Intelligent control system and method for shower valve production process

Abstract

The invention relates to the technical field of production process control, in particular to an intelligent control system and method for a shower valve production process, and the system comprises an environment monitoring module which collects the operation state of a machine, the batch quality of raw materials, and the temperature and humidity data of a surrounding environment, carries out the real-time monitoring of the operation state of the machine, and carries out the tracking analysis of the batch of the raw materials. According to the method, by continuously monitoring the machine state and the environment condition, key variables in the production process are controlled, dynamic data analysis is conducted, production parameters are adjusted in time, optimal matching of raw material use and the production rhythm is ensured, waste is effectively reduced, the operation efficiency of a production line is improved, and the risk assessment model is utilized; potential problems in the production process can be predicted and relieved, production safety is improved, energy consumption is optimized by applying an evolutionary algorithm, balance between environmental protection and cost effectiveness is achieved, production configuration is continuously optimized through a real-time feedback mechanism, it is ensured that production activities meet the latest operation standard, and overall production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production process control, and in particular to an intelligent control system and method for a shower valve production process. Background Art

[0002] Production process control is a field involving automation technology, the core purpose of which is to optimize and monitor the operation of the production line through a high degree of technical integration to improve efficiency, quality and safety. This technology field is widely used in various manufacturing and production industries, such as automobile manufacturing, food processing, chemical production, etc. It achieves precise control and monitoring of production equipment through the use of sensors, controllers and software systems to ensure the continuity and efficiency of the production process. Production process control technology also involves data collection and analysis in order to continuously improve and optimize the production process.

[0003] Among them, the intelligent control system for the production process of shower valves refers to an intelligent system specially designed to manage and optimize the production process of shower valves. The purpose of this system is mainly to improve the production efficiency and product quality of shower valves, reduce resource waste, and ensure production safety. By integrating automation technologies such as robots, sensors and artificial intelligence algorithms, the system can monitor various parameters on the production line in real time, automatically adjust production settings or warn of potential production problems, thereby achieving efficient and precise control of the entire production process. This system is a typical representative of the trend of modern manufacturing industry towards high efficiency, low cost and high automation level.

[0004] In existing technologies, the monitoring of the environment and production line status is usually not comprehensive enough and cannot reflect changes in raw materials or the environment in real time, which often leads to damage to product quality and production efficiency. The lack of effective risk assessment tools makes it difficult to predict and manage potential problems in production, increasing the risk of production interruptions. In addition, traditional production lines often do not adopt data-driven optimization methods, resulting in inefficient use of energy and resources. For example, unoptimized energy management not only increases costs, but also places unnecessary burdens on the environment. These problems limit the competitiveness of enterprises in the market and hinder the improvement of production efficiency and quality. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent control system and method for the production process of shower valves.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: an intelligent control system for the production process of shower valves comprises:

[0007] The environmental monitoring module collects data on machine operation status, raw material batch quality, ambient temperature and humidity, monitors machine operation status in real time, tracks and analyzes raw material batches, and continuously records ambient temperature and humidity to form multi-dimensional indicators of environmental data;

[0008] The Bayesian network update module inputs the multi-dimensional indicators of the environmental data into the Bayesian network, performs weighted adjustments on the nodes of the Bayesian network, reconfigures the network links, and generates a dynamic risk assessment;

[0009] The decision optimization module uses the dynamic risk assessment to adjust the production speed and the ratio of raw material usage, analyzes the impact of the production speed on the overall operating efficiency, adjusts the ratio of raw material usage to match the production demand, and determines the production line operation parameters;

[0010] The multi-objective evolutionary algorithm module uses the production line operating parameters, applies the evolutionary algorithm to optimize the relationship between energy consumption and production efficiency, conducts parameter setting tests, evaluates the impact of various parameter settings on energy consumption and efficiency, and selects the optimal production configuration that matches the current production needs;

[0011] The real-time feedback and adjustment module compares the optimal production configuration with the current production data, analyzes the difference between the configuration and the actual operation, adjusts the production parameters, and establishes an adjusted production process.

[0012] The multi-dimensional indicators of environmental data specifically include machine operating status, raw material batch quality, ambient temperature, and ambient humidity; the dynamic risk assessment specifically includes node weight adjustment and network link reconfiguration; the production line operating parameters specifically include production speed and raw material usage ratio; the optimal production configuration specifically includes energy consumption relationship and production efficiency; the adjusted production process includes parameter updates and actual and expected differences.

[0013] As a further solution of the present invention, the steps of obtaining the multi-dimensional indicators of the environmental data are specifically as follows:

[0014] Monitor the machine's operating status in real time and continuously record the temperature and humidity of the environment. Collect data through sensor technology and integrate them into a set of raw data records of the machine and environment.

[0015] Filtering the operating status data and environmental data directly related to the machine performance from the raw data record set, performing data preprocessing including standardization and noise removal, and generating a preprocessed data set;

[0016] Multidimensional data analysis technology is applied to the preprocessed data set to analyze the correlation between the machine operation status and environmental factors, using the formula:

[0017]

[0018] Adjust the sensitivity of differentiated environmental factors, calculate the impact of environmental factors on machine performance, and generate multi-dimensional indicators of environmental data;

[0019] Among them, E ij is the jth environmental factor index of the ith sample, x ik is the kth original data point of the ith sample, μ k , σ k are the mean and standard deviation of the kth data point, w k represents the weight coefficient, which adjusts the influence of multiple environmental factors in performance evaluation, k represents the index of the data point, and n represents the total number of data points.

[0020] As a further solution of the present invention, the steps of obtaining the dynamic risk assessment are specifically as follows:

[0021] Import the multi-dimensional indicators of the environmental data into the Bayesian network, analyze the weights of the network nodes in real time, and dynamically adjust the weights according to the current data to match the changing data pattern and generate real-time adjusted node weights;

[0022] According to the real-time adjusted node weights, the links of the Bayesian network are optimized and reconfigured, and the optimal connection configuration is ensured by using a structural learning algorithm to generate an optimized network structure;

[0023] In the optimized Bayesian network, the risk is calculated using the formula:

[0024]

[0025] Generate dynamic risk assessments;

[0026] Among them, R t represents the total risk assessment at time t, w i Adjust the contribution weight of each node, v ti is the observed value of the node at time t, σ i Yes ti The standard deviation of i is the baseline-adjusted value, Represents the normalized square of the node observation value, emphasizing the impact of outliers.

[0027] As a further solution of the present invention, the steps of obtaining the operating parameters of the production line are specifically as follows:

[0028] Utilizing the data obtained from the dynamic risk assessment, analyzing the impact of the current production speed on the overall operating efficiency, calculating the necessary production speed adjustment, matching the risk tolerance of the production line, and generating adjusted production speed parameters;

[0029] Re-evaluate and optimize the raw material usage ratio in combination with the production demand and the adjusted production speed parameters to ensure the best match between raw material input and production efficiency, and generate optimized raw material usage ratio parameters;

[0030] Combining the adjusted production speed parameters and the optimized raw material ratio parameters, the formula is adopted:

[0031]

[0032] Calculate and set production line operating parameters;

[0033] Among them, P opt Represents the production line operating parameters, S adj is the adjusted production speed, R opt is the optimized raw material usage ratio, γ is the influence of adjusting the production speed, and δ is the influence of the weighted raw material ratio.

[0034] As a further solution of the present invention, the step of obtaining the optimal production configuration is specifically as follows:

[0035] Using the production line operation parameters, the evolutionary algorithm model is initialized, the initial population of the algorithm is set based on the current production parameters, the impact of differentiated production configurations on energy consumption and efficiency is simulated, and initial test results are generated;

[0036] Analyze the initial test results, identify key influencing factors between energy consumption and production efficiency, conduct multi-parameter sensitivity analysis, determine key parameters that affect production efficiency and energy consumption, and generate parameter impact results;

[0037] According to the parameter impact results, the selection, crossover and mutation strategies in the evolutionary algorithm are adjusted to optimize the algorithm to match production needs, the optimized model is run, multiple rounds of iterative testing are performed, and optimized test results are generated;

[0038] The production configuration is selected from the optimization test results using the formula:

[0039]

[0040] Select the optimal production configuration that matches current production needs;

[0041] Among them, C opt represents the optimal production configuration, E eff is the optimized production efficiency, E con is the corresponding energy consumption value, ξ and ζ are the weight coefficients for adjusting the impact of production efficiency and energy consumption.

[0042] As a further solution of the present invention, the steps for obtaining the adjusted production process are specifically:

[0043] Collect current production data, including production speed, raw material usage rate and product quality, analyze the difference between the data and the optimal production configuration, and generate a difference analysis result;

[0044] According to the difference analysis results, determine the production parameters that need to be adjusted, including production speed and raw material ratio, and use statistical methods to calculate the current parameter settings to minimize production costs and resource waste, and generate the current parameter settings;

[0045] Applying the current parameter settings to the production process simulation, performing multiple rounds of testing to evaluate the adjustment effect, and generating simulation test results;

[0046] According to the simulation test results, the operating parameters of the production line are determined using the formula:

[0047]

[0048] Generate an adjusted production process by referring to the combined impact of current and optimal parameters and the impact of differences in energy consumption on configuration adjustment;

[0049] Among them, R new represents the adjusted production process, P current is the current production parameter, P opt is the optimal production configuration, α represents the weight adjusted according to efficiency, β represents the sensitivity of energy consumption difference to parameter adjustment, E diff Represents the standard deviation of energy consumption obtained from data analysis.

[0050] A method for intelligently controlling a shower valve production process is provided. The method is executed based on the above-mentioned intelligent control system for the shower valve production process, and comprises the following steps:

[0051] S1: Collect machine operation status data, raw material batch quality data, ambient temperature and humidity data, monitor the quality of raw materials, continuously record environmental factors, form multi-dimensional indicators of environmental data through data integration, and generate environmental monitoring results;

[0052] S2: input the multi-dimensional indicators in the environmental monitoring results, adjust the Bayesian network node weights, reconfigure the network links, and generate a dynamic risk assessment through node weight adjustment and network link reconfiguration;

[0053] S3: According to the dynamic risk assessment, the production speed is adjusted to match the raw material usage ratio with the production demand, the raw material usage ratio and the production speed are optimized, the impact on the overall operating efficiency is analyzed, the optimal production line operating parameters are determined, and the optimized production configuration is obtained;

[0054] S4: Utilize the optimized production configuration to optimize the relationship between energy consumption and production efficiency, test various parameter settings, evaluate the impact of various settings on energy consumption and efficiency, select the optimal production configuration, compare it with current production data, analyze the difference between the configuration and actual operation, adjust production parameters, and establish an adjusted production process.

[0055] Compared with the prior art, the advantages and positive effects of the present invention are:

[0056] In the present invention, precise control of key variables in the production process is achieved by continuously monitoring the machine status and environmental conditions. Dynamic data analysis helps to adjust production parameters in a timely manner to ensure the best match between raw material use and production rhythm, effectively reduce waste, and improve the operating efficiency of the production line. Using risk assessment models, potential problems in the production process can be predicted and mitigated, increasing production safety. Evolutionary algorithms are used to optimize energy consumption to achieve a balance between environmental protection and cost-effectiveness. Through real-time feedback mechanisms, production configurations are continuously optimized to ensure that production activities meet the latest operating standards and improve overall production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a system flow chart of the present invention;

[0058] Figure 2 A flowchart of the steps for obtaining multi-dimensional indicators of environmental data of the present invention;

[0059] Figure 3 A flowchart of the acquisition steps of the dynamic risk assessment of the present invention;

[0060] Figure 4 A flow chart of the steps for obtaining the operating parameters of the production line of the present invention;

[0061] Figure 5 A flow chart of the steps for obtaining the optimal production configuration of the present invention;

[0062] Figure 6 The figure is a flowchart of the acquisition steps of the production process adjusted by the present invention. DETAILED DESCRIPTION

[0063] 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.

[0064] 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.

[0065] Embodiment 1

[0066] See also Figure 1 The present invention provides a technical solution: an intelligent control system for the production process of shower valves includes:

[0067] The environmental monitoring module collects data on machine operation status, raw material batch quality, ambient temperature and humidity, monitors machine operation status in real time, tracks and analyzes raw material batches, and continuously records ambient temperature and humidity to form multi-dimensional indicators of environmental data;

[0068] The Bayesian network update module inputs multi-dimensional indicators of environmental data into the Bayesian network, performs weighted adjustments to the nodes of the Bayesian network, reconfigures the network links, and generates dynamic risk assessments;

[0069] The decision optimization module uses dynamic risk assessment to adjust the production speed and raw material usage ratio, analyzes the impact of production speed on overall operating efficiency, adjusts the raw material usage ratio to match production demand, and determines the production line operating parameters;

[0070] The multi-objective evolutionary algorithm module uses the production line operating parameters and applies the evolutionary algorithm to optimize the relationship between energy consumption and production efficiency, conduct parameter setting tests, evaluate the impact of various parameter settings on energy consumption and efficiency, and select the optimal production configuration that matches the current production needs;

[0071] The real-time feedback and adjustment module compares the optimal production configuration with the current production data, analyzes the difference between the configuration and the actual operation, adjusts the production parameters, and establishes the adjusted production process.

[0072] The multi-dimensional indicators of environmental data include machine operating status, raw material batch quality, ambient temperature, and ambient humidity. Dynamic risk assessment includes node weight adjustment and network link reconfiguration. Production line operating parameters include production speed and raw material usage ratio. The optimal production configuration includes energy consumption relationship and production efficiency. The adjusted production process includes parameter updates and actual and expected differences.

[0073] See also Figure 2 ,The specific steps for obtaining multi-dimensional indicators of environmental data are as follows:

[0074] Monitor the machine's operating status in real time and continuously record the temperature and humidity of the environment. Collect data through sensor technology and integrate them into a set of raw data records of the machine and environment.

[0075] In the process of monitoring the machine's operating status and recording environmental data, sensors are first deployed to collect data, including the machine's operating speed, temperature, vibration parameters, and the temperature and humidity of the surrounding environment. The sensor data is transmitted to the data center in real time via a wireless network. The data center uses a real-time data processing system to perform preliminary filtering and sorting on the collected data, eliminating obvious outliers, including readings where the temperature suddenly jumps below zero or exceeds the working limit of the equipment. The data is then standardized, that is, converted into values ​​within the range of standard deviation and mean for more in-depth analysis. The preprocessed data is stored in a high-performance database to support query and analysis, and the generated raw data record set provides the input basis for subsequent analysis.

[0076] Filter the operating status data and environmental data directly related to the machine performance from the original data record set, perform data preprocessing including standardization and noise removal, and generate a preprocessed data set;

[0077] Based on the collected raw data, data availability and accuracy are enhanced through data preprocessing. Preprocessing includes data cleaning and data conversion. The key to data cleaning is to remove invalid and erroneous data and fill in missing values, and use statistical methods to identify and replace outliers. Data conversion involves adjusting all data to the same scale and comparing them in an unbiased manner, including standardization through the z-score method to convert the data into a format with a mean of 0 and a standard deviation of 1. The processed data set includes all the key indicators required for the correlation analysis of machine operating status and environmental conditions, making further multidimensional data analysis possible.

[0078] Multidimensional data analysis techniques are applied to the preprocessed data set to analyze the correlation between the machine operation status and environmental factors, using the formula:

[0079]

[0080] Adjust the sensitivity of differentiated environmental factors, calculate the impact of environmental factors on machine performance, and generate multi-dimensional indicators of environmental data;

[0081] Among them, E ij is the jth environmental factor index of the ith sample, x ik is the kth original data point of the ith sample, μ k , σ kare the mean and standard deviation of the kth data point, w k represents the weight coefficient, which adjusts the influence of multiple environmental factors in performance evaluation, k represents the index of the data point, and n represents the total number of data points.

[0082] formula:

[0083]

[0084] The benefit of the formula is that it weights the data points by reference to the differentiated criticality of each environmental factor. This approach increases the model’s sensitivity to key variables, allowing for more accurate prediction and analysis of the actual impact of environmental factors on machine performance.

[0085] Detailed explanation of the formula and the process of formula calculation and derivation:

[0086] There are three environmental factors: temperature, humidity and vibration level. The weights of each factor are 0.5, 0.3 and 0.2 respectively. The environmental data points are as follows: temperature (x1 = 20°C, μ1 = 15°C, σ1 = 5°C), humidity (x2 = 30%, μ2 = 40%, σ2 = 10%), vibration level (x3 = 0.5, μ3 = 0.3, σ3 = 0.1). Calculate the score of each factor:

[0087]

[0088] E j =(0.5·1)+(0.3·-1)+(0.2·2)

[0089] E ij =0.5-0.3+0.4=0.6

[0090] The results show that after referring to the weights and deviations of multiple environmental factors, the total environmental score is 0.6, indicating that the impact of the environment on machine performance is at an upper-middle level. The collated data can be used to adjust the machine's operating parameters or make maintenance decisions.

[0091] See also Figure 3 , the specific steps for obtaining dynamic risk assessment are:

[0092] Import multi-dimensional indicators of environmental data into the Bayesian network, analyze the weights of network nodes in real time, and dynamically adjust the weights according to current data to match the changing data patterns and generate real-time adjusted node weights;

[0093] Importing multi-dimensional indicators of environmental data into the Bayesian network, firstly, by connecting environmental sensors and data acquisition systems, real-time collection of ambient temperature, humidity and other related data is performed, and then transmitted to the data processing center through the network. The data center uses a preliminary data cleaning algorithm to remove noise and outliers to ensure that the data quality meets the requirements of model input. In addition, by applying data standardization processing, all collected data is converted into a standard format to facilitate the algorithm processing of the Bayesian network. During the standardization process, the scale of the data is adjusted to ensure that the data is at the same level to reduce errors during model training.

[0094] According to the real-time adjusted node weights, the links of the Bayesian network are optimized and reconfigured, and the structural learning algorithm is used to ensure the optimal connection configuration and generate the optimized network structure;

[0095] The links of the Bayesian network are reconfigured according to the real-time adjustment of node weights. This is based on current network optimization research and algorithms. First, network analysis is used to determine which node links cause bottlenecks or redundancy in information flow. Then, the strength of these links is adjusted or they are completely disconnected according to the algorithm solution. The reconfigured network will undergo a series of performance tests to ensure that each adjustment can actually improve the performance of the network. During the optimization process, the network's scalability and ability to match future data changes will also be referenced to make the network more robust.

[0096] In the optimized Bayesian network, the risk is calculated using the formula:

[0097]

[0098] Generate dynamic risk assessments;

[0099] Among them, R t represents the total risk assessment at time t, w i Adjust the contribution weight of each node, v ti is the observed value of the node at time t, σ i Yes ti The standard deviation of i is the baseline-adjusted value, Represents the normalized square of the node observation value, emphasizing the impact of outliers.

[0100] formula:

[0101]

[0102] The benefit of the formula is that by increasing the square of each node's observation value divided by the standard deviation, it emphasizes the impact of outliers, making the model more sensitive to mutations, helping to quickly respond to changes in risks in a dynamically changing environment, and improving the model's predictive accuracy and the timeliness of risk assessment.

[0103] Detailed explanation of the formula and the process of formula calculation: Setting the environment variable v ti The number of observations at time t is 50, with a standard deviation σ i is 10, weight w i is 0.5, the baseline adjustment value b i is 1.5. The calculation process is as follows:

[0104] 1.

[0105] 2.

[0106] 3.

[0107] 4.R t =125+1.5=126.5

[0108] The results show that at a given time point t, the risk assessment of the overall network is 126.5, which means that the system predicts a high risk level based on the current environmental data and network configuration.

[0109] See also Figure 4 , the specific steps for obtaining the production line operation parameters are:

[0110] Using the data obtained from the dynamic risk assessment, analyze the impact of the current production speed on the overall operating efficiency, calculate the necessary production speed adjustment, match the risk tolerance of the production line, and generate the adjusted production speed parameters;

[0111] The process of adjusting production speed parameters involves obtaining data from dynamic risk assessment, monitoring the production line's operating data and risk levels in real time, using data analysis technology to evaluate the relationship between production speed and overall operating efficiency, and adjusting production speed to minimize risk while maximizing output. This requires continuous monitoring and analysis of current machine performance, raw material supply conditions, and market demand to generate adjusted production speed parameters to ensure that production efficiency remains optimal in a changing market and risk environment.

[0112] Re-evaluate and optimize the raw material usage ratio based on production demand and adjusted production speed parameters to ensure the best match between raw material input and production efficiency, and generate optimized raw material usage ratio parameters;

[0113] In the process of adjusting the raw material usage ratio, it is necessary to refer to the raw material input amount that matches the adjusted production speed parameters. By analyzing the raw material market supply chain, the quality and quantity of available raw materials are determined. Combined with changes in production demand, the algorithm model is used to calculate the optimal raw material usage ratio. This ratio needs to be dynamically adjusted to match the real-time operating status of the production line and external supply conditions. The process of re-evaluating raw material configuration involves a comprehensive reference to raw material costs and supply stability. The optimized raw material ratio parameters are the comprehensive results based on production efficiency and cost control.

[0114] Comprehensively adjust the production speed parameters and optimize the raw material ratio parameters, using the formula:

[0115]

[0116] Calculate and set production line operating parameters;

[0117] Among them, P opt Represents the production line operating parameters, S adj is the adjusted production speed, R opt is the optimized raw material usage ratio, γ is the influence of adjusting the production speed, and δ is the influence of the weighted raw material ratio.

[0118] formula:

[0119]

[0120] The benefit of the formula is that by adjusting the weight coefficients γ and δ, the impact of production speed and raw material usage ratio on production line operating parameters can be flexibly controlled, thereby optimizing the balance between production efficiency and cost efficiency.

[0121] Detailed explanation of the formula and the process of formula calculation and derivation:

[0122] Set the current production speed adjustment value S adj The raw material usage ratio adjustment value is 200 units / hour. opt is 0.5, the weight coefficients γ and δ are set to 1.5 and 0.75 respectively, and the production line operation parameters are calculated according to the formula:

[0123] P opt =1.5·200+0.75·(0.5) 2 =300+0.1875=300.1875

[0124] The results show that the optimal operating parameters of the production line are 300.1875, referring to the adjusted production speed and raw material ratio. This means that in order to achieve the best operating efficiency and cost efficiency, the parameters of the production line need to be set at this value.

[0125] See also Figure 5, the steps to obtain the optimal production configuration are as follows:

[0126] Using the production line operating parameters, the evolutionary algorithm model is initialized, the initial population of the algorithm is set based on the current production parameters, the impact of differentiated production configurations on energy consumption and efficiency is simulated, and initial test results are generated;

[0127] When initializing the evolutionary algorithm model, it is necessary to set the basic conditions of the algorithm according to the existing production line operating parameters, including the size of the population, the crossover probability and the mutation probability in the genetic algorithm. These parameters directly affect the convergence speed of the algorithm and the quality of the solution. According to production needs, adjust the parameters to simulate the impact of differentiated configuration on energy consumption and efficiency. Through multiple iterations, collect operating data under differentiated parameter settings. These data will be used for iterative analysis and optimization to ensure that the algorithm can capture the optimal solution in the true sense, thereby achieving the optimal balance between production efficiency and energy consumption.

[0128] Analyze the initial test results, identify the key influencing factors between energy consumption and production efficiency, conduct multi-parameter sensitivity analysis, determine the key parameters that affect production efficiency and energy consumption, and generate parameter impact results;

[0129] When analyzing initial test results, the key is to identify those parameters that have the greatest impact on energy consumption and production efficiency. Through sensitivity analysis, determine the extent to which a small change in a parameter affects the output. These analyses rely on principal component analysis (PCA) or regression analysis. These technologies help analyze how differentiated parameters interact with each other and then determine which parameters are the key factors in adjusting the production configuration. Such analysis is the key to ensuring that the optimization results are both scientific and applicable to actual production.

[0130] According to the parameter impact results, adjust the selection, crossover and mutation strategies in the evolutionary algorithm, optimize the algorithm to match production needs, run the optimized model, conduct multiple rounds of iterative testing, and generate optimized test results;

[0131] Adjusting the evolutionary algorithm according to the evaluation results of influencing factors and optimizing the selection strategy, crossover and mutation methods are key steps in matching the evolutionary algorithm with production needs. This requires adjusting the algorithm parameters based on the data in the evaluation results to improve the matching and efficiency of the algorithm. The optimized algorithm will be tested with actual production data to ensure that each iteration is closer to actual production needs. After multiple rounds of iterative testing, the data obtained will more accurately reflect the effect of differentiated production configuration, thereby providing a scientific basis for production parameter setting.

[0132] The production configuration is selected from the optimization test results using the formula:

[0133]

[0134] Select the optimal production configuration that matches current production needs;

[0135] Among them, C opt represents the optimal production configuration, E eff is the optimized production efficiency, E con is the corresponding energy consumption value, ξ and ζ are the weight coefficients for adjusting the impact of production efficiency and energy consumption.

[0136] formula:

[0137]

[0138] The benefit of the formula is that by adjusting the weight coefficients ξ and ζ, the balance between production efficiency and energy consumption can be flexibly controlled to achieve the goal of reducing costs while increasing output.

[0139] Detailed explanation of the formula and the process of formula calculation and derivation:

[0140] The initial settings are ξ=0.5,ζ=0.3,E eff =85, E con =120, calculate:

[0141]

[0142] calculate:

[0143] log(E con +1)=log(121)≈4.8

[0144] Then calculate:

[0145] C opt =0.5·7225-0.3·4.8≈3612.56-1.44=3611.12

[0146] The results show that through the current parameter configuration, higher production efficiency can be achieved while keeping energy consumption within a reasonable range. Each parameter in the formula is obtained through actual data measurement and market demand analysis, ensuring the practical feasibility and scientificity of the formula application.

[0147] See also Figure 6 , the specific steps for obtaining the adjusted production process are:

[0148] Collect current production data, including production speed, raw material usage rate and product quality, analyze the difference between the data and the optimal production configuration, and generate difference analysis results;

[0149] Collect current production data including production speed, raw material utilization rate and product quality, analyze the difference between the data and the optimal production configuration, analyze the data at multiple stages in the production process through data mining, identify production bottlenecks and waste points, so as to adjust parameters in subsequent steps to eliminate differences, make production more efficient and save resources.

[0150] Based on the results of the difference analysis, determine the production parameters that need to be adjusted, including production speed and raw material ratio, and use statistical methods to calculate the current parameter settings to minimize production costs and resource waste, and generate the current parameter settings;

[0151] Based on the results of the difference analysis, the production parameters that need to be adjusted include production speed and raw material ratio. The current parameter settings are calculated by using statistical methods to minimize production costs and resource waste, and the current parameter settings are generated, including the use of regression analysis and variance analysis to optimize the parameter settings, ensuring that the optimization of parameters can effectively reduce costs in the production process and improve raw material utilization.

[0152] Apply current parameter settings to production process simulation, perform multiple rounds of testing to evaluate adjustment effects, and generate simulation test results;

[0153] Apply the current parameter settings to the production process simulation, conduct multiple rounds of simulation tests to verify the effectiveness of the parameter settings, and generate simulation test results to ensure that the actual application effect of the new parameters is consistent with the theoretical analysis. The simulation test includes setting differentiated production conditions and environmental parameters, and analyzing the impact of parameter changes on production efficiency through real-time data monitoring and collection, so as to determine which parameter adjustments can bring optimal production efficiency.

[0154] According to the simulation test results, the operating parameters of the production line are determined using the formula:

[0155]

[0156] Generate an adjusted production process by referring to the combined impact of current and optimal parameters and the impact of differences in energy consumption on configuration adjustment;

[0157] Among them, R new represents the adjusted production process, P current is the current production parameter, P opt is the optimal production configuration, α represents the weight adjusted according to efficiency, β represents the sensitivity of energy consumption difference to parameter adjustment, E diff Represents the standard deviation of energy consumption obtained from data analysis.

[0158] formula:

[0159]

[0160] The benefit of the formula is that it provides a method for dynamically adjusting production parameters by combining current production parameters and optimal configuration with reference to energy consumption differences, which helps to optimize cost-effectiveness and efficiency.

[0161] Detailed explanation of the formula and the process of formula calculation and derivation:

[0162] Set α = 0.5, β = 0.3, P current =100, P opt =150, E diff =20. The calculation process is as follows:

[0163] 1. Calculate P current +P opt =100+150=250

[0164] 2. Find the square root

[0165] 3. Calculate |E in the denominator diff |=20, then β·|E diff |+1=0.3×20+1=7

[0166] 4. Apply the formula to calculate

[0167] The results show that the adjusted production process is more efficient than the current configuration, with lower energy consumption and relatively less cost investment. This effect is achieved by adjusting production parameters and iteratively optimizing the production process.

[0168] A shower valve production process intelligent control method is provided, and the shower valve production process intelligent control method is performed based on the shower valve production process intelligent control system, and comprises the following steps:

[0169] S1: Collect machine operation status data, raw material batch quality data, ambient temperature and humidity data, monitor the quality of raw materials, continuously record environmental factors, form multi-dimensional indicators of environmental data through data integration, and generate environmental monitoring results;

[0170] S2: Input the multi-dimensional indicators in the environmental monitoring results, adjust the Bayesian network node weights, reconfigure the network links, and generate dynamic risk assessment through node weight adjustment and network link reconfiguration;

[0171] S3: According to dynamic risk assessment, adjust the production speed, match the raw material usage ratio with production demand, optimize the raw material usage ratio and production speed, analyze their impact on the overall operating efficiency, determine the optimal production line operating parameters, and obtain the optimized production configuration;

[0172] S4: Optimize the relationship between energy consumption and production efficiency by optimizing production configuration, test various parameter settings, evaluate the impact of various settings on energy consumption and efficiency, select the optimal production configuration, compare it with the current production data, analyze the difference between the configuration and actual operation, adjust production parameters, and establish an adjusted production process.

[0173] 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. An intelligent control system for the production process of shower valves, characterized in that: The system comprises: The environmental monitoring module collects data on machine operation status, raw material batch quality, ambient temperature and humidity, monitors machine operation status in real time, tracks and analyzes raw material batches, and continuously records ambient temperature and humidity to form multi-dimensional indicators of environmental data; The Bayesian network update module inputs the multi-dimensional indicators of the environmental data into the Bayesian network, performs weighted adjustments on the nodes of the Bayesian network, reconfigures the network links, and generates a dynamic risk assessment; The decision optimization module uses the dynamic risk assessment to adjust the production speed and the ratio of raw material usage, analyzes the impact of the production speed on the overall operating efficiency, adjusts the ratio of raw material usage to match the production demand, and determines the production line operation parameters; The multi-objective evolutionary algorithm module uses the production line operating parameters, applies the evolutionary algorithm to optimize the relationship between energy consumption and production efficiency, conducts parameter setting tests, evaluates the impact of various parameter settings on energy consumption and efficiency, and selects the optimal production configuration that matches the current production needs; The real-time feedback and adjustment module compares the optimal production configuration with the current production data, analyzes the difference between the configuration and the actual operation, adjusts the production parameters, and establishes an adjusted production process.

2. The intelligent control system for shower valve production process according to claim 1 is characterized in that: The multi-dimensional indicators of environmental data specifically include machine operating status, raw material batch quality, ambient temperature, and ambient humidity; the dynamic risk assessment specifically includes node weight adjustment and network link reconfiguration; the production line operating parameters specifically include production speed and raw material usage ratio; the optimal production configuration specifically includes energy consumption relationship and production efficiency; the adjusted production process includes parameter updates and actual and expected differences.

3. The intelligent control system for shower valve production process according to claim 2 is characterized in that: The steps for obtaining the multi-dimensional indicators of environmental data are specifically as follows: Monitor the machine's operating status in real time and continuously record the temperature and humidity of the environment. Collect data through sensor technology and integrate them into a set of raw data records of the machine and environment. Filtering the operating status data and environmental data directly related to the machine performance from the raw data record set, performing data preprocessing including standardization and noise removal, and generating a preprocessed data set; Multidimensional data analysis technology is applied to the preprocessed data set to analyze the correlation between the machine operation status and environmental factors, using the formula: Adjust the sensitivity of differentiated environmental factors, calculate the impact of environmental factors on machine performance, and generate multi-dimensional indicators of environmental data; Among them, E ij is the jth environmental factor index of the ith sample, x ik is the kth original data point of the ith sample, μ k , σ k are the mean and standard deviation of the kth data point, w k represents the weight coefficient, which adjusts the influence of multiple environmental factors in performance evaluation, k represents the index of the data point, and n represents the total number of data points.

4. The intelligent control system for shower valve production process according to claim 3 is characterized in that: The steps for obtaining the dynamic risk assessment are specifically as follows: Import the multi-dimensional indicators of the environmental data into the Bayesian network, analyze the weights of the network nodes in real time, and dynamically adjust the weights according to the current data to match the changing data pattern and generate real-time adjusted node weights; According to the real-time adjusted node weights, the links of the Bayesian network are optimized and reconfigured, and the optimal connection configuration is ensured by using a structural learning algorithm to generate an optimized network structure; In the optimized Bayesian network, the risk is calculated using the formula: Generate dynamic risk assessments; Among them, R t represents the total risk assessment at time t, w i Adjust the contribution weight of each node, v ti is the observed value of the node at time t, σ i Yes ti The standard deviation of i is the baseline-adjusted value, Represents the normalized square of the node observation value, emphasizing the impact of outliers.

5. The intelligent control system for shower valve production process according to claim 4 is characterized in that: The steps for obtaining the operating parameters of the production line are specifically as follows: Utilizing the data obtained from the dynamic risk assessment, analyzing the impact of the current production speed on the overall operating efficiency, calculating the necessary production speed adjustment, matching the risk tolerance of the production line, and generating adjusted production speed parameters; Re-evaluate and optimize the raw material usage ratio in combination with the production demand and the adjusted production speed parameters to ensure the best match between raw material input and production efficiency, and generate optimized raw material usage ratio parameters; Combining the adjusted production speed parameters and the optimized raw material ratio parameters, the formula is adopted: Calculate and set production line operating parameters; Among them, P opt Represents the production line operating parameters, S adj is the adjusted production speed, R opt is the optimized raw material usage ratio, γ is the influence of adjusting the production speed, and δ is the influence of the weighted raw material ratio.

6. The intelligent control system for the production process of shower valves according to claim 5, characterized in that: The steps for obtaining the optimal production configuration are specifically as follows: Using the production line operation parameters, the evolutionary algorithm model is initialized, the initial population of the algorithm is set based on the current production parameters, the impact of differentiated production configurations on energy consumption and efficiency is simulated, and initial test results are generated; Analyze the initial test results, identify key influencing factors between energy consumption and production efficiency, conduct multi-parameter sensitivity analysis, determine key parameters that affect production efficiency and energy consumption, and generate parameter impact results; According to the parameter impact results, the selection, crossover and mutation strategies in the evolutionary algorithm are adjusted to optimize the algorithm to match production needs, the optimized model is run, multiple rounds of iterative testing are performed, and optimized test results are generated; The production configuration is selected from the optimization test results using the formula: Select the optimal production configuration that matches current production needs; Among them, C opt represents the optimal production configuration, E eff is the optimized production efficiency, E con is the corresponding energy consumption value, ξ and ζ are the weight coefficients for adjusting the impact of production efficiency and energy consumption.

7. The intelligent control system for shower valve production process according to claim 6, characterized in that: The steps for obtaining the adjusted production process are specifically as follows: Collect current production data, including production speed, raw material usage rate and product quality, analyze the difference between the data and the optimal production configuration, and generate a difference analysis result; According to the difference analysis results, determine the production parameters that need to be adjusted, including production speed and raw material ratio, and use statistical methods to calculate the current parameter settings to minimize production costs and resource waste, and generate the current parameter settings; Applying the current parameter settings to the production process simulation, performing multiple rounds of testing to evaluate the adjustment effect, and generating simulation test results; According to the simulation test results, the operating parameters of the production line are determined using the formula: Generate an adjusted production process by referring to the combined impact of current and optimal parameters and the impact of differences in energy consumption on configuration adjustment; Among them, R new represents the adjusted production process, P current is the current production parameter, P opt is the optimal production configuration, α represents the weight adjusted according to efficiency, β represents the sensitivity of energy consumption difference to parameter adjustment, E diff Represents the standard deviation of energy consumption obtained from data analysis.

8. A method for intelligent control of shower valve production process, characterized in that: The intelligent control system for the production process of shower valves according to any one of claims 1 to 7 comprises the following steps: Collect machine operation status data, raw material batch quality data, ambient temperature and humidity data, monitor the quality of raw materials, continuously record environmental factors, form multi-dimensional indicators of environmental data through data integration, and generate environmental monitoring results; Input the multi-dimensional indicators in the environmental monitoring results, adjust the Bayesian network node weights, reconfigure the network links, and generate a dynamic risk assessment through node weight adjustment and network link reconfiguration; According to the dynamic risk assessment, the production speed is adjusted to match the raw material usage ratio with the production demand, the raw material usage ratio and the production speed are optimized, the impact on the overall operating efficiency is analyzed, the optimal production line operating parameters are determined, and the optimized production configuration is obtained; The optimized production configuration is used to optimize the relationship between energy consumption and production efficiency, test various parameter settings, evaluate the impact of various settings on energy consumption and efficiency, select the optimal production configuration, compare it with current production data, analyze the difference between the configuration and actual operation, adjust production parameters, and establish an adjusted production process.

Images