiliary raw material provided by the embodiment of the present invention.

[0039] Figure 5 This is a record form of the discharging data of the No. 7 of the 2# auxiliary raw material provided by the embodiment of the present invention.

[0040] Figure 6 This is a record form of the discharging of the No. 3 auxiliary raw material scale provided by the embodiment of the present invention.

[0041] Figure 7 This is a record form of the discharging of the No. 9 auxiliary raw material scale provided by the embodiment of the present invention. Detailed implementation manners

[0042] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0043] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0044] In the field of steelmaking, the structure of traditional hopper batching scale weighing equipment usually consists of an analog sensor, an analog junction box, an analog instrument, a programmable logic controller (PLC), and an industrial control computer working together. During the signal acquisition and transmission process, the analog sensor is vulnerable to environmental interference, which affects the accuracy and stability of the signal. When connecting multiple sensors, the analog junction box may have problems such as poor contact and wire aging, thus affecting the signal transmission quality. The accuracy of the analog instrument in data processing and display is relatively low and it is difficult to meet the requirements of high-precision weighing. In addition, there may be certain limitations in the compatibility and coordination among the components in the entire system, increasing the complexity and maintenance difficulty of the system.

[0045] To overcome the many deficiencies of the above traditional structure, the hopper batching scale has been transformed. As Figure 1 shown, the present invention provides an intelligent transformation system for a steelmaking hopper batching scale based on the Internet of Things, including: digital sensors, Y-type connectors, terminators, Internet of Things digital instruments, and a PLC core analysis unit, where:

[0046] The digital sensors are provided in multiple numbers and are respectively installed at the bottom or side of the hopper for accurately measuring the weight of the materials in the hopper; the digital sensors significantly improve the accuracy and stability of signal acquisition, can effectively resist external interference, and ensure the accuracy of weighing data.

[0047] The number of Y-type connectors provided is the same as the number of digital sensors, and each digital sensor is connected to the Y-type connector through a cable; the design of the Y-type connector can optimize the connection method, reduce signal loss, and improve the integrity and stability of signal transmission.

[0048] The terminator is connected to the cable coming out of the Y-type connector. The function of the terminator is to further ensure the integrity and stability of signal transmission and prevent signal attenuation or interference during transmission;

[0049] The Internet of Things digital instrument is connected to the terminator and is used for displaying and processing data from the sensors; the digital touchscreen instrument can provide a user interface, enabling operators to monitor and control the batching process. The digital instrument performs well in data processing and display, has higher accuracy and richer functions, and can provide clear and accurate weighing information for operators.

[0050] The PLC core analysis unit is integrated with an intelligent self-learning algorithm module for dynamically adjusting the drop value and realizing intelligent self-learning deviation correction.

[0051] In specific implementation, as a preferred implementation manner of the present invention, the digital sensor transmits the collected signals to the Internet of Things digital instrument through the serial port 485 communication method. The Internet of Things digital instrument is connected to the switch through an RJ45 network cable. The switch transmits the data to the PLC core analysis unit. The PLC core analysis unit analyzes the received data and applies the analyzed data to the actual weighing control link. In this embodiment, in the operation mechanism of the hopper batching weighing system, the sensors inside play a crucial role. These sensors transmit the collected signals to the digital instrument in series through the serial port 485 communication method. At the external architecture level of the system, the digital instrument establishes an effective connection with the switch through an RJ45 network cable. Based on such a network layout, within the coverage of the local area network, the collected data can be smoothly transmitted to the PLC core analysis unit, and the PLC core analysis unit deeply and carefully analyzes the received data. These data after analysis and processing will be directly applied to the actual weighing control link, providing key decision-making basis and technical support for ensuring the accuracy, stability and efficiency of the weighing process.

[0052] In specific implementation, as a preferred implementation manner of the present invention, the intelligent self-learning algorithm module includes: a dynamic drop adjustment module and an intelligent self-learning deviation correction module, where:

[0053] The dynamic drop adjustment module is used to dynamically adjust the drop value according to the actual particle sizes of the auxiliary raw materials and alloy materials, the angle of the vibrating hopper, and the tightness of the vibrating screen pull rod spring to ensure the accuracy of the feeding amount;

[0054] The intelligent self-learning deviation correction module is used to receive the weighing data through the self-learning control module and compare it with the preset target value, analyze the data reliability, recalculate the frequency conversion point and the drop closing point, and implement fuzzy control to ensure that the deviation between the actual feeding amount and the set value is controlled within ±3 kg.

[0055] In specific implementation, as a preferred implementation manner of the present invention, the dynamic drop adjustment module can obtain the change rule of the drop and dynamically adjust the drop value according to the change rule.

[0056] In this embodiment, the auxiliary raw materials and alloy materials are the blending agents for steelmaking production. To produce different varieties of steel, it mainly depends on adjusting the components of the auxiliary raw materials and alloy materials. To produce qualified and high-quality steel, the feeding amounts of the auxiliary raw materials and alloy materials need to be accurately measured. Adding less or more cannot produce high-quality steel. Especially when adding more, it will increase costs and waste the auxiliary raw materials and alloy materials. Therefore, accurate feeding is very important for steelmaking, which can not only improve product quality but also achieve the effect of energy conservation and emission reduction. During the actual production process, due to different manufacturers of the auxiliary raw materials and alloy materials, the particle sizes of the materials will be inconsistent. Even for the same manufacturer, different batches will also have this phenomenon. In addition, when workers perform maintenance, adjusting the angle of the vibrating hopper, the tightness of the vibrating screen pull rod spring, and the change of the vibration frequency after replacing the vibrating screen will all cause changes in the actual drop amount. However, in the original software, the drop amount is a fixed value, resulting in a larger actual feeding error. The improved system can obtain the change law of the drop, and then dynamically adjust the drop value, as Figure 3 shown.

[0057] When specifically implemented, as a preferred implementation manner of the present invention, the intelligent self-learning deviation correction module realizes deviation correction through the following steps:

[0058] Receive the weighing data, the corresponding bin, frequency conversion point, drop-off point in the PLC core analysis unit, and the target value preset by the terminal;

[0059] Compare the actual feeding value with the target value, and analyze the reliability of the data according to certain instructions;

[0060] Recalculate the new frequency conversion point and drop-off point, and replace the original frequency conversion point and drop-off point;

[0061] When feeding again, the PLC core analysis unit controls the feeding according to the newly set frequency conversion point and drop-off point;

[0062] The self-learning control module collects and analyzes the deviation information again, performs loop calculations, and realizes the fuzzy control of the self-learning control module to meet the expected batching control requirements.

[0063] In this embodiment, through the analysis of the influencing factors and data of the hopper scale weighing, according to the variation law of the drop amount, combined with the actual on-site working conditions, a set of self-learning control modules is added to the PLC core analysis unit for the feeding control of the modified hopper batching scale. The self-learning control module receives the weighing data, the corresponding bin positions, frequency conversion points, drop closing points in the PLC, and the target values preset by the terminal. By comparing the actual feeding value with the target value, it analyzes the reliability of the data according to certain instructions, recalculates the new frequency conversion points and drop closing points, and replaces the original frequency conversion points and drop closing points. When feeding again, the PLC controls the feeding according to the newly set frequency conversion points and drop closing points. At the same time, the self-learning control module collects and analyzes the deviation information again, performs cyclic calculations, and realizes the fuzzy control of the control system to meet the expected batching control requirements. When the drop amount changes greatly, the self-learning algorithm will automatically start working to adjust the drop amount and speed change points, ensuring that the deviation between the actual feeding amount per hopper and the set value is controlled within ±5 kg. Through multiple modifications and data analysis, the current deviation has reached within ±3 kg, greatly reducing the feeding error. For details, see Figures 4 - 7 。 Figure 4 、 5 In the 4# feeding record table in 5 , the average absolute value of the deviation is 2.13 kg, and all 15 weighings meet the 5 kg technical index requirements, and only 3 times exceed 3 kg. Figure 4 、 5 In the 7# feeding record table in 5 , the average absolute value of the deviation is 1.07 kg, and all data do not exceed 3 kg, which is better than the 5 kg technical index requirements in a positive direction. Figure 6 、 7 In the feeding records of the 3# and 9# auxiliary raw materials in 7 , the average absolute values of the deviation are 0.9 kg and 1.8 kg respectively, and all errors are less than ±3 kg. In summary, it can be concluded that through the transformation of the current digital and intelligent hopper scale weighing system, the actual batching weighing requirements can already be met.

[0064] In summary, through the Internet of Things, digital and intelligent technologies, the present invention has comprehensively transformed and upgraded the traditional steelmaking hopper batching scale, significantly improving the accuracy, stability and intelligent level of the batching scale. The transformed system can meet the requirements of modern steelmaking production for high precision and high stability, and has significant economic and social benefits.

[0065] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent transformation system for steelmaking hopper batching scale based on the Internet of Things, characterized in that: include: Digital sensors, Y-connectors, terminators, IoT digital meters, PLC core analysis units, including: The digital sensors are provided in plurality and are respectively installed at the bottom or side of the hopper to accurately measure the weight of the material in the hopper; The number of the Y-type connectors is the same as the number of the digital sensors, and each digital sensor is connected to the Y-type connector via a cable; The terminator is connected to the cable coming out of the Y-type connector; The IoT digital instrument is connected to a terminal device and is used to display and process data from sensors; The PLC core analysis unit is integrated with an intelligent self-learning algorithm module for dynamically adjusting the drop value and realizing intelligent self-learning deviation correction.

2. According to the Internet of Things-based steelmaking hopper batching scale intelligent transformation system of claim 1, it is characterized in that: The digital sensor transmits the collected signal to the IoT digital instrument via serial port 485 communication. The IoT digital instrument is connected to the switch via an RJ45 network cable. The switch transmits the data to the PLC core analysis unit. The PLC core analysis unit parses the received data and applies the parsed data to the actual weighing control link.

3. The intelligent transformation system of steelmaking hopper batching scale based on Internet of Things according to claim 1 is characterized in that: The intelligent self-learning algorithm module includes: a dynamic drop adjustment module and an intelligent self-learning deviation correction module, wherein: The dynamic drop adjustment module is used to dynamically adjust the drop value according to the actual particle size of the auxiliary raw material and the alloy material, the angle of the vibration bucket, and the tension of the vibration screen rod spring to ensure the accuracy of the feeding amount; The intelligent self-learning deviation correction module is used to receive weighing data through the self-learning control module and compare it with the preset target value, analyze the data reliability, recalculate the frequency conversion point and the drop closing point, realize fuzzy control, and ensure that the deviation between the actual feeding amount and the set value is controlled within ±3kg.

4. The intelligent transformation system of steelmaking hopper batching scale based on Internet of Things according to claim 3 is characterized in that: The dynamic height difference adjustment module can obtain the variation rule of the height difference and dynamically adjust the height difference value according to the variation rule.

5. The intelligent transformation system of steelmaking hopper batching scale based on Internet of Things according to claim 3 is characterized in that: The intelligent self-learning deviation correction module realizes deviation correction through the following steps: Receive weighing data, corresponding bin positions, frequency conversion points, drop closing points in the PLC core analysis unit, and target values ​​preset in the terminal; Compare the actual value of material discharge with the target value and analyze the reliability of the data according to certain instructions; Recalculate new frequency conversion points and drop closing points, and replace the original frequency conversion points and drop closing points; When unloading again, the PLC core analysis unit controls unloading according to the newly set frequency conversion point and drop closing point; The self-learning control module collects and analyzes the deviation information again, performs cyclic calculations, and implements the fuzzy control of the self-learning control module to achieve the expected batching control requirements.

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