Converter and refining stock bin feeding control system and control method thereof

Abstract

The invention discloses a converter and refining stock bin feeding control system and method in the technical field of feeding control, and the system comprises a priority division module which is used for obtaining a priority classification data set; the stock bin state monitoring module is used for outputting a stock bin state adjusting result; the feeding amount optimization module is used for outputting the optimized feeding amount; the dynamic adjustment control module is used for outputting the adjusted feeding rate of the stock bin; and the feeding process adjusting module is used for generating a correction curve set according to the adjusted real-time filling rate data of the stock bin and the temperature data change amplitude before and after adjustment, obtaining the length of a fitting section according to the correction curve set, adjusting the feeding rate increment of the stock bin based on the fitting distinguishing length, and outputting an adjusted material input distribution result. The technical problems that a traditional control system cannot quickly adapt to emergencies in a complex production environment, and the production efficiency and reasonable utilization of resources are affected can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of feeding control, in particular to a converter and refining bin feeding control system and a control method thereof. BACKGROUND

[0002] The technical field of feeding control includes related technologies such as material conveying, control and monitoring systems, and is widely used in various automated equipment in industrial production. The core content is to accurately adjust the feeding, conveying and distribution of materials through various control devices and systems to ensure the efficiency and stability of the production process. In the feeding control technology, including but not limited to sensor technology, automation control technology, material detection and tracking technology, etc., which jointly act on the quantitative feeding, uniform distribution and precise control of materials, especially in the metallurgical, chemical, food processing and other industries. It has important application value.

[0003] Among them, the converter and refining bin feeding control system refers to a system for controlling the feeding process of the converter and refining bin in the metallurgical industry. For the accurate control of the converter and refining bin feeding, the problem of how to accurately feed the raw materials to the designated position under different operating conditions of the converter and refining bin is solved. Specifically, by combining bin state monitoring and control algorithm, the feeding process is monitored and adjusted in real time to ensure the accuracy of material conveying and avoid resource waste or production instability caused by improper operation. The control system uses advanced sensors and automatic adjustment technology to accurately control the feeding amount according to real-time data.

[0004] The prior art has a lag in responding to real-time data changes, and cannot effectively distinguish the priority and timeliness of different data, resulting in untimely or excessive material flow rate adjustment. When the temperature and pressure change greatly, the system cannot accurately adjust, and uneven or unstable material distribution may occur. This makes the traditional control system unable to quickly adapt to unexpected situations in complex production environments, affecting production efficiency and rational use of resources. SUMMARY

[0005] The purpose of the present application is to overcome the shortcomings of the prior art and provide a converter and refining bin feeding control system and a control method thereof, which can solve the technical problem that the traditional control system cannot quickly adapt to unexpected situations in complex production environments, affecting production efficiency and rational use of resources.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solutions: In a first aspect, the present application provides a converter and refining bin feeding control system, comprising: A priority classification module is configured to obtain converter and refining bin data, and output a priority classification data set based on the converter and refining bin data. The bunker state monitoring module is configured to call the bunker pressure data and the bunker charging rate data in the priority classification dataset, and output a bunker state adjustment result based on the bunker pressure data and the bunker charging rate data. The charging amount optimization module is configured to fit a flow rate change curve and a temperature range according to the bunker state adjustment result, calculate a section distribution volume deviation amount of each bunker, perform direction correction based on the section distribution volume deviation amount, and output an optimized charging amount. The dynamic adjustment control module is configured to adjust the bunker charging rate in real time based on the optimized charging amount, and output an adjusted bunker charging rate. The charging flow adjustment module is configured to call the adjusted bunker charging rate, generate a correction curve set according to real-time filling rate data of the adjusted bunker and a temperature data change amplitude before and after adjustment, obtain a fitting section length according to the correction curve set, adjust a bunker charging rate increment based on the fitting section length, and output an adjusted material input distribution result.

[0007] Further, the priority classification dataset includes temperature data, bunker pressure data, bunker charging rate data, and real-time filling rate data of the bunker classified by the priority classification module.

[0008] Further, the priority classification module includes: The data grouping processing submodule is configured to collect temperature data, bunker pressure data, bunker charging rate data, and real-time filling rate data of the bunker at different times of the converter and the refining bunker, group each type of data according to a time sequence after matching, and generate a grouped dataset according to a sampling frequency and a physical response time difference. The fluctuation cycle analysis submodule is configured to call the grouped dataset, extract a fluctuation cycle and a variable amplitude of each group of data in the grouped dataset, calculate an instantaneous variable amplitude according to a difference absolute value of adjacent sampling points of the variable amplitude, determine a length of the fluctuation cycle by using continuous variable amplitude direction consistency, and generate a range difference coefficient based on the fluctuation cycle length and the instantaneous variable amplitude. The priority label generation submodule is configured to sequentially sort each group of data according to the range difference coefficient, calculate a proportion of each group of coefficients in a total sum of all coefficients, compare the proportion with a preset proportion threshold, assign label numbers in descending order of proportion to groups higher than the threshold, and generate a priority classification dataset.

[0009] Further, the bunker state monitoring module includes: The data synchronization comparison submodule is configured to call the bin pressure data and the bin feeding rate data in the priority classification data set, extract the pressure response time mean value before and after the change of the bin feeding rate data based on the bin pressure data, generate a pressure response difference value based on the pressure response time mean value before and after the change, obtain an absolute difference value between the maximum value of the bin level after the change of the bin feeding rate data and the initial value of the bin level, and generate a bin level change amplitude based on the absolute difference value. The trend grouping analysis submodule is configured to call the pressure response difference value and the bin level change amplitude, calculate the pressure response delay of each group of data based on the pressure response difference value and the bin level change amplitude, and the calculation formula includes: , mark the data group with the pressure response delay exceeding the preset delay threshold as an abnormal group, and generate a delay determination coefficient. wherein, is the first group pressure response delay, is the first group changed pressure response time, is the unchanged response time, is the first group bin level change amplitude, is the reference bin level amplitude. The state determination submodule is configured to call the delay determination coefficient, count the proportion of the number of abnormal groups in the total number of groups, determine whether there is input deviation or uneven input based on the proportion of the number of abnormal groups in the total number of groups, and output a bin state adjustment result.

[0010] Further, the feeding amount optimization module includes: The mapping relationship fitting submodule is configured to call the bin state adjustment result, extract the temperature range of the temperature data after the bin state adjustment, analyze the corresponding relationship between the temperature data and the bin feeding rate data in the temperature range, calculate the segmented linear fitting slope of the flow rate change curve in the temperature range, and generate a temperature flow rate mapping coefficient. The deviation amount calculation submodule is configured to call the temperature flow rate mapping coefficient, obtain the absolute difference value between the real-time input bin feeding rate and the reference bin feeding rate, calculate the cumulative volume of the absolute difference value between the real-time input bin feeding rate and the reference bin feeding rate in time, calculate the sectional distribution volume deviation amount in combination with the temperature response time and the historical fluctuation coefficient, and the calculation formula includes: ; wherein, is the sectional distribution volume deviation amount, is the real-time input flow rate, is the reference flow rate, is the temperature response time, is the equipment rated response time. the real-time bin loading time, the bin start loading time, the historical fluctuation coefficient; generating a volume deviation amount set according to the segment distribution volume deviation amount; a direction correction submodule, configured to call the volume deviation amount set, compare the volume deviation amount of each bin with a preset threshold, calculate a correction direction ratio for the deviation amount exceeding the threshold, generate a correction ratio set, and output an optimized loading amount.

[0011] Further, the dynamic adjustment control module comprises: a pressure flow rate deviation rate calculation submodule, configured to call the optimized loading amount, analyze a dynamic change trend of bin pressure data after the loading amount is optimized, calculate a pressure change rate according to the dynamic change trend, extract an absolute difference of bin loading rates before and after optimization, generate a deviation rate set based on the pressure change rate and the absolute difference of bin loading rates before and after optimization; a direction consistency judgment submodule, configured to call the deviation rate set, confirm a pressure change direction and a flow rate deviation direction based on the deviation rate set, compare the correlation of the pressure change direction and the flow rate deviation direction, judge whether a direction consistency mark is a matching item or an abnormal item, and generate a direction consistency signal; a flow distribution matrix adjustment submodule, configured to call the direction consistency signal, adjust a weight coefficient of a flow distribution matrix according to a matching result, limit the weight coefficient in a preset interval, and output an adjusted bin loading rate after adjusting the flow distribution matrix.

[0012] Further, the loading process adjustment module comprises: a correction curve construction submodule, configured to call the adjusted bin loading rate, collect real-time filling rate data of the bin after the bin loading rate is adjusted and temperature data change amplitudes before and after the bin loading rate is adjusted, calculate a difference value of the filling rate and the temperature data change amplitude of each bin, draw a correction curve according to the difference value, and generate a correction curve set; a fitting segment calculation submodule, configured to call the correction curve set, calculate an absolute difference value of each correction curve and a preset target filling rate, screen a continuous segment with an absolute difference value less than a fitting threshold, measure a fitting segment length comprehensive value of each segment, and the calculation formula comprises: , wherein, is the fitting segment length comprehensive value, is the absolute difference value of the i th bin, is the absolute difference value of the i th bin, is the absolute difference value of the i th bin, a temperature data variation range of the bin, a dimensionless temperature variation ratio, a reference temperature, an error correction term, a first a fluctuation variance of the correction curve; obtaining a fitting section length according to the fitting section length comprehensive value; The input mode adjustment sub-module is used for calling the fitting section length, extracting a fitting section exceeding a preset length threshold, obtaining input configuration parameters of the fitting section exceeding the preset length threshold, comparing a current bin feeding rate with the configuration parameters, adjusting a bin feeding rate increment, and generating a material input distribution result.

[0013] In a second aspect, the present application provides a converter and refining bin feeding control method, which is used for the converter and refining bin feeding control system in any one of the first aspect, and comprises the following steps: obtaining converter and refining bin data, and outputting a priority classification data set based on the converter and refining bin data; calling bin pressure data and bin feeding rate data in the priority classification data set, and outputting a bin state adjustment result based on the bin pressure data and the bin feeding rate data; fitting a flow rate variation curve and a temperature range according to the bin state adjustment result, calculating a section distribution volume deviation amount of each bin, performing direction correction based on the section distribution volume deviation amount, and outputting an optimized feeding amount; based on the optimized feeding amount, adjusting a bin feeding rate in real time, and outputting an adjusted bin feeding rate; calling the adjusted bin feeding rate, generating a correction curve set according to real-time filling rate data of the adjusted bin and a temperature data variation range before and after adjustment, obtaining a fitting section length according to the correction curve set, adjusting a bin feeding rate increment based on the fitting section length, and outputting an adjusted material input distribution result.

[0014] Compared with the prior art, the present application has the following beneficial effects: The present application improves the pertinence and timeliness of data response in feeding control by grouping and classifying temperature, pressure and other data and priority assessment. Based on interval mapping and deviation correction between bin feeding rate, pressure and temperature, dynamic optimization and adjustment of input flow rate are realized. Combined with the filling state change trend, the real-time matching of material input and bin state is ensured, the fluctuation risk caused by input deviation is reduced, and the material distribution accuracy and process stability are improved. The system can quickly adapt to changes in multiple data sources, ensuring continuous, accurate and efficient feeding control under complex working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a system flowchart of a converter and refining silo feeding control system provided by the present application, and the converter and refining silo feeding control system comprises: Figure 2 is an acquisition flowchart of a priority classification module of the present application; Figure 3 is an acquisition flowchart of a silo state monitoring module of the present application; Figure 4 is an acquisition flowchart of a feeding quantity optimization module of the present application; Figure 5 is an acquisition flowchart of a dynamic adjustment control module of the present application; Figure 6 is an acquisition flowchart of a feeding process optimization evaluation module of the present application. DETAILED DESCRIPTION

[0016] The technical solutions of the present application will be described in detail below with the aid of the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limitations of the technical solutions of the present application. In the case of no conflict, the technical features in the embodiments and the embodiments of the present application can be combined with each other.

[0017] The term "and / or" in this paper is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are in an "or" relationship. Embodiment 1:

[0018] Figure 1 is a system flowchart of a converter and refining silo feeding control system provided by the present application, and the converter and refining silo feeding control system comprises: a priority classification module, configured to acquire converter and refining silo data, and output a priority classification data set based on the converter and refining silo data; a silo state monitoring module, configured to call silo pressure data and silo feeding rate data in the priority classification data set, and output a silo state adjustment result based on the silo pressure data and the silo feeding rate data; a feeding quantity optimization module, configured to fit a flow rate change curve and a temperature range according to the silo state adjustment result, calculate a section distribution volume deviation amount of each silo, perform direction correction based on the section distribution volume deviation amount, and output an optimized feeding quantity; a dynamic adjustment control module, configured to adjust a silo feeding rate in real time based on the optimized feeding quantity, and output an adjusted silo feeding rate; The loading flow adjustment module is configured to call the adjusted loading rate of the stock bin, generate a correction curve set according to the real-time filling rate data of the stock bin and the change range of the temperature data before and after the adjustment, obtain a fitting section length according to the correction curve set, adjust a loading rate increment of the stock bin based on the fitting section length, and output an adjusted material input distribution result.

[0019] Specifically, the priority classification data set includes temperature data, stock bin pressure data, stock bin loading rate data, and real-time filling rate data of the stock bin classified by the priority division module.

[0020] Referring to Figure 2 The priority division module includes: The data grouping processing submodule is configured to collect temperature data, stock bin pressure data, stock bin loading rate data, and real-time filling rate data of the stock bin at different moments of the converter and the refining stock bin, group each type of data according to time sequence after matching, and generate a grouped data set: The temperature data can be collected by a temperature sensor at a frequency of 1 Hz, and the stock bin pressure data can be collected by a pressure sensor at a frequency of 0.5 Hz. The fluctuation period analysis submodule is configured to call the grouped data set, extract the fluctuation period and amplitude range of each group of data in the grouped data set, calculate the instantaneous amplitude range according to the absolute value difference of adjacent sampling points, determine the length of the fluctuation period by using the consistency of the continuous amplitude direction, and generate a range difference coefficient based on the fluctuation period length and the instantaneous amplitude range. Specifically, the fluctuation period of the temperature data is extracted, and it is assumed that the temperature change directions of three consecutive sampling points are consistent (such as 0.5℃→0.8℃→1.1℃ for continuous rise), the period length is determined to be 3 seconds, the instantaneous amplitude range is the average of the absolute value differences of adjacent points, that is, (0.8−0.5)+(1.1−0.8) / 2=0.3, the range difference coefficient is the product of the period length 3 seconds and the amplitude 0.3, which is 0.9, the pressure data period length is 4 seconds, the amplitude is (1.2MPa−1.0MPa)+(1.4MPa−1.2MPa) / 2=0.2, the range difference coefficient is 4×0.2=0.8, the vibration data period length is 2 seconds, the amplitude is (0.05mm→0.12mm→0.08mm), the directions are inconsistent, only the maximum difference 0.12−0.05=0.07 is taken, and the range difference coefficient is 2×0.07=0.14, and the range difference coefficient including the temperature 0.9, the pressure 0.8, and the vibration 0.14 is generated.

[0021] The priority label generation submodule is configured to sequentially sort each group of data according to the range difference coefficient, calculate the proportion of each group of coefficients in the total sum of coefficients, compare the proportion with a preset proportion threshold, and assign label numbers to groups higher than the threshold in descending order of proportion to generate a priority classification data set: Specifically, the range difference coefficient is called, the total coefficient sum is calculated as 0.9 (temperature) + 0.8 (pressure) + 0.14 (vibration) = 1.84, the temperature proportion is 0.9 / 1.84 = 0.489 (48.9%), the pressure proportion is 0.8 / 1.84 = 0.435 (43.5%), and the vibration proportion is 0.14 / 1.84 = 0.076 (7.6%). The preset proportion threshold is 30%, and based on the minimum response proportion requirement of the device abnormal fluctuation in the historical operation record, the temperature and pressure data groups are both higher than the threshold, and the vibration data group is lower than the threshold. In descending order of temperature 48.9% and pressure 43.5%, the temperature group is assigned label number 1, the pressure group is assigned label number 2, and the vibration group is not assigned a label. A priority classification data set containing label 1 (temperature, priority 48.9%) and label 2 (pressure, priority 43.5%) is generated.

[0022] Please refer to Figure 3 , the silo state monitoring module comprises: The data synchronization comparison submodule is configured to call the silo pressure data and the silo feeding rate data in the priority classification data set, extract the pressure response time mean value before and after the change of the silo feeding rate data based on the silo pressure data, generate a pressure response difference value based on the pressure response time mean value before and after the change, obtain the absolute difference value between the maximum value of the material level after the change of the silo feeding rate data and the initial value of the material level, and generate a material level change amplitude based on the absolute difference value. Specifically, the silo pressure data contains a pressure response time sequence before the change of the silo feeding rate data (for example, the pressure response time is 2.3 seconds at 10:00:00 and 2.8 seconds at 10:00:05), and the silo feeding rate data contains a material level change value (for example, the initial material level is 500 mm, and the peak value of the material level after the rate change is 520 mm). The pressure response time mean value before the change of the feeding rate is extracted by calculating the arithmetic mean value of all pressure response times in the time window (for example, 10 minutes) (for example, the average value of 2.3 seconds, 2.5 seconds and 2.4 seconds is seconds), and the mean value after the change is extracted in the same way (for example, the time sequence after the rate change is 3.1 seconds, 3.0 seconds and 3.2 seconds, and the calculation result is seconds), and the difference value is calculated as seconds, and the material level change amplitude is extracted synchronously , for example, the absolute difference between the maximum value of the material level after the rate change, 520 mm, and the initial value, 500 mm, is The absolute difference between the maximum value of the data change rate of the material bin and the initial value of the material level and the material level change range.

[0023] The trend grouping analysis submodule is configured to call the pressure response difference and the material level change range, calculate the pressure response delay of each group of data based on the pressure response difference and the material level change range, and the calculation formula comprises: , The data group with the pressure response delay exceeding the preset delay threshold is marked as an abnormal group, and a delay judgment coefficient is generated. wherein, is the pressure response delay of the first group, is the changed pressure response time of the first group, is the response time before the change, is the material level change range of the first group, is the reference material level range. Specifically, it is assumed that during 10:00:00-10:00:30, the material level increments of adjacent time points are +5 mm, +3 mm, -2 mm, +4 mm, +6 mm and +8 mm, three consecutive increments are grouped, and the following data groups are obtained: The first group: the increment sequence is +5, +3 and -2, the absolute value of the component is , and the material level change range is mm (the initial value is 500 mm, and the peak value is 520 mm); The second group: the increment sequence is +3, -2 and +4, the absolute value of the component is , and the material level change range is mm (the peak value is 518 mm); The third group: the increment sequence is -2, +4 and +6, the absolute value of the component is , and the material level change range is mm (the peak value is 522 mm); The fourth group: the increment sequence is +4, +6 and +8, the absolute value of the component is , and the material level change range is mm (the peak value is 530 mm).

[0024] The calculation process is as follows: The first group calculation: seconds, seconds, and the time difference is seconds; mm, the correction coefficient is , seconds, compared with the threshold value seconds, it is determined to be a normal group​​​ Group 2 calculation: seconds, seconds, time difference seconds; millimeters, correction factor , seconds, determined as a normal group ( ); Group 3 calculation: seconds, seconds, time difference seconds; millimeters, correction factor ; seconds, determined as a normal group ( ); Group 4: seconds, seconds, time difference seconds; millimeters, correction factor , seconds; because seconds, determined as an abnormal group ( ); The results show that 3 groups are normal and 1 group is abnormal in the 4 groups of data, and a delay determination coefficient ( ) is generated.

[0025] The state determination submodule is configured to call the delay determination coefficient, count the proportion of the number of abnormal groups in the total number of groups, determine whether there is input offset or uneven input based on the proportion of the number of abnormal groups in the total number of groups, and output a stock bin state adjustment result. Specifically, assuming that the number of abnormal groups in the 4 groups is 1, the proportion of abnormal groups is counted , according to a preset determination rule ( input offset, local unevenness, normal), the current belongs to local unevenness, and the output stock bin state adjustment result is “local unevenness is detected, it is recommended to check the feeding pipe blockage or uneven distribution of the 4th group corresponding to the time period (10:01:00 to 10:01:30)”. In the specific determination process, the original data timestamp is traced back by the abnormal group number, the pressure response time (increased from 2.0 seconds to 4.5 seconds) and the material level fluctuation data (increment sequence +8, -5, +10 millimeters) in the period are extracted, the local feeding abnormality is verified, and the maintenance instruction for the specific pipe is triggered. If the number of abnormal groups is 2 ( ), it is determined that there is input offset and it is recommended to globally adjust the input rate to 90% of the standard range, and if the number of abnormal groups is 0, it is determined that it is normal and a continuous monitoring instruction is output.​

[0026] Please refer to Figure 4 , the said feeding amount optimization module comprises: The mapping relationship fitting sub-module is configured to call the stock bin state adjustment result, extract the temperature range of the temperature data after the stock bin state adjustment, analyze the corresponding relationship between the temperature data and the stock bin feeding rate data in the temperature range, calculate the segmented linear fitting slope of the flow rate change curve in the temperature interval, and generate the temperature flow rate mapping coefficient. Specifically, for example, if the temperature interval is 10°C to 50°C, the flow rate records in the same temperature interval in the past 24 hours are called from the historical database, and the discrete points of the average flow rate of 1.2m³ / s at 10°C, 1.3m³ / s at 20°C, and 1.5m³ / s at 30°C are selected. The least square method is used to calculate the segmented linear fitting slope. For example, in the 10°C-30°C interval, the fitting equation is ( for temperature), the slope , and the temperature flow rate mapping coefficient is generated. For example, at the temperature median value of 20°C, the reference flow rate m³ / s.

[0027] The deviation amount calculation sub-module is configured to call the temperature flow rate mapping coefficient, obtain the absolute difference between the real-time input stock bin feeding rate and the reference stock bin feeding rate, calculate the cumulative volume of the absolute difference between the real-time input stock bin feeding rate and the reference stock bin feeding rate in time, combine the temperature response time and the historical fluctuation coefficient, calculate the sectional distribution volume deviation amount, and the calculation formula includes: ; Wherein, is the sectional distribution volume deviation amount, is the real-time input flow rate, is the reference flow rate, is the temperature response time, is the equipment rated response time, is the real-time stock bin feeding time, is the stock bin start feeding time, is the historical fluctuation coefficient. Specifically, the real-time input stock bin feeding rate and the reference stock bin feeding rate are obtained. It is assumed that the current rate is m³ / s collected by the flowmeter. According to the temperature-flow rate mapping equation ( ), if the temperature sensor measures , m³ / s, then the flow rate difference is m³ / s. From the start of the flow rate change, the time consumption is monitored, and the temperature response time seconds (device rated response time seconds), response time correction term: , assuming the standard deviation of flow rate in the first 10 minutes is 0.2 m³ / s, ; Substitute into the formula and calculate: m³; ; Assume that the preset volume deviation threshold is 2.5 m³ (5% of the device capacity of 50 m³), because , the silo needs to perform direction correction, and the result shows that the cumulative volume deviation of the current silo is 3.26 m³, which exceeds the safety threshold of 2.5 m³, and the deviation needs to be adjusted by a correction ratio to reduce the deviation.

[0028] Generate a volume deviation amount set according to the sectional distribution volume deviation amount; A direction correction submodule is configured to call the volume deviation amount set, compare the volume deviation amount of each silo with a preset threshold, calculate a correction direction ratio for the deviation amount that exceeds the threshold, generate a correction ratio set, and output an optimized feeding amount; Specifically, a preset volume deviation threshold of 2.5 m³ (set according to 5% of the device capacity of 50 m³) is extracted, and the volume deviation amount of each silo is calculated m³ correction ratio , limit in the interval , take , generate a correction ratio set, and output a flow rate adjustment amount m³ / s, for example, when m³, , output m³ / s, and the over-limit silo (such as m³) is forced to correct to , output m³ / s, and the low-deviation silo (such as m³) outputs m³ / s, and generates an optimized feeding amount.

[0029] Please refer to Figure 5 , the dynamic adjustment control module comprises: A pressure flow rate deviation rate calculation submodule is configured to call the optimized feeding amount, analyze the dynamic change trend of the silo pressure data after the feeding amount is optimized, calculate the pressure change rate according to the dynamic change trend of the data, extract the absolute difference of the silo feeding rate before and after optimization, and generate a deviation rate set based on the pressure change rate and the absolute difference of the silo feeding rate before and after optimization. Specifically, the real-time pressure data collected by the extraction bin pressure sensor is extracted, for example, the current pressure value is monitored to be 52 kPa, the pressure value in the previous 10 seconds is 49 kPa, the pressure change rate is calculated to be (52-49) / 10=0.3 kPa / s, the real-time flow rate data of the flow rate sensor is called synchronously, for example, the current feeding rate is 1.6 m³ / s, the optimized feeding rate is 1.5 m³ / s, the feeding rate difference is calculated to be |1.6-1.5|=0.1 m³ / s, the pressure change rate 0.3 kPa / s and the feeding rate difference 0.1 m³ / s are input into the deviation rate formula , the deviation rate 0.3×0.1=0.03 kPa·m³ / (s²) is obtained, if the preset deviation rate threshold is 0.05 kPa·m³ / (s²), it is determined that 0.03 does not exceed the threshold, and a deviation rate set is generated.

[0030] The direction consistency judgment submodule is used to call the deviation rate set, confirm the pressure change direction and the flow rate deviation direction based on the deviation rate set, compare the relevance of the pressure change direction and the flow rate deviation direction, judge whether the direction consistency mark is a matching item or an abnormal item, and generate a direction consistency signal; The pressure change direction is extracted, for example, the current pressure change rate is +0.3 kPa / s (rising), the flow rate difference is +0.1 m³ / s (positive deviation), and the direction consistency is determined. If the pressure rises and the flow rate positively deviates, it is marked as consistent, for example, the current pressure rise and the positive flow rate deviation match, the direction consistency signal value is 1, if the pressure change rate is -0.2 kPa / s (falling) and the flow rate difference is -0.1 m³ / s (negative deviation), it is also marked as consistent, if the pressure rises but the flow rate negatively deviates, it is marked as inconsistent, and the signal value is 0, and the direction consistency signal is generated.

[0031] The flow distribution matrix adjustment submodule is used to call the direction consistency signal, adjust the weight coefficient of the flow distribution matrix according to the matching result, limit the weight coefficient in a preset interval, and output the adjusted bin feeding rate after adjusting the flow distribution matrix; Specifically, the weight parameter of the current flow distribution matrix is obtained, for example, the initial weight is 0.5, if the signal value is 1 (consistent), the weight is increased to 0.6, if the signal value is 0 (inconsistent), the weight is reduced to 0.4, and the weight range is limited to 0.2 to 0.8, for example, the weight 0.6 meets the range, the adjusted rate is calculated after updating the matrix parameter, for example, the optimized rate 1.5 m³ / s×0.6=0.9 m³ / s, if the signal value is 0 and the weight is reduced to 0.4, the rate is 1.5×0.4=0.6 m³ / s, and the adjusted bin feeding rate is generated.

[0032] Please refer to Figure 6 , the feeding process adjustment module comprises: The correction curve construction submodule is used to call the adjusted hopper feeding rate, collect the real-time filling rate data of the hopper after the hopper feeding rate is adjusted and the temperature data change range before and after the hopper feeding rate is adjusted, calculate the difference between the filling rate and the temperature data change range of each hopper, draw the correction curve based on the difference, and generate a set of correction curves. The instantaneous filling rate and temperature change amplitude of the silo are collected. Taking silo B03 as an example, its instantaneous filling rate is 87%. The temperature sensor records a temperature of 25°C at t=15s, the initial temperature (t=0s) is 23.5°C, and the temperature change amplitude is 1.5°C. The difference between the filling rate and the temperature change amplitude is calculated to be 87%-1.5=85.5%. The timestamp of 15s is used as the horizontal axis and the difference of 85.5% is used as the vertical axis to generate the coordinate point (15,85.5). Traverse all time points (e.g., t=10s, filling rate 82%, temperature change 1.0°C, difference 81%, coordinate point (10,81)) to draw the correction curve of silo B03, which includes the point sequence [(10,81),(15,85.5),(20,88)]. Repeat this process to generate the correction curves of all silos, forming a set of correction curves.

[0033] The bonding section calculation submodule is used to call the set of correction curves, calculate the absolute difference between each correction curve and the preset target fill rate, filter continuous sections whose absolute difference is less than the bonding threshold, and measure the comprehensive value of the bonding section length of each section. The calculation formula includes: , in, To match the comprehensive value of the section length, For the first The absolute difference between the individual silos For the first Temperature variation data for each silo This is a dimensionless ratio of temperature changes. As the reference temperature, This is the error correction term. Let be the variance of the fluctuation of the i-th correction curve; Specifically, by calling the correction curve data of silo B03, if the time interval is [10s, 25s], the fill rate difference sequence is as follows: Fill rate difference at t=10s (Take the absolute value of 8%) At t=15s (Take the absolute value of 3%) At t=25s (Take the absolute value of 2%) Assume the temperature change range of silo B03 within [10s, 25s] ( measured by temperature sensor, initial temperature 23.5°C, current temperature 25°C), set according to the median value in historical temperature data , ( experimental calibration value, prevent denominator from being zero), calculate the variance of the modified curve difference sequence [8%, 3%, 2%]: Mean ; Variance ; Plug into the formula to calculate: Temperature change ratio: ; Denominator: ; Numerator: ; ; .

[0034] Summation term: ; Square root term: ; Finally get ; Assume the fit threshold is set to 5% (absolute value of difference ≤ 5% is a valid section), length threshold: preset to 30 (non-dimensional score ≥ 30 triggers adjustment), represents the comprehensive fit score of this section (10s-25s), the higher the score, the more significant the deviation of the fill rate from the target value (90%), and the greater the influence of temperature change on the fit degree. The results show that the fit degree of the current section is low (the score 83.87 is far beyond the threshold 30), and the input mode adjustment module needs to be triggered to modify the material input rate.

[0035] According to the fit section length comprehensive value, the fit section length is obtained; The input mode adjustment submodule is used to call the fit section length, extract the fit section that exceeds the preset length threshold, obtain the input configuration parameters of the fit section that exceeds the preset length threshold, compare the current stock bin feeding rate with the configuration parameters, adjust the stock bin feeding rate increment, and generate a material input distribution result: Call the fitting section length 31.75, extract the length threshold 30, determine the valid section as t=15s to t=20s, get the historical optimal input configuration parameters of the section (get the input rate as 1.2m³ / s by counting the sections with a fitting score>90%), read the current input rate as 1.0m³ / s, calculate the rate increment 1.2-1.0=0.2m³ / s, check whether the increment range is within the device allowed range ±0.3m³ / s (0.2≤0.3, compliance), adjust the input rate from 1.0m³ / s to 1.2m³ / s, update to the control system, generate material input distribution results, for example, the input rate of silo B03 is adjusted to 1.2m³ / s, and the input rate of silo A07 is adjusted to 0.8m³ / s (calculated synchronously according to other silo correction curves), output the rate configuration table of all silos.

[0036] Embodiment two: The embodiment two of the present application provides a converter and refining silo feeding control method, comprising: Obtaining converter and refining silo data, based on the converter and refining silo data, output priority classification data set; Call the silo pressure data and the silo feeding rate data in the priority classification data set, based on the silo pressure data and the silo feeding rate data, output silo state adjustment result; According to the silo state adjustment result, fit the flow rate change curve and the temperature range, calculate the section distribution volume deviation of each silo, based on the section distribution volume deviation, execute direction correction, output the optimized feeding amount; Based on the optimized feeding amount, adjust the silo feeding rate in real time, output the adjusted silo feeding rate; Call the adjusted silo feeding rate, generate a correction curve set according to the real-time filling rate data of the adjusted silo and the temperature data change amplitude before and after adjustment, obtain the fitting section length according to the correction curve set, adjust the silo feeding rate increment based on the fitting section length, and output the adjusted material input distribution result.

[0037] The converter and refining silo feeding control method provided by the embodiment two of the present application can be executed by the converter and refining silo feeding control system provided by the embodiment one of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0038] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, an apparatus, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0039] The present application is described in reference to the flowchart and / or block diagrams of the methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing system or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0040] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0041] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0042] The above description is only preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the technical principles of the present application, can make a number of improvements and variations, these improvements and variations should be considered as the protection scope of the present application.

Claims

1. A converter and refining silo feeding control system, characterized in that, include: The priority classification module is used to acquire converter and refining silo data, and output a priority classification dataset based on the converter and refining silo data; The silo status monitoring module is used to call the silo pressure data and silo feeding rate data in the priority classification dataset, and output the silo status adjustment result based on the silo pressure data and silo feeding rate data; The feeding quantity optimization module is used to fit the flow rate change curve and temperature range according to the adjustment result of the silo status, calculate the segment distribution volume deviation of each silo, perform directional correction based on the segment distribution volume deviation, and output the optimized feeding quantity. The dynamic adjustment control module is used to adjust the hopper feeding rate in real time based on the optimized feeding amount and output the adjusted hopper feeding rate. The material feeding process adjustment module is used to call the adjusted silo feeding rate, generate a set of correction curves based on the real-time filling rate data of the adjusted silo and the temperature data change range before and after the adjustment, obtain the length of the bonding section based on the set of correction curves, adjust the silo feeding rate increment based on the bonding difference length, and output the adjusted material input allocation result.

2. The converter and refining silo feeding control system according to claim 1, characterized in that, The priority classification dataset includes temperature data, silo pressure data, silo feeding rate data, and silo real-time fill rate data, all classified according to priority modules.

3. The converter and refining silo feeding control system according to claim 2, characterized in that, The priority allocation module includes: The data grouping and processing submodule is used to collect temperature data, silo pressure data, silo feeding rate data, and silo real-time filling rate data at different times in the converter and refining silos. Based on the sampling frequency and the difference in physical response time difference, each type of data is matched and grouped according to time series to generate a grouped dataset. The fluctuation cycle analysis submodule is used to call the grouped dataset, extract the fluctuation cycle and amplitude of each group of data in the grouped dataset, calculate the instantaneous amplitude based on the absolute value of the difference between adjacent sampling points of the amplitude, determine the length of the fluctuation cycle by using the consistency of the continuous amplitude direction, and generate a range difference coefficient based on the fluctuation cycle length and the instantaneous amplitude. The priority label generation submodule is used to sort each group of data in order according to the range difference coefficient, calculate the proportion of each group's coefficient to the total of all coefficients, compare the proportion with a preset proportion threshold, and assign label numbers to groups that are higher than the threshold in descending order of proportion, thereby generating a priority classification dataset.

4. The converter and refining silo feeding control system according to claim 1, characterized in that, The silo status monitoring module includes: The data synchronization and comparison submodule is used to call the silo pressure data and silo feeding rate data in the priority classification dataset. Based on the silo pressure data, it extracts the average pressure response time before and after the change in the silo feeding rate data, generates a pressure response difference based on the average pressure response time before and after the change, obtains the absolute difference between the maximum material level and the initial material level after the change in the silo feeding rate data, and generates the material level change amplitude based on the absolute difference. The trend grouping analysis submodule is used to retrieve the pressure response difference and material level change amplitude, and calculate the pressure response delay for each group of data based on the pressure response difference and material level change amplitude. The calculation formula includes: ， Data groups whose pressure response delay exceeds a preset delay threshold are marked as abnormal groups, and delay determination coefficients are generated; in, For the pressure response delay of the first group, For the first Pressure response time after group change The response time before the change. The change range of the material level in the first group. The reference material level range; The status determination submodule is used to call the delay determination coefficient, count the proportion of abnormal groups to the total number of groups, determine whether there is input offset or unbalanced input based on the proportion of abnormal groups to the total number of groups, and output the silo status adjustment result.

5. The converter and refining silo feeding control system according to claim 4, characterized in that, The material feeding optimization module includes: The mapping relationship fitting submodule is used to call the silo state adjustment result, extract the temperature range of the temperature data after the silo state adjustment, analyze the correspondence between the temperature data and the silo feeding rate data within the temperature range, calculate the piecewise linear fitting slope of the flow rate change curve in the temperature range, and generate the temperature flow rate mapping coefficient. The deviation calculation submodule is used to call the temperature-flow rate mapping coefficient to obtain the absolute difference between the real-time input hopper feeding rate and the reference hopper feeding rate, calculate the cumulative volume of the absolute difference between the real-time input hopper feeding rate and the reference hopper feeding rate over time, and calculate the segment distribution volume deviation by combining the temperature response time and historical fluctuation coefficient. The calculation formula includes: ； in, This represents the volume deviation in the segment distribution. To input the flow rate in real time, As the reference flow rate, Temperature response time The rated response time of the equipment. This refers to the real-time material loading time from the hopper. This is the time when the material hopper begins loading. This is the historical volatility coefficient; A set of volume deviations is generated based on the volume deviations distributed in the aforementioned sections; The direction correction submodule is used to call the volume deviation set, compare the volume deviation of each hopper with a preset threshold, calculate the correction direction ratio for deviations exceeding the threshold, generate a correction ratio set, and output the optimized feeding amount.

6. The converter and refining silo feeding control system according to claim 1, characterized in that: The dynamic adjustment control module includes: The pressure-flow rate deviation calculation submodule is used to call the optimized feeding amount, analyze the dynamic change trend of the silo pressure data after the feeding amount is optimized, calculate the pressure change rate according to the dynamic change trend of the data, extract the absolute difference of the silo feeding rate before and after optimization, and generate a deviation rate set based on the pressure change rate and the absolute difference of the silo feeding rate before and after optimization. The directional consistency judgment submodule is used to call the deviation rate set, confirm the pressure change direction and the flow velocity deviation direction based on the deviation rate set, compare the correlation between the pressure change direction and the flow velocity deviation direction, determine whether the direction is consistent and mark it as a matching item, otherwise mark it as an abnormal item, and generate a directional consistency signal. The flow distribution matrix adjustment submodule is used to call the direction consistency signal, adjust the weight coefficients of the flow distribution matrix according to the matching result, limit the weight coefficients within a preset range, and output the adjusted hopper feeding rate after adjusting the flow distribution matrix.

7. The converter and refining silo feeding control system according to claim 1, characterized in that: The material feeding process adjustment module includes: The correction curve construction submodule is used to call the adjusted hopper feeding rate, collect the real-time filling rate data of the hopper after the hopper feeding rate is adjusted and the temperature data change range before and after the hopper feeding rate is adjusted, calculate the difference between the filling rate and the temperature data change range of each hopper, draw the correction curve based on the difference, and generate a set of correction curves. The bonding section calculation submodule is used to call the set of correction curves, calculate the absolute difference between each correction curve and the preset target fill rate, filter continuous sections whose absolute difference is less than the bonding threshold, and measure the comprehensive value of the bonding section length of each section. The calculation formula includes: ， in, To match the comprehensive value of the section length, For the first The absolute difference between the individual silos For the first Temperature variation data for each silo This is a dimensionless ratio of temperature changes. As the reference temperature, This is the error correction term. For the first The variance of the fluctuation of the correction curve; The length of the bonding section is obtained based on the comprehensive value of the bonding section length; The input mode adjustment submodule is used to call the bonding section length, extract bonding sections that exceed the preset length threshold, obtain the input configuration parameters of the bonding sections that exceed the preset length threshold, compare the current hopper feeding rate with the configuration parameters, adjust the hopper feeding rate increment, and generate material input allocation results.

8. A method for controlling the feeding of materials into converters and refining silos, characterized in that, The method is used in the converter and refining silo feeding control system according to any one of claims 1-7, and includes the following steps: Acquire converter and refining silo data, and output a priority classification dataset based on the converter and refining silo data; The system retrieves the silo pressure data and silo feeding rate data from the priority classification dataset, and outputs the silo status adjustment result based on the silo pressure data and silo feeding rate data. Based on the silo status adjustment results, the flow rate change curve and temperature range are fitted, the section distribution volume deviation of each silo is calculated, the direction correction is performed based on the section distribution volume deviation, and the optimized feeding amount is output. Based on the optimized feeding amount, the feeding rate of the hopper is adjusted in real time, and the adjusted feeding rate of the hopper is output. The adjusted hopper feeding rate is invoked, and a set of correction curves is generated based on the real-time filling rate data of the adjusted hopper and the temperature data change before and after the adjustment. The length of the bonding section is obtained based on the set of correction curves, and the increment of the hopper feeding rate is adjusted based on the bonding difference length. The adjusted material input allocation result is then output.

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