Production line efficiency monitoring method and device

Abstract

The invention discloses a production line efficiency monitoring method and device, and belongs to the technical field of steel coil production. The method comprises the following steps: acquiring production process data and process parameter thresholds in a current sampling period on a steel coil production line; generating periodic start-stop information of the production line according to the production process data, the process parameter threshold and a start-stop judgment model; and obtaining a production index in the current sampling period, and inputting the production process data, the period start-stop information and the production index into an efficiency evaluation model to calculate the period efficiency of the current sampling period. According to the method, accurate perception of production line start-stop state judgment is realized through the production process data on the production line, and the accuracy of the production line start-stop state judgment is improved. The method is advantaged in that comprehensive period efficiency determination is carried out, deviation between an efficiency evaluation result and an actual production condition is reduced, limitation of single process and light integral cooperation of a traditional monitoring method is eliminated, and intelligent degree of a steel coil production line is improved.

Description

Technical Field

[0001] This application relates to the field of steel coil production technology, and more specifically to a method and apparatus for monitoring production line efficiency. Background Technology

[0002] In the production process of rolling steel billets into steel coils, effective monitoring of the overall efficiency of the production line is crucial for improving production efficiency. The start-up and shutdown status of the production line significantly affects its overall efficiency. Traditional production line start-up and shutdown judgments often rely on manual observation of equipment operating status, such as whether the motor is running or whether the steel billet is being conveyed. This approach suffers from delays in judgment and strong subjectivity, easily leading to misjudgments of start-up and shutdown status, which in turn affects production scheduling and equipment maintenance planning.

[0003] Furthermore, existing comprehensive efficiency monitoring only calculates overall efficiency for a single process and does not deeply link start-up and shutdown status with efficiency calculation. When production lines start and stop frequently, traditional efficiency monitoring methods do not deduct the impact of ineffective downtime on capacity, resulting in a large deviation between efficiency assessment results and actual production conditions, and failing to provide accurate guidance for production line optimization. Summary of the Invention

[0004] Therefore, it is necessary to provide a production line efficiency monitoring method and device to address the aforementioned technical problems.

[0005] In a first aspect, this application provides a production line efficiency monitoring method, the method comprising: acquiring production process data and process parameter thresholds within the current sampling period of a steel coil production line; generating cycle start-up and shutdown information of the production line based on the production process data, the process parameter thresholds, and a start-up and shutdown judgment model; acquiring production indicators within the current sampling period; and inputting the production process data, the cycle start-up and shutdown information, and the production indicators into an efficiency evaluation model to calculate the cycle efficiency of the current sampling period.

[0006] In one embodiment, the step of generating periodic start-stop information of the production line based on the production process data, the process parameter thresholds, and the start-stop judgment model includes: calculating the parameter coordination matching degree of the production process data corresponding to each time point in the current sampling period based on the process parameter thresholds and the start-stop judgment model; determining the start-stop state corresponding to each time point based on the parameter coordination matching degree and the start-stop thresholds; and statistically analyzing the start-stop states of all time points in the current sampling period to generate periodic start-stop information.

[0007] In one embodiment, the calculation of the parameter coordination degree of the production process data satisfies the following formula:

[0008] ; In the formula, C represents the parameter matching degree. This refers to the number of key process parameters in the production process data used to construct the start-stop judgment model. Let be the weighting coefficient of the i-th critical process parameter. is the standardized state coefficient of the i-th critical process parameter.

[0009] In one embodiment, the calculation of the standardized state coefficients satisfies the following formula: ; In the formula, This represents the value of the i-th key process parameter in the production process data. and These are the lower and upper limits of the threshold values ​​for the i-th critical process parameter during stable operation of the production line. This represents the shutdown threshold value for the i-th critical process parameter. This represents the overrange critical value of the i-th key process parameter.

[0010] In one embodiment, the parameters in the production process data include at least the furnace temperature, total furnace energy consumption, billet quality, billet thickness deviation, coiling speed, and coil roundness deviation. The step of inputting the production process data, the cycle start / stop information, and the production indicators into the efficiency evaluation model to calculate the cycle efficiency of the current sampling cycle includes: generating a first process energy efficiency based on the production indicators, the total furnace energy consumption, and the furnace temperature; generating a second process energy efficiency based on the production indicators, the billet quality, and the billet thickness deviation; generating a third process energy efficiency based on the production indicators, the coiling speed, and the coil roundness deviation; and generating the cycle efficiency by weighting the first process energy efficiency, the second process energy efficiency, the third process energy efficiency, and the cycle start / stop information.

[0011] In one embodiment, the energy efficiency calculation formula for the first process is as follows: ; In the formula, The energy efficiency of the first process. The theoretical energy consumption required for steel billet production. This represents the actual total energy consumption for billet production. This indicates the maximum temperature difference within the heating furnace. This indicates the target rolling temperature within the heating furnace temperature range. The heating furnace load correction factor; or the energy efficiency calculation formula for the second process is as follows: ; In the formula, For the energy efficiency of the second process, This indicates the actual mass of the steel billet rolled per unit time. This represents the theoretical mass of steel billets rolled per unit time. This represents the maximum deviation value of the steel coil thickness. Indicates the target thickness of the steel coil. The roll wear correction factor is indicated; or the energy efficiency calculation formula for the third process is as follows: ; In the formula, The energy efficiency of the third process, This represents the actual winding speed of the winding machine per unit time. The target winding speed of the winding machine per unit time. This represents the maximum roundness deviation after the steel coil is formed. Indicates the target forming radius of the steel coil. This represents the winding tension stability coefficient.

[0012] In one embodiment, the step of generating the cycle efficiency by weighting the energy efficiency of the first process, the energy efficiency of the second process, the energy efficiency of the third process, and the cycle start-stop information includes: generating a comprehensive process energy efficiency based on the energy efficiency of the first process, the energy efficiency of the second process, the energy efficiency of the third process, and the corresponding weighting coefficients; generating an efficiency loss coefficient based on the cycle start-stop information and the corresponding start-stop loss coefficients; and calculating the cycle efficiency based on the comprehensive process energy efficiency and the efficiency loss coefficients.

[0013] In one embodiment, the cycle efficiency calculation satisfies the following formula: ; In the formula, The cycle efficiency is mentioned above. , , These are the energy efficiency of the first process, the energy efficiency of the second process, and the energy efficiency of the third process, respectively. , , These are the weighting coefficients corresponding to the energy efficiency of the first process, the second process, and the third process, respectively. The start-stop loss coefficient is mentioned above; The total downtime of the production line during the current sampling period. The duration of the start / stop transition phase for the current sampling period. The total duration of the current sampling period.

[0014] In one embodiment, the method further includes: acquiring the cycle efficiency within multiple sampling periods; sending an efficiency warning message when the cycle efficiency of a consecutive preset number of sampling periods is lower than an efficiency warning threshold or when the energy efficiency of any process in a consecutive specified number of sampling periods is lower than the corresponding process warning threshold; or sending a start / stop warning message when it is determined that there is a start / stop abnormality based on the cycle start / stop information of the current sampling period.

[0015] Secondly, this application also provides a production line efficiency monitoring device, comprising: a data acquisition module for acquiring production process data and process parameter thresholds within the current sampling period on a steel coil production line; a start / stop judgment module for generating cycle start / stop information of the production line based on the production process data, the process parameter thresholds, and a start / stop judgment model; and an efficiency monitoring module for acquiring production indicators within the current sampling period, and inputting the production process data, the cycle start / stop information, and the production indicators into an efficiency evaluation model to calculate the cycle efficiency of the current sampling period.

[0016] One of the above technical solutions has the following advantages or beneficial effects: by acquiring production process data and process parameter thresholds within the current sampling period on the steel coil production line; generating cycle start-up and shutdown information of the production line based on the production process data, the process parameter thresholds, and the start-up and shutdown judgment model; acquiring production indicators within the current sampling period; and inputting the production process data, the cycle start-up and shutdown information, and the production indicators into the efficiency evaluation model to calculate the cycle efficiency of the current sampling period, this method achieves accurate perception of the start-up and shutdown status of the production line through production process data on the production line, enabling comprehensive cycle efficiency judgment, reducing the deviation between efficiency evaluation results and actual production conditions, overcoming the limitations of traditional monitoring methods that focus on single processes and neglect overall coordination, and improving the intelligence level of the steel coil production line.

[0017] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 This is a flowchart illustrating a production line efficiency monitoring method in one embodiment; Figure 2 This is a structural block diagram of a production line efficiency monitoring device in one embodiment; Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0020] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0021] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0022] In one embodiment, such as Figure 1 As shown, a production line efficiency monitoring method is provided, the method including: S102, obtain the production process data and process parameter thresholds within the current sampling period on the steel coil production line.

[0023] The current sampling period refers to a pre-set fixed time window for efficiency evaluation, enabling periodic monitoring of the production line's operating status. Specific time intervals, such as 1 hour or 1 day, can be configured by the user. Process parameter thresholds refer to pre-set baseline values ​​for judging whether the production process status is normal, based on historical production process data. Production process data refers to the set of production status parameters collected in real-time from sensors and control systems at each stage of the steel coil production line within the current sampling period. This may include furnace temperature, real-time rolling force and speed of the rolling mill, real-time tension and speed of the coiler, real-time power of the total power grid of the production line, and coiling speed of the coiler. It should be understood that production process data can be collected by a parameter acquisition system deployed on the hot continuous rolling production line. Data can be collected in real-time or with a sampling frequency set according to actual operating conditions. The collected production process data will be stored in a database.

[0024] S104 generates cycle start-up and shutdown information for the production line based on production process data, process parameter thresholds, and start-up / shutdown judgment models.

[0025] The start-stop judgment model is used to determine whether a production line is in an operating or stopped state. It can be implemented based on a single logical rule, mathematical algorithm, or machine learning model. For example, the production line's operating status can be determined by the real-time rolling speed of the mill, or by whether multiple parameters simultaneously exceed a certain threshold. Periodic start-stop information refers to the comprehensive statistical information about the production line's start-stop status output by the start-stop judgment model within the current sampling period. This information may include runtime, pause duration, number of start-stops, and current status. This periodic start-stop information divides the continuous production process into different operating states, providing a status basis for subsequent efficiency evaluation and start-stop anomaly analysis.

[0026] S106: Obtain the production indicators within the current sampling period, and input the production process data, cycle start / stop information, and production indicators into the efficiency evaluation model to calculate the cycle efficiency of the current sampling period.

[0027] Production indicators refer to outcome-based metrics used to evaluate production performance by comparing them to benchmark targets. These can include output-related metrics such as target billet rolling quality, and coil quality-related metrics such as target coil thickness deviation. Optionally, production indicators may include theoretical furnace energy consumption (or theoretical energy consumption required for billet production), target rolling temperature, theoretical billet quality, target coil thickness, target coiling speed, and target coil forming radius, etc. Efficiency assessment models are used to comprehensively analyze production process data, cycle start / stop information, and production indicators to reflect production line operating efficiency. They can be implemented based on a single logical rule, mathematical algorithm, or machine learning model. Cycle efficiency refers to the overall production line efficiency within the current sampling cycle, representing the production line's operating level during that cycle. This cycle efficiency comprehensively considers the multi-dimensional impacts of output, quality, energy consumption, and production line status, reducing the deviation between efficiency assessment results and actual production conditions, and providing precise guidance for production line optimization.

[0028] This method provides a benchmark for subsequent start-up and shutdown judgments and efficiency assessments by acquiring production process data and process parameter thresholds from the steel coil production line. It achieves multi-parameter coupled start-up and shutdown judgments based on production process data, process parameter thresholds, and start-up and shutdown judgment models, which can avoid misjudgments caused by fluctuations in a single parameter and improve the accuracy of start-up and shutdown status identification. By inputting production process data, cycle start-up and shutdown information, and production indicators into the efficiency assessment model, the cycle efficiency calculated comprehensively considers multiple influencing factors, which can more comprehensively reflect the production line efficiency and provide a comprehensive and efficient production line efficiency monitoring method.

[0029] In one embodiment, the step of generating cycle start-stop information of the production line based on production process data and start-stop judgment model includes: calculating the parameter coordination matching degree of the production process data corresponding to each time point in the current sampling period based on the process parameter threshold and start-stop judgment model; determining the start-stop state corresponding to each time point based on the parameter coordination matching degree and start-stop threshold; and statistically analyzing the start-stop state of all time points in the current sampling period to generate cycle start-stop information.

[0030] Parameter matching degree refers to the degree of similarity between the overall state of multiple process parameters at the current moment and the ideal operating state benchmark. Start-up and shutdown status is the result of classifying the production line operating status based on parameter matching degree, which can be divided into running or shutdown status.

[0031] In one embodiment, the start / stop judgment model determines the production line status by calculating the parameter coordination matching degree C. Specifically, the calculation of the parameter coordination matching degree of the production process data satisfies the following formula: ; In the formula, C represents the parameter co-matching degree. This refers to the number of key process parameters in the production process data used to construct the start-stop judgment model. Let be the weighting coefficient of the i-th critical process parameter. Let be the standardized state coefficient of the i-th critical process parameter, where satisfy The value of n is determined based on the complexity of the production line process and monitoring requirements, and is usually no less than three, to achieve coordinated judgment of multiple parameters. In some specific preferred embodiments, n can be configured as 8, and the key process parameters can be any of the following: furnace temperature T (or furnace temperature gradient), fuel consumption rate, rolling force F of the rolling mill (which reflects the roll bite situation), coiling speed v of the coiler, coiling tension of the coiling process, steel coil forming diameter D, and main grid power P and billet conveying speed v0 of the production line's common system, thus forming an eight-dimensional real-time parameter matrix. Of course, n can also be configured as 5, in which case the key process parameters can be any of the following: furnace temperature T, rolling force F of the rolling mill, coiling speed v of the coiler, main motor current I, and billet conveying speed v0, thus forming a five-dimensional real-time parameter matrix. This method reflects the overall coordination level by weighting the matching degree of multiple parameters with linear terms, while combining nonlinear terms to suppress the influence of extreme values ​​and enhance attention to local anomalies. It should be understood that the weights... Standardized state coefficients can be adjusted based on historical data or real-time operating conditions. It can be calibrated in conjunction with process parameter thresholds, specifically, the standardized state coefficient. It can be calculated by comparing the real-time collected process parameter values ​​with the pre-set process parameter thresholds for the corresponding parameters.

[0032] In one embodiment, the standardized state coefficient The calculation satisfies the following formula: ; In the formula, This represents the value of the i-th critical process parameter in the production process data. and These are the lower and upper limits of the threshold values ​​for the i-th critical process parameter during stable production line operation, respectively. This represents the shutdown threshold value for the i-th critical process parameter. This represents the over-range critical value of the i-th key process parameter. In this embodiment, different calculation methods can be used for the standardized state coefficient depending on the range in which the parameter is located. When the process parameter is in... and Within the ideal operating range, the standardized state coefficient is 1. When the process parameters are within the offset range, the standardized state coefficient is calculated through linear interpolation. When the process parameters are in the abnormal range, the standardized state coefficient is 0, indicating a shutdown is triggered. This method provides standardized input for production line start-up and shutdown decisions by fusing segmented quantized coefficients with multiple parameters.

[0033] In a specific embodiment, the key process parameters used to construct the start-stop judgment model in the production process data are preferably any multiple of the following: furnace temperature T, rolling mill force F, coiling speed v of the coiler, main motor current I, and billet conveying speed v0. Taking the above-mentioned five-dimensional real-time parameter matrix as an example, a multi-dimensional parameter acquisition system can be built to obtain production process data. The furnace temperature can be acquired by 10 thermocouple sensors arranged inside the furnace, with a sampling frequency of 1Hz and a data accuracy of ±1℃; the rolling mill force can be acquired by pressure sensors on the mill stand, with a sampling frequency of 5Hz and a data accuracy of ±10kN; the coiling speed of the coiler can be acquired by the coiler spindle encoder, with a sampling frequency of 10Hz and a data accuracy of ±0.1m / s; the main motor current can be acquired by the current transformer in the motor control cabinet, with a sampling frequency of 2Hz and a data accuracy of ±1A; and the billet conveying speed can be acquired by the conveyor roller encoder, with a sampling frequency of 5Hz and a data accuracy of ±0.05m / s.

[0034] The production process data collected at a certain moment are: furnace temperature T = 1200℃, rolling force F = 12000kN, coiling speed v = 3.0m / s, main motor current I = 1200A, and billet conveying speed v0 = 1.5m / s. Based on the historical operating data of this production line and the rated parameters of the equipment, the process parameter thresholds are determined as follows: furnace temperature =1150℃, =1250℃; Rolling force of the rolling mill =8000kN, =18000kN; winding speed of the winding machine =1.5m / s, =5.0m / s; Main motor current =800A, =1500A; billet conveying speed =0.8m / s, =2.5m / s.

[0035] If all parameters are within the stable operating range of the process parameter thresholds, the coefficients of multiple parameters are all 1, the start-stop threshold is 0.7, and the multi-parameter coordination matching degree C is calculated by substituting into the formula: C=0.25×1+0.25×1+0.2×1+0.15×1+0.15×1=1.0≥0.7, the production line is determined to be in the operating state; if the parameters obtained at a certain moment are T=700℃, F=800kN, v=0.1m / s, I=80A, v0=0.05m / s, all parameters are below the over-range critical value, the coefficients are all 0, C=0<0.7, the line is determined to be in the shutdown state.

[0036] In one embodiment, the parameters in the production process data include at least the furnace temperature, total furnace energy consumption, billet quality, billet thickness deviation, coiling speed, and coil roundness deviation. The step of inputting the production process data, cycle start / stop information, and production indicators into the efficiency evaluation model to calculate the cycle efficiency of the current sampling cycle includes: generating the first process energy efficiency based on the production indicators, total furnace energy consumption, and furnace temperature; generating the second process energy efficiency based on the production indicators, billet quality, and billet thickness deviation; generating the third process energy efficiency based on the production indicators, coiling speed, and coil roundness deviation; and generating the cycle efficiency by weighting the first process energy efficiency, the second process energy efficiency, the third process energy efficiency, and the cycle start / stop information.

[0037] In this embodiment, the efficiency evaluation model can measure the conversion efficiency of a single production process based on production process data and production indicators to generate the corresponding process efficiency, which may include the energy efficiency of heating process, rolling process, coiling process, etc., and then generate cycle efficiency by weighting the energy efficiency of each process and cycle start-stop information.

[0038] In one embodiment, the energy efficiency calculation formula for the first process is as follows: ; In the formula, For the energy efficiency of the first process, The theoretical energy consumption required for steel billet production. This represents the actual total energy consumption for billet production. This indicates the maximum temperature difference in the three-dimensional temperature field of the steel billet inside the heating furnace. This indicates the target rolling temperature within the heating furnace temperature range. For the heating furnace load correction factor, 0.92≤ ≤1.0, which can linearly change with the ratio of the heating furnace charge to the rated charge. In this embodiment, the ratio of theoretical energy consumption to actual total energy consumption directly reflects energy utilization efficiency. The closer the actual total energy consumption is to the theoretical energy consumption, the higher the basic energy efficiency. In addition, large temperature fluctuations in the heating furnace can lead to uneven billet structure, requiring additional energy compensation. The ratio of the actual maximum temperature difference in the heating furnace to the target rolling temperature is used as a temperature control term to reflect the implicit loss of overall efficiency due to temperature runaway. It should be understood that theoretical energy consumption is defined as the theoretical energy consumption required for the billet to reach the target rolling temperature. This is a reference value calculated under ideal and standard conditions, which can be calculated from the target billet mass m, specific heat capacity c, and target temperature rise ΔT predetermined according to the production plan and product specifications. =m×c×ΔT; In a specific embodiment, the energy efficiency of the first process can be the energy efficiency of the heating process, which can be used as the starting point for multi-process energy efficiency evaluation, and the operating status of the heating furnace can be monitored through the energy efficiency of the heating process to trigger an abnormal warning of the heating process.

[0039] In one embodiment, the energy efficiency calculation formula for the second process is as follows: ; In the formula, For the energy efficiency of the second process, This indicates the actual mass of the steel billet rolled per unit time. This represents the theoretical mass of steel billets rolled per unit time. This represents the maximum deviation value of the steel coil thickness. Indicates the target thickness of the steel coil. This represents the roll wear correction factor, 0.90≤ ≤1.0, linearly decreasing with the cumulative rolling amount of the rolls; in a specific embodiment, the energy efficiency of the second process can be the energy efficiency of the rolling process, wherein the mass ratio directly reflects the speed of rolling the steel billet. The closer the actual steel billet mass is to the theoretical steel billet mass, the higher the production efficiency. However, the greater the thickness deviation or the more severe the roll wear, the more energy efficiency penalty will be imposed, affecting the effect of rolling the steel billet. Therefore, this method can correlate rolling accuracy with capacity utilization rate to achieve multi-dimensional evaluation of the rolling process.

[0040] In one embodiment, the energy efficiency calculation formula for the third process is as follows: ; In the formula, For the energy efficiency of the third process, This represents the actual winding speed of the winding machine per unit time. The target winding speed of the winding machine per unit time. This represents the maximum roundness deviation after the steel coil is formed. Indicates the target forming radius of the steel coil. This represents the winding tension stability coefficient, 0.91≤ ≤1.0, changing inversely with the standard deviation of winding tension fluctuation. In a specific embodiment, the energy efficiency of the third process can be the energy efficiency of the winding process. The smaller the deviation between the actual winding speed and the target winding speed, the higher the energy efficiency. At the same time, the smaller the roundness deviation after the steel coil is formed, the better the quality of the steel coil after forming. This process can effectively correlate the quality and speed stability of winding forming.

[0041] In one embodiment, the step of generating cycle efficiency by weighting the energy efficiency of the first process, the energy efficiency of the second process, the energy efficiency of the third process, and the cycle start-stop information includes: generating a comprehensive process energy efficiency based on the energy efficiency of the first process, the energy efficiency of the second process, the energy efficiency of the third process, and the corresponding weighting coefficients; generating an efficiency loss coefficient based on the cycle start-stop information and the corresponding start-stop loss coefficients; and calculating the cycle efficiency based on the comprehensive process energy efficiency and the efficiency loss coefficients.

[0042] Comprehensive process energy efficiency refers to the overall process energy efficiency obtained by weighting the energy efficiency of three processes according to certain weighting coefficients. Start-up and shutdown loss coefficient refers to the proportion of efficiency loss caused by start-up and shutdown events (such as start-up and shutdown) that occur in the production line during the sampling period. It represents the part of efficiency lost due to start-up and shutdown. It can be a fixed value preset based on historical data, or it can be dynamically calculated based on the number of start-up and shutdown events, start-up and shutdown duration, etc. Efficiency loss coefficient refers to the total efficiency loss proportion after comprehensively considering start-up and shutdown losses. This method can evaluate the impact of start-up and shutdown from multiple dimensions such as time, frequency, and number of times, and provide dynamic and accurate efficiency loss positioning.

[0043] In one embodiment, the cycle efficiency calculation satisfies the following equation: ; In the formula, For cycle efficiency, , , These are the energy efficiency of the first process, the energy efficiency of the second process, and the energy efficiency of the third process, respectively. , , These are the weighting coefficients for the energy efficiency of the first, second, and third processes, respectively, with a total weighting coefficient of 1. The start-stop loss coefficient is 1.1≤ ≤1.3, the higher the percentage of downtime, the better. The larger the value; This represents the total downtime of the production line during the current sampling period. This refers to the duration of the start-up / shutdown transition phase during the current sampling cycle, i.e., the transition period from shutdown to stable operation or from operation to shutdown of the production line. This represents the total duration of the current sampling period. In this embodiment, global cycle efficiency is assessed by quantifying the energy efficiency and start-up / shutdown losses of multiple processes. The contribution of each process to the overall efficiency is reflected in a linear weighted manner. The weighting coefficient corresponding to energy efficiency can be determined based on historical production line failure statistics, and the impact of downtime and transition phases on the total production time is quantified. The longer the start-up / shutdown time, the smaller this item is, thereby reducing the overall efficiency.

[0044] In one specific embodiment, the cycle efficiency of the production line is calculated using a 30-minute current sampling period, where the heating process efficiency is: within this cycle... =2.5×10 6 kJ (calculated from the target billet mass, specific heat capacity and temperature difference). =2.63×10 6 kJ, temperature difference between the upper and lower surfaces of the billet after exiting the furnace =150℃, =2.5×10 6 ℃, therefore =0.98, substituting into the energy efficiency formula for the heating process, we get that the heating process efficiency is approximately 0.931 (93.1%). It should be understood that due to uncertainties such as measurement error, empirical coefficient, and dynamic correction, errors and estimated values ​​will also occur in engineering calculations.

[0045] Rolling process efficiency: The theoretical billet mass rolled by the rolling mill per unit time =120t / h, the actual billet mass rolled within this cycle =112t / h, billet thickness deviation =0.1mm, =112mm, therefore =0.97, substituting into the rolling process energy efficiency formula, we get the rolling process efficiency ≈0.904 (90.4%). Winding process efficiency: Winding machine set speed =4.0m / s, the average actual winding speed during this period =3.8m / s, steel coil roundness deviation =2mm, =3.8mm, therefore =0.96, substituting into the energy efficiency formula for the winding process, we get the winding process efficiency ≈0.822 (82.2%). Cycle efficiency: within this cycle =30min, =2min (single downtime 1min, 2 downtimes total), waiting time for conveying between adjacent processes =0.5min, =0.99, , , Substituting 0.35, 0.4, and 0.25 into the production line overall efficiency formula, we get a cycle efficiency of ≈0.819 (81.9%).

[0046] In one embodiment, the method further includes: acquiring the cycle efficiency within multiple sampling periods; sending an efficiency warning message when the cycle efficiency of a consecutive preset number of sampling periods is lower than the efficiency warning threshold or when the energy efficiency of any process in a consecutive specified number of sampling periods is lower than the corresponding process warning threshold; or sending a start / stop warning message when it is determined that there is a start / stop abnormality based on the cycle start / stop information of the current sampling period.

[0047] Efficiency warning thresholds refer to the minimum level of overall production line operating efficiency, which can be set based on historical data, industry standards, and capacity targets. Process warning thresholds refer to the minimum acceptable level of energy efficiency for each production process, which can be set separately for different processes. If the energy efficiency of any process is consistently lower than the corresponding threshold, it indicates that there may be a problem with that process, requiring close monitoring by staff. Warning information is an automatically generated notification message that can include warning type, problem description, time range, severity, and improvement suggestions, etc. Efficiency warning information can include inefficient processes, key influencing parameters, and the extent to which parameters deviate from the optimal range. Start-up and shutdown warning information can include abnormal start-up and shutdown periods, corresponding parameter fluctuation curves, and analysis of potential equipment failure points. Among these, when determining start-up and shutdown situations based on periodic start-up and shutdown information, abnormal start-up and shutdown situations can include the number of production line start-up and shutdown switching times ≥ N within the sampling period, or the proportion of start-up and shutdown transition phase duration ≥ 25% of the total running time, or the frequency of single shutdown duration t satisfying 5min < t < 20min ≥ 70% of the total shutdown frequency.

[0048] In one specific embodiment, under another hypothetical scenario, if the cycle efficiency of 84.9% is lower than the warning threshold of 85%, and is lower than this threshold for 5 consecutive cycles, an efficiency anomaly warning will be triggered. The warning message will show "The rolling process efficiency is the lowest (90.1%), and the main influencing parameter is the fluctuation of the rolling mill force (fluctuation range ±500kN)". The optimization suggestion can be output as "Stable control of the rolling mill force in the range of 11000-13000kN, and adjust the pressure compensation coefficient of the rolling mill hydraulic system to 1.05".

[0049] If the production line starts and stops 6 times within a 2-hour cycle, an abnormal start / stop warning will be triggered. The warning message will show "3 start / stop switchings during the 10:00-10:30 period, corresponding to a main motor current fluctuation range of 800-1400A". Optimization suggestions can be output: "Prioritize checking the tightness of the main motor wiring terminals, adjust the matching coefficient between the billet conveying speed and the rolling mill speed to 0.98, and reduce the frequency of start / stop switching."

[0050] This method analyzes trends over multiple consecutive cycles, focusing on both the overall cycle efficiency of the production line and the energy efficiency of individual processes. It can pinpoint problematic processes and issue warnings when efficiency deviates slightly, enabling faster problem diagnosis. Furthermore, each warning and subsequent results are recorded to facilitate analysis of the triggering frequency and type of various warnings within consecutive cycles. This allows for the identification of common problems in the production line and the provision of optimization suggestions, offering data support for longer-term process improvement and management optimization.

[0051] In one embodiment, such as Figure 2 As shown, this application also provides a production line efficiency monitoring device 200, comprising: The data acquisition module 201 is used to acquire production process data and process parameter thresholds within the current sampling period on the steel coil production line.

[0052] The start / stop judgment module 202 is used to generate the cycle start / stop information of the production line based on the production process data, process parameter thresholds and start / stop judgment model.

[0053] The efficiency monitoring module 203 is used to obtain production indicators within the current sampling period, and input production process data, cycle start and stop information and production indicators into the efficiency evaluation model to calculate the cycle efficiency of the current sampling period.

[0054] In one embodiment, the start / stop judgment module 202 is specifically used to calculate the parameter coordination matching degree of the production process data corresponding to each time point in the current sampling period based on the process parameter threshold and the start / stop judgment model; determine the start / stop status corresponding to each time point based on the parameter coordination matching degree and the start / stop threshold; and statistically analyze the start / stop status of all time points in the current sampling period to generate periodic start / stop information.

[0055] In one embodiment, the calculation of the parameter coordination degree of the production process data satisfies the following formula: ; In the formula, C represents the parameter co-matching degree. This refers to the number of key process parameters in the production process data used to construct the start-stop judgment model. Let be the weighting coefficient of the i-th critical process parameter. is the standardized state coefficient of the i-th critical process parameter.

[0056] In one embodiment, the calculation of the standardized state coefficients satisfies the following equation: ; In the formula, This represents the value of the i-th critical process parameter in the production process data. and These are the lower and upper limits of the threshold values ​​for the i-th critical process parameter during stable production line operation, respectively. This represents the shutdown threshold value for the i-th critical process parameter. This represents the overrange critical value of the i-th key process parameter.

[0057] In one embodiment, the parameters in the production process data include at least the furnace temperature, total furnace energy consumption, billet quality, billet (coil) thickness deviation, coiling speed, and coil roundness deviation. Here, total furnace energy consumption can be understood as the actual total energy consumption of the furnace or billet production; billet quality can be understood as the actual billet quality; and coiling speed can be understood as the actual coiling speed. The efficiency monitoring module 203 is specifically used to generate the first process energy efficiency based on production indicators, total furnace energy consumption, and furnace temperature; generate the second process energy efficiency based on production indicators, billet quality, and billet thickness deviation; generate the third process energy efficiency based on production indicators, coiling speed, and coil roundness deviation; and generate the cycle efficiency by weighting the first process energy efficiency, second process energy efficiency, third process energy efficiency, and cycle start / stop information.

[0058] In one embodiment, the energy efficiency calculation formula for the first process is as follows: ; In the formula, For the energy efficiency of the first process, The theoretical energy consumption required for steel billet production. This represents the actual total energy consumption for billet production. This indicates the maximum temperature difference within the heating furnace. This indicates the target rolling temperature within the heating furnace temperature range. The heating furnace load correction factor; or the energy efficiency calculation formula for the second process is as follows: ; In the formula, For the energy efficiency of the second process, This indicates the actual mass of the steel billet rolled per unit time. This represents the theoretical mass of steel billets rolled per unit time. This represents the maximum deviation value of the steel coil thickness. Indicates the target thickness of the steel coil. The formula for calculating the energy efficiency of the third process is as follows: (This refers to the roll wear correction factor.) ; In the formula, For the energy efficiency of the third process, This represents the actual winding speed of the winding machine per unit time. The target winding speed of the winding machine per unit time. This represents the maximum roundness deviation after the steel coil is formed. Indicates the target forming radius of the steel coil. This represents the winding tension stability coefficient.

[0059] In one embodiment, the efficiency monitoring module 203 includes a comprehensive efficiency module, which is used to generate a comprehensive process energy efficiency based on the energy efficiency of the first process, the energy efficiency of the second process, the energy efficiency of the third process and the corresponding weighting coefficients; generate an efficiency loss coefficient based on the cycle start-stop information and the corresponding start-stop loss coefficients; and calculate the cycle efficiency based on the comprehensive process energy efficiency and the efficiency loss coefficients.

[0060] In one embodiment, the cycle efficiency calculation satisfies the following equation: ; In the formula, For cycle efficiency, , , These are the energy efficiency of the first process, the energy efficiency of the second process, and the energy efficiency of the third process, respectively. , , These are the weighting coefficients corresponding to the energy efficiency of the first, second, and third processes, respectively. This is the start-stop loss coefficient; This represents the total downtime of the production line during the current sampling period. This refers to the duration of the start / stop transition phase for the current sampling period. This represents the total duration of the current sampling period.

[0061] In one embodiment, the device further includes an early warning module, which is used to acquire the cycle efficiency within multiple sampling periods; send efficiency early warning information when the cycle efficiency of a consecutive preset number of sampling periods is lower than the efficiency early warning threshold or when the energy efficiency of any process is lower than the corresponding process early warning threshold for a consecutive specified number of sampling periods; or send start / stop early warning information when it is determined that there is a start / stop abnormality based on the cycle start / stop information of the current sampling period.

[0062] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0063] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0064] This invention provides a processor for running a program, wherein the program executes a production line efficiency monitoring method during runtime.

[0065] In one embodiment, a computer device is provided, which may be a mobile terminal, and the internal structure diagram of the computer device may be as follows: Figure 3 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, and a display unit. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface and display unit are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a production line efficiency monitoring method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display (LCD) or an e-ink display.

[0066] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0067] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0068] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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 processor, 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0069] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0070] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for monitoring production line efficiency, characterized in that, include: Obtain production process data and process parameter thresholds within the current sampling period on the steel coil production line; The production line's cycle start-up and shutdown information is generated based on the production process data, the process parameter thresholds, and the start-up and shutdown judgment model. The production indicators within the current sampling period are obtained, and the production process data, the cycle start / stop information, and the production indicators are input into the efficiency evaluation model to calculate the cycle efficiency of the current sampling period.

2. The method according to claim 1, characterized in that, The step of generating the production line's cycle start-up and shutdown information based on the production process data, the process parameter thresholds, and the start-up and shutdown judgment model includes: Based on the process parameter thresholds and the start / stop judgment model, calculate the parameter coordination matching degree of the production process data corresponding to each time point in the current sampling period; Based on the parameter matching degree and start / stop threshold, the start / stop status corresponding to each time point is determined; The start / stop status of all time points within the current sampling period is statistically analyzed to generate periodic start / stop information.

3. The method according to claim 2, characterized in that, The calculation of the parameter coordination degree of the production process data satisfies the following formula: ； In the formula, C represents the parameter's collaborative matching degree. This refers to the number of key process parameters in the production process data used to construct the start-stop judgment model. Let be the weighting coefficient of the i-th critical process parameter. is the standardized state coefficient of the i-th critical process parameter.

4. The method according to claim 3, characterized in that, The calculation of the standardized state coefficients satisfies the following formula: ； In the formula, This represents the value of the i-th key process parameter in the production process data. and These are the lower and upper limits of the threshold values ​​for the i-th critical process parameter during stable operation of the production line. This represents the shutdown threshold value for the i-th critical process parameter. This represents the overrange critical value of the i-th key process parameter.

5. The method according to claim 1, characterized in that, The parameters in the production process data include at least the furnace temperature, total furnace energy consumption, billet mass, billet thickness deviation, coiling speed, and coil roundness deviation. The step of inputting the production process data, the cycle start / stop information, and the production indicators into the efficiency evaluation model to calculate the cycle efficiency of the current sampling cycle includes: The energy efficiency of the first process is generated based on the production indicators, the total energy consumption of the heating furnace, and the temperature of the heating furnace. The energy efficiency of the second process is generated based on the production indicators, the billet quality, and the billet thickness deviation. The energy efficiency of the third process is generated based on the production indicators, the winding speed, and the roundness deviation of the steel coil. The cycle efficiency is generated by weighting the energy efficiency of the first process, the energy efficiency of the second process, the energy efficiency of the third process, and the cycle start-stop information.

6. The method according to claim 5, characterized in that, The energy efficiency calculation formula for the first process is as follows: ； In the formula, The energy efficiency of the first process. The theoretical energy consumption required for steel billet production. This represents the actual total energy consumption for billet production. This indicates the maximum temperature difference within the heating furnace. This indicates the target rolling temperature within the heating furnace temperature range. This is a correction factor for the heating furnace load. And / or, the energy efficiency calculation formula for the second process is as follows: ； In the formula, For the energy efficiency of the second process, This indicates the actual mass of the steel billet rolled per unit time. This represents the theoretical mass of steel billets rolled per unit time. This represents the maximum deviation in the thickness of the steel coil. Indicates the target thickness of the steel coil. This represents the roll wear correction factor; And / or, the energy efficiency calculation formula for the third process is as follows: ； In the formula, The energy efficiency of the third process, This represents the actual winding speed of the winding machine per unit time. The target winding speed of the winding machine per unit time. This represents the maximum roundness deviation after the steel coil is formed. Indicates the target forming radius of the steel coil. This represents the winding tension stability coefficient.

7. The method according to claim 5, characterized in that, The step of generating the cycle efficiency by weighting the energy efficiency of the first process, the energy efficiency of the second process, the energy efficiency of the third process, and the cycle start / stop information includes: A comprehensive process energy efficiency is generated based on the energy efficiency of the first process, the energy efficiency of the second process, the energy efficiency of the third process, and the corresponding weighting coefficients. An efficiency loss coefficient is generated based on the cycle start-stop information and the corresponding start-stop loss coefficient. The cycle efficiency is calculated based on the overall process energy efficiency and the efficiency loss coefficient.

8. The method according to claim 7, characterized in that, The periodic efficiency calculation satisfies the following formula: ； In the formula, The cycle efficiency is mentioned above. , , These are the energy efficiency of the first process, the energy efficiency of the second process, and the energy efficiency of the third process, respectively. , , These are the weighting coefficients corresponding to the energy efficiency of the first process, the second process, and the third process, respectively. The start-stop loss coefficient is mentioned above; The total downtime of the production line during the current sampling period. The duration of the start / stop transition phase for the current sampling period. The total duration of the current sampling period.

9. The method according to any one of claims 5-8, characterized in that, The method further includes: Get the cycle efficiency within multiple sampling periods; send an efficiency warning message if the cycle efficiency is lower than the efficiency warning threshold for a consecutive preset number of sampling periods or if the energy efficiency of any process is lower than the corresponding process warning threshold for a consecutive specified number of sampling periods. Alternatively, if an abnormal start / stop condition is determined based on the start / stop information of the current sampling period, a start / stop warning message may be sent.

10. A production line efficiency monitoring device, characterized in that, include: The data acquisition module is used to acquire production process data and process parameter thresholds within the current sampling period on the steel coil production line; The start / stop determination module is used to generate cycle start / stop information of the production line based on the production process data, the process parameter thresholds, and the start / stop determination model. The efficiency monitoring module is used to obtain the production indicators within the current sampling period, and input the production process data, the cycle start and stop information and the production indicators into the efficiency evaluation model to calculate the cycle efficiency of the current sampling period.

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