A smart manufacturing control method and system based on Internet of Things data

Abstract

The present invention discloses an intelligent manufacturing control method and system based on Internet of Things data, which relates to the field of intelligent manufacturing technology, including: integrating the details of equipment power lines, types, specifications, locations, etc., constructing a spatial model of manufacturing equipment spacing, calculating the impact index of manufacturing equipment operation status, and determining the preferred installation position of sensors to deploy the network. Subsequently, based on the sensor network, real-time data on equipment operation, production process, product quality, materials, and production environment are collected, and product quality standards are clarified based on production process data and equipment information, the proportion of manufacturing equipment processing quality is calculated, and a product quality impact function is constructed, thereby obtaining a product processing quality index, and then calculating a manufacturing quality impact index. Finally, combined with the manufacturing quality impact index, product processing quality index, real-time data of equipment operation status, and manufacturing equipment information, thresholds are set to dynamically control the operating parameters of manufacturing equipment to achieve intelligent manufacturing control.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to an intelligent manufacturing control method and system based on Internet of Things data. Background Art

[0002] As the manufacturing industry transforms towards intelligence and digitalization, traditional production methods can no longer meet the needs of efficiency and flexibility. The application of Internet of Things (IoT) technology provides real-time monitoring, data collection and remote control capabilities for the production process. Intelligent manufacturing control methods based on IoT data can achieve intelligent optimization and automatic adjustment of the production process by obtaining equipment status, production quality and environmental parameters in real time. This method not only improves production efficiency, product quality and equipment utilization, but also reduces failure rates and energy consumption, with significant economic and social benefits. Promote the development of intelligent manufacturing and enhance the competitiveness of the global manufacturing industry.

[0003] The existing intelligent manufacturing control methods and systems based on IoT data have problems such as inappropriate distance between sensors and manufacturing equipment, and great influence of electromagnetic interference or power line noise, which affects the accuracy and reliability of data collected by sensors, and further affects the subsequent monitoring and control of the status of manufacturing equipment. At the same time, the existing technology fails to comprehensively analyze the in-depth relationship between factors such as production process data, manufacturing equipment information and real-time data of production environment and product quality, resulting in the inability to accurately construct product quality impact functions and it is difficult to accurately evaluate the relationship between product quality and various factors. Therefore, it is necessary to provide an intelligent manufacturing control method and system based on IoT data to solve the above-mentioned problems. Summary of the invention

[0004] In order to solve the above technical problems, an intelligent manufacturing control method and system based on Internet of Things data are provided. This technical solution solves the problem that the existing intelligent manufacturing control method and system based on Internet of Things data proposed in the above background technology have improper distances between sensors and manufacturing equipment, and fail to conduct in-depth analysis of the impact of factors such as production process data, manufacturing equipment information and real-time data of production environment on product quality, resulting in the inability to accurately construct product quality impact functions and difficulty in accurately evaluating the relationship between product quality and various factors.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: An intelligent manufacturing control method based on Internet of Things data, comprising: Acquire manufacturing equipment information and manufacturing scene information, and deploy a sensor network based on the manufacturing equipment information and manufacturing scene information; Based on the sensor network, real-time data on equipment operation status, production process data, product quality data, material information data and production environment data are obtained; Obtain product quality impact functions based on production process data, manufacturing equipment information and real-time production environment data; Obtain product processing quality index based on product quality impact function and real-time product quality data; Obtain the manufacturing quality impact index based on product quality impact function, product processing quality index, manufacturing scenario information and real-time material information data; Based on the manufacturing quality impact index, product processing quality index, real-time data of equipment operation status and manufacturing equipment information, the manufacturing equipment is controlled to achieve intelligent manufacturing control.

[0006] In an optional embodiment, the obtaining of manufacturing equipment information and manufacturing scene information, and deploying a sensor network based on the manufacturing equipment information and the manufacturing scene information, specifically includes: Based on the manufacturing equipment information, obtaining manufacturing equipment power line information, manufacturing equipment type information, manufacturing equipment specification information and manufacturing equipment location information; Determine manufacturing equipment spacing information based on manufacturing equipment location information, manufacturing equipment specification information, and manufacturing scene information; According to the manufacturing equipment spacing information and the manufacturing equipment specification information, a manufacturing equipment spacing space model is constructed, and the initial installation position of the sensor is set based on the manufacturing equipment spacing space model; Based on the initial installation position of the sensor, simulate the installation of the sensor and deploy the transmission line to obtain the simulated sensor installation information. Then, combined with the manufacturing equipment information, obtain the manufacturing equipment operation status impact index. The initial installation position of the sensor corresponding to the minimum impact index of the manufacturing equipment operation status is used as the preferred installation position of the sensor; Complete the deployment of the sensor network according to the preferred installation locations of the sensors; The calculation formula of the manufacturing equipment operation status impact index is: ; In the formula, is the impact index of manufacturing equipment operation status, is the weight coefficient of the impact factor, is the impact factor, is the distance factor between the sensor and the manufacturing equipment, is a constant, is the distance between the sensor and the manufacturing equipment, is the attenuation factor, is the electromagnetic interference factor between the sensor and the manufacturing equipment, is the intensity of electromagnetic interference, is a constant, is the shared power line noise factor between the sensor and the manufacturing equipment, is the electrical noise level, is a constant, is the factor affecting the shared network line between the sensor and the manufacturing equipment, It's network delay. is the packet loss rate, is a constant, is the influence factor of environmental factors on the sensor, , and They are the impact indexes of temperature, humidity and vibration on the sensor. is a constant, is the noise impact factor of the sensor transmission line itself, is the power line noise level, is a constant.

[0007] In an optional embodiment, the obtaining of the product quality impact function according to the production process data, the manufacturing equipment information and the real-time data of the production environment specifically includes: According to the production process data, clarify the product quality standard information, production process sequence information and key process parameters, and determine the product quality standard index based on the product quality standard information; Based on the production process sequence information and the manufacturing equipment information, determine the manufacturing equipment processing area information, the manufacturing equipment related information and the association information between the manufacturing equipment; Determine the standard product volume value and the manufacturing equipment processing area area based on product quality standard information and manufacturing equipment processing area information; The manufacturing equipment processing area and the standard product volume value are normalized, and then the ratio of the normalized manufacturing equipment processing area and the standard product volume value is used as the manufacturing equipment processing quality ratio; The product value of the manufacturing equipment processing quality ratio and the product quality standard index is used as the product quality standard sub-index; According to the manufacturing equipment information and the manufacturing equipment processing area information, obtaining the manufacturing equipment processing repeated area information; Based on the manufacturing equipment processing repeated area information and the association information between the manufacturing equipment, determine the manufacturing equipment processing repeated area area and the manufacturing equipment association coefficient, wherein the value of the manufacturing equipment association coefficient is the total number of different manufacturing equipment participating in the same production process; Normalizing the manufacturing equipment correlation coefficient, and taking the product of the normalized manufacturing equipment correlation coefficient and the area of ​​the manufacturing equipment processing repeated region as the first product quality influence coefficient; Obtain historical manufacturing equipment status data based on manufacturing equipment related information, and obtain corresponding production environment historical data based on production environment real-time data; Constructing a product quality impact function based on historical manufacturing equipment status data, production environment historical data, the first product quality impact coefficient, the product quality standard sub-index, and the product quality standard index; Among them, the expression formula of the product quality impact function is: In the formula, is the product quality standard index, For the The product quality standard sub-index corresponding to the historical manufacturing equipment, is the total number of all manufacturing equipment involved in the production process, is the first product quality influence coefficient, For the The first historical manufacturing equipment Historical device status data, is the total number of historical device status data, represents a quadratic function, For the The historical temperature of the historical manufacturing equipment when processing the product, For the The historical pressure of the historical manufacturing equipment when processing the product, For the The historical time of the historical manufacturing equipment in processing the product, Respectively The equipment accuracy and equipment failure rate of historical manufacturing equipment.

[0008] In an optional embodiment, the obtaining of the product processing quality index based on the product quality influence function and the product quality real-time data specifically includes: Determine the real-time data of the equipment operation status at a certain moment and the real-time data of the production environment corresponding to the manufacturing equipment, and perform data preprocessing on the real-time data of the equipment operation status and the real-time data of the production environment to obtain parameters affecting product quality; Substitute the product quality influencing parameters into the product quality influencing function (2) to obtain the product processing quality index.

[0009] In an optional embodiment, obtaining the manufacturing quality impact index according to the product quality impact function, the product processing quality index, the manufacturing scene information and the real-time data of the material information specifically includes: According to formula (1) in the product quality influence function, obtain the real-time processing standard quality index of the product; The difference between the product processing quality index and the product real-time processing standard quality index is used as the first processing quality impact index; Setting a first processing quality impact index threshold, and determining a processing link response plan based on the first processing quality impact index and the first processing quality impact index threshold; Calculate the implementation time of the processing link response plan based on real-time material information data, manufacturing scenario information and processing link response plan; The planned implementation time of the processing link is quantified to obtain the manufacturing quality impact index.

[0010] In an optional embodiment, the manufacturing equipment is controlled based on the manufacturing quality impact index, the product processing quality index, the real-time data of the equipment operation status and the manufacturing equipment information to realize intelligent manufacturing control, which specifically includes: Setting a manufacturing quality impact index threshold, and determining a first manufacturing equipment control parameter based on the manufacturing quality impact index, the manufacturing quality impact index threshold, and real-time data of equipment operation status; Based on the first manufacturing equipment control parameter, adjusting the operating parameters of the corresponding manufacturing equipment, and obtaining the processing quality index of the product processed by the corresponding manufacturing equipment at that moment; The difference between the product processing quality index processed by the manufacturing equipment corresponding to the moment and the product real-time processing standard quality index is used as the second processing quality impact index; Determine a second manufacturing equipment control parameter according to the second processing quality influence index and the first processing quality influence index threshold, and dynamically adjust the operating parameters of the corresponding manufacturing equipment according to the second manufacturing equipment control parameter; Based on the production process data and manufacturing equipment information, obtain the manufacturing equipment information of the next process of the corresponding manufacturing equipment; According to the manufacturing equipment information and formula (1), the product processing prediction quality standard index corresponding to the manufacturing equipment of the next process is calculated; Let equation (1) be equal to equation (2) to obtain equation (3), and obtain the manufacturing equipment state prediction data and production environment prediction data corresponding to the manufacturing equipment of the next process; Substitute the manufacturing equipment status prediction data, production environment prediction data and the first product quality impact coefficient into formula (3) to obtain the predicted processing time corresponding to the manufacturing equipment of the next process; Quantify the predicted processing time and optimize the processing response plan in combination with the manufacturing quality impact index to achieve intelligent manufacturing control; Among them, formula (3) is: .

[0011] Furthermore, an intelligent manufacturing control system based on Internet of Things data is proposed, which is used to implement any of the control methods mentioned above, including: A collection module, which is used to obtain manufacturing equipment information, manufacturing scene information, real-time data on equipment operation status, production process data, real-time data on product quality, real-time data on material information, and real-time data on production environment; A main control module, the main control module is used to deploy a sensor network based on manufacturing equipment information and manufacturing scenario information, obtain a product quality impact function based on production process data, manufacturing equipment information and real-time data of production environment, obtain a product processing quality index based on the product quality impact function and real-time data of product quality, and obtain a manufacturing quality impact index based on the product quality impact function, the product processing quality index, manufacturing scenario information and real-time data of material information; A control module, the control module is used to control the sensor network and control the manufacturing equipment based on the manufacturing quality impact index, the product processing quality index, the real-time data of the equipment operation status and the manufacturing equipment information; The display module is used to display the collection process and results of the collection module, the deployment process and results of the main control module, and the control process and results of the control module.

[0012] In an optional embodiment, the acquisition module includes: A first acquisition unit, the first acquisition unit is used to obtain manufacturing equipment information and manufacturing scene information; The second acquisition unit is used to obtain real-time data on equipment operation status, production process data, product quality data, material information data and production environment data.

[0013] In an optional embodiment, the main control module includes: A deployment unit, the deployment unit is used to deploy a sensor network based on manufacturing equipment information and manufacturing scene information; A data processing unit, wherein the data processing unit is used to obtain a product quality impact function based on production process data, manufacturing equipment information and real-time data of production environment, obtain a product processing quality index based on the product quality impact function and real-time data of product quality, and obtain a manufacturing quality impact index based on the product quality impact function, product processing quality index, manufacturing scenario information and real-time data of material information.

[0014] In an optional embodiment, the control module includes: a first control unit, the first control unit being used to control the sensor network; The second control unit is used to control the manufacturing equipment based on the manufacturing quality impact index, the product processing quality index, the real-time data of the equipment operation status and the manufacturing equipment information.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This scheme proposes an intelligent manufacturing control method and system based on IoT data. Based on various information of manufacturing equipment, a spatial model of the spacing between manufacturing equipment is constructed. By calculating the impact index of the operating status of the manufacturing equipment, the position corresponding to the minimum index is used as the preferred installation position of the sensor, which optimizes the deployment of the sensor and improves the quality of data collection. This scheme proposes an intelligent manufacturing control method and system based on IoT data. By clarifying product quality standard information, production process sequence, key process parameters, etc., combined with manufacturing equipment processing area, related information, historical manufacturing equipment status and production environment historical data, a more comprehensive product quality impact function is constructed, which can more accurately reflect the relationship between product quality and various factors; This scheme proposes an intelligent manufacturing control method and system based on IoT data. By preprocessing the real-time data of equipment operation status and production environment, the product quality influencing parameters are obtained, and then the product quality influencing function is substituted to obtain the product processing quality index, thereby improving the accuracy of product processing quality evaluation. This scheme proposes an intelligent manufacturing control method and system based on Internet of Things data. By integrating product quality impact function, product processing quality index, manufacturing scenario information and real-time material information data, the manufacturing quality impact index is timely and accurately evaluated, and a reasonable processing link response plan is timely formulated, so that the manufacturing quality impact assessment is more comprehensive and the response strategy is more targeted and timely. At the same time, by fully combining various information such as manufacturing quality impact index, product processing quality index and real-time data of equipment operation status, dynamic adjustment is carried out to accurately adjust the operating parameters of manufacturing equipment and realize efficient intelligent manufacturing control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of an intelligent manufacturing control method based on Internet of Things data proposed by the present invention; Figure 2 A flow chart of the deployment of the sensor network in the present invention; Figure 3 It is a flow chart for obtaining the product quality influence function in the present invention; Figure 4 This is a system framework diagram of an intelligent manufacturing control system based on Internet of Things data proposed in the present invention. DETAILED DESCRIPTION

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0018] Reference Figure 1 - Figure 4 As shown, a smart manufacturing control method based on Internet of Things data includes: Acquire manufacturing equipment information and manufacturing scene information, and deploy a sensor network based on the manufacturing equipment information and manufacturing scene information; Based on the sensor network, real-time data on equipment operation status, production process data, product quality data, material information data and production environment data are obtained; Obtain product quality impact functions based on production process data, manufacturing equipment information and real-time production environment data; Obtain product processing quality index based on product quality impact function and real-time product quality data; Obtain the manufacturing quality impact index based on product quality impact function, product processing quality index, manufacturing scenario information and real-time material information data; Based on the manufacturing quality impact index, product processing quality index, real-time data of equipment operation status and manufacturing equipment information, the manufacturing equipment is controlled to achieve intelligent manufacturing control.

[0019] Specifically, manufacturing scenario information is used to describe the layout of the production environment, process flow, workshop topology, equipment associations, etc. (such as production line number, workstation distribution, and logistics path). The real-time data on equipment operation status is the real-time operating parameters of the equipment monitored by the sensor network (such as vibration frequency, current, and energy consumption). It is used to predict equipment failures (such as abnormal vibration detection) and optimize energy consumption. It includes equipment ID, timestamp, sensor type (vibration / temperature), etc. The production process data is the set value and actual value of the process parameters in the production process (such as injection temperature and CNC processing speed). The purpose is to ensure process compliance and analyze process bottlenecks. It includes process ID, parameter name (temperature / pressure), target value, actual value, deviation, etc., and product quality is realized. Real-time data refers to the key quality characteristics of products (such as dimensional tolerances, surface defects, etc.) detected online by the sensor network. It is used to provide real-time feedback on quality anomalies and trigger shutdowns or adjustments. It includes batch number, inspection items (size / weight), qualified status, defect type, etc. Real-time data on material information is used to track the flow status of materials (such as raw material batches, inventory locations, usage), and is used for error prevention (such as material matching verification) and lean production (JIT scheduling). It includes information such as material ID, supplier batch, current location, remaining quantity, expiration date, etc. Real-time data on production environment refers to workshop environmental conditions (such as temperature) monitored by the sensor network. It includes sensor location, parameter type (temperature), value, etc., providing a data basis for the calculation of product quality influencing functions.

[0020] It can be understood that the product quality impact function quantifies the weight of the impact of process parameters, equipment status, environmental factors, etc. on product quality, and the manufacturing quality impact index is an indicator for globally evaluating the overall quality of the manufacturing process, integrating multi-dimensional data such as equipment, process, and materials.

[0021] Furthermore, the manufacturing equipment information and the manufacturing scene information are obtained, and a sensor network is deployed based on the manufacturing equipment information and the manufacturing scene information, specifically including: Based on the manufacturing equipment information, obtaining manufacturing equipment power line information, manufacturing equipment type information, manufacturing equipment specification information and manufacturing equipment location information; Determine manufacturing equipment spacing information based on manufacturing equipment location information, manufacturing equipment specification information, and manufacturing scene information; According to the manufacturing equipment spacing information and the manufacturing equipment specification information, a manufacturing equipment spacing space model is constructed, and the initial installation position of the sensor is set based on the manufacturing equipment spacing space model; Based on the initial installation position of the sensor, simulate the installation of the sensor and deploy the transmission line to obtain the simulated sensor installation information. Then, combined with the manufacturing equipment information, obtain the manufacturing equipment operation status impact index. The initial installation position of the sensor corresponding to the minimum impact index of the manufacturing equipment operation status is used as the preferred installation position of the sensor; Complete the deployment of the sensor network according to the preferred installation locations of the sensors; Among them, the calculation formula of the manufacturing equipment operation status impact index is: ; In the formula, is the impact index of manufacturing equipment operation status, is the weight coefficient of the impact factor, is the impact factor, is the distance factor between the sensor and the manufacturing equipment, is a constant, is the distance between the sensor and the manufacturing equipment, is the attenuation factor, is the electromagnetic interference factor between the sensor and the manufacturing equipment, is the intensity of electromagnetic interference, is a constant, is the shared power line noise factor between the sensor and the manufacturing equipment, is the electrical noise level, is a constant, is the factor affecting the shared network line between the sensor and the manufacturing equipment, It's network delay. is the packet loss rate, is a constant, is the influence factor of environmental factors on the sensor, , and They are the impact indexes of temperature, humidity and vibration on the sensor. is a constant, is the noise impact factor of the sensor transmission line itself, is the power line noise level, is a constant.

[0022] Specifically, the manufacturing equipment power line information describes the topology of the equipment power supply, line direction, load distribution, etc. (such as cable specifications, circuit breaker location), to avoid conflicts with power lines when sensors are deployed (such as electromagnetic interference). Manufacturing equipment type information is to identify the classification of equipment (such as injection molding machines, robots, etc.), and determine the special requirements of equipment for sensors (such as high temperature sensors required in high temperature environments). Manufacturing equipment specification information is the physical size, weight, power consumption and other parameters of the equipment, which are used to ensure safe channels and operating space when calculating equipment spacing. Manufacturing equipment location information is the absolute coordinates of the equipment in the workshop. When determining the manufacturing equipment spacing information, it is necessary to input equipment location information (coordinates), equipment specification information (size, weight) and manufacturing scenario information (workshop layout constraints, safety specifications) to output the minimum safety distance, maintenance channel distance and electrical safety distance to prevent collisions or operational interference between equipment (such as forklift passage width). The minimum safety distance prevents collisions or operational interference between equipment (such as forklift passage width), the maintenance channel distance reserves equipment maintenance space (such as ≥1m channel), and the electrical safety distance avoids electromagnetic interference between power lines and sensors (such as a distance of more than 3m around high-voltage equipment).

[0023] Specifically, the distance influencing factor between the sensor and the manufacturing equipment usually depends on the transmission method (such as wireless, copper wire, optical fiber, etc.), which can be calculated using the signal attenuation formula. The electromagnetic interference factor between the sensor and the manufacturing equipment is often generated by the electrical noise source around the equipment, affecting the signal quality. It is expressed by interference power (noise amplitude) and system noise tolerance. The shared power line noise influencing factor between the sensor and the manufacturing equipment is expressed by power fluctuations or distortion of the current waveform. The power noise level is measured by the power spectrum. The shared network line influencing factor between the sensor and the manufacturing equipment has a significant impact on the data transmission quality due to the shared bandwidth, delay and packet loss rate of the network. Delay and packet loss rate are used to quantify it in the formula. The environmental factors affect the sensor. Among the environmental factors, temperature and humidity will affect the accuracy and stability of the sensor. The impact index of temperature, humidity and vibration on the sensor can be obtained by the type of sensor, thereby establishing an environmental impact model. The noise influencing factor of the sensor transmission line itself is that the power line itself causes additional interference, which affects the data transmission of the sensor.

[0024] Understandably, signal attenuation increases inversely with distance. This means that as distance increases, the quality and strength of the signal gradually decreases. The impact of electromagnetic interference is usually related to the strength of the interference source, and the interference strength tends to increase linearly with the increase of the interference source, or in some cases, the impact of interference may increase logarithmically. As electromagnetic interference increases, the impact increases exponentially. However, due to the use of a logarithmic function, the impact of interference increases logarithmically, that is, the rate of increase of the impact exponentially decreases as the interference strength increases. Power line noise usually increases with power fluctuations or instabilities. Usually, the noise impact increases with the square root of the power noise level, that is, as the noise increases, the impact gradually increases, but the growth rate slows down. As the power noise increases, the impact exponentially increases, but the rate of increase is the square root, that is, as the noise increases, the increase of the impact gradually slows down. Network latency and packet loss rate directly affect the quality of data transmission, usually latency and packet loss rate increase linearly, that is, as the network load increases, the increase of latency and packet loss is relatively uniform. The impact of changes in environmental factors such as temperature, humidity, vibration, etc. on device performance usually increases linearly, although some extreme environments may cause more drastic changes. The noise of power lines usually increases with voltage fluctuations or current instability, and this noise usually grows logarithmically. As the noise intensity increases, the rate at which system performance decreases also gradually slows down.

[0025] Furthermore, the product quality impact function is obtained based on the production process data, manufacturing equipment information and real-time production environment data, including: According to the production process data, clarify the product quality standard information, production process sequence information and key process parameters, and determine the product quality standard index based on the product quality standard information; Based on the production process sequence information and the manufacturing equipment information, determine the manufacturing equipment processing area information, the manufacturing equipment related information and the association information between the manufacturing equipment; Determine the standard product volume value and the manufacturing equipment processing area area based on product quality standard information and manufacturing equipment processing area information; The manufacturing equipment processing area and the standard product volume value are normalized, and then the ratio of the normalized manufacturing equipment processing area and the standard product volume value is used as the manufacturing equipment processing quality ratio; The product value of the manufacturing equipment processing quality ratio and the product quality standard index is used as the product quality standard sub-index; According to the manufacturing equipment information and the manufacturing equipment processing area information, obtaining the manufacturing equipment processing repeated area information; Based on the manufacturing equipment processing repeated area information and the association information between the manufacturing equipment, determine the manufacturing equipment processing repeated area area and the manufacturing equipment association coefficient, the value of the manufacturing equipment association coefficient is the total number of different manufacturing equipment participating in the same production process; Normalizing the manufacturing equipment correlation coefficient, and taking the product of the normalized manufacturing equipment correlation coefficient and the area of ​​the manufacturing equipment processing repeated region as the first product quality influence coefficient; Obtain historical manufacturing equipment status data based on manufacturing equipment related information, and obtain corresponding production environment historical data based on production environment real-time data; Constructing a product quality impact function based on historical manufacturing equipment status data, production environment historical data, the first product quality impact coefficient, the product quality standard sub-index, and the product quality standard index; Among them, the expression formula of product quality impact function is: In the formula, is the product quality standard index, For the The product quality standard sub-index corresponding to the historical manufacturing equipment, is the total number of all manufacturing equipment involved in the production process, is the first product quality influence coefficient, For the The first historical manufacturing equipment Historical device status data, is the total number of historical device status data, represents a quadratic function, For the The historical temperature of the historical manufacturing equipment when processing the product, For the The historical pressure of the historical manufacturing equipment when processing the product is The historical time of the historical manufacturing equipment in processing the product, Respectively The equipment accuracy and equipment failure rate of historical manufacturing equipment.

[0026] Specifically, normalizing the correlation coefficient of manufacturing equipment is to scale the correlation coefficient of manufacturing equipment to the interval [0, 1]. Product quality standard information is the provisions of the quality level and characteristics that the product should achieve, including various quality requirements, test methods, acceptance criteria, etc. of the product, among which the "product quality standard index" is a numerical value used to quantitatively measure the overall quality standard level of the product, which can comprehensively reflect the expected performance of the product in various quality indicators. Production process sequence information refers to the sequence of various process steps in the product production process, which clarifies the entire production process path from raw materials to finished products. Key process parameters are process variables that have a significant impact on product quality and performance in the production process, such as temperature, pressure, time, speed, etc. Manufacturing equipment information contains various detailed information about manufacturing equipment, such as equipment model, specifications, functions, processing capacity, equipment status, etc. Manufacturing equipment processing area information refers to the relevant information of the specific area where the manufacturing equipment is used to process the product, such as the size, shape, location, etc. of the area. Manufacturing equipment related information is other equipment-related information except the processing area, such as equipment maintenance records, service life, etc. The association information between manufacturing equipment indicates the relationship between different manufacturing equipment in the production process, such as which equipment will participate in the same production process, the order of cooperation between equipment, etc. The real-time data of the production environment refers to the environmental parameter data of the production site at a certain moment, such as temperature, humidity, air pressure, etc. The historical data of the production environment is the data record of the production environment in the past period of time. The historical manufacturing equipment status data records various status information of the manufacturing equipment in the past operation process, such as equipment failure records, operation time, maintenance records, etc.

[0027] Furthermore, based on the product quality impact function and the real-time product quality data, the product processing quality index is obtained, including: Determine the real-time data of the equipment operation status at a certain moment and the real-time data of the production environment corresponding to the manufacturing equipment, and perform data preprocessing on the real-time data of the equipment operation status and the real-time data of the production environment to obtain parameters affecting product quality; Substitute the product quality influencing parameters into the product quality influencing function (2) to obtain the product processing quality index.

[0028] Specifically, according to formula (2), we can know the specific corresponding values ​​of the parameters affecting product quality, including equipment status data, temperature, etc.

[0029] Furthermore, according to the product quality impact function, product processing quality index, manufacturing scene information and material information real-time data, the manufacturing quality impact index is obtained, including: According to formula (1) in the product quality influence function, obtain the real-time processing standard quality index of the product; The difference between the product processing quality index and the product real-time processing standard quality index is used as the first processing quality impact index; Setting a first processing quality impact index threshold, and determining a processing link response plan based on the first processing quality impact index and the first processing quality impact index threshold; Calculate the implementation time of the processing link response plan based on real-time material information data, manufacturing scenario information and processing link response plan; The planned implementation time of the processing link is quantified to obtain the manufacturing quality impact index.

[0030] Specifically, the threshold of the first processing quality impact index is set (set by the operator), and the response plan for the processing link is determined based on the first processing quality impact index and the threshold of the first processing quality impact index. If the first processing quality impact index is greater than or equal to the threshold of the first processing quality impact index, the processing quality impact of the product is too large and the processed product needs to be removed, and the transportation equipment is controlled to replenish new processed products or materials. If the first processing quality impact index is greater than or equal to 0.5 times the threshold of the first processing quality impact index but less than the threshold of the first processing quality impact index, the control parameters of the manufacturing equipment are optimized. The response plan for the processing link includes the need to remove the processed product if the processing quality impact of the product is too large, and control the transportation equipment to replenish new processed products or materials, and also includes optimizing the control parameters of the manufacturing equipment. By adjusting the equipment parameters (such as temperature, pressure, speed, etc.), the product processing quality index is as close to the standard value as possible, and at the same time, the time required for parameter adjustment is shortened while ensuring quality. Based on the real-time data of material information, manufacturing scenario information and the response plan for the processing link, the implementation time of the response plan for the processing link is calculated, and the control system traverses the historical data entered by the operator (the average time used when the event occurred in the past time period). The planned implementation time of the processing link is quantified to obtain the manufacturing quality impact index, and the implementation time is converted into a value in seconds.

[0031] Furthermore, based on the manufacturing quality impact index, product processing quality index, real-time data of equipment operation status and manufacturing equipment information, the manufacturing equipment is controlled to realize intelligent manufacturing control, including: Setting a manufacturing quality impact index threshold, and determining a first manufacturing equipment control parameter based on the manufacturing quality impact index, the manufacturing quality impact index threshold, and real-time data of equipment operation status; Based on the first manufacturing equipment control parameter, adjusting the operating parameters of the corresponding manufacturing equipment, and obtaining the processing quality index of the product processed by the corresponding manufacturing equipment at that moment; The difference between the product processing quality index processed by the manufacturing equipment corresponding to the moment and the product real-time processing standard quality index is used as the second processing quality impact index; Determine a second manufacturing equipment control parameter according to the second processing quality influence index and the first processing quality influence index threshold, and dynamically adjust the operating parameters of the corresponding manufacturing equipment according to the second manufacturing equipment control parameter; Based on the production process data and manufacturing equipment information, obtain the manufacturing equipment information of the next process of the corresponding manufacturing equipment; According to the manufacturing equipment information and formula (1), the product processing prediction quality standard index corresponding to the manufacturing equipment of the next process is calculated; Let equation (1) be equal to equation (2) to obtain equation (3), and obtain the manufacturing equipment state prediction data and production environment prediction data corresponding to the manufacturing equipment of the next process; Substitute the manufacturing equipment status prediction data, production environment prediction data and the first product quality impact coefficient into formula (3) to obtain the predicted processing time corresponding to the manufacturing equipment of the next process; The predicted processing time is quantified, and the processing response plan is optimized in combination with the manufacturing quality impact index to achieve intelligent manufacturing control.

[0032] Specifically, a manufacturing quality impact index threshold is set, and the first manufacturing equipment control parameter is determined based on the manufacturing quality impact index, the manufacturing quality impact index threshold and the real-time data of the equipment operating status. The threshold is set by the operator through the control system. If the manufacturing quality impact index is greater than or equal to the manufacturing quality impact index threshold, it is necessary to record the real-time data of the equipment operating status and obtain the corresponding equipment operating parameters, reduce the manufacturing quality impact index to the current 0.8 (the reduction multiple is set by the operator before manufacturing), record the equipment operating parameters after the manufacturing quality impact index is reduced, and use this operating parameter as the first manufacturing equipment control parameter (including the control parameters for manufacturing equipment and environmental control equipment).

[0033] It can be understood that, according to the manufacturing equipment information and formula (1), the product processing prediction quality standard index corresponding to the manufacturing equipment of the next process is calculated, and formula (1) is equal to formula (2) to obtain formula (3), and the manufacturing equipment state prediction data and production environment prediction data corresponding to the manufacturing equipment of the next process are obtained. The manufacturing equipment state prediction data, the production environment prediction data and the first product quality impact coefficient are substituted into formula (3) to obtain the predicted processing time corresponding to the manufacturing equipment of the next process, and the predicted processing time is quantified. In combination with the manufacturing quality impact index, the processing link response plan is optimized to realize intelligent manufacturing control. The manufacturing equipment state prediction data and the production environment prediction data can obtain corresponding parameters according to formula (2); Among them, formula (3) is: .

[0034] Furthermore, an intelligent manufacturing control system based on Internet of Things data is proposed, which is used to implement any of the control methods mentioned above, including: The acquisition module is used to obtain manufacturing equipment information, manufacturing scene information, real-time data on equipment operation status, production process data, real-time data on product quality, real-time data on material information, and real-time data on production environment; A main control module, which is used to deploy a sensor network based on manufacturing equipment information and manufacturing scenario information, obtain a product quality impact function based on production process data, manufacturing equipment information and real-time production environment data, obtain a product processing quality index based on the product quality impact function and real-time product quality data, and obtain a manufacturing quality impact index based on the product quality impact function, product processing quality index, manufacturing scenario information and real-time material information data; A control module, the control module is used to control the sensor network and control the manufacturing equipment based on the manufacturing quality impact index, the product processing quality index, the real-time data of the equipment operation status and the manufacturing equipment information; Display module: The display module is used to display the collection process and results of the collection module, the deployment process and results of the main control module, and the control process and results of the control module.

[0035] Furthermore, the acquisition module includes: A first acquisition unit, the first acquisition unit is used to acquire manufacturing equipment information and manufacturing scene information; The second acquisition unit is used to obtain real-time data on equipment operation status, production process data, product quality data, material information data and production environment data.

[0036] Furthermore, the main control module includes: A deployment unit, the deployment unit is used to deploy a sensor network based on manufacturing equipment information and manufacturing scene information; The data processing unit is used to obtain the product quality impact function according to the production process data, manufacturing equipment information and real-time data of the production environment, and obtain the product processing quality index based on the product quality impact function and the real-time data of product quality, and obtain the manufacturing quality impact index according to the product quality impact function, the product processing quality index, the manufacturing scene information and the real-time data of material information.

[0037] Furthermore, the control module includes: A first control unit, the first control unit is used to control the sensor network; The second control unit, the first control unit is used to control the manufacturing equipment based on the manufacturing quality impact index, the product processing quality index, the real-time data of the equipment operation status and the manufacturing equipment information.

[0038] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent manufacturing control method based on Internet of Things data, characterized in that: include: Acquire manufacturing equipment information and manufacturing scene information, and deploy a sensor network based on the manufacturing equipment information and manufacturing scene information; Based on the sensor network, real-time data on equipment operation status, production process data, product quality data, material information data and production environment data are obtained; Obtain product quality impact functions based on production process data, manufacturing equipment information and real-time production environment data; Obtain product processing quality index based on product quality impact function and real-time product quality data; Obtain the manufacturing quality impact index based on product quality impact function, product processing quality index, manufacturing scenario information and real-time material information data; Based on the manufacturing quality impact index, product processing quality index, real-time data of equipment operation status and manufacturing equipment information, the manufacturing equipment is controlled to achieve intelligent manufacturing control.

2. According to the method of claim 1, the intelligent manufacturing control method based on Internet of Things data is characterized in that: The obtaining of manufacturing equipment information and manufacturing scene information, and deploying a sensor network based on the manufacturing equipment information and the manufacturing scene information, specifically includes: Based on the manufacturing equipment information, obtaining manufacturing equipment power line information, manufacturing equipment type information, manufacturing equipment specification information and manufacturing equipment location information; Determine manufacturing equipment spacing information based on manufacturing equipment location information, manufacturing equipment specification information, and manufacturing scene information; According to the manufacturing equipment spacing information and the manufacturing equipment specification information, a manufacturing equipment spacing space model is constructed, and the initial installation position of the sensor is set based on the manufacturing equipment spacing space model; Based on the initial installation position of the sensor, simulate the installation of the sensor and deploy the transmission line to obtain the simulated sensor installation information. Then, combined with the manufacturing equipment information, the impact index of the manufacturing equipment operation status is obtained. The initial installation position of the sensor corresponding to the minimum impact index of the manufacturing equipment operation status is used as the preferred installation position of the sensor; Complete the deployment of the sensor network according to the optimal installation location of the sensors; The calculation formula of the manufacturing equipment operation status impact index is: In the formula, is the impact index of manufacturing equipment operation status, is the weight coefficient of the impact factor, is the impact factor, is the distance factor between the sensor and the manufacturing equipment, is a constant, is the distance between the sensor and the manufacturing equipment, is the attenuation factor, is the electromagnetic interference factor between the sensor and the manufacturing equipment, is the intensity of electromagnetic interference, is a constant, is the shared power line noise factor between the sensor and the manufacturing equipment, is the electrical noise level, is a constant, is the factor affecting the shared network line between the sensor and the manufacturing equipment, It's network delay. is the packet loss rate, is a constant, is the influence factor of environmental factors on the sensor, , and They are the impact indexes of temperature, humidity and vibration on the sensor. is a constant, is the noise impact factor of the sensor transmission line itself, is the power line noise level, is a constant.

3. According to the intelligent manufacturing control method based on Internet of Things data in claim 2, it is characterized in that: The method of obtaining the product quality impact function according to the production process data, manufacturing equipment information and real-time production environment data specifically includes: According to the production process data, clarify the product quality standard information, production process sequence information and key process parameters, and determine the product quality standard index based on the product quality standard information; Based on the production process sequence information and the manufacturing equipment information, determine the manufacturing equipment processing area information, the manufacturing equipment related information and the association information between the manufacturing equipment; Determine the standard product volume value and the manufacturing equipment processing area area based on product quality standard information and manufacturing equipment processing area information; The manufacturing equipment processing area and the standard product volume value are normalized, and then the ratio of the normalized manufacturing equipment processing area and the standard product volume value is used as the manufacturing equipment processing quality ratio; The product value of the manufacturing equipment processing quality ratio and the product quality standard index is used as the product quality standard sub-index; According to the manufacturing equipment information and the manufacturing equipment processing area information, obtaining the manufacturing equipment processing repeated area information; Based on the manufacturing equipment processing repeated area information and the association information between the manufacturing equipment, determine the manufacturing equipment processing repeated area area and the manufacturing equipment association coefficient, wherein the value of the manufacturing equipment association coefficient is the total number of different manufacturing equipment participating in the same production process; Normalizing the manufacturing equipment correlation coefficient, and taking the product of the normalized manufacturing equipment correlation coefficient and the area of ​​the manufacturing equipment processing repeated region as the first product quality influence coefficient; Obtain historical manufacturing equipment status data based on manufacturing equipment related information, and obtain corresponding production environment historical data based on production environment real-time data; Constructing a product quality impact function based on historical manufacturing equipment status data, production environment historical data, the first product quality impact coefficient, the product quality standard sub-index, and the product quality standard index; Among them, the expression formula of the product quality impact function is: In the formula, is the product quality standard index, For the The product quality standard sub-index corresponding to the historical manufacturing equipment, is the total number of all manufacturing equipment involved in the production process, is the first product quality influence coefficient, For the The first historical manufacturing equipment Historical device status data, is the total number of historical device status data, represents a quadratic function, For the The historical temperature of the historical manufacturing equipment when processing the product, For the The historical pressure of the historical manufacturing equipment when processing the product, For the The historical time of the historical manufacturing equipment in processing the product, Respectively The equipment accuracy and equipment failure rate of historical manufacturing equipment.

4. The intelligent manufacturing control method based on Internet of Things data according to claim 3 is characterized in that: The obtaining of the product processing quality index based on the product quality influence function and the product quality real-time data specifically includes: Determine the real-time data of the equipment operation status at a certain moment and the real-time data of the production environment corresponding to the manufacturing equipment, and perform data preprocessing on the real-time data of the equipment operation status and the real-time data of the production environment to obtain parameters affecting product quality; Substitute the product quality influencing parameters into the product quality influencing function (2) to obtain the product processing quality index.

5. The intelligent manufacturing control method based on Internet of Things data according to claim 4 is characterized in that: The obtaining of the manufacturing quality impact index according to the product quality impact function, the product processing quality index, the manufacturing scenario information and the real-time data of the material information specifically includes: According to formula (1) in the product quality influence function, obtain the real-time processing standard quality index of the product; The difference between the product processing quality index and the product real-time processing standard quality index is used as the first processing quality impact index; Setting a first processing quality impact index threshold, and determining a processing link response plan based on the first processing quality impact index and the first processing quality impact index threshold; Calculate the implementation time of the processing link response plan based on real-time material information data, manufacturing scenario information and processing link response plan; The planned implementation time of the processing link is quantified to obtain the manufacturing quality impact index.

6. The intelligent manufacturing control method based on Internet of Things data according to claim 5 is characterized in that: The manufacturing equipment is controlled based on the manufacturing quality impact index, product processing quality index, real-time data of equipment operation status and manufacturing equipment information to realize intelligent manufacturing control, specifically including: Setting a manufacturing quality impact index threshold, and determining a first manufacturing equipment control parameter based on the manufacturing quality impact index, the manufacturing quality impact index threshold, and real-time data of equipment operation status; Based on the first manufacturing equipment control parameter, adjusting the operating parameters of the corresponding manufacturing equipment, and obtaining the processing quality index of the product processed by the corresponding manufacturing equipment at that moment; The difference between the product processing quality index processed by the manufacturing equipment corresponding to the moment and the product real-time processing standard quality index is used as the second processing quality impact index; Determine a second manufacturing equipment control parameter according to the second processing quality influence index and the first processing quality influence index threshold, and dynamically adjust the operating parameters of the corresponding manufacturing equipment according to the second manufacturing equipment control parameter; Based on the production process data and manufacturing equipment information, obtain the manufacturing equipment information of the next process of the corresponding manufacturing equipment; According to the manufacturing equipment information and formula (1), the product processing prediction quality standard index corresponding to the manufacturing equipment of the next process is calculated; Let equation (1) be equal to equation (2) to obtain equation (3), and obtain the manufacturing equipment state prediction data and production environment prediction data corresponding to the manufacturing equipment of the next process; Substitute the manufacturing equipment status prediction data, production environment prediction data and the first product quality impact coefficient into formula (3) to obtain the predicted processing time corresponding to the manufacturing equipment of the next process; Quantify the predicted processing time and optimize the processing response plan in combination with the manufacturing quality impact index to achieve intelligent manufacturing control; Among them, formula (3) is: 。 7. An intelligent manufacturing control system based on Internet of Things data, used to implement the control method according to any one of claims 1 to 6, characterized in that: include: A collection module, which is used to obtain manufacturing equipment information, manufacturing scene information, real-time data on equipment operation status, production process data, real-time data on product quality, real-time data on material information, and real-time data on production environment; A main control module, the main control module is used to deploy a sensor network based on manufacturing equipment information and manufacturing scenario information, obtain a product quality impact function based on production process data, manufacturing equipment information and real-time data of production environment, obtain a product processing quality index based on the product quality impact function and real-time data of product quality, and obtain a manufacturing quality impact index based on the product quality impact function, the product processing quality index, manufacturing scenario information and real-time data of material information; A control module, the control module is used to control the sensor network and control the manufacturing equipment based on the manufacturing quality impact index, the product processing quality index, the real-time data of the equipment operation status and the manufacturing equipment information; The display module is used to display the collection process and results of the collection module, the deployment process and results of the main control module, and the control process and results of the control module.

8. The intelligent manufacturing control system based on Internet of Things data according to claim 7 is characterized in that: The acquisition module comprises: A first acquisition unit, the first acquisition unit is used to obtain manufacturing equipment information and manufacturing scene information; The second acquisition unit is used to obtain real-time data on equipment operation status, production process data, product quality data, material information data and production environment data.

9. The intelligent manufacturing control system based on Internet of Things data according to claim 7 is characterized in that: The main control module comprises: A deployment unit, the deployment unit is used to deploy a sensor network based on manufacturing equipment information and manufacturing scene information; A data processing unit, wherein the data processing unit is used to obtain a product quality impact function based on production process data, manufacturing equipment information and real-time data of production environment, obtain a product processing quality index based on the product quality impact function and real-time data of product quality, and obtain a manufacturing quality impact index based on the product quality impact function, product processing quality index, manufacturing scenario information and real-time data of material information.

10. The intelligent manufacturing control system based on Internet of Things data according to claim 7, characterized in that: The control module comprises: a first control unit, the first control unit being used to control the sensor network; The second control unit is used to control the product processing quality based on the manufacturing quality impact index, The manufacturing equipment can be controlled by using the quantity index, real-time data of equipment operation status and manufacturing equipment information.

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