Dynamic Optimization Method of Permeable Brick Production Line

Abstract

The present invention relates to the technical field of production management, specifically to a dynamic optimization method for a permeable brick production line, comprising the following steps: based on the energy consumption in the permeable brick production line, recording the energy consumption data of a mixer and a press in the process of manufacturing permeable bricks through an energy consumption recorder, obtaining an energy consumption data set, and adjusting the energy consumption limit of the equipment to obtain the adjusted energy consumption parameters. In the present invention, by adjusting the energy consumption parameters of the permeable brick production line, the production efficiency and product quality are significantly improved, the energy consumption of the mixer and the press is accurately recorded and adjusted, the production process is optimized, the maximum utilization of energy is achieved, the production data is monitored in real time, the operating conditions such as mold temperature and press pressure are adjusted, the optimization directly improves the cost efficiency and product consistency, the experiment is designed to test the actual impact of the adjustment on production, and the effectiveness of the adjustment strategy is verified. The optimized production strategy systematically manages the energy consumption of the entire production line, significantly reduces costs and shortens the production cycle.

Description

Technical Field

[0001] The invention relates to the technical field of production management, and in particular to a dynamic optimization method for a permeable brick production line. Background Art

[0002] The dynamic optimization method of the permeable brick production line refers to the use of systematic methods and technologies to analyze and adjust the production process of permeable bricks in real time to improve production efficiency and product quality. This topic involves the use of mathematical models and computer algorithms to predict the performance of the production line, identify bottlenecks and inefficient areas, and adjust production parameters or processes accordingly. The purpose of the optimization method is to reduce production costs and shorten production cycles while improving product consistency and meeting market demand. This topic also includes the implementation of advanced monitoring systems such as sensors and data analysis platforms to continuously collect and analyze production data to support dynamic optimization decisions.

[0003] Existing technologies lack in-depth analysis and application of real-time data in the field of production management, which limits the dynamic adjustment capabilities in the production process. Despite integrated monitoring systems and automated controls, in actual operations, methods for predicting production line performance, identifying bottlenecks, and adjusting production parameters still rely on intermittent data analysis rather than continuous stream real-time data processing. This delayed response leads to a decrease in production efficiency and a waste of resources, because production parameters cannot be adjusted in time to cope with fluctuations in raw material quality or changes in equipment performance. In terms of ensuring product quality and meeting delivery deadlines, existing technologies face production delays and cost overruns due to the lack of real-time feedback and predictive adjustments. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a dynamic optimization method for a permeable brick production line.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme: a dynamic optimization method for a permeable brick production line, comprising the following steps:

[0006] S1: Based on the energy consumption in the permeable brick production line, the energy consumption data of the mixer and the press in the permeable brick manufacturing process are recorded by the energy consumption recorder, the energy consumption data set is obtained, and the energy consumption limit of the equipment is adjusted to obtain the adjusted energy consumption parameters;

[0007] S2: extracting key control points from the adjusted energy consumption parameters, fine-tuning the operating conditions of the permeable brick production line, matching the new energy consumption requirements, generating a fine-tuning operation strategy, implementing the adjustments in the fine-tuning operation strategy, optimizing the production parameters according to the monitoring data of production efficiency and energy consumption, and obtaining optimized operating parameters;

[0008] S3: using the optimized operating parameters, designing experiments and testing the effects of the parameters on the real-time production of permeable bricks. The experiments compare the production data before and after the adjustment, measure the operating efficiency and the quality standard of permeable bricks, generate experimental effect analysis results, and adjust the production strategy according to the experimental effect analysis results to obtain an optimized strategy adjustment plan;

[0009] S4: Apply the optimized strategy adjustment plan to the fully permeable brick production line, monitor the implementation effect of the measures, evaluate the changes in production costs and efficiency, generate cost-benefit analysis results, and based on the cost-benefit analysis results, continuously optimize energy consumption and improve efficiency, and conduct monitoring and evaluation to obtain a continuously improved production strategy.

[0010] As a further solution of the present invention, the step of obtaining the adjusted energy consumption parameter is specifically as follows:

[0011] Based on the energy consumption in the permeable brick production line, the energy consumption data of the mixer and press in the permeable brick production are obtained from the energy consumption recorder, the data are screened and processed, abnormal and noise data are excluded, and the formula is used:

[0012] ;

[0013] The processed total energy consumption data is obtained, where Indicates Energy consumption data for each time period, Represents the total energy consumption data, is the total number of time periods;

[0014] Based on the processed total energy consumption data, identify the devices and time periods with high energy consumption, using the formula:

[0015] ;

[0016] Calculate and obtain average energy consumption data, where: and represent the minimum and maximum energy consumption respectively, Indicates average energy consumption data, is the total energy consumption data, is the total number of time periods, and They are adjustment parameters respectively;

[0017] The average energy consumption data is used to adjust the energy consumption parameters of the equipment, and the equipment operation parameters are optimized through real-time feedback control configuration to obtain the adjusted energy consumption parameters.

[0018] As a further solution of the present invention, the step of obtaining the fine-tuning operation strategy is specifically:

[0019] According to the adjusted energy consumption parameters, analyze the key control points, including the temperature of the mold and the pressure of the press, obtain the original energy consumption data, calculate the energy consumption change rate of each control point, and use the formula:

[0020] ;

[0021] Generate energy consumption change rate data set, where Represents the adjusted energy consumption value, Represents the energy consumption value before adjustment, represents the rate of change of energy consumption;

[0022] Analyze the energy consumption change rate data set, set the adjustment range of temperature and pressure, and compare the change rate with the control point of the set threshold using the formula:

[0023] ;

[0024] Generate an adjustment proposal dataset, where represents the total number of data points, Representative The energy consumption change rate of each data point, represents the thresholds for temperature and pressure adjustments;

[0025] The adjustment proposal data set is combined with the control point analysis results to formulate operation steps, adjust the mold temperature and press pressure, and generate a fine-tuning operation strategy.

[0026] As a further solution of the present invention, the step of obtaining the optimized operating parameters is specifically as follows:

[0027] Implement the adjustments in the fine-tuning operation strategy, collect current production parameter data based on the monitoring data of production efficiency and energy consumption, analyze the difference between real-time production parameters and theoretical optimization parameters, calculate the range of production parameter changes, and use the formula:

[0028] ;

[0029] Generate preliminary optimization parameter change data, where: represents the adjusted production parameter value, Represents the production parameter value before adjustment, Represents the range of changes in production parameters;

[0030] The preliminary optimization parameter change data is analyzed, and the data is combined with the relationship between production efficiency and energy consumption, through the formula:

[0031] ;

[0032] Generate an optimized and adjusted dataset, where is the energy consumption weight coefficient, Representative The energy consumption variation of each data point is represents the total number of data points, Representative The production parameter variation range of each data point, represents the optimized and adjusted dataset;

[0033] Apply the optimized and adjusted data set to the real-time production parameter adjustment process, refer to the production efficiency and energy consumption, adjust the production parameters to the optimized state, and obtain the optimized operating parameters.

[0034] As a further solution of the present invention, the steps for obtaining the experimental effect analysis results are specifically as follows:

[0035] The optimized operating parameters were used to design experiments, set up experimental groups and control groups, record production data of multiple groups, and use the formula:

[0036] ;

[0037] The production data difference analysis results are calculated, where: is the production data of the experimental group, The production data of the control group, Indicates the results of production data difference analysis;

[0038] Analyze the impact of the production data difference analysis results on the quality of permeable bricks, calculate the quality index difference value, and use the formula:

[0039] ;

[0040] Generate quality indicator difference analysis results, where: is the total number of samples, Representative The quality index of the experimental group of samples, Representative The quality index of the control group of samples, Represents the difference value of quality index;

[0041] Combined with the above-mentioned quality index difference analysis results and production data difference analysis results, and analyzed to evaluate the impact of optimized operating parameters on permeable brick production, the formula is used:

[0042] ;

[0043] Generate experimental effect analysis results, where: and are the weight coefficients of production data and quality indicators, It represents the results of experimental effect analysis. Indicates the results of production data difference analysis, Represents the quality index difference value.

[0044] As a further solution of the present invention, the step of obtaining the optimized strategy adjustment solution is specifically as follows:

[0045] According to the experimental effect analysis results, analyze the experimental effect data, identify the key factors affecting production efficiency, collect the changes in experimental effects and production strategies, and use the formula:

[0046] ;

[0047] Generate the influence coefficients of multiple influencing factors, where represents the change in the experimental effect, represents the change in production strategy, Represents the influence coefficient of multiple influencing factors;

[0048] Based on the influence coefficients of the multiple influencing factors and the experimental effect data, the optimization effect of each production strategy is calculated, and the experimental effect data of each production strategy is captured, using the formula:

[0049] ;

[0050] Generate an optimization effect data set, where is the total number of strategies, For the The influence coefficient of the strategy, For the The experimental effect data of the strategy, To adjust the parameters, To optimize the effect data set;

[0051] According to the optimization effect data set, a sorting and selection method is used to determine the production strategy with the best optimization effect, the data with the best effect is selected as the strategy, and a post-optimization strategy adjustment plan is generated.

[0052] As a further solution of the present invention, the steps for obtaining the cost-benefit analysis result are specifically as follows:

[0053] Apply the optimized strategy adjustment scheme to the full permeable brick production line, monitor the implementation effect, record the production cost and production efficiency of the production line before and after optimization, and form a production cost data set and a production efficiency data set;

[0054] According to the production cost data set and the production efficiency data set, the production cost change and the production efficiency change before and after the optimization are calculated using the formula:

[0055] ;

[0056] and

[0057] ;

[0058] Calculate the changes in production costs and production efficiency, where: To optimize production costs, To optimize the pre-production cost, To optimize production efficiency, To optimize production efficiency, represents the change in production cost, Indicates changes in production efficiency;

[0059] According to the changes in production costs and production efficiency, the benefits of production costs and production efficiency are evaluated using the formula:

[0060] ;

[0061] Generate cost-benefit analysis results, where For production efficiency changes, For changes in production costs, To optimize the pre-production cost, To optimize production efficiency, Represents the results of cost-benefit analysis.

[0062] As a further solution of the present invention, the steps of obtaining the continuous improvement production strategy are specifically:

[0063] According to the cost-benefit analysis results, analyzing the cost-benefit data, recording the energy consumption data and production efficiency data of the current production line, extracting related data and creating a preliminary data analysis basic set;

[0064] Based on the preliminary data analysis foundation set, the current data is normalized, and statistical analysis methods are applied to the normalized data for trend and pattern recognition, using the formula:

[0065] ;

[0066] and ;

[0067] Calculate the energy efficiency ratio and optimization potential, where: Represents the optimized production efficiency, Represents the optimized energy consumption, Represents the production efficiency before optimization, represents the energy consumption before optimization, It represents the energy efficiency ratio, Indicates optimization potential;

[0068] Based on the energy efficiency ratio and optimization potential, evaluate the current production line operation process, identify key energy consumption peaks and efficiency bottlenecks, and monitor and evaluate them using the formula:

[0069] ;

[0070] Continuously improve production strategies, including: To optimize the energy efficiency ratio, To optimize potential, To optimize the energy efficiency ratio before Indicates continuous improvement of production strategy.

[0071] Compared with the prior art, the advantages and positive effects of the present invention are:

[0072] In the present invention, by adjusting the energy consumption parameters in the permeable brick production line, the production efficiency and product quality are significantly improved. By accurately recording and adjusting the energy consumption of the mixer and the press, the production process is fine-tuned to ensure maximum energy utilization. By real-time monitoring of production data, operating conditions such as mold temperature and press pressure are optimized, which is directly reflected in cost efficiency and product consistency. The impact of experimental design test adjustments on actual production is further verified to verify the effectiveness of the adjustment strategy. After adjusting the production strategy for optimization, the energy consumption of the entire production line is systematically managed and significantly reduced. This comprehensive energy consumption management and optimization strategy not only reduces production costs, but also shortens the production cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0074] Figure 2 It is a flow chart of the energy consumption parameters adjusted in the present invention;

[0075] Figure 3 A flow chart of the fine-tuning operation strategy in the present invention;

[0076] Figure 4 A flow chart of the operating parameters optimized in the present invention;

[0077] Figure 5 It is a flow chart of the experimental effect analysis results in the present invention;

[0078] Figure 6 This is a flow chart of the optimized strategy adjustment scheme in the present invention;

[0079] Figure 7 A flow chart showing the cost-benefit analysis results of the present invention;

[0080] Figure 8 It is a flow chart of continuously improving the production strategy in the present invention. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0082] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0083] See also Figure 1 The present invention provides a technical solution: a dynamic optimization method for a permeable brick production line, comprising the following steps:

[0084] S1: Based on the energy consumption in the permeable brick production line, the energy consumption data of the mixer and the press in the permeable brick manufacturing process are recorded by the energy consumption recorder, the energy consumption data set is obtained, and the energy consumption limit of the equipment is adjusted to obtain the adjusted energy consumption parameters;

[0085] S2: Extract key control points from the adjusted energy consumption parameters, fine-tune the operating conditions of the permeable brick production line, including adjusting the temperature of the mold and the pressure of the press to match the new energy consumption requirements, generate a fine-tuning operation strategy, implement the adjustments in the fine-tuning operation strategy, optimize the production parameters according to the monitoring data of production efficiency and energy consumption, and obtain the optimized operating parameters;

[0086] S3: Using the optimized operating parameters, design experiments and test the impact of the parameters on the real-time production of permeable bricks. The experiment compares the production data before and after the adjustment, measures the operating efficiency and the quality standard of permeable bricks, generates the experimental effect analysis results, and adjusts the production strategy according to the experimental effect analysis results to obtain the optimized strategy adjustment plan;

[0087] S4: Apply the optimized strategy adjustment plan to the fully permeable brick production line, monitor the implementation effect of the measures, evaluate the changes in production costs and efficiency, generate cost-benefit analysis results, and continuously optimize energy consumption and improve efficiency based on the cost-benefit analysis results, and conduct monitoring and evaluation to obtain a continuously improved production strategy.

[0088] The energy consumption data set includes power readings, operating time, and machine numbers. The adjusted energy consumption parameters include maximum power limit, energy consumption rate, and continuous operating time. The fine-tuning operation strategy includes mold temperature setting, pressure adjustment steps, and energy consumption adjustment plan. The optimized operation parameters include adjusted temperature, set pressure value, and energy usage upper limit. The experimental effect analysis results include production rate improvement, product quality standards, and energy utilization efficiency. The optimized strategy adjustment plan includes process optimization measures, equipment upgrade plans, and operating procedure updates. The cost-benefit analysis results include direct cost reduction, investment return improvement, and production efficiency growth. Continuous improvement of production strategies includes periodic energy consumption monitoring, quality audit process, and cost control strategy.

[0089] See also Figure 2 , the specific steps for obtaining the adjusted energy consumption parameters are:

[0090] S111: Based on the energy consumption in the permeable brick production line, the energy consumption data of the mixer and press in the permeable brick production are obtained from the energy consumption recorder, the data are screened and processed, abnormal and noise data are excluded, and the formula is used:

[0091] ;

[0092] The processed total energy consumption data is obtained, where Indicates Energy consumption data for each time period, Represents the total energy consumption data, is the total number of time periods;

[0093] S112: Based on the processed total energy consumption data, identify the devices and time periods with high energy consumption, using the formula:

[0094] ;

[0095] Calculate and obtain average energy consumption data, where: and represent the minimum and maximum energy consumption respectively, Indicates average energy consumption data, is the total energy consumption data, is the total number of time periods, and They are adjustment parameters respectively;

[0096] S113: using the average energy consumption data, adjusting energy consumption parameters of the equipment, optimizing equipment operation parameters through real-time feedback control configuration, and obtaining adjusted energy consumption parameters.

[0097] The formula for total energy consumption data is:

[0098] ;

[0099] : Represents the total energy consumption after screening and cleaning, in kilowatt-hours (kWh);

[0100] : represents the energy consumption data after cleaning in the i-th time period, in kilowatt-hours (kWh);

[0101] : The number of time periods, that is, the number of time periods divided within the considered time;

[0102] A day is divided into 24 time periods. After the energy consumption data recorded in each time period is filtered and processed, the energy consumption per hour is set as follows:

[0103] ;

[0104] The total energy consumption The calculation of is as follows:

[0105] ;

[0106] The formula for average energy consumption data is:

[0107] ;

[0108] : adjusted average energy consumption, taking into account the adjustment of minimum and maximum energy consumption;

[0109] and : Weight coefficient adjusted according to device type, here set and;

[0110] : The minimum energy consumption recorded in a day, set to 12kWh;

[0111] : The maximum energy consumption recorded in a day, set to 19kWh;

[0112] Set the parameters to:

[0113] ;

[0114] calculate :

[0115] ;

[0116] here, kWh represents the average energy consumption after adjusting for the maximum and minimum energy consumption of the equipment. It is used to further analyze the energy consumption distribution and identify equipment or time periods with high energy consumption.

[0117] See also Figure 3 , the specific steps for obtaining the fine-tuning operation strategy are:

[0118] S211: According to the adjusted energy consumption parameters, analyze the key control points, including the temperature of the mold and the pressure of the press, obtain the original energy consumption data, calculate the energy consumption change rate of each control point, and use the formula:

[0119] ;

[0120] Generate energy consumption change rate data set, where Represents the adjusted energy consumption value, Represents the energy consumption value before adjustment, represents the rate of change of energy consumption;

[0121] S212: Analyze the energy consumption change rate data set, set the adjustment range of temperature and pressure, and compare the change rate with the control point of the set threshold value using the formula:

[0122] ;

[0123] Generate an adjustment proposal dataset, where represents the total number of data points, Representative The energy consumption change rate of each data point, represents the thresholds for temperature and pressure adjustments;

[0124] S213: Combining the adjustment proposal data set with the control point analysis result, formulating operation steps, adjusting the mold temperature and the press pressure, and generating a fine-tuning operation strategy.

[0125] The formula for energy consumption change rate is: ;

[0126] : The adjusted energy consumption value is obtained through actual measurement, for example, from the energy consumption monitoring system of the adjusted production line;

[0127] : The energy consumption value before adjustment is obtained through actual measurement, for example, from the energy consumption monitoring system of the production line before adjustment;

[0128] The following data were obtained from the production line:

[0129] Energy consumption value before adjustment ;

[0130] Adjusted energy consumption value ;

[0131] Calculate the rate of change of energy consumption : ;

[0132] Energy consumption change rate -20kWh means that the energy consumption is reduced by 20kWh.

[0133] Threshold formula for temperature and pressure adjustment: ;

[0134] : The total number of data points is determined by the actual number of data points collected, for example, 5 data points are monitored;

[0135] : No. The energy consumption change rate of each data point is calculated in the previous step;

[0136] Set the following energy consumption change rate data to be obtained from 5 data points:

[0137] , ;

[0138] Calculate the absolute value and find the average :

[0139] ;

[0140] Adjusting the Threshold It is 20kWh, which means that the energy consumption change rate of the control point is greater than 20kWh, which is an important adjustment point.

[0141] To generate fine-tuning operation strategies, first combine the energy consumption change rate data with the adjustment threshold, analyze the energy consumption change rate of the control point and determine whether it exceeds the threshold. control points, develop specific operating steps, adjust mold temperature and press pressure, and create a fine-tuning operating strategy document that includes all adjustment steps and suggestions.

[0142] See also Figure 4 , the steps of obtaining the optimized operating parameters are specifically as follows:

[0143] S221: Implement the adjustments in the fine-tuning operation strategy, collect current production parameter data based on the monitoring data of production efficiency and energy consumption, analyze the difference between real-time production parameters and theoretical optimization parameters, calculate the variation range of production parameters, and use the formula:

[0144] ;

[0145] Generate preliminary optimization parameter change data, where: represents the adjusted production parameter value, Represents the production parameter value before adjustment, Represents the range of changes in production parameters;

[0146] S222: Analyze the preliminary optimization parameter change data, combine the data with the relationship between production efficiency and energy consumption, and use the formula:

[0147] ;

[0148] Generate an optimized and adjusted dataset, where is the energy consumption weight coefficient, Representative The energy consumption variation of each data point is represents the total number of data points, Representative The production parameter variation range of each data point, represents the optimized and adjusted dataset;

[0149] S223: Apply the optimized and adjusted data set to the real-time production parameter adjustment process, refer to the production efficiency and energy consumption, adjust the production parameters to the optimized state, and obtain the optimized operating parameters.

[0150] The formula for the variation range of production parameters is: ;

[0151] : adjusted production parameter value;

[0152] : Production parameter value before adjustment;

[0153] : The range of variation of production parameters;

[0154] By monitoring the current production parameters of the production line (such as mold temperature, press pressure, etc.), record the production parameter values ​​before adjustment , set it to be derived from the actual monitoring data ;

[0155] Re-measure the production parameters after adjustment and record the adjusted production parameter values , set the adjusted value to ;

[0156] Substitute the formula into the calculation :

[0157] ;

[0158] , which means that after the production parameters are adjusted, the parameter variation is 20℃ compared with before the adjustment.

[0159] Optimized and adjusted data set formula: ;

[0160] : total number of data points;

[0161] : No. The variation range of production parameters for each data point;

[0162] : Energy consumption weight coefficient;

[0163] : No. The energy consumption variation of each data point;

[0164] : Recommended dataset for optimization and adjustment;

[0165] Obtain the variation range of production parameters at several data points from the production line and energy consumption fluctuations ;

[0166] There are 3 data points, and their ranges of change are:

[0167] , , , , , ;

[0168] Energy consumption weight coefficient (determined from energy consumption analysis);

[0169] Calculate the recommended optimization adjustment for each data point:

[0170] For data point 1:

[0171] ;

[0172] For data point 2: ;

[0173] For data point 3: ;

[0174] Compute optimization and adjust the recommended data set :

[0175] ;

[0176] , the result represents the comprehensive value of optimizing the adjustment of the proposed dataset.

[0177] According to the calculation in the previous step , adjust the actual production parameter settings to ensure that they meet the optimization recommendation data set. In the actual production environment, according to The production parameters are optimized based on the results, and the final optimized operating parameter document is recorded to generate the optimized operating parameters, and all parameter configurations after adjustment, including mold temperature, press pressure, etc., are recorded as standard operating parameters for production optimization.

[0178] See also Figure 5 , the specific steps for obtaining the experimental effect analysis results are:

[0179] S311: Design an experiment using the optimized operating parameters, set up an experimental group and a control group, record production data of multiple groups, and use the formula:

[0180] ;

[0181] The production data difference analysis results are calculated, where: is the production data of the experimental group, The production data of the control group, Indicates the results of production data difference analysis;

[0182] S312: Analyze the impact of the production data difference analysis results on the quality of the permeable bricks, calculate the quality index difference value, and use the formula:

[0183] ;

[0184] Generate quality indicator difference analysis results, where: is the total number of samples, Representative The quality index of the experimental group of samples, Representative The quality index of the control group of samples, Represents the difference value of quality index;

[0185] S313: Combine the quality index difference analysis results and the production data difference analysis results, and analyze and evaluate the impact of the optimized operating parameters on the permeable brick production, using the formula:

[0186] ;

[0187] Generate experimental effect analysis results, where: and are the weight coefficients of production data and quality indicators, It represents the results of experimental effect analysis. Indicates the results of production data difference analysis, Represents the quality index difference value.

[0188] The formula for the production data difference analysis results is: ;

[0189] : Production data of the experimental group (such as production efficiency);

[0190] : Production data of the control group (such as production efficiency);

[0191] : Differences in production data;

[0192] Through experiments, record the production efficiency of the experimental group 120 units / hour;

[0193] Record the production efficiency of the control group is 100 units / hour;

[0194] Substitute into the formula to calculate :

[0195] Unit / hour;

[0196] Differences in production efficiency Unit / hour, indicating the improvement in production efficiency after optimizing operating parameters.

[0197] The formula for the quality index difference value is:

[0198] ;

[0199] : total number of samples;

[0200] : No. Quality indicators of the experimental group of samples;

[0201] : No. Quality indicators of the control group of samples;

[0202] : Differences in quality indicators;

[0203] From the experimental data, the quality indicators of the five samples were recorded. The quality data of the experimental group and the control group were unit, Unit, and so on, set the total number of samples ;

[0204] Calculate the difference in quality index for each sample, for example: |2.5-2.3|=0.2 units;

[0205] Summarize the differences in quality indicators of all samples and calculate the average value, and substitute it into the formula to calculate :

[0206] unit;

[0207] Differences in quality indicators Units, representing the average difference in quality indicators.

[0208] Experimental effect analysis result formula: ;

[0209] : Weight coefficient of production data;

[0210] : Weight coefficient of quality index;

[0211] : Differences in production data;

[0212] : Differences in quality indicators;

[0213] : Comprehensive analysis results of experimental effects;

[0214] The data obtained from the previous two steps Unit / hour, unit;

[0215] Weight coefficient and According to the actual application settings, for example: ;

[0216] Substitute into the formula to calculate :

[0217] ;

[0218] Comprehensive analysis results of experimental effects , which represents the comprehensive effect of optimizing the operating parameters.

[0219] See also Figure 6 The steps for obtaining the optimized strategy adjustment scheme are specifically as follows:

[0220] S321: According to the experimental effect analysis results, analyze the experimental effect data, identify the key factors affecting production efficiency, collect the changes in experimental effects and production strategies, and use the formula:

[0221] ;

[0222] Generate the influence coefficients of multiple influencing factors, where represents the change in the experimental effect, represents the change in production strategy, Represents the influence coefficient of multiple influencing factors;

[0223] S322: Based on the influence coefficients of the multiple influencing factors and the experimental effect data, the optimization effect of each production strategy is calculated, and the experimental effect data of each production strategy is captured, using the formula:

[0224] ;

[0225] Generate an optimization effect data set, where is the total number of strategies, For the The influence coefficient of the strategy, For the The experimental effect data of the strategy, To adjust the parameters, To optimize the effect data set;

[0226] S323: According to the optimization effect data set, use the sorting and selection method to determine the production strategy with the best optimization effect, select the data with the best effect as the strategy, and generate the optimized strategy adjustment plan.

[0227] The formula for the influence coefficient of multiple influencing factors is: ;

[0228] : the change in the experimental effect;

[0229] : The amount of change in production strategy;

[0230] : influence coefficient;

[0231] Obtained through actual monitoring and data collection:

[0232] Experimental effect change :For example, in an experiment, the production efficiency is increased from 80 units to 95 units, then unit;

[0233] Production strategy changes :For example, after the production strategy is adjusted, the production cost increases from 5000 yuan to 5500 yuan, then Yuan;

[0234] calculate : ;

[0235] Influence coefficient Represents the impact of each unit change in production strategy on the experimental effect.

[0236] Formula for optimizing the performance dataset: ;

[0237] : No. The influence coefficient of the strategy;

[0238] : No. Experimental effect data of the strategies;

[0239] : Adjust parameters;

[0240] : Optimization effect data set;

[0241] There are 3 strategies set up, and their influence coefficients And experimental effect data After actual monitoring and calculation, the following results are obtained:

[0242] Strategy 1: ;

[0243] Strategy 2: ;

[0244] Strategy 3: ;

[0245] Adjusting parameters ;

[0246] calculate :

[0247] ;

[0248] Optimization effect data set Represents the effect of optimized strategy adjustment.

[0249] The optimization effect data set in the previous step Sorting and selection are performed to obtain the production strategy with the best optimization effect. The strategy with the best optimization effect obtained by calculation and sorting is set. Its optimization effect data is 2.12. This strategy is selected as the optimized strategy adjustment plan.

[0250] The optimized strategy adjustment plan is the result of sorting and selecting based on the optimization effect data set, and represents the best production strategy adjustment plan.

[0251] See also Figure 7 The steps for obtaining the cost-benefit analysis results are specifically as follows:

[0252] S411: applying the optimized strategy adjustment scheme to the full permeable brick production line, monitoring the implementation effect, recording the production cost and production efficiency of the production line before and after the optimization, and forming a production cost data set and a production efficiency data set;

[0253] S412: Calculate the production cost change and production efficiency change before and after optimization based on the production cost data set and the production efficiency data set, using the formula:

[0254] ;

[0255] and

[0256] ;

[0257] Calculate the changes in production costs and production efficiency, where: To optimize production costs, To optimize the pre-production cost, To optimize production efficiency, To optimize production efficiency, represents the change in production cost, Indicates changes in production efficiency;

[0258] S413: According to the production cost change and production efficiency change, the benefits of production cost and production efficiency are evaluated, using the formula:

[0259] ;

[0260] Generate cost-benefit analysis results, where For production efficiency changes, For changes in production costs, To optimize the pre-production cost, To optimize production efficiency, Represents the results of cost-benefit analysis.

[0261] The formula for production cost change is:

[0262] ;

[0263] : Optimized production cost

[0264] : Production cost before optimization

[0265] Production cost data is obtained by actually monitoring the expenditure of the production line, which is divided into two stages: before optimization and after optimization;

[0266] The settings are obtained through data monitoring:

[0267] Production cost before optimization Yuan;

[0268] Optimized production cost Yuan;

[0269] calculate :

[0270] Yuan;

[0271] The production cost changes to It is -50,000 yuan, which means that the production cost is reduced by 50,000 yuan after optimization.

[0272] The formula for production efficiency change is: ;

[0273] : Optimized production efficiency;

[0274] : Production efficiency before optimization;

[0275] Production efficiency data is obtained by actually monitoring the output of the production line, which is divided into two stages: before optimization and after optimization;

[0276] The settings are obtained through data monitoring:

[0277] Production efficiency before optimization Unit / hour;

[0278] Optimized production efficiency Unit / hour;

[0279] calculate : Unit / hour;

[0280] Changes in production efficiency It is 200 units / hour, which means that the production efficiency is increased by 200 units / hour after optimization.

[0281] The formula for cost-benefit analysis results is:

[0282] ;

[0283] : Changes in production efficiency;

[0284] : Changes in production costs;

[0285] : Production cost before optimization;

[0286] : Production efficiency before optimization;

[0287] Calculated based on the results of the previous steps and data for further calculation of cost-effectiveness ratios;

[0288] According to the above results: Yuan, Unit / hour, Yuan, Unit / hour;

[0289] calculate :

[0290] ;

[0291] Cost-effectiveness ratio It is about -0.0072, which means that in the calculation, the cost-effectiveness ratio is negative, indicating that during the optimization process, the cost is not enough to completely offset the improvement in production efficiency.

[0292] See also Figure 8 The specific steps for obtaining the continuous improvement production strategy are:

[0293] S421: Analyze cost-benefit data according to the cost-benefit analysis result, record energy consumption data and production efficiency data of the current production line, extract related data and create a preliminary data analysis basic set;

[0294] S422: Based on the preliminary data analysis basic set, normalize the current data, apply statistical analysis methods to identify trends and patterns on the normalized data, and use the formula:

[0295] ;

[0296] and ;

[0297] Calculate the energy efficiency ratio and optimization potential, where: Represents the optimized production efficiency, Represents the optimized energy consumption, Represents the production efficiency before optimization, represents the energy consumption before optimization, It represents the energy efficiency ratio, Indicates optimization potential;

[0298] S423: Based on the energy efficiency ratio and optimization potential, evaluate the current production line operation process, identify key energy consumption peaks and efficiency bottlenecks, and monitor and evaluate them using the formula:

[0299] ;

[0300] Continuously improve production strategies, including: To optimize the energy efficiency ratio, To optimize potential, To optimize the energy efficiency ratio before Indicates continuous improvement of production strategy.

[0301] Parameter acquisition:

[0302] Energy consumption data: obtained through the energy consumption monitoring system of the actual production line, the energy consumption data of the production line is set as follows: before optimization: , after optimization, ;

[0303] Production efficiency data: obtained through the production efficiency monitoring system of the actual production line, the production efficiency data is set as follows: before optimization Unit, after optimization unit;

[0304] The formula of energy efficiency ratio is: ;

[0305] Bring in actual data : Unit: / kWh;

[0306] Energy efficiency ratio : The optimized production efficiency and energy consumption are calculated to be 2.20 units / kWh.

[0307] Formula for optimization potential:

[0308] ;

[0309] First calculate the energy efficiency ratio before optimization : Unit: / kWh;

[0310] Calculate the square root:

[0311] ;

[0312] Then calculate the optimization potential :

[0313] ;

[0314] Optimization potential : It is calculated by dividing the difference in energy efficiency ratio before and after optimization by the square root of the energy consumption and production efficiency before optimization, and the result is 0.0034.

[0315] The formula for continuous improvement of production strategy: ;

[0316] Calculate the square root:

[0317] ;

[0318] Bring in actual data calculation :

[0319] ;

[0320] Continuously improve production strategy : Calculated based on the energy efficiency ratio and optimization potential, the result is 0.00695.

[0321] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A dynamic optimization method for a permeable brick production line, characterized in that: The following steps are involved: Based on the energy consumption in the permeable brick production line, the energy consumption data of the mixer and the press in the permeable brick manufacturing process are recorded by the energy consumption recorder to obtain the energy consumption data set, and the energy consumption limit of the equipment is adjusted to obtain the adjusted energy consumption parameters; The steps for obtaining the adjusted energy consumption parameters are specifically as follows: Based on the energy consumption in the permeable brick production line, the energy consumption data of the mixer and press in the permeable brick production are obtained from the energy consumption recorder, the data are screened and processed, abnormal and noise data are excluded, and the formula is used: ； The processed total energy consumption data is obtained, where represents the energy consumption data of the i-th time period, Represents the total energy consumption data, is the total number of time periods; Based on the processed total energy consumption data, identify the devices and time periods with high energy consumption, using the formula: ； Calculate and obtain average energy consumption data, where: and represent the minimum and maximum energy consumption respectively, Indicates average energy consumption data, is the total energy consumption data, is the total number of time periods, and They are adjustment parameters respectively; Using the average energy consumption data, adjusting the energy consumption parameters of the equipment, optimizing the equipment operation parameters through real-time feedback control configuration, and obtaining the adjusted energy consumption parameters; Extract key control points from the adjusted energy consumption parameters, fine-tune the operating conditions of the permeable brick production line and match the new energy consumption requirements, generate a fine-tuning operation strategy, implement the adjustments in the fine-tuning operation strategy, optimize the production parameters according to the monitoring data of production efficiency and energy consumption, and obtain optimized operating parameters; The steps for obtaining the fine-tuning operation strategy are specifically as follows: According to the adjusted energy consumption parameters, analyze the key control points, including the temperature of the mold and the pressure of the press, obtain the original energy consumption data, and calculate the energy consumption change rate of each control point using the formula: ； Generate energy consumption change rate data set, where Represents the adjusted energy consumption value, Represents the energy consumption value before adjustment, represents the rate of change of energy consumption; Analyze the energy consumption change rate data set, set the adjustment range of temperature and pressure, and compare the change rate with the control point of the set threshold using the formula: ； Generate an adjustment proposal dataset, where represents the total number of data points, Representative The energy consumption change rate of each data point, represents the thresholds for temperature and pressure adjustments; Combining the adjustment proposal data set with the control point analysis results, formulating operation steps, adjusting the mold temperature and the press pressure, and generating a fine-tuning operation strategy; The steps for obtaining the optimized operating parameters are specifically as follows: Implement the adjustments in the fine-tuning operation strategy, collect current production parameter data based on the monitoring data of production efficiency and energy consumption, analyze the difference between real-time production parameters and theoretical optimization parameters, calculate the range of production parameter changes, and use the formula: ； Generate preliminary optimization parameter change data, where: represents the adjusted production parameter value, Represents the production parameter value before adjustment, Represents the range of changes in production parameters; The preliminary optimization parameter change data is analyzed, and the data is combined with the relationship between production efficiency and energy consumption, through the formula: ； Generate an optimized and adjusted dataset, where is the energy consumption weight coefficient, Representative The energy consumption variation of each data point is represents the total number of data points, Representative The production parameter variation range of each data point, represents the optimized and adjusted dataset; Applying the optimized and adjusted data set to the real-time production parameter adjustment process, adjusting the production parameters to an optimized state with reference to production efficiency and energy consumption, and obtaining optimized operating parameters; Using the optimized operating parameters, an experiment is designed to test the effect of the parameters on the real-time production of permeable bricks. The experiment compares the production data before and after the adjustment, measures the operating efficiency and the quality standard of the permeable bricks, generates an experimental effect analysis result, and adjusts the production strategy according to the experimental effect analysis result to obtain an optimized strategy adjustment plan; The steps for obtaining the experimental effect analysis results are specifically as follows: The optimized operating parameters were used to design experiments, set up experimental groups and control groups, record production data of multiple groups, and use the formula: ； The production data difference analysis results are calculated, where: is the production data of the experimental group, The production data of the control group, Indicates the results of production data difference analysis; Analyze the impact of the production data difference analysis results on the quality of permeable bricks, calculate the quality index difference value, and use the formula: ； Generate quality indicator difference analysis results, where: is the total number of samples, Representative The quality index of the experimental group of samples, Representative The quality index of the control group of samples, Represents the difference value of quality index; Combined with the above-mentioned quality index difference analysis results and production data difference analysis results, and analyzed to evaluate the impact of optimized operating parameters on permeable brick production, the formula is used: ； Generate experimental effect analysis results, where: and are the weight coefficients of production data and quality indicators, It represents the results of the experimental effect analysis. Indicates the results of production data difference analysis, Represents the difference value of quality index; The steps for obtaining the optimized strategy adjustment scheme are specifically as follows: According to the experimental effect analysis results, analyze the experimental effect data, identify the key factors affecting production efficiency, collect the changes in experimental effects and production strategies, and use the formula: ； Generate the influence coefficients of multiple influencing factors, where represents the change in the experimental effect, represents the change in production strategy, Represents the influence coefficient of multiple influencing factors; Based on the influence coefficients of the multiple influencing factors and the experimental effect data, the optimization effect of each production strategy is calculated, and the experimental effect data of each production strategy is captured, using the formula: ； Generate an optimization effect data set, where is the total number of strategies, For the The influence coefficient of the strategy, For the The experimental effect data of the strategy, To adjust the parameters, To optimize the effect data set; According to the optimization effect data set, using the sorting and selection method, determine the production strategy with the best optimization effect, select the data with the best effect as the strategy, and generate the optimized strategy adjustment plan; Apply the optimized strategy adjustment plan to the fully permeable brick production line, monitor the execution effect of the measures, evaluate the changes in production cost and efficiency, generate cost-benefit analysis results, and continuously optimize energy consumption and improve efficiency based on the cost-benefit analysis results, and conduct monitoring and evaluation to obtain a continuous improvement production strategy; The steps for obtaining the cost-benefit analysis results are specifically as follows: Apply the optimized strategy adjustment scheme to the full permeable brick production line, monitor the implementation effect, record the production cost and production efficiency of the production line before and after optimization, and form a production cost data set and a production efficiency data set; According to the production cost data set and the production efficiency data set, the production cost change and the production efficiency change before and after the optimization are calculated using the formula: and ; Calculate the changes in production costs and production efficiency, where: To optimize production costs, To optimize the pre-production cost, To optimize production efficiency, To optimize production efficiency, represents the change in production cost, Indicates changes in production efficiency; According to the changes in production costs and production efficiency, the benefits of production costs and production efficiency are evaluated using the formula: ； Generate cost-benefit analysis results, where For production efficiency changes, For changes in production costs, To optimize the pre-production cost, To optimize production efficiency, Present the results of cost-benefit analysis; The steps for obtaining the continuous improvement production strategy are specifically as follows: According to the cost-benefit analysis results, analyzing the cost-benefit data, recording the energy consumption data and production efficiency data of the current production line, extracting related data and creating a preliminary data analysis basic set; Based on the preliminary data analysis foundation set, the current data is normalized, and statistical analysis methods are applied to the normalized data for trend and pattern recognition, using the formula: and ; Calculate the energy efficiency ratio and optimization potential, where: Represents the optimized production efficiency, Represents the optimized energy consumption, Represents the production efficiency before optimization, represents the energy consumption before optimization, represents the energy efficiency ratio, Indicates optimization potential; Based on the energy efficiency ratio and optimization potential, evaluate the current production line operation process, identify key energy consumption peaks and efficiency bottlenecks, and monitor and evaluate them using the formula: ； Continuously improve production strategies, including: To optimize the energy efficiency ratio, To optimize potential, To optimize the energy efficiency ratio before Indicates continuous improvement of production strategy.

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