Industrial production process optimization management system based on the Internet of Things

Abstract

The present invention provides an industrial production process optimization management system based on the Internet of Things, which relates to the field of production management technology. The system includes a data acquisition module, a data preprocessing module, a data analysis module, an evaluation index acquisition module, a production improvement module, and a feedback optimization module. The data acquisition module is used to collect production data; the data preprocessing module is used to obtain preprocessed data; the data analysis module is used to build a linear evaluation model, input preprocessed data, and output production parameters; the evaluation index acquisition module is used to obtain optimal production parameters; the production improvement module is used to formulate a production improvement strategy; and the feedback optimization module is used to continuously optimize the linear evaluation model and the production improvement strategy. Through the collaborative work of each module, the present invention realizes data-driven production optimization decision-making, improves product quality and production efficiency, reduces costs and resource consumption, and brings innovative changes and sustainable development momentum to the field of industrial production.

Description

Technical Field

[0001] The present invention relates to the technical field of production management, and in particular to an industrial production process optimization management system based on the Internet of Things. Background Art

[0002] In today's society, industrial production is a key pillar of economic development. With the continuous advancement of science and technology and the increasing diversification of market demand, the industrial production process has undergone a long and profound transformation. Early industrial production relied primarily on manual labor and simple machine tools, resulting in low efficiency and uneven product quality. With the invention and widespread use of the steam engine, the First Industrial Revolution began. Mechanized production gradually replaced manual labor, the factory system was established, and large-scale production became possible. This significantly increased production efficiency, reduced product costs, and provided society with a richer supply of goods. During the Second Industrial Revolution, the widespread use of electricity and the invention of the internal combustion engine further promoted the development of industrial production. The emergence of production lines led to more standardized and specialized production processes, and a more refined division of labor. Major breakthroughs have been achieved in the chemical and steel industries, with the continuous emergence of new materials and processes, resulting in a wider range of products and significantly improved quality. In modern times, the rapid development of information technology has brought new opportunities and challenges to industrial production. The application of computer technology, automated control technology, and sensor technology has enabled the automation and intelligence of industrial production. Production equipment can self-monitor, self-diagnose, and self-adjust, greatly ensuring the precision and stability of the production process. At the same time, the popularity of the Internet enables companies to achieve global resource allocation and supply chain management, improving production flexibility and responsiveness.

[0003] While IoT-based industrial production process optimization and management systems offer numerous advantages, they also suffer from some significant technical drawbacks. Product quality control often relies on manual spot checks and limited testing equipment, making it difficult to achieve comprehensive, real-time monitoring of the production process. This results in delayed detection of quality issues and the inability to adjust production parameters in a timely manner, impacting the stability and consistency of product quality. Furthermore, the lack of precise data analysis makes it difficult to accurately trace the root causes of quality issues, making quality improvement efforts less targeted and effective. Traditional production processes often lack effective coordination and optimization. Poor information flow between production links can easily lead to overproduction or undersupply, resulting in wasted resources and extended production cycles. Summary of the Invention

[0004] The present invention provides an industrial production process optimization management system based on the Internet of Things to solve the defects in the prior art.

[0005] The present invention provides an industrial production process optimization management system based on the Internet of Things, comprising:

[0006] The data acquisition module is used to collect production data in the industrial production process in real time. The production data includes equipment parameters and product quality parameters.

[0007] The data preprocessing module is used to preprocess the collected production data to obtain preprocessed data.

[0008] The data analysis module is used to establish a linear evaluation model between equipment parameters and product quality parameters based on the linear regression algorithm, input preprocessed data, and output production parameters.

[0009] The evaluation index acquisition module is used to analyze the equipment parameters and product quality parameters in the historical production process, and obtain the optimal production parameters in the historical production process based on the linear evaluation model.

[0010] The production improvement module is used to compare the production management data in the current production process with the optimal production parameters, obtain production differences, and formulate production improvement strategies based on the production differences.

[0011] The feedback optimization module is used to establish a real-time monitoring feedback mechanism, evaluate the implementation effect of the production improvement strategy, and continuously optimize the linear evaluation model and production improvement strategy based on the evaluation results.

[0012] According to the IoT-based industrial production process optimization and management system provided by the present invention, the data acquisition module includes a sensor unit and a data acquisition unit. The sensor unit is used to acquire production data from the industrial production process and convert the production data into electrical signals. The data acquisition unit is used to receive the electrical signals and convert them into digital signals using an analog-to-digital converter.

[0013] According to the IoT-based industrial production process optimization and management system provided by the present invention, the data preprocessing module includes a data cleaning unit, a data conversion unit, and a data compression unit. The data cleaning unit is used to screen and filter production data, removing invalid, erroneous, or duplicate data. The data conversion unit is used to convert production data into the same format and units. The data compression unit is used to compress production data using the Huffman coding algorithm to reduce storage space for production data.

[0014] According to the Internet of Things-based industrial production process optimization management system provided by the present invention, the data analysis module includes a data modeling unit, which is used to construct a linear evaluation model based on a linear regression algorithm. The process includes:

[0015] Divide the preprocessed data into training and testing sets.

[0016] Use the training set to build a linear evaluation model based on the linear regression algorithm. The formula is expressed as:

[0017]

[0018] In the formula, y represents the product quality parameter, 、 、…、 Indicates device parameters, represents the intercept, 、 、…、 represents the regression coefficient, represents the error term.

[0019] Add in the loss function Constrain the regression coefficients to prevent overfitting.

[0020] The mean square error is used as the evaluation indicator, the linear evaluation model is evaluated using the test set, and the model parameters are adjusted according to the evaluation results until the preset model performance is met.

[0021] According to the IoT-based industrial production process optimization and management system provided by the present invention, the data analysis module also includes a coefficient and intercept calculation unit and a goodness-of-fit assessment unit. The coefficient and intercept calculation unit is used to calculate the coefficients and intercept in the linear evaluation model. The goodness-of-fit assessment unit is used to evaluate the goodness of fit of the linear evaluation model and determine the degree of fit of the linear evaluation model to the production data.

[0022] According to the IoT-based industrial production process optimization and management system provided by the present invention, the evaluation index acquisition module includes a historical data acquisition unit, an optimal production parameter calculation unit, and an index storage and retrieval unit. The historical data acquisition unit is used to collect historical production data from the industrial production process, including historical equipment parameters and corresponding historical product quality parameters. The optimal production parameter calculation unit is used to obtain the optimal production parameters for the historical production process based on the historical production data using a linear evaluation model. The index storage and retrieval unit is used to store the optimal production parameters and provide retrieval and query functions.

[0023] According to the Internet of Things-based industrial production process optimization management system provided by the present invention, the process of obtaining optimal production parameters includes:

[0024] Input historical production data into the linear evaluation model and output historical production parameters.

[0025] The historical production parameters are encoded and expressed in the form of gene strings.

[0026] Genetic optimization of gene strings, including:

[0027] A set of initial gene strings is randomly generated to form an initial population.

[0028] For each gene string in the initial population, it is decoded into the corresponding production parameter value and substituted into the objective function to calculate the fitness value.

[0029] According to the fitness value, a roulette wheel selection strategy is used to select a part of individuals from the current population as parent individuals to reproduce the next generation.

[0030] Perform a crossover operation on the selected parent individuals to generate new offspring individuals.

[0031] Perform mutation operations on offspring individuals to increase the diversity of the population.

[0032] The genetic optimization process is repeated until the preset number of iterations is reached, and the individual with the highest fitness value is selected and decoded as the optimal production parameter.

[0033] According to the IoT-based industrial production process optimization and management system provided by the present invention, the production improvement module includes a difference comparison unit, a policy customization unit, and a policy implementation unit. The difference comparison unit is used to compare production parameters with optimal production parameters and use the difference between the production parameters and the optimal production parameters as the production difference. The policy customization unit is used to formulate corresponding production improvement strategies based on the production difference using a rule engine method. The policy implementation unit is used to convert the production improvement strategies into production control instructions and send the production control instructions to the production equipment via a communication interface.

[0034] According to the IoT-based industrial production process optimization and management system provided by the present invention, the feedback optimization module includes a real-time monitoring unit and an effect evaluation unit. The real-time monitoring unit is used to monitor the implementation process of the production improvement strategy in real time and obtain monitoring data, which includes improvement parameters and improved product quality. The effect evaluation unit is used to evaluate the implementation effect of the production improvement strategy based on the monitoring data.

[0035] According to the Internet of Things-based industrial production process optimization management system provided by the present invention, the feedback optimization module also includes a model and strategy optimization unit, which is used to continuously optimize the linear evaluation model and production improvement strategy based on the evaluation results of the effect evaluation unit.

[0036] The IoT-based industrial production process optimization and management system provided by the present invention uses a data acquisition module to collect equipment parameters and product quality parameters in real time during the industrial production process, ensuring the timeliness and comprehensiveness of the data. This enables accurate acquisition of various key information from the production process, providing a solid data foundation for subsequent analysis and optimization, and avoiding decision-making errors caused by missing or delayed data. The data preprocessing module preprocesses the collected data, improving its quality and usability. It removes noise and outliers, making the data more accurate and reliable, providing a clean data environment for subsequent analysis and modeling, and enhancing the accuracy and stability of the model. The data analysis module uses a linear regression algorithm to establish a linear evaluation model between equipment parameters and product quality parameters, clearly revealing the inherent relationship between the two. This facilitates a deeper understanding of the cause-and-effect relationships within the production process, enabling more targeted adjustments to production parameters, achieving precise production control, and improving the consistency and stability of product quality. The evaluation indicator acquisition module analyzes historical production data and obtains optimal production parameters, providing valuable reference for current production. Enterprises can learn from past successful experiences, avoid repeating mistakes, quickly identify directions for production optimization, significantly improve production efficiency and resource utilization, and reduce production costs. The production improvement module compares current production management data with optimal production parameters, identifies production discrepancies, and develops improvement strategies. This allows the production process to dynamically adjust based on actual conditions, promptly addressing deficiencies and ensuring optimal production, thereby enhancing the company's market competitiveness. The feedback optimization module establishes a real-time monitoring and feedback mechanism that promptly evaluates the effectiveness of production improvement strategies. Based on the evaluation results, the linear evaluation model and production improvement strategies are continuously optimized, forming a continuously improving and evolving closed-loop system. This enables the entire production management system to adapt to the ever-changing production environment and market demands, possessing powerful self-adjustment and optimization capabilities, ensuring the company maintains its leading position in long-term production operations. In summary, this industrial production process optimization management system, through the collaborative work of various modules, achieves data-driven production optimization decisions. This not only improves product quality and production efficiency, reduces costs and resource consumption, but also enhances the company's market adaptability and competitiveness, bringing innovative changes and sustainable development momentum to the industrial production sector. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1It is a structural diagram of an industrial production process optimization management system based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0040] The following combination Figure 1 The present invention describes an industrial production process optimization management system based on the Internet of Things.

[0041] Figure 1 This is a flow chart of an industrial production process optimization management system based on the Internet of Things provided by an embodiment of the present invention.

[0042] like Figure 1 As shown, the industrial production process optimization management system based on the Internet of Things provided by the embodiment of the present invention includes a data acquisition module, a data preprocessing module, a data analysis module, an evaluation index acquisition module, a production improvement module and a feedback optimization module.

[0043] The data acquisition module is used to collect production data in the industrial production process in real time. The production data includes equipment parameters and product quality parameters.

[0044] The data acquisition module includes a sensor unit and a data acquisition unit. The sensor unit is used to obtain production data from the industrial production process and convert the production data into electrical signals. The data acquisition unit is used to receive the electrical signals and convert them into digital signals using an analog-to-digital converter.

[0045] In this embodiment, the sensor unit plays a key role. First, appropriate sensors are selected based on the characteristics of the industrial production process and the type of parameters to be monitored. For example, for temperature monitoring, a thermistor sensor might be used, while for pressure monitoring, a pressure sensor might be used. These sensors are installed at key locations on production equipment to accurately obtain equipment parameters and product quality parameters. As the production process progresses, the sensors sense corresponding changes in physical quantities and convert them into electrical signals. The strength of the electrical signals is proportional to the magnitude of the monitored physical quantity. Next, the data acquisition unit begins operation. It receives the electrical signals from the sensor unit and converts these analog electrical signals into digital signals using an analog-to-digital converter (ADC). The ADC operates by sampling and quantizing the input analog electrical signals at a specific sampling frequency. During the sampling process, it acquires instantaneous values ​​of the electrical signal at fixed time intervals. These sampled values ​​are then converted into discrete digital quantities through quantization. The quantization accuracy depends on the number of bits in the ADC. A higher number of bits results in higher quantization accuracy, enabling a more accurate representation of the original analog signal. The converted digital signals are then transmitted to the subsequent data processing and analysis modules. During the transmission process, data verification and error correction mechanisms may be employed to ensure data accuracy and integrity. Furthermore, the data acquisition unit must possess a certain degree of anti-interference capability to address factors such as electromagnetic interference that may exist in industrial production environments, ensuring the authenticity and reliability of the collected data. The collaborative operation of the sensor unit and the data acquisition unit enables real-time and accurate collection of production data from the industrial production process, providing a foundation for subsequent analysis and optimization.

[0046] The data preprocessing module is used to preprocess the collected production data to obtain preprocessed data.

[0047] The data preprocessing module includes a data cleaning unit, a data conversion unit, and a data compression unit. The data cleaning unit is used to screen and filter production data, removing invalid, erroneous, or duplicate data. The data conversion unit is used to convert production data into the same format and units. The data compression unit is used to compress production data using the Huffman coding algorithm to reduce storage space.

[0048] In this embodiment, once production data is collected, the data cleaning unit immediately screens and filters it. It uses a series of rules and algorithms to identify invalid data. For example, if the value of a device parameter exceeds its reasonable range, or if a product quality parameter exhibits an extreme value that is clearly inconsistent with actual conditions, these data will be marked as invalid. Furthermore, for duplicate data records, the cleaning unit retains only one copy to avoid data redundancy. Erroneous data may be corrected through comparison with other relevant data and logical analysis, or simply deleted. Next comes the data conversion unit. Because different equipment and sensors may generate production data in different formats and units, the data conversion unit converts this data to a unified standard format and units. For example, it converts temperature data from Fahrenheit to Celsius and pressure data from pounds-force per square inch to Pascals. The conversion unit uses mathematical operations and data mapping based on pre-set conversion rules and standards to complete the conversion process. This allows subsequent data analysis and processing to be performed on a unified data basis, avoiding confusion and errors caused by inconsistent data formats and units. Next comes the data compression unit. The cleaned and converted data enters the data compression unit. Huffman coding is a commonly used lossless compression algorithm. The compression unit first counts the frequency of characters or values ​​in the data. Frequent characters or values ​​are assigned shorter codes, while less frequent ones are assigned longer codes. For example, if the value "0" appears frequently in production data, it might be assigned a shorter code, such as "00." Meanwhile, the less frequent value "999" might be assigned a longer code, such as "1101." This significantly reduces the space occupied by data during storage and transmission, thereby improving data storage and transmission efficiency. Throughout the preprocessing process, each unit continuously optimizes and adjusts its parameters and rules to accommodate the different types and characteristics of production data. Furthermore, the preprocessing module performs quality assessment and verification on the processed data to ensure that it is accurate, complete, valid, and meets the requirements of subsequent analysis and processing. Through the meticulous processing of the data preprocessing module, a high-quality, easy-to-process, and storable data foundation is provided for subsequent data analysis and decision-making.

[0049] The data analysis module is used to establish a linear evaluation model between equipment parameters and product quality parameters based on the linear regression algorithm, input preprocessed data, and output production parameters.

[0050] The data analysis module includes a data modeling unit, which is used to build a linear evaluation model based on a linear regression algorithm. The process includes:

[0051] Divide the preprocessed data into training and testing sets.

[0052] Use the training set to build a linear evaluation model based on the linear regression algorithm. The formula is expressed as:

[0053]

[0054] In the formula, y represents the product quality parameter, 、 、…、 Indicates device parameters, represents the intercept, 、 、…、 represents the regression coefficient, represents the error term.

[0055] Add in the loss function Constrain the regression coefficients to prevent overfitting.

[0056] The mean square error is used as the evaluation indicator, the linear evaluation model is evaluated using the test set, and the model parameters are adjusted according to the evaluation results until the preset model performance is met.

[0057] The data analysis module also includes a coefficient and intercept calculation unit and a goodness-of-fit assessment unit. The coefficient and intercept calculation unit is used to calculate the coefficients and intercept in the linear evaluation model. The goodness-of-fit assessment unit is used to evaluate the goodness of fit of the linear evaluation model and determine how well the linear evaluation model fits the production data.

[0058] The evaluation index acquisition module is used to analyze the equipment parameters and product quality parameters in the historical production process, and obtain the optimal production parameters in the historical production process based on the linear evaluation model.

[0059] The evaluation index acquisition module includes a historical data acquisition unit, an optimal production parameter calculation unit, and an index storage and retrieval unit. The historical data acquisition unit is used to collect historical production data from the industrial production process, including historical equipment parameters and corresponding historical product quality parameters. The optimal production parameter calculation unit is used to obtain the optimal production parameters for the historical production process based on the historical production data using a linear evaluation model. The index storage and retrieval unit is used to store the optimal production parameters and provide retrieval and query functions.

[0060] The process of obtaining optimal production parameters includes:

[0061] Input historical production data into the linear evaluation model and output historical production parameters.

[0062] The historical production parameters are encoded and expressed in the form of gene strings.

[0063] A set of initial gene strings is randomly generated to form an initial population.

[0064] Genetic optimization of gene strings, including:

[0065] For each gene string in the initial population, it is decoded into the corresponding production parameter value and substituted into the objective function to calculate the fitness value.

[0066] According to the fitness value, a roulette wheel selection strategy is used to select a part of individuals from the current population as parent individuals to reproduce the next generation.

[0067] Perform a crossover operation on the selected parent individuals to generate new offspring individuals.

[0068] Perform mutation operations on offspring individuals to increase the diversity of the population.

[0069] The genetic optimization process is repeated until the preset number of iterations is reached, and the individual with the highest fitness value is selected and decoded as the optimal production parameter.

[0070] In this embodiment, the various units in the evaluation index acquisition module work closely together to obtain the optimal production parameters from historical production processes. First, the historical data acquisition unit takes effect. It collects historical production data from industrial production processes from a database or data storage system. This data covers historical equipment parameters and the corresponding historical product quality parameters. During the acquisition process, the integrity and accuracy of the data are ensured, and missing or abnormal data is marked and processed. Next, the optimal production parameter calculation unit begins operation. It inputs the collected historical production data into a linear evaluation model. The linear evaluation model uses the historical production data as input variables. After calculation and processing within the model, the historical production parameters are output. This then enters the core step of obtaining the optimal production parameters. The historical production parameters are encoded and represented as gene strings. This encoding method enables the production parameters to be presented in a form suitable for genetic algorithm processing. An initial set of gene strings is randomly generated to form an initial population. This initial population represents the possible combinations of production parameters. For each gene string in the initial population, the corresponding production parameter value is decoded and substituted into the objective function to calculate the fitness value. The fitness value reflects the degree to which the production parameter combination represented by the gene string achieves the production target. Based on fitness values, a roulette wheel selection strategy is used to select a subset of individuals from the current population to serve as parents for the next generation. In roulette wheel selection, individuals with higher fitness values ​​have a greater probability of being selected, thus increasing the chances that superior genetic combinations will be passed on to the next generation. A crossover operation is then performed on the selected parent individuals. This operation randomly selects gene segments from the parent individuals and exchanges them to generate new offspring individuals, creating genetic combinations with new characteristics. Mutation is then performed on the offspring individuals. Mutation randomly changes the gene values ​​of offspring individuals with a small probability, increasing population diversity and preventing the algorithm from falling into local optima. The genetic optimization process—generating new populations, evaluating fitness, and performing selection, crossover, and mutation operations—is repeated until a preset number of iterations is reached. With each iteration, the overall fitness of the population gradually improves, gradually approaching the optimal solution. Ultimately, the individual with the highest fitness value is selected and decoded as the optimal production parameters. The indicator storage and retrieval unit is responsible for storing the acquired optimal production parameters. This storage utilizes efficient data structures and database management techniques to ensure fast writing and reading. At the same time, it provides retrieval and query functions so that these optimal production parameters can be quickly obtained and used when needed. Through the above detailed steps and the collaborative work of the units, the evaluation index acquisition module can accurately and efficiently obtain the optimal production parameters in the historical production process, providing strong support for the optimization and improvement of industrial production.

[0071] The production improvement module is used to compare the production management data in the current production process with the optimal production parameters, obtain production differences, and formulate production improvement strategies based on the production differences.

[0072] The production improvement module includes a difference comparison unit, a strategy customization unit, and a strategy implementation unit. The difference comparison unit compares production parameters with the optimal production parameters and uses the difference between the production parameters and the optimal production parameters as the production difference. The strategy customization unit uses a rule engine to develop corresponding production improvement strategies based on the production difference. The strategy implementation unit converts the production improvement strategies into production control instructions and sends them to the production equipment through the communication interface.

[0073] In this embodiment, the production improvement module is first initiated by the difference comparison unit. This unit obtains production parameters contained in the production management data of the current production process and extracts the optimal production parameters. It then compares the two sets of parameters one by one, calculates the numerical difference between each parameter, and combines these differences as the production variance. Next, the policy customization unit begins its work based on the production variance determined by the difference comparison unit. This is where the rule engine comes into play. It has a series of pre-set rules tailored to different production variance scenarios. For example, if the difference in a key equipment parameter exceeds a certain threshold, it may indicate a strategy requiring adjustment of the equipment's operating speed or replacement of a component. Large differences in product quality parameters may indicate a need for process improvements or enhanced quality inspection. By matching the production variance with pre-set rules, the policy customization unit generates a corresponding production improvement strategy. The policy implementation unit then takes over. It converts the formulated production improvement strategy into production control instructions that the production equipment can understand and execute. This conversion process requires consideration of the production equipment's communication protocols and control interface requirements. After the conversion is complete, the production control instructions are accurately and promptly transmitted to the production equipment via communication interfaces such as industrial Ethernet or wireless networks. Throughout the entire process, various units work closely together to monitor changes in production parameters in real time, and quickly formulate and implement improvement strategies based on the comparison results to ensure that the production process can continuously approach the optimal state, improve production efficiency and product quality, and reduce production costs and resource consumption.

[0074] The feedback optimization module is used to establish a real-time monitoring feedback mechanism, evaluate the implementation effect of the production improvement strategy, and continuously optimize the linear evaluation model and production improvement strategy based on the evaluation results.

[0075] The feedback optimization module includes a real-time monitoring unit and an effect evaluation unit. The real-time monitoring unit is used to monitor the implementation process of the production improvement strategy and obtain monitoring data, including improvement parameters and improved product quality. The effect evaluation unit is used to evaluate the implementation effect of the production improvement strategy based on the monitoring data.

[0076] The feedback optimization module also includes a model and strategy optimization unit, which is used to continuously optimize the linear evaluation model and production improvement strategy based on the evaluation results of the effect evaluation unit.

[0077] In this embodiment, the feedback optimization module is first initiated by the real-time monitoring unit. This unit, connected to production equipment and quality inspection systems, monitors the implementation of the production improvement strategy in real time. It continuously collects various data from the improvement process, including key information such as improved equipment parameters and product quality, generating detailed monitoring data. The effectiveness evaluation unit then analyzes and processes this monitoring data. It compares the monitored improved parameters and improved product quality with the expected targets. For example, it compares whether improved parameters such as equipment operating speed and temperature meet the optimization expectations and whether product quality indicators have significantly improved. Using a series of evaluation indicators and algorithms, it comprehensively evaluates the effectiveness of the production improvement strategy and provides a quantitative evaluation result. The model and strategy optimization unit takes action based on the evaluation results of the effectiveness evaluation unit. If the evaluation results indicate that the production improvement strategy has achieved good results, it further strengthens and consolidates the relevant strategy and, where appropriate, applies it to similar production scenarios. If the evaluation results are unsatisfactory, it conducts an in-depth analysis of the reasons, which may be due to inaccurate parameters in the linear evaluation model or defects in the production improvement strategy. For linear assessment models, the model and strategy optimization unit adjusts parameters such as coefficients and variable weights to improve model accuracy and predictive power. For production improvement strategies, it re-examines the strategy formulation logic and steps, potentially modifying certain aspects of the strategy, adding new control variables, or adjusting the optimization direction. This allows for continuous optimization of the linear assessment model and production improvement strategy, ensuring they better adapt to actual production conditions and continuously improving production efficiency and product quality.

[0078] In summary, this embodiment provides an IoT-based industrial production process optimization and management system. The data acquisition module collects equipment parameters and product quality parameters in real time during the industrial production process, ensuring the timeliness and comprehensiveness of the data. This enables accurate acquisition of various key information from the production process, providing a solid data foundation for subsequent analysis and optimization, and avoiding decision-making errors caused by missing or delayed data. The data preprocessing module preprocesses the collected data, improving its quality and usability. It removes noise and outliers, making the data more accurate and reliable, providing a clean data environment for subsequent analysis and modeling, and enhancing the accuracy and stability of the model. The data analysis module uses a linear regression algorithm to establish a linear evaluation model between equipment parameters and product quality parameters, clearly revealing the inherent relationship between the two. This facilitates a deeper understanding of the cause-and-effect relationships within the production process, enabling more targeted adjustments to production parameters, achieving precise production control, and improving the consistency and stability of product quality. The evaluation indicator acquisition module analyzes historical production data and obtains optimal production parameters, providing valuable reference for current production. Enterprises can draw on past successes to avoid repeating mistakes and quickly identify production optimization strategies, significantly improving production efficiency and resource utilization while reducing production costs. The production improvement module compares current production management data with optimal production parameters, identifying production discrepancies and formulating improvement strategies. This allows the production process to dynamically adjust based on actual conditions, promptly addressing deficiencies and ensuring optimal production, thereby enhancing the enterprise's market competitiveness. The feedback optimization module establishes a real-time monitoring and feedback mechanism that promptly evaluates the effectiveness of production improvement strategies. Based on the evaluation results, the linear evaluation model and production improvement strategies are continuously optimized, forming a continuously improving and evolving closed-loop system. This enables the entire production management system to adapt to the ever-changing production environment and market demands, possessing strong self-adjustment and optimization capabilities, ensuring that the enterprise maintains a leading position in long-term production operations. In summary, this industrial production process optimization management system, through the collaborative work of various modules, achieves data-driven production optimization decisions. This not only improves product quality and production efficiency, reduces costs and resource consumption, but also enhances the enterprise's market adaptability and competitiveness, bringing innovative changes and sustainable development momentum to the industrial production sector.

[0079] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. The industrial production process optimization management system based on the Internet of Things is characterized by: include: A data acquisition module is used to collect production data in the industrial production process in real time, wherein the production data includes equipment parameters and product quality parameters; A data preprocessing module, configured to preprocess the collected production data to obtain preprocessed data; A data analysis module, configured to establish a linear evaluation model between the equipment parameters and the product quality parameters based on a linear regression algorithm, input the preprocessed data, and output production parameters; The data analysis module further includes a coefficient and intercept calculation unit and a goodness of fit evaluation unit; the coefficient and intercept calculation unit is used to calculate the coefficients and intercepts in the linear evaluation model; the goodness of fit evaluation unit is used to evaluate the goodness of fit of the linear evaluation model and determine the degree of fit of the linear evaluation model to the production data; An evaluation index acquisition module is used to analyze equipment parameters and product quality parameters in the historical production process and obtain the optimal production parameters in the historical production process based on the linear evaluation model; The evaluation index acquisition module includes a historical data acquisition unit, an optimal production parameter calculation unit, and an index storage and retrieval unit; the historical data acquisition unit is used to collect historical production data in the industrial production process, and the historical production data includes historical equipment parameters and corresponding historical product quality parameters; the optimal production parameter calculation unit is used to obtain the optimal production parameters in the historical production process based on the historical production data through the linear evaluation model; The index storage and retrieval unit is used to store the optimal production parameters and provide retrieval and query functions; The process of obtaining the optimal production parameters includes: Inputting the historical production data into the linear evaluation model and outputting historical production parameters; Encoding the historical production parameters and expressing them in the form of gene strings; The gene string is genetically optimized, comprising: Randomly generate a set of initial gene strings to form an initial population; For each gene string in the initial population, decode it into a corresponding production parameter value and substitute it into the objective function to calculate the fitness value; According to the fitness value, a roulette wheel selection strategy is used to select a portion of individuals from the current population to serve as parent individuals to reproduce the next generation; Perform crossover operation on the selected parent individuals to generate new offspring individuals; Perform mutation operations on offspring individuals to increase the diversity of the population; Repeat the genetic optimization process until a preset number of iterations is reached, select the individual with the highest fitness value, and decode it as the optimal production parameter; A production improvement module is used to compare the production management data in the current production process with the optimal production parameters to obtain production differences and formulate a production improvement strategy based on the production differences; The feedback optimization module is used to establish a real-time monitoring feedback mechanism, evaluate the implementation effect of the production improvement strategy, and continuously optimize the linear evaluation model and the production improvement strategy based on the evaluation results.

2. The industrial production process optimization management system based on the Internet of Things according to claim 1 is characterized in that: The data acquisition module includes a sensor unit and a data acquisition unit; the sensor unit is used to obtain production data in the industrial production process and convert the production data into an electrical signal; the data acquisition unit is used to receive the electrical signal and convert the electrical signal into a digital signal using an analog-to-digital converter.

3. The industrial production process optimization management system based on the Internet of Things according to claim 1 is characterized in that: The data preprocessing module includes a data cleaning unit, a data conversion unit and a data compression unit; the data cleaning unit is used to screen and filter the production data to remove invalid, erroneous or duplicate data in the production data; the data conversion unit is used to convert the production data into the same format and unit; the data compression unit is used to compress the production data using a Huffman coding algorithm to reduce the storage space of the production data.

4. The industrial production process optimization management system based on the Internet of Things according to claim 1 is characterized in that: The data analysis module includes a data modeling unit, which is used to construct a linear evaluation model based on a linear regression algorithm. The process includes: Dividing the preprocessed data into a training set and a test set; The training set is used to construct a linear evaluation model based on the linear regression algorithm, and the formula is expressed as: In the formula, y represents the product quality parameter, 、 、…、 Indicates device parameters, represents the intercept, 、 、…、 represents the regression coefficient, represents the error term; Add in the loss function Constraining the regression coefficients to prevent overfitting; The mean square error is used as the evaluation indicator, the linear evaluation model is evaluated using the test set, and the model parameters are adjusted according to the evaluation results until the preset model performance is met.

5. The industrial production process optimization management system based on the Internet of Things according to claim 1 is characterized in that: The production improvement module includes a difference comparison unit, a strategy customization unit and a strategy implementation unit; the difference comparison unit is used to compare the production parameters with the optimal production parameters, and take the difference value between the production parameters and the optimal production parameters as the production difference; the strategy customization unit is used to formulate corresponding production improvement strategies based on the production difference using a rule engine method; the strategy implementation unit is used to convert the production improvement strategy into a production control instruction, and send the production control instruction to the production equipment through a communication interface.

6. The industrial production process optimization management system based on the Internet of Things according to claim 1 is characterized in that: The feedback optimization module includes a real-time monitoring unit and an effect evaluation unit; the real-time monitoring unit is used to monitor the implementation process of the production improvement strategy and obtain monitoring data, and the monitoring data includes improvement parameters and improved product quality; the effect evaluation unit is used to evaluate the implementation effect of the production improvement strategy based on the monitoring data.

7. The industrial production process optimization management system based on the Internet of Things according to claim 6 is characterized in that: The feedback optimization module further includes a model and strategy optimization unit, which is used to continuously optimize the linear evaluation model and the production improvement strategy according to the evaluation result of the effect evaluation unit.

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