Intelligent control method and system for shoemaking production line

Abstract

The invention relates to the technical field of industrial automation, in particular to an intelligent control method and system for a shoemaking production line, and the method comprises the following steps: based on a shoemaking three-dimensional design model, recognizing size parameters of a plurality of sizes by analyzing the design size and shape of shoes, adjusting the production parameters of the plurality of sizes, and generating multi-size production parameter configuration. According to the method, the production parameters of various sizes are adjusted according to the design model of the shoe, so that the production line flexibly meets the production requirements of multiple sizes of products, production delay and resource waste caused by mismatching of specifications are reduced, the utilization rate of raw materials is increased through real-time image data collection and defect detection, material waste in the cutting process is reduced, and the production efficiency is improved. The system analyzes production parameters and material consumption, predicts material requirements, responds to market changes in real time, optimizes inventory management, reduces the defective product rate in combination with automatic quality monitoring, improves the energy efficiency of a production line by analyzing and optimizing energy consumption, and supports sustainable production and environmental protection.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation, and in particular to an intelligent control method and system for a shoe production line. Background Art

[0002] The field of industrial automation technology focuses on using control systems, information technology and mechanical automation equipment to improve production efficiency and reduce manpower requirements, including programmable logic controllers, industrial robots, sensors and actuators, human-machine interfaces and real-time data processing systems to achieve automatic control of the production process. Industrial automation aims to improve production speed, quality control and flexibility, reduce operating costs and human errors, and achieve continuous production and operation in dangerous and unsuitable environments for human work by using automation technology to precisely control the production environment. It is applied to multiple industrial fields such as automobile manufacturing, food processing, and chemical production to meet modern high-efficiency and high-standard production needs.

[0003] Among them, an intelligent control method for a shoe production line focuses on the application of industrial automation and intelligent technology in the shoe production process, aiming to optimize the production process, improve product quality and production efficiency, including automated assembly lines, intelligent monitoring systems and data analysis tools, aiming to reduce manual operations, and use precise control of production parameters to ensure the standardization and personalized needs of footwear products. By using intelligent control methods, it can reduce production costs and improve product consistency, optimize production cycles and increase production flexibility. Combined with sensors that monitor shoe quality in real time, it adjusts production line parameters to match the automated scheduling of various production needs, reduce waste and costs, and improve market competitiveness.

[0004] The control methods of traditional shoe production lines have problems with insufficient flexibility and response speed in the face of high-change production demands and diversified market demands. Traditional industrial automation systems are fixed to specific product configurations and production processes, and are difficult to quickly adapt to changes in market demand for personalized and diversified products. They rely on preset standards and manual intervention in terms of raw material utilization efficiency and quality control, resulting in waste of resources and low production efficiency. In terms of energy management, there is a lack of effective real-time data analysis and optimization mechanisms, which makes it difficult to optimize energy consumption, increases production costs and affects the realization of environmental protection goals. In terms of maintenance and fault diagnosis, there is a lag in response, and maintenance strategies are based on post-event response rather than prevention, which increases downtime and maintenance costs, affects production efficiency and the economic benefits of the enterprise, and limits the ability of existing technologies to respond to rapid market changes and high-quality production requirements. 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 method and system for a shoe production line.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, an intelligent control method for a shoe production line, comprising the following steps: S1: Based on the three-dimensional design model of shoemaking, by analyzing the design size and shape of the shoe, identifying the size parameters of multiple sizes, and adjusting the production parameters of multiple sizes to generate multi-size production parameter configurations; S2: Based on the multi-size production parameter configuration, by real-time acquisition of image data collected on the surface of the production material, the defect position is identified and recorded, and a cutting path is calculated to generate a cutting path calculation result; S3: Based on the calculation result of the cutting path, the production parameters and material consumption data are recorded in real time, the material demand is calculated and predicted, and the inventory level of the production raw materials is compared and adjusted to generate inventory adjustment data; S4: Based on the inventory adjustment data, image data of finished shoes are collected in real time, unqualified products are identified and recorded, inventory information is updated in real time, and quality inspection data is generated; S5: Based on the quality detection data, monitor the operation data of the air compressor in real time, evaluate the change trend of the operation data and identify faults through time series analysis, adjust the maintenance schedule, and generate compressed air management results; S6: Based on the compressed air management results, energy consumption data of multiple devices are collected, and by analyzing the data, the equipment operation modes and energy efficiency are compared, the equipment parameters are adjusted to optimize energy consumption, and production line control information is generated.

[0007] As a further solution of the present invention, the multi-size production parameter configuration includes shoe width adjustment data, sole length adjustment parameters, and mold pressure setting parameters; the cutting path calculation results include defect location identification information, material consumption forecast data, and material saving ratio; the inventory adjustment data includes predicted monthly material demand, seasonal inventory ratio, and surplus and shortage warning information; the quality inspection data includes surface smoothness rating, color difference abnormal area, and structural integrity checkpoint; the compressed air management results include pressure release valve setting parameters, temperature control thresholds, and vibration alarm levels; the production line control information includes power usage time periods, automatic shutdown start conditions, and energy-saving parameter adjustment records.

[0008] As a further solution of the present invention, based on the three-dimensional design model of shoemaking, by analyzing the design size and shape of the shoe, identifying the size parameters of multiple sizes, and adjusting the production parameters of the multiple sizes, the steps of generating the multi-size production parameter configuration are specifically as follows: S101: Based on the three-dimensional design model of shoemaking, the three-dimensional model is analyzed to analyze the length, width and height parameters of the shoe type, and the geometric characteristics of the shoe are recorded to obtain shoe type analysis data; S102: Analyze and calculate size parameters corresponding to multiple sizes based on the shoe type analysis data, including length, width, and height, and generate a size parameter adjustment configuration; S103: adjusting the configuration according to the size parameters, adjusting the mold size, injection pressure and mechanical operation path on the production line, and generating a multi-size production parameter configuration.

[0009] As a further solution of the present invention, based on the multi-size production parameter configuration, by real-time acquisition of image data collected on the surface of the production material, the defect position is identified and recorded, and the cutting path is calculated. The steps of generating the cutting path calculation result are specifically as follows: S201: Based on the multi-size production parameter configuration, using an image acquisition device, real-time acquisition of image data of the surface of production materials, including cloth and leather, to generate raw material image data; S202: Analyze the image data based on the raw material image data, identify the defect location by analyzing the edge clarity and texture depth, and generate a material defect analysis result; S203: According to the material defect analysis result, taking into account material utilization and cutting efficiency, calculating the raw material cutting path, and generating a cutting path calculation result.

[0010] As a further solution of the present invention, according to the clipping path calculation result, the production parameters and material consumption data are recorded in real time, the material demand is calculated and predicted, and the inventory level of the production raw materials is compared and adjusted. The steps of generating the inventory adjustment data are specifically as follows: S301: According to the calculation result of the cutting path and the real-time material usage data, the data of the amount of cloth and leather consumed in multiple batches are recorded to generate a real-time material consumption record; S302: Based on the real-time material consumption record, material demand is calculated by comparing the current inventory and production demand, and material demand forecast data is generated; S303: According to the material demand forecast data, by comparing the actual inventory and the forecast demand information, the inventory order is adjusted, and an early warning message is sent to the management personnel to generate inventory adjustment data.

[0011] As a further solution of the present invention, based on the inventory adjustment data, the image data of finished shoes is collected in real time, unqualified products are identified and recorded, and inventory information is updated in real time. The steps of generating quality inspection data are specifically as follows: S401: Based on the inventory adjustment data, using an image acquisition device, real-time acquisition of image data of finished shoes to generate finished product image acquisition records; S402: Based on the finished product image acquisition record, analyzing the color consistency, surface quality and structural integrity of the shoe surface in the image, identifying defective products that do not meet the standards, and generating a product quality inspection record; S403: Using the product quality inspection record, by recording the quantity of qualified and unqualified products in the inventory, adjusting the inventory information in real time, including the quantity of salable inventory, and generating quality inspection data.

[0012] As a further solution of the present invention, based on the quality detection data, the operation data of the air compressor is monitored in real time, and through time series analysis, the change trend of the operation data is evaluated and faults are identified, and the maintenance schedule is adjusted to generate the compressed air management results. Specifically, the steps are as follows: S501: Based on the quality detection data, real-time collection of operating pressure, temperature and vibration data of the air compressor is performed, and time information of the data is recorded to generate compressor operating data; S502: Based on the compressor operation data, by performing trend analysis on the data, calculating an abnormal index value, identifying abnormal fluctuations and data points beyond the normal operating range, and generating data abnormality identification information; S503: Utilizing the data anomaly identification information, according to the change trend of the operation data, analyzing and predicting the maintenance needs of the equipment, and adjusting the maintenance plan to generate a compressed air management result.

[0013] As a further solution of the present invention, the specific formula for calculating the abnormal index value is: in, represents the compressor pressure reading at time t, represents the target time point, represents the average pressure reading over the past n data points, represents the standard deviation of the pressure readings over the past n data points, Represents the length of the time window considered, that is, the number of data points contained in the window , The anomaly index representing the compressor data at time t is used to identify short-term fluctuations and abnormal changes in the data through a formula.

[0014] As a further solution of the present invention, according to the compressed air management result, the energy consumption data of multiple devices are collected, and the steps of analyzing the data, comparing the equipment operation mode and energy efficiency, adjusting the equipment parameters to optimize energy consumption, and generating production line control information are specifically as follows: S601: According to the compressed air management result, energy consumption data of multiple devices on the production line are collected in real time, including electricity, water and gas usage, and equipment energy usage records are generated; S602: Based on the energy usage records of the devices, by comparing the energy efficiency performance of multiple devices, analyzing the energy usage efficiency of multiple devices, and generating energy efficiency analysis information; S603: Apply the energy efficiency analysis information to adjust the operating parameters of multiple devices on the production line, including start and stop time, operating speed and workload, optimize energy consumption and utilization efficiency, and generate production line control information.

[0015] An intelligent control system for a shoe production line, the intelligent control system for the shoe production line is used to execute the intelligent control method for the shoe production line, the system comprising: The size parameter configuration module adjusts the mechanical setting parameters corresponding to various shoe sizes based on the shoemaking 3D design model, including the position and pressure setting of the tool, uses the image acquisition device to detect the surface image of the raw material, identifies the defect location and plans the cutting path, and generates production raw material analysis data; The inventory analysis management module, based on the production raw material analysis data, monitors the material usage and production speed in real time, predicts material demand, adjusts the inventory level, and generates material demand calculation information; The product defect recognition module detects product defects by collecting finished shoe images based on the material demand calculation information, records unqualified products, and updates the inventory status in real time to generate product inspection results; The compressor management module monitors the pressure, temperature and vibration data of the air compressor in real time based on the product detection results, evaluates the equipment status and predicts failures, adjusts maintenance time, and generates compressor monitoring data; The energy usage optimization module collects and analyzes the energy consumption data of multiple devices in the production line based on the compressor monitoring data, adjusts the equipment operating parameters to optimize the energy usage efficiency, and generates production line control information.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by adjusting the production parameters of various sizes according to the design model of the shoe, the production line can flexibly respond to the production needs of multiple sizes of products, reduce production delays and resource waste caused by specification mismatch, use real-time image data acquisition and defect detection to improve the utilization rate of raw materials, reduce material waste in the cutting process, analyze production parameters and material consumption, predict material demand, respond to market changes in real time and optimize inventory management, combine automated quality monitoring to reduce the defective rate, and improve the energy efficiency of the production line by analyzing and optimizing energy consumption, supporting sustainable production and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2This is a detailed flow chart of S1 of the present invention; Figure 3 This is a detailed flow chart of S2 of the present invention; Figure 4 This is a detailed flow chart of S3 of the present invention; Figure 5 This is a detailed flow chart of S4 of the present invention; Figure 6 This is a detailed flow chart of S5 of the present invention; Figure 7 This is a detailed flow chart of S6 of the present invention; Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] 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.

[0020] See also Figure 1 The present invention provides a technical solution, an intelligent control method for a shoe production line, comprising the following steps: S1: Based on the three-dimensional design model of shoemaking, by analyzing the design size and shape of the shoe, identifying the size parameters of multiple sizes, and adjusting the production parameters of multiple sizes to generate multi-size production parameter configurations; S2: Based on the multi-size production parameter configuration, the image data collected from the surface of the production material is collected in real time to identify and record the defect location, calculate the cutting path, and generate the cutting path calculation result; S3: Based on the calculation results of the cutting path, the production parameters and material consumption data are recorded in real time, the material demand is calculated and predicted, and the inventory level of production raw materials is compared and adjusted to generate inventory adjustment data; S4: Based on inventory adjustment data, collect image data of finished shoes in real time, identify and record unqualified products, update inventory information in real time, and generate quality inspection data; S5: Based on the quality inspection data, the operating data of the air compressor is monitored in real time. Through time series analysis, the changing trend of the operating data is evaluated and faults are identified, the maintenance schedule is adjusted, and the compressed air management results are generated; S6: Based on the compressed air management results, collect energy consumption data of multiple devices, analyze the data, compare the equipment operation mode and energy efficiency, adjust the equipment parameters to optimize energy consumption, and generate production line control information.

[0021] Multi-size production parameter configuration includes shoe width adjustment data, sole length adjustment parameters, and mold pressure setting parameters. The cutting path calculation results include defect location identification information, material consumption forecast data, and material savings ratio. Inventory adjustment data includes predicted monthly material demand, seasonal inventory ratio, and surplus and shortage warning information. Quality inspection data includes surface smoothness rating, color difference abnormality area, and structural integrity checkpoints. Compressed air management results include pressure release valve setting parameters, temperature control thresholds, and vibration alarm levels. Production line control information includes power usage time periods, automatic shutdown start conditions, and energy-saving parameter adjustment records.

[0022] See also Figure 2 Based on the three-dimensional design model of shoemaking, by analyzing the design size and shape of the shoe, identifying the size parameters of multiple sizes, and adjusting the production parameters of multiple sizes, the steps of generating the multi-size production parameter configuration are as follows: S101: Based on the three-dimensional design model of shoemaking, the three-dimensional model is analyzed to analyze the length, width and height parameters of the shoe type, and the geometric characteristics of the shoe are recorded to obtain shoe type analysis data; In sub-step S101, based on the three-dimensional design model of shoemaking, the three-dimensional model is analyzed. By analyzing the length, width and height of the model, the key geometric features of the shoe are recorded. The target data include the height of the heel, the width of the forefoot, and the overall length. The physical shape of the shoe is described by the target data, and the shoe analysis data is formed to influence subsequent design and production decisions, ensure the accurate delivery and efficient production of each design, and identify potential design problems, including uneven sizes or structural weaknesses, so as to make adjustments in advance and optimize the overall product quality. The analysis results are used to ensure the accuracy and practicality of the data.

[0023] S102: Analyze and calculate size parameters corresponding to multiple sizes based on the shoe type analysis data, including length, width, and height, and generate a size parameter adjustment configuration; In the above content, through in-depth analysis of shoe type analysis data, according to the formula , , , calculate the size parameters of different sizes; In the formula, , , Represent the original length, width, and height respectively. , , Represents the corresponding adjustment factor, which is set based on market demand and design specifications; Detailed explanation of the formula and the process of formula calculation and derivation: Assuming the original length of the shoe mm, width mm, height mm, adjustment factor , , , calculate the adjusted length: width: high: Adjustments are used to ensure production accuracy of different sizes to meet the diverse needs of the market.

[0024] S103: Adjust the configuration according to the size parameters, adjust the mold size, injection pressure and mechanical operation path on the production line, and generate multi-size production parameter configuration; In sub-step S103, based on the size parameter adjustment configuration, key components on the production line are adjusted, including mold size, injection pressure and mechanical operation path. The mold size adjustment is used to adapt to different product sizes, and the pressure adjustment is used to ensure that the material is evenly distributed in the mold to prevent defects. The mechanical path adjustment ensures the accuracy and efficiency of production operations, ensuring that multiple parameters can meet the design requirements when producing shoes of different sizes, and ensuring that the size of the shoes produced is accurate. The adjustment is made based on the previous size analysis results and actual production needs to ensure the smooth progress of the production process. The generated multi-size production parameter configuration provides strong support for production, optimizes production efficiency, and reduces material waste.

[0025] See also Figure 3 Based on the multi-size production parameter configuration, the image data collected from the surface of the production material is collected in real time to identify and record the defect position, and the cutting path is calculated. The specific steps for generating the cutting path calculation result are as follows: S201: Based on the multi-size production parameter configuration, use the image acquisition device to collect image data of the surface of production materials, including cloth and leather, in real time to generate raw material image data; In sub-step S201, based on the multi-size production parameter configuration, image acquisition equipment is set at key production line nodes to ensure that multiple raw material types, including cloth and leather, can be covered. The acquisition equipment automatically adjusts the focus and exposure according to the predetermined program to match raw materials with differentiated colors and textures, and collects high-resolution images of the raw material surface in real time. Each material is imaged at multiple angles and multiple focal points to ensure the comprehensiveness and accuracy of the image data. The image data is instantly transmitted back to the data processing center through transmission technology. The generated raw material image data records the visual characteristics of the material, providing basic data for subsequent quality control and material utilization.

[0026] S202: Analyze the image data based on the raw material image data, identify the defect location by analyzing the edge clarity and texture depth, and generate a material defect analysis result; In sub-step S202, based on the raw material image data, the collected images are pre-processed by image processing software, including color correction and noise reduction to improve image quality, and edge detection algorithms are applied to analyze edge clarity in the image. At the same time, texture analysis technology is used to evaluate the texture depth of the material surface and locate defects on the material surface, including cracks, stains, and uneven dyeing. A machine learning model is used to improve the accuracy and speed of defect recognition based on the results of historical data training. The generated material defect analysis results play an important role in improving product quality and reducing material waste.

[0027] S203: Calculate the raw material cutting path based on the material defect analysis result, taking into account the material utilization rate and cutting efficiency, and generate the cutting path calculation result; In sub-step S203, based on the material defect analysis results, each defect position is marked and quantified, including the evaluation of the defect size, shape and position. Based on the marked defect data and material utilization requirements, a cutting path algorithm is designed for calculation. The algorithm optimizes the cutting route to avoid defective areas while maintaining the maximum utilization efficiency of the material. Taking into account the cutting efficiency, the cutting path design also needs to consider the speed and accuracy of mechanical cutting to ensure the optimal operating state of the machine during the cutting process. The model self-adjusts and optimizes according to actual operation feedback. The generated cutting path calculation results are used to meet the production efficiency requirements and reduce material waste, ensuring the dual optimization of cost control and production quality.

[0028] See also Figure 4 , based on the calculation results of the cutting path, the production parameters and material consumption data are recorded in real time, the material demand is calculated and predicted, and the inventory level of production raw materials is compared and adjusted. The specific steps for generating inventory adjustment data are: S301: According to the calculation result of the cutting path and the real-time material usage data, the data of the amount of cloth and leather consumed in multiple batches are recorded to generate a real-time material consumption record; In sub-step S301, based on the calculation results of the cutting path, the sensors installed on the production line automatically record the amount of fabric and leather consumed in the cutting process of each batch, measure the material usage, and transmit the data to the central monitoring system in real time. By collating and summarizing the collected data, a material consumption record is generated, which includes the total consumption of each material and is marked with a timestamp and batch number for optimizing production planning and cost control.

[0029] S302: Based on the real-time material consumption record, by comparing the current inventory and production demand, the material demand quantity is calculated to generate material demand forecast data; In sub-step S302, based on the real-time material consumption records, the current inventory is reviewed and evaluated to ensure the accuracy of the records. By comparing the consumption data and the production plan, statistical analysis methods are used to calculate the material demand in the future period, including trend analysis and consumption forecasting, to predict the demand for different materials. Material demand forecast data is generated to help the procurement department make procurement decisions in advance, ensure that production is not interrupted due to material shortages, adjust production strategies and optimize costs.

[0030] S303: According to the material demand forecast data, by comparing the actual inventory and the forecast demand information, the inventory order is adjusted, and an early warning message is sent to the management personnel to generate inventory adjustment data; In the above content, based on the material demand forecast data, compare the actual inventory and forecast demand information, according to the formula , adjust inventory orders, send early warning information to managers, and generate inventory adjustment data; In the formula, represents the inventory adjustment, Represents the forecast demand, Represents the actual inventory quantity; Detailed explanation of the formula and the process of formula calculation and derivation: Assuming current inventory The demand for next month is 500 units. is 800 units, the inventory difference is: The results show that an additional 300 units of inventory are needed to meet production demand. Inventory management can be adjusted dynamically to ensure that production is not affected by material shortages. It also enables companies to effectively control inventory costs and avoid excessive or insufficient inventory. Inventory adjustment data provides important decision-making basis for management, helping to achieve efficient use of resources and cost optimization.

[0031] See also Figure 5, based on inventory adjustment data, real-time image data of finished shoes is collected, unqualified products are identified and recorded, inventory information is updated in real time, and the steps for generating quality inspection data are as follows: S401: Based on the inventory adjustment data, using an image acquisition device, real-time image data of finished shoes are acquired to generate finished product image acquisition records; In sub-step S401, based on the inventory adjustment data, the image acquisition equipment of the finished shoes is installed and adjusted to ensure that the equipment can capture images of the finished shoes from multiple angles. The high-resolution images of the finished shoes are acquired in real time by the equipment. During the acquisition process, the equipment automatically focuses and adjusts the light to obtain the best image quality. The acquired image data is instantly transmitted to the data processing center for storage and backup. The generated finished product image acquisition records record the visual information of each finished shoe in detail. The target data provides a basis for quality control and provides support for subsequent product tracking and customer service.

[0032] S402: Based on the finished product image acquisition record, analyzing the color consistency, surface quality and structural integrity of the shoe surface in the image, identifying defective products that do not meet the standards, and generating product quality inspection records; In sub-step S402, based on the finished product image acquisition records, the images of the finished shoes are analyzed through professional image processing software, including the evaluation of color consistency, surface quality and structural integrity. Image recognition technology and deep learning algorithms are used to automatically identify and mark any defects in the image, including color difference, wear or structural damage, quickly and accurately identify products that do not meet quality standards, and generate product quality inspection records. The records provide accurate data support for formulating improvement measures and optimizing production processes.

[0033] S403: Using product quality inspection records, by recording the number of qualified and unqualified products in inventory, adjusting inventory information in real time, including the number of salable inventory, and generating quality inspection data; In the above content, using product quality inspection records, according to the formula Adjust inventory information; In the formula, represents the adjusted inventory sales quantity, Represents the number of qualified products in inventory, Represents the number of non-conforming products; Detailed explanation of the formula and the process of formula calculation and derivation: By recording the number of qualified and unqualified products in real time, the quantity of inventory that can be sold is calculated. Assuming that the number of qualified products is 500 units and the number of unqualified products is 50 units, the adjustment process is as follows: The results reflect the actual number of products available for sale and are used for inventory management and quality control to ensure the accuracy of inventory data and the effectiveness of business decisions.

[0034] See also Figure 6 Based on the quality inspection data, the operating data of the air compressor is monitored in real time. Through time series analysis, the changing trend of the operating data is evaluated and faults are identified. The maintenance schedule is adjusted and the steps to generate compressed air management results are as follows: S501: Based on the quality inspection data, the operating pressure, temperature and vibration data of the air compressor are collected in real time, and the time information of the data is recorded to generate the compressor operation data; In sub-step S501, based on the quality inspection data, various key operating parameters of the compressor, including operating pressure, temperature and vibration, are collected in real time through IoT sensors. By collecting multiple sets of data per second, the timeliness and accuracy of the data are ensured. The automated data collection system is used to record the time information of each data point, and historical data comparison and trend analysis are performed. The generated compressor operating data provides a real-time basis for equipment maintenance and fault prediction, and the records reflect the operating status of the compressor, providing key data for the formulation of subsequent maintenance strategies.

[0035] S502: Based on the compressor operation data, by performing trend analysis on the data, calculating the abnormal index value, identifying abnormal fluctuations and data points beyond the normal operating range, and generating data abnormality identification information; The specific formula for calculating the abnormal index value is: in, represents the compressor pressure reading at time t, represents the target time point, represents the average pressure reading over the past n data points, represents the standard deviation of the pressure readings over the past n data points, Represents the length of the time window considered, that is, the number of data points contained in the window , The anomaly index representing the compressor data at time t is used to identify short-term fluctuations and abnormal changes in the data through a formula.

[0036] formula: Detailed explanation of the formula and the process of formula calculation and derivation: The formula is used to calculate the abnormal index of the compressor operation data at a specific time point t The abnormal index reflects the pressure reading of the compressor at time point t and the pressure in the past time window The deviation from the mean value of the internal pressure readings.

[0037] Parameter meaning and setting value: is the compressor pressure reading at time point t, assuming it is 85PSI, is the average pressure reading of the past n data points, assuming it is 80PSI, is the standard deviation of the pressure readings for the past n data points, assuming it is 5PSI, is the time window length, assuming 30 data points, which means one data point is collected every minute. is the actual duration of the time window, corresponding to 30 minutes.

[0038] Substitute the parameters into the formula for calculation: result It shows that the current pressure reading of 85PSI deviates from the 30-minute average of 80PSI by 1 standard deviation, which is within the normal operating range, indicating that the compressor is running stably and no additional intervention is required. The numerical results are used to help maintenance personnel monitor the equipment status and ensure operating efficiency and safety.

[0039] S503: Analyze and predict the maintenance needs of the equipment based on the change trend of the operation data by using the data anomaly identification information, adjust the maintenance plan, and generate compressed air management results; In sub-step S503, data anomaly identification information is used to analyze the changing trend of compressor operation data. The abnormal data is analyzed through data analysis software to identify the trends and patterns that lead to equipment failures. Based on the analysis results, the equipment maintenance needs are predicted, and the maintenance plan and cycle are adjusted in time. The adjustments are based on actual operation data and predictive analysis, aiming to reduce downtime and maintenance costs. The generated compressed air management results help the maintenance team optimize resource allocation and time planning to ensure the efficiency and reliability of equipment operation.

[0040] See also Figure 7 ,According to the compressed air management results, the energy consumption data of multiple devices are collected, and the steps of analyzing the data, comparing the equipment operation mode and energy efficiency, adjusting the equipment parameters to optimize energy consumption and generating production line control information are as follows: S601: Based on the compressed air management results, real-time energy consumption data of multiple devices on the production line is collected, including electricity, water and gas usage, to generate equipment energy usage records; In sub-step S601, based on the compressed air management results, the energy consumption data of multiple devices on the production line are monitored and recorded in real time. The target data includes the usage of electricity, water and gas. Special sensors and metering equipment are configured for each energy source to ensure that the data of each energy consumption is accurately collected and updated in real time. The generated equipment energy usage records reflect the energy flow and consumption patterns of the production line. The energy management of the production line is comprehensively monitored and analyzed through the records. The real-time collected data is used to track energy usage, providing a data basis for formulating energy-saving measures and cost control strategies, ensuring the effective use of energy resources and reducing production costs.

[0041] S602: Based on the device energy usage records, by comparing the energy efficiency performance of multiple devices, analyzing the energy usage efficiency of multiple devices, and generating energy efficiency analysis information; In sub-step S602, based on the equipment energy usage records, data analysis technology is used to compare the energy efficiency performance of multiple equipment on the production line, analyze the energy usage efficiency, and help identify inefficient links and efficient practices in energy usage, providing a basis for equipment energy optimization. The energy efficiency analysis information points out improvement points, including equipment aging, unreasonable configuration, and improper operation. According to the analysis results, the operation strategy is adjusted and the technology is upgraded to improve the overall energy usage efficiency, reduce energy waste, improve production cost-effectiveness, and achieve sustainable production.

[0042] S603: Apply the energy efficiency analysis information to adjust the operating parameters of multiple devices on the production line, including start and stop time, operating speed and workload, optimize energy consumption and utilization efficiency, and generate production line control information; In the above content, the energy efficiency analysis information is used to adjust the operating parameters of multiple equipment on the production line, including start and stop time, operating speed and workload, according to the formula , optimize energy consumption and usage efficiency, and generate production line control signals; In the formula, Represents the amount of energy saved, represents the energy consumption before adjustment, represents the adjusted energy consumption; Detailed explanation of the formula and the process of formula calculation and derivation: Assume that the daily operation of a device consumes energy For 1000 units of energy, after optimizing the operating parameters, the energy consumption is reduced to Units, calculate the amount of energy saved: By adjusting operating parameters, energy consumption is optimized, energy demand in the production process is reduced, energy efficiency is improved and operating costs are reduced. By ensuring that the equipment in the production line works according to the optimal operating parameters, dual optimization of energy and cost is achieved.

[0043] See also Figure 8 , an intelligent control system for a shoe production line, the intelligent control system for a shoe production line is used to execute the intelligent control method for the shoe production line, the system comprises: The size parameter configuration module adjusts the mechanical setting parameters corresponding to various shoe sizes based on the shoemaking 3D design model, including the position and pressure setting of the tool, uses the image acquisition device to detect the surface image of the raw material, identifies the defect location and plans the cutting path, and generates production raw material analysis data; The inventory analysis management module is based on production raw material analysis data, monitors material usage and production speed in real time, predicts material demand, adjusts inventory levels, and generates material demand calculation information; The product defect recognition module detects product defects by collecting images of finished shoes based on material demand calculation information, records unqualified products, and updates inventory status in real time to generate product inspection results; The compressor management module monitors the pressure, temperature and vibration data of the air compressor in real time based on the product test results, evaluates the equipment status and predicts failures, adjusts maintenance time, and generates compressor monitoring data; The energy usage optimization module collects and analyzes the energy consumption data of multiple devices in the production line based on the compressor monitoring data, adjusts the equipment operating parameters to optimize energy usage efficiency, and generates production line control information.

[0044] 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 method for a shoe production line, characterized in that: The following steps are involved: Based on the three-dimensional design model of shoemaking, by analyzing the design size and shape of the shoe, the size parameters of multiple sizes are identified, and the production parameters of multiple sizes are adjusted to generate multi-size production parameter configurations; Based on the multi-size production parameter configuration, by real-time acquisition of image data collected on the surface of the production material, the defect position is identified and recorded, and the cutting path is calculated to generate a cutting path calculation result; According to the calculation results of the cutting path, the production parameters and material consumption data are recorded in real time, the material demand is calculated and predicted, and the inventory level of the production raw materials is compared and adjusted to generate inventory adjustment data; Based on the inventory adjustment data, image data of finished shoes is collected in real time, unqualified products are identified and recorded, inventory information is updated in real time, and quality inspection data is generated; Based on the quality inspection data, the operating data of the air compressor is monitored in real time, and through time series analysis, the changing trend of the operating data is evaluated and faults are identified, the maintenance schedule is adjusted, and compressed air management results are generated; According to the compressed air management results, energy consumption data of multiple devices are collected, and by analyzing the data, the equipment operation modes and energy efficiency are compared, the equipment parameters are adjusted to optimize energy consumption, and the production line control information is generated.

2. The intelligent control method for a shoe production line according to claim 1, characterized in that: The multi-size production parameter configuration includes shoe width adjustment data, sole length adjustment parameters, and mold pressure setting parameters; the cutting path calculation results include defect location identification information, material consumption forecast data, and material saving ratio; the inventory adjustment data includes predicted monthly material demand, seasonal inventory ratio, and surplus and shortage warning information; the quality inspection data includes surface smoothness rating, color difference abnormal area, and structural integrity checkpoint; the compressed air management results include pressure release valve setting parameters, temperature control thresholds, and vibration alarm levels; the production line control information includes power usage time periods, automatic shutdown start conditions, and energy-saving parameter adjustment records.

3. The intelligent control method for a shoe production line according to claim 1, characterized in that: Based on the three-dimensional design model of shoemaking, by analyzing the design size and shape of the shoe, identifying the size parameters of multiple sizes, and adjusting the production parameters of multiple sizes, the steps of generating the multi-size production parameter configuration are as follows: Based on the three-dimensional design model of shoemaking, the length, width and height parameters of the shoe type are analyzed by analyzing the three-dimensional model, the geometric characteristics of the shoe are recorded, and the shoe type analysis data is obtained; Based on the shoe type analysis data, analyzing and calculating size parameters corresponding to multiple sizes, including length, width, and height, and generating size parameter adjustment configurations; According to the size parameter adjustment configuration, the mold size, injection pressure and mechanical operation path on the production line are adjusted to generate multi-size production parameter configurations.

4. The intelligent control method for a shoe production line according to claim 1, characterized in that: Based on the multi-size production parameter configuration, the image data collected on the surface of the production material is collected in real time, the defect position is identified and recorded, and the cutting path is calculated. The steps of generating the cutting path calculation result are specifically as follows: Based on the multi-size production parameter configuration, using an image acquisition device, real-time acquisition of image data of the surface of production materials, including cloth and leather, to generate raw material image data; Based on the raw material image data, the image data is analyzed, and the defect location is identified by analyzing the edge clarity and texture depth, thereby generating a material defect analysis result; According to the material defect analysis result, the material utilization rate and cutting efficiency are taken into consideration, the cutting path of the raw material is calculated, and the cutting path calculation result is generated.

5. The intelligent control method for a shoe production line according to claim 1, characterized in that: According to the calculation results of the cutting path, the production parameters and material consumption data are recorded in real time, the material demand is calculated and predicted, and the inventory level of the production raw materials is compared and adjusted. The steps of generating the inventory adjustment data are as follows: According to the calculation results of the cutting path and the real-time material usage data, the data of the amount of cloth and leather consumed in multiple batches are recorded to generate a real-time material consumption record; Based on the real-time material consumption records, material demand is calculated by comparing current inventory and production demand to generate material demand forecast data; According to the material demand forecast data, by comparing the actual inventory and the forecast demand information, the inventory order is adjusted, and an early warning message is sent to the management personnel to generate inventory adjustment data.

6. The intelligent control method for a shoe production line according to claim 1, characterized in that: Based on the inventory adjustment data, the steps of collecting image data of finished shoes in real time, identifying and recording unqualified products, updating inventory information in real time, and generating quality inspection data are specifically as follows: Based on the inventory adjustment data, using an image acquisition device to acquire image data of finished shoes in real time and generate a finished product image acquisition record; Based on the finished product image acquisition record, analyzing the color consistency, surface quality and structural integrity of the shoe surface in the image, identifying defective products that do not meet the standards, and generating a product quality inspection record; By using the product quality inspection records, the quantity of qualified and unqualified products in the inventory is recorded, and the inventory information, including the quantity of salable inventory, is adjusted in real time to generate quality inspection data.

7. The intelligent control method for a shoe production line according to claim 1, characterized in that: Based on the quality inspection data, the operation data of the air compressor is monitored in real time, and through time series analysis, the change trend of the operation data is evaluated and faults are identified, and the maintenance schedule is adjusted to generate the compressed air management results. The specific steps are: Based on the quality detection data, the operating pressure, temperature and vibration data of the air compressor are collected in real time, and the time information of the data is recorded to generate compressor operating data; Based on the compressor operation data, by performing trend analysis on the data, calculating an abnormal index value, identifying abnormal fluctuations and data points beyond the normal operating range, and generating data abnormality identification information; By using the data anomaly identification information, the maintenance needs of the equipment are analyzed and predicted according to the changing trend of the operating data, and the maintenance plan is adjusted to generate compressed air management results.

8. The intelligent control method for a shoe production line according to claim 7, characterized in that: The specific formula for calculating the abnormal index value is: in, represents the compressor pressure reading at time t, represents the target time point, represents the average pressure reading over the past n data points, represents the standard deviation of the pressure readings over the past n data points, Represents the length of the time window considered, that is, the number of data points contained in the window , The anomaly index representing the compressor data at time t is used to identify short-term fluctuations and abnormal changes in the data through a formula.

9. The intelligent control method for a shoe production line according to claim 1, characterized in that: According to the compressed air management results, the energy consumption data of multiple devices are collected, the data are analyzed, the equipment operation modes and energy efficiency are compared, the equipment parameters are adjusted to optimize energy consumption, and the steps of generating production line control information are as follows: According to the compressed air management results, energy consumption data of multiple devices on the production line are collected in real time, including electricity, water and gas usage, to generate equipment energy usage records; Based on the energy usage records of the equipment, by comparing the energy efficiency performance of multiple equipment, analyzing the energy usage efficiency of multiple equipment, and generating energy efficiency analysis information; The energy efficiency analysis information is applied to adjust the operating parameters of multiple devices on the production line, including start and stop time, operating speed and workload, optimize energy consumption and utilization efficiency, and generate production line control information.

10. An intelligent control system for a shoe production line, characterized in that: According to the intelligent control method of a shoe production line according to any one of claims 1 to 9, the system comprises: The size parameter configuration module adjusts the mechanical setting parameters corresponding to various shoe sizes based on the shoemaking 3D design model, including the position and pressure setting of the tool, uses the image acquisition device to detect the surface image of the raw material, identifies the defect location and plans the cutting path, and generates production raw material analysis data; The inventory analysis management module, based on the production raw material analysis data, monitors the material usage and production speed in real time, predicts material demand, adjusts the inventory level, and generates material demand calculation information; The product defect recognition module detects product defects by collecting finished shoe images based on the material demand calculation information, records unqualified products, and updates the inventory status in real time to generate product inspection results; The compressor management module monitors the pressure, temperature and vibration data of the air compressor in real time based on the product detection results, evaluates the equipment status and predicts failures, adjusts maintenance time, and generates compressor monitoring data; The energy usage optimization module collects and analyzes the energy consumption data of multiple devices in the production line based on the compressor monitoring data, adjusts the equipment operating parameters to optimize energy usage efficiency, and generates production line control information.

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