Intelligent industrial automatic control system

Abstract

The invention, which relates to the technical field of industrial automatic control, discloses an intelligent industrial automatic control system comprising an intelligent acquisition module, an intelligent analysis module and an intelligent control module. In an intelligent industrial automatic control system, complexity calculation in a production process is insufficient, dynamic adjustment is lacked in the production process, and monitoring and intervention in the production process are not accurate enough. The intelligent acquisition module ensures data accuracy through accurate image acquisition and illumination compensation, the intelligent analysis module analyzes production complexity and assembly complexity and optimizes efficiency and quality, and the intelligent control module adjusts the production process according to a threshold value to ensure efficient operation. Through cooperative work of the intelligent acquisition module, the intelligent analysis module and the intelligent control module, automation, intelligence and high efficiency of the production process are achieved, the overall production quality can be improved, the failure rate is reduced, and the production cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automatic control, and in particular to an intelligent industrial automatic control system. Background Art

[0002] Intelligent industrial automatic control systems rely on the deep integration of modern sensor technology, communication technology, artificial intelligence, big data analysis, and automated control equipment to form an industrial production management method with high efficiency, precision, and intelligence as the core. By deploying multifunctional, high-precision sensors to collect real-time environmental and equipment parameters in the production process, and using the Industrial Internet of Things (IIoT) and high-speed communication networks (such as 5G, Industrial Ethernet) to achieve rapid data transmission and sharing, combined with edge computing and cloud big data analysis, the system can dynamically optimize control strategies, predict equipment status, and maximize resource utilization. The introduction of machine learning and digital twin technology has further enhanced the capabilities of fault prediction, process optimization, and intelligent decision-making, laying a solid foundation for promoting intelligent manufacturing and sustainable development of Industry 4.0;

[0003] However, the background technology generally has some defects. First, the calculation of the complexity of the production process usually relies on static and fixed rules, lacking dynamic adjustment and accurate prediction. Second, many existing industrial automatic control systems lack flexible dynamic adjustment mechanisms in the production process and cannot cope with real-time changes in production complexity. In addition, in the existing technology, the monitoring and intervention of the complexity of the production process is often not based on real-time and comprehensive data analysis, but on fixed parameters or preset rules, which can easily miss some important dynamic changes. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned problems and to provide an intelligent industrial automatic control system.

[0005] The purpose of the present invention can be achieved through the following technical solutions: an intelligent industrial automatic control system, including an intelligent analysis module; characterized in that:

[0006] The intelligent analysis module performs product quality analysis on each step, specifically:

[0007] Analyze the complexity of product production in each step, and obtain the time, materials and process accuracy requirements for product production in the corresponding step for analysis:

[0008] Analyze the time within the step to obtain the time factor value T1 in the product production complexity;

[0009] Analyze the materials in the steps to obtain the material processing complexity M1 in the product production complexity;

[0010] Analyze the process accuracy requirements within the steps to obtain the process accuracy requirements E1 in the production complexity of the output product;

[0011] The product production complexity coefficient PCF is obtained by comprehensively analyzing the process accuracy requirement E1, time factor value T1 and material processing complexity M1 in the product production complexity;

[0012] Analyze the complexity of product assembly in each step, specifically:

[0013] The analysis of assembly complexity is done by calculating the matching of component geometry, tolerance matching, material matching and complexity of assembly methods;

[0014] The geometric shape matching degree of the parts is analyzed to obtain the geometric shape matching degree Pj;

[0015] The tolerance matching degree is analyzed to obtain the tolerance matching degree Pg;

[0016] Analyze the material matching degree to obtain the material matching degree Pc;

[0017] The complexity of the assembly method is analyzed to obtain the complexity of the assembly method Pf;

[0018] Finally, the geometric shape matching degree Pj, material matching degree Pc, tolerance matching degree Pg and assembly method complexity Pf are comprehensively calculated to obtain the product assembly complexity CFZ.

[0019] As a preferred embodiment of the present invention, it also includes an intelligent acquisition module and an intelligent control module;

[0020] The intelligent collection module is used to collect product appearance data during production, specifically:

[0021] A camera is set up at each step of the production process to collect image data at each step. The sensor detects the product position in real time, accurately triggers the shooting, captures the best angle and complete appearance of the product, and matches the graphics with the preset graphics. If the overlap rate between the two is greater than 99%, it is determined to be the same model specification, and the time and process accuracy data corresponding to each step are collected;

[0022] The intelligent control module is used to obtain the analyzed production complexity coefficient PCF and product assembly complexity CFZ, and perform intelligent dynamic monitoring, specifically:

[0023] Compare the production complexity coefficient PCF and the product assembly complexity CFZ with their respective corresponding preset threshold ranges; if the production complexity coefficient PCF or the product assembly complexity CFZ belongs to their respective low threshold ranges, reduce the monitoring intensity; if one of the production complexity coefficient PCF or the product assembly complexity CFZ belongs to their respective upper threshold ranges, increase the monitoring intensity, and conduct real-time feedback and adjustments; if one of the production complexity coefficient PCF or the product assembly complexity CFZ belongs to their respective over-threshold ranges, take high-intensity intervention measures immediately.

[0024] As a preferred embodiment of the present invention, the specific process of analyzing the time in the step to obtain the time factor value T1 in the product production complexity is:

[0025] By formula: Output the time factor value T1 in the complexity of product production, where Tz is the total time spent on the actual production step, including the ideal time and the extra time due to various factors; Tl is the time required to complete the step under ideal conditions; Tq is the waiting time wasted due to some reasons in the production process; Tg is the time the equipment is shut down due to failure, maintenance or other reasons.

[0026] As a preferred embodiment of the present invention, the specific process of analyzing the materials in the step to obtain the material processing complexity M1 in the product production complexity is:

[0027] By formula: The material processing complexity M1, M2 in the output product production complexity f (k) is the complexity function of the material in the kth step. The calculation process is as follows: Each material can be given a weight coefficient Wm. The hardness of the material affects its processing difficulty. The hardness value is used to adjust the material complexity: Get the hardness adjustment factor HF and set the additional adjustment factor Am, and substitute it into the formula: M f (k) = Wm × HF × Am to obtain the material complexity function of each step; Cx is the cost of the processing tool; Cy is the cost of the raw material.

[0028] As a preferred embodiment of the present invention, the specific process of analyzing the process accuracy requirements in the steps to obtain the process accuracy requirements E1 in the production complexity of the output product is:

[0029] Obtain the machining tolerance range, equipment accuracy and error, and automation and manual intervention through the formula: The process accuracy requirement E1 in the complexity of output product production; Eg is the specification requirement set by the process, Es is the accuracy achieved in actual processing, and Ew is the accuracy error of the equipment itself.

[0030] As a preferred embodiment of the present invention, the specific process of analyzing the geometric shape matching degree of the parts to obtain the geometric shape matching degree Pj is:

[0031] Calculated by comparing the ratio of the mating area of ​​the parts in the actual assembly process to the ideal mating area: Output geometry matching degree Pj, As is the actual matching area, and Al is the ideal matching area.

[0032] As a preferred embodiment of the present invention, the specific process of analyzing the tolerance matching degree to obtain the tolerance matching degree Pg is:

[0033] During the assembly process, the tolerance matching is calculated by setting up the formula: Output tolerance matching degree Pg, Δts is the tolerance range of the actual component, and Δtl is the tolerance range predetermined during design.

[0034] As a preferred embodiment of the present invention, the specific process of analyzing the material matching degree to obtain the material matching degree Pc is as follows:

[0035] The material matching degree is calculated by the established formula: The output material matching degree Pc,Lx is the sum of the material differences of the same material properties.

[0036] As a preferred embodiment of the present invention, the specific process of analyzing the complexity of the assembly method to obtain the complexity of the assembly method Pf is:

[0037] Obtain the requirements of tools, equipment, and processes for different connection methods; the complexity of the assembly method, assign values ​​to the types of assembly tools, obtain the volume of various assembly tools, and define a volume value for each tool; the volume size follows the normal distribution; there are volume data of multiple tools, calculate the mean μ and standard deviation σ of the data; and then perform normal distribution, through the formula: Output volume complexity value Ct, x volume value; then through the established formula: Output assembly tool complexity GJ, tc j is the usage time of the jth corresponding tool;

[0038] For different types of connection methods, the complexity of each connection method is different; we assign a complexity value to each connection method according to the actual situation, including welding connection, brazing connection and adhesive connection; for connection methods with high precision requirements, calculate the tolerance requirements of component matching, the size of the contact area, and whether the connection surface is flat; if the connection method requires a higher matching accuracy, add an adjustment factor fu; output the connection method value GL through the established formula: GL = Lu × fu, Lu is the general term for the connection method assignment;

[0039] The assembly sequence complexity is calculated according to the dependency between the steps in the assembly process: non-dependent steps, dependent steps and highly dependent steps are assigned values; if the assembly process involves N different devices working together, and there is a high coordination requirement between the work contents of each device, the calculation formula is: SX = Cs × XT Output assembly sequence complexity SX, XT is the coordination value between the work contents of the equipment, and Cs is the general term for the dependency assignment;

[0040] The assembly sequence complexity SX, connection mode value GL and assembly tool complexity GJ are calculated comprehensively to obtain the assembly mode complexity Pf, through the established formula: Get the assembly complexity Pf, GL j is the jth connection mode value.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention comprehensively considers the time factor, material complexity and process precision requirements in the production process through the intelligent analysis module, and can accurately evaluate the complexity of each production step. This module not only considers the difference between the actual production time and the ideal time, but also combines the equipment efficiency, the consistency of the process flow and the operation status of the production line. By carefully analyzing factors such as waiting time, equipment downtime and material delays that may occur in the production process, the intelligent analysis module provides a detailed production complexity assessment for each step, thereby effectively predicting and optimizing production efficiency, avoiding bottlenecks and reducing resource waste.

[0043] 2. The present invention realizes accurate monitoring and adjustment functions by combining the dynamic comparison of the production complexity coefficient and the assembly complexity coefficient with the preset threshold value through the intelligent control module. When the complexity coefficient is within the threshold range, the system can increase the monitoring intensity in a timely manner, and provide real-time feedback or adjust the production process to prevent it from entering an overly complex state. When the complexity coefficient exceeds the threshold, the intelligent control module will immediately take high-intensity intervention measures to optimize the production process, adjust the assembly process or increase resources to ensure the smoothness of the production process and the stability of product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0045] Figure 1 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0048] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0049] See also Figure 1 As shown, an intelligent industrial automatic control system includes: an intelligent acquisition module, an intelligent analysis module and an intelligent control module;

[0050] The intelligent collection module is used to collect product appearance data during production, specifically:

[0051] By setting up cameras at each step in the production and collecting image data in each step of the production, the product position is detected in real time through sensors, the shooting is accurately triggered, and the best angle and complete appearance of the product are captured. The graphics are matched with the preset graphics. If the overlap rate of the two is greater than 99%, they are determined to be the same model specification; as well as data such as the time and process accuracy corresponding to each step; it should be noted that: a ring LED light is arranged around the camera to provide uniform and stable lighting, and combined with a dynamic lighting compensation algorithm to effectively eliminate shadow and reflection interference; during the acquisition process, the camera automatically adjusts the focal length, shutter speed and resolution to cope with the needs of high-speed movement of the product or capturing tiny features;

[0052] The intelligent analysis module is used to analyze product quality at each step, specifically:

[0053] Analyze the complexity of product production in each step, and obtain the time, materials and process accuracy requirements for product production in the corresponding step for analysis:

[0054] Among them, time is not just the difference between "actual time" and "ideal time", it is also closely related to factors such as the work efficiency of the production line, the operating status of the equipment, and the continuity of the process flow; if there is equipment failure, downtime or bottleneck process in the production process, it will affect the time required for each step; it is also necessary to consider: for large-scale production, the unit time of production may be shorter, but for small-batch or customized production, the time complexity may increase; in addition to process time, the waiting time of operators or equipment should also be considered; for example, slow scheduling of equipment conveyor belts may cause time delays; the formula is: Output the time factor value T1 in the product production complexity, where Tz is the total time spent on the actual production step, including the ideal time and the additional time due to various factors (waiting, downtime, etc.); Tl is the time required to complete the step under ideal conditions; Tq is the waiting time wasted due to certain reasons in the production process (such as untimely material preparation, upstream process delays, etc.); Tg is the time the equipment is downtime due to failure, maintenance or other reasons;

[0055] Materials not only refer to their types, but also include their processing difficulty, tool wear, and process requirements. The hardness of the material will affect the cutting force, tool wear, and process accuracy. For example, steel with higher hardness may require more complex cutting tools, which in turn affects production speed and cost. Some products require special surface treatment (such as spraying, coating, polishing, etc.) during production, which increases the complexity of the material and also leads to additional time consumption. If the raw materials used in production are highly diverse or the supply chain is unstable, the complexity of the production process will increase, especially when the production line frequently switches between different materials. The formula is: Output product production complexity in the material processing complexity M1, where M f (k) is the complexity function of the material in the kth step, which is an indicator to measure the difficulty of material operation in this step. It may include the type, shape, hardness and other properties of the material. The specific calculation process is as follows: Each material can be given a weight coefficient Wm, for example: ordinary steel: Wg = 1; aluminum alloy: Wl = 1.2; titanium alloy: Wt = 1.5; ceramic: Wc = 2.0; the hardness of the material affects its processing difficulty, and the hardness value is used to adjust the material complexity: Get the hardness adjustment factor HF, for example, the material with a hardness of 300HB (Brinell hardness) has a hardness factor of 3.0; then set additional adjustment factors according to the shape and size of the material; for example, for complex shapes (such as thin-walled structures or asymmetric parts), give an additional factor Am, for example: simple shapes (such as long strips, circles): Am = 1; complex shapes (such as thin-walled structures, complex curved surfaces): Am = 1.5; then combine the weight coefficient Wm, the hardness adjustment factor HF and the additional factor Am to get the formula: M f (k) = Wm × HF × Am to obtain the material complexity function of each step; Cx is the cost of the processing tool; Cy is the cost of the raw material;

[0056] The process accuracy requirement is a static value. It should be noted that it is closely related to the production environment, equipment accuracy and error compensation technology. To improve the guarantee and optimization of accuracy, the processing tolerance range (divided into tolerance ranges for different production steps, the accuracy requirements for different components can be calculated according to different weights), equipment accuracy and error (the accuracy of the equipment itself also directly affects the accuracy of the product, especially in CNC machine tools (CNC) and automation equipment, equipment errors may accumulate and affect the final product quality; equipment accuracy should be considered as a dynamic factor, and over time, the accuracy of the equipment will change with wear) and automation and manual intervention (highly automated production lines may be more stable and accurate than those with more manual intervention, but they will still be affected by equipment maintenance and environmental factors; manual intervention is considered as one of the factors for adjusting accuracy), then the formula is: Output the process accuracy requirement E1 in the complexity of product production; Eg is the specification requirement set by the process, that is, the standard accuracy required to be achieved in the production process, usually the ideal accuracy specified in product design or quality control; Es is the accuracy achieved in actual processing, which means the actual accuracy achieved by the process under specific production conditions; Ew is the error of the equipment, which means the accuracy error of the equipment itself;

[0057] Then, the process accuracy requirement E1, time factor value T1 and material processing complexity M1 in the product production complexity are comprehensively analyzed, and the product production complexity coefficient PCF is output through the established formula: PCF = T1 × M1 × E1;

[0058] Then analyze the complexity of product assembly in each step, specifically:

[0059] Assembly complexity refers to the difficulty of matching and connecting parts during product assembly. It is affected by many factors, including the geometry of parts, dimensional tolerances, material matching, assembly methods, etc. The level of assembly complexity determines the time, precision requirements, and tool and equipment configuration required during the assembly process.

[0060] Analysis of assembly complexity, calculation of component geometry matching, tolerance matching, material matching and assembly complexity;

[0061] The geometric matching degree of parts is an indicator to measure whether the geometric shapes of parts can be matched smoothly; the size, shape and matching accuracy of parts will directly affect the difficulty of assembly; this indicator is calculated by comparing the ratio of the matching area of ​​parts in the actual assembly process to the ideal matching area: Output the geometric shape matching degree Pj, where As is the actual matching area and Al is the ideal matching area. If the geometric shape matching degree Pj is larger, the assembly process will be smoother and the complexity will be lower. On the contrary, if the geometric shape does not match or is not accurate, the assembly complexity will increase significantly.

[0062] The tolerance matching degree reflects the degree of conformity between the dimensional deviations of the parts. During the assembly process, a tolerance that is too large may cause the parts to loosen, while a tolerance that is too small may increase the difficulty of assembly. The calculation of the tolerance matching degree is based on the established formula: Output the tolerance matching degree Pg, where Δts is the tolerance range of the actual parts and Δtl is the tolerance range predetermined during design. The closer the tolerance matching degree is to 1, the lower the assembly complexity.

[0063] Material matching considers the compatibility between component materials; the differences in expansion coefficient, hardness, toughness and other characteristics of different materials will affect the quality of fit and connection; the calculation of material matching is based on the established formula: Output material matching degree Pc, Lx is the sum of material differences of the same material properties; the same material properties Lx refers to the similarity between component materials, such as thermal expansion coefficient, hardness, etc., and the sum of material differences Lh is a weighted value reflecting the differences between materials; the higher the material matching degree Pc, the lower the assembly complexity;

[0064] Assembly complexity quantifies the impact of component connection methods (such as screws, welding, brazing, etc.) on the assembly process; different connection methods have different effects on tools, equipment, process requirements, etc.; Assembly complexity;

[0065] Assign values ​​to the types of assembly tools, obtain the volume sizes of various assembly tools, and define a volume value for each tool; the volume size follows a normal distribution; there are volume data of multiple tools, calculate the mean μ and standard deviation σ of the data; and then perform a normal distribution, through the formula: Output volume complexity value Ct, x volume value; in normal distribution, tools with larger or smaller volumes have a higher impact on the complexity value; then obtain the usage time of the corresponding tool; and then use the established formula: Output assembly tool complexity GJ, tcj is the usage time of the jth corresponding tool, j = 1, 2, 3, ..., m;

[0066] For different types of connection methods, the complexity of each connection method is different. We assign a complexity score to each connection method according to the actual situation (for example, screw connection, welding, brazing, gluing, etc.), for example: screw connection (standard thread): Lu = 0.5 welding connection: Lu = 1.5 brazing connection: Lu = 1.0 gluing connection: Lu = 0.8, etc.; connection methods with high precision requirements (such as welding and brazing) are usually more complicated; factors such as the tolerance requirements of component matching, the size of the contact area, and whether the connection surface is flat need to be considered; if the connection method requires a higher matching accuracy, add an adjustment factor fu; output the connection method value GL through the established formula: GL = Lu × fu;

[0067] Assembly sequence complexity is the influence of the connection sequence of parts on the entire assembly process during the assembly process. If the assembly sequence is strict and there are many dependencies, the assembly complexity will increase;

[0068] The calculation is based on the dependency between the steps: non-dependent steps (parts can be assembled in any order): Cs = 0.5; dependent steps (need to be assembled strictly in order): Cs = 1.0; highly dependent steps (multiple steps in the assembly are interdependent and have a large impact): Cs = 1.5; ...; if the equipment needs to complete different assembly steps at a specific time point, and there are high coordination requirements between these steps, the complexity of the assembly sequence will also increase; if the assembly process involves N different devices working together, and there is a high coordination requirement between the work content of each device, the calculation formula is: SX = Cs × X T outputs the assembly sequence complexity SX, XT is the coordination value between the work contents of the equipment; and XT = (number of equipment) × Cs × (synchronization complexity coefficient), where the synchronization complexity coefficient includes low synchronization requirements (poor synchronization, strong time independence between tasks) and high synchronization requirements (precise time coordination is required between tasks); if there is no need for precise synchronization between tasks, but only sequential execution or accidental collaboration, it is a low synchronization requirement, and the synchronization complexity coefficient is 1; if multiple devices must complete tasks within a very precise time, it is a high synchronization requirement, and the synchronization complexity coefficient is 1+q, q>0, q is the value obtained after a certain ratio of the preset accuracy is converted;

[0069] Then the assembly sequence complexity SX, connection mode value GL and assembly tool complexity GJ are comprehensively calculated to obtain the assembly mode complexity Pf, through the established formula: Get the assembly complexity Pf, GL j is the jth connection mode value;

[0070] Finally, the geometric shape matching degree Pj, material matching degree Pc, tolerance matching degree Pg and assembly mode complexity Pf are comprehensively calculated to obtain the product assembly complexity CFZ, through the established formula: get.

[0071] The intelligent control module is used to obtain the analyzed production complexity coefficient PCF and product assembly complexity CFZ, and perform intelligent dynamic monitoring, specifically:

[0072] By comparing the production complexity coefficient PCF and the product assembly complexity CFZ with their respective corresponding preset threshold ranges; the preset threshold ranges include a low threshold range (low complexity), a near-threshold range and an over-threshold range. If the production complexity coefficient PCF or the product assembly complexity CFZ belongs to the respective low threshold range, the monitoring intensity is reduced, resource consumption is reduced, and the natural operation of the process is maintained; if one of the production complexity coefficient PCF or the product assembly complexity CFZ belongs to the respective near-threshold range, the monitoring intensity is increased, and real-time feedback and adjustments may be made to prevent entering a high-complexity state; if one of the production complexity coefficient PCF or the product assembly complexity CFZ belongs to the respective over-threshold range, high-intensity intervention measures are immediately taken, such as adjusting the production process, optimizing the assembly process, increasing human resources, adjusting the production speed, etc.

[0073] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent industrial automatic control system, comprising an intelligent analysis module; characterized in that: The intelligent analysis module performs product quality analysis on each step, specifically: Analyze the complexity of product production in each step, and obtain the time, materials and process accuracy requirements for product production in the corresponding step for analysis: Analyze the time within the step to obtain the time factor value T1 in the product production complexity; Analyze the materials in the steps to obtain the material processing complexity M1 in the product production complexity; Analyze the process accuracy requirements within the steps to obtain the process accuracy requirements E1 in the production complexity of the output product; The product production complexity coefficient PCF is obtained by comprehensively analyzing the process accuracy requirement E1, time factor value T1 and material processing complexity M1 in the product production complexity; Analyze the complexity of product assembly in each step, specifically: The analysis of assembly complexity is done by calculating the matching of component geometry, tolerance matching, material matching and complexity of assembly methods; The geometric shape matching degree of the parts is analyzed to obtain the geometric shape matching degree Pj; The tolerance matching degree is analyzed to obtain the tolerance matching degree Pg; Analyze the material matching degree to obtain the material matching degree Pc; The complexity of the assembly method is analyzed to obtain the complexity of the assembly method Pf; Finally, the geometric shape matching degree Pj, material matching degree Pc, tolerance matching degree Pg and assembly method complexity Pf are comprehensively calculated to obtain the product assembly complexity CFZ.

2. An intelligent industrial automatic control system according to claim 1, characterized in that: It also includes an intelligent acquisition module and an intelligent control module; The intelligent collection module is used to collect product appearance data during production, specifically: A camera is set up at each step of the production process to collect image data at each step. The sensor detects the product position in real time, accurately triggers the shooting, captures the best angle and complete appearance of the product, and matches the graphics with the preset graphics. If the overlap rate between the two is greater than 99%, it is determined to be the same model specification, and the time and process accuracy data corresponding to each step are collected; The intelligent control module is used to obtain the analyzed production complexity coefficient PCF and product assembly complexity CFZ, and perform intelligent dynamic monitoring, specifically: Compare the production complexity coefficient PCF and the product assembly complexity CFZ with their respective corresponding preset threshold ranges; if the production complexity coefficient PCF or the product assembly complexity CFZ belongs to their respective low threshold ranges, reduce the monitoring intensity; if one of the production complexity coefficient PCF or the product assembly complexity CFZ belongs to their respective upper threshold ranges, increase the monitoring intensity, and conduct real-time feedback and adjustments; if one of the production complexity coefficient PCF or the product assembly complexity CFZ belongs to their respective over-threshold ranges, take high-intensity intervention measures immediately.

3. The intelligent industrial automatic control system according to claim 1 is characterized in that: The specific process of analyzing the time in the step to obtain the time factor value T1 in the product production complexity is: By formula: Output the time factor value T1 in the complexity of product production, where Tz is the total time spent on the actual production step, including the ideal time and the extra time due to various factors; Tl is the time required to complete the step under ideal conditions; Tq is the waiting time wasted due to some reasons in the production process; Tg is the time the equipment is shut down due to failure, maintenance or other reasons.

4. An intelligent industrial automatic control system according to claim 3, characterized in that: The specific process of analyzing the materials in the step to obtain the material processing complexity M1 in the product production complexity is: By formula: The material processing complexity M1, M2 in the output product production complexity f (k) is the complexity function of the material in the kth step. The calculation process is as follows: Each material can be given a weight coefficient Wm. The hardness of the material affects its processing difficulty. The hardness value is used to adjust the material complexity: Get the hardness adjustment factor HF and set the additional adjustment factor Am, and substitute it into the formula: M f (k) = Wm × HF × Am to obtain the material complexity function of each step; Cx is the cost of the processing tool; Cy is the cost of the raw material.

5. An intelligent industrial automatic control system according to claim 4, characterized in that: The specific process of analyzing the process accuracy requirements in the steps to obtain the process accuracy requirements E1 in the production complexity of the output product is: Obtain the machining tolerance range, equipment accuracy and error, and automation and manual intervention through the formula: The process accuracy requirement E1 in the complexity of output product production; Eg is the specification requirement set by the process, Es is the accuracy achieved in actual processing, and Ew is the accuracy error of the equipment itself.

6. The intelligent industrial automatic control system according to claim 1, characterized in that: The specific process of analyzing the geometric shape matching degree of the parts to obtain the geometric shape matching degree Pj is as follows: Calculated by comparing the ratio of the mating area of ​​the parts in the actual assembly process to the ideal mating area: Output geometry matching degree Pj, As is the actual matching area, and Al is the ideal matching area.

7. An intelligent industrial automatic control system according to claim 6, characterized in that: The specific process of analyzing the tolerance matching degree to obtain the tolerance matching degree Pg is as follows: During the assembly process, the tolerance matching is calculated by setting up the formula: Output tolerance matching degree Pg, Δts is the tolerance range of the actual component, and Δtl is the tolerance range predetermined during design.

8. An intelligent industrial automatic control system according to claim 7, characterized in that: The specific process of analyzing the material matching degree to obtain the material matching degree Pc is as follows: The material matching degree is calculated by the established formula: The output material matching degree Pc,Lx is the sum of the material differences of the same material properties.

9. An intelligent industrial automatic control system according to claim 8, characterized in that: The specific process of analyzing the complexity of the assembly method to obtain the complexity of the assembly method Pf is as follows: Obtain the requirements of tools, equipment, and processes for different connection methods; the complexity of the assembly method, assign values ​​to the types of assembly tools, obtain the volume of various assembly tools, and define a volume value for each tool; the volume size follows the normal distribution; there are volume data of multiple tools, calculate the mean μ and standard deviation σ of the data; and then perform normal distribution, through the formula: Output volume complexity value Ct, x volume value; then through the established formula: Output assembly tool complexity GJ, tc j is the usage time of the jth corresponding tool; For different types of connection methods, the complexity of each connection method is different; we assign a complexity value to each connection method according to the actual situation, including welding connection, brazing connection and adhesive connection; for connection methods with high precision requirements, calculate the tolerance requirements of component matching, the size of the contact area, and whether the connection surface is flat; if the connection method requires a higher matching accuracy, add an adjustment factor fu; output the connection method value GL through the established formula: GL = Lu × fu, Lu is the general term for the connection method assignment; The assembly sequence complexity is calculated based on the dependencies between steps during the assembly process: non-dependent steps, dependent steps, and high-dependency steps are assigned values; If the assembly process involves N different devices working together, and there is a high coordination requirement between the work contents of each device, the calculation formula is: SX = Cs × XT Output assembly sequence complexity SX, XT is the coordination value between the work contents of the devices, and Cs is a general term for the dependency assignment; The assembly sequence complexity SX, connection mode value GL and assembly tool complexity GJ are calculated comprehensively to obtain the assembly mode complexity Pf, through the established formula: Get the assembly complexity Pf, GL j is the jth connection mode value.

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