A method and system for intelligently controlling a microphone production line

Abstract

The present invention relates to the field of microphone manufacturing technology, and in particular to an intelligent control method and system for microphone production lines. The method comprises the following steps: obtaining microphone production line sensor data and microphone production process data, and performing microphone production topology structure analysis to obtain a microphone production topology structure model; performing production demand forecasting on the microphone production topology structure model and the microphone production line sensor data to obtain a microphone production demand forecasting data set; performing production process link parameter optimization on the microphone production topology structure model to obtain a process optimization data set; performing production link product quality inspection based on the microphone production line sensor network to obtain a quality inspection data set; performing production line equipment scheduling optimization on the microphone production topology structure model to obtain an equipment scheduling optimization data set. The present invention can improve the production efficiency of microphone production lines.

Description

Technical Field

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

[0002] With the rapid development of modern electronic communication technology, microphones are widely used in broadcasting, conferences, performances, recording and other scenarios as an important audio acquisition device. With the continuous upgrading of consumer demand, the production process and quality requirements of microphones are also increasing. In the traditional production process of microphones, manual control and simple automation equipment are often relied on. The control process of the production line is relatively simple and extensive, and it has not been able to fully realize intelligence and efficiency. Although these traditional methods have been able to meet production needs in the past period of time, with the expansion of production scale and the improvement of quality requirements, some serious defects have gradually been exposed, and it is urgent to optimize and improve them through intelligent control technology. In the traditional production process of microphones, the production line mainly relies on manual operation to complete multiple production links, such as material delivery, assembly of components, quality inspection and packaging. Although automation equipment has been introduced in some links, due to the lack of intelligent control systems, there are still problems such as low operating efficiency and inaccurate quality control. For example, the equipment of the production line can only run according to the predetermined program, lacks adaptability and flexibility, and cannot be dynamically adjusted according to changes in production needs. Especially in the production of high-precision microphones, due to the influence of environmental factors and equipment errors, problems such as high production failure rate and long production cycle often occur. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide an intelligent control method and system for a microphone production line to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for intelligently controlling a microphone production line includes the following steps:

[0005] Step S1: acquiring sensor data of a microphone production line and microphone production process data, and performing microphone production topology structure analysis according to the microphone production process data, thereby obtaining a microphone production topology structure model;

[0006] Step S2: performing production line sensor data fusion on the microphone production topology model and the microphone production line sensor data, thereby obtaining the microphone production line sensor network; performing production demand prediction based on the microphone production line sensor network, thereby obtaining a microphone production demand prediction data set;

[0007] Step S3: Optimize the production process parameters of the microphone production topology model according to the microphone production demand prediction data set, so as to obtain a process optimization data set, and transmit it to the microphone production control platform to perform the microphone production process parameter adjustment task;

[0008] Step S4: performing real-time production line sensor data collection based on the microphone production line sensor network to obtain real-time microphone production line sensor data, and performing product quality inspection in the production link based on the real-time microphone production line sensor data and the process optimization data set to obtain a quality inspection data set;

[0009] Step S5: Optimize the production line equipment scheduling of the microphone production topology model according to the quality inspection data set and the microphone production demand prediction data set, so as to obtain the equipment scheduling optimization data set, and transmit it to the microphone production control platform to execute the equipment scheduling task.

[0010] The present invention can deeply understand each link in the microphone production process by acquiring the sensor data and production process data of the microphone production line and analyzing the production topology model formed by these data, accurately depict the relationship between each process and equipment of the production line, and then provide detailed basic data for subsequent optimization. This topology analysis provides a reliable basis for further production line sensor data fusion and production demand prediction, ensuring the accuracy and reliability of the prediction data set. In addition, the production topology model and sensor data are combined for data fusion, effectively integrating the real-time data of each production link, so that the dynamic changes of the production line, equipment status and process parameters can be monitored and analyzed in real time, thereby providing high-quality data input for production demand prediction and ensuring the accurate formulation of production plans. Through production demand prediction, key data such as output, production cycle, equipment load, etc. required for future production can be accurately predicted, thereby providing a scientific basis for production process optimization. On the basis of production demand prediction data, the parameters of the production process link are further optimized to achieve a comprehensive improvement in production efficiency and process quality. Through these optimizations, not only can the process parameters be flexibly adjusted according to demand changes during the production process, but also the waste of resources and quality problems caused by improper processes can be effectively reduced, ensuring the high efficiency and high quality of the production process. This optimization solution is directly transmitted to the production control platform, which can adjust the operating status of production equipment in real time, ensure that the production line continues to operate efficiently under dynamically changing demands, and reduce production stagnation and quality fluctuations caused by untimely equipment adjustment. Real-time production line sensor data collection and quality inspection mechanism enable quality problems in the entire production process to be quickly discovered and solved in a timely manner. Quality inspection not only relies on traditional manual inspection, but also combines modern visual inspection and vibration analysis technology to accurately control every link of the product to ensure that the quality of the final product meets the expected standards. Through real-time monitoring, equipment failures or production deviations can be quickly adjusted and resolved during the production process, reducing the production of unqualified products and improving the production pass rate. Based on the combination of quality inspection data and production demand forecast data, equipment scheduling optimization is carried out, which effectively solves the problems of low utilization rate and uneven load of production equipment, maximizes the output efficiency of equipment, and ensures the reasonable allocation of production resources. The generation and transmission of equipment scheduling optimization data to the production control platform ensures the accurate execution of equipment scheduling tasks, avoids the waste of equipment resources and delays in production progress, and ensures the continuity and efficiency of the production process. By optimizing production line equipment scheduling and real-time feedback mechanisms, the production control platform can flexibly adjust equipment operation plans according to actual needs to ensure efficient production and stable quality.To sum up, through intelligent production scheduling, real-time data collection and analysis, and precise production demand prediction and process optimization, the present invention realizes a high degree of automation and intelligence in the microphone production process, greatly improves production efficiency, product quality and production flexibility, overcomes the various shortcomings of traditional production methods, and provides strong technical support for large-scale, high-precision microphone production.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: Acquire microphone production line sensor data and microphone production process data;

[0013] Step S12: performing data preprocessing on the microphone production line sensor data and the microphone production process data respectively, so as to obtain the production line sensor data to be analyzed and the production process data to be analyzed;

[0014] Step S13: extracting production line equipment sensor data from the production line sensor data to be analyzed, thereby obtaining production line equipment sensor data;

[0015] Step S14: Arranging the equipment sensor data points in time sequence according to the production line equipment sensor data, thereby obtaining the production equipment operation time sequence data;

[0016] Step S15: Perform production line topology analysis based on the production process data to be analyzed and the production equipment operation timing data, so as to obtain a microphone production topology model.

[0017] The present invention can accurately understand each link in the production process, optimize the resource allocation of the production line, and realize the high automation and intelligence of the production process through dynamic adjustment by acquiring the sensor data and production process data of the microphone production line, combining data preprocessing, equipment sensor data extraction, time sequence arrangement and production topology structure analysis. By acquiring and preprocessing the sensor data and production process data of the microphone production line, the noise and inconsistency in the data can be eliminated, ensuring that high-quality data is used in the subsequent analysis, and providing a solid foundation for the accurate monitoring and optimization of the production process. After the production line sensor data and the production process data are preprocessed respectively, effective information that can be used for subsequent analysis can be extracted, and this information can be integrated into a unified data set, laying the foundation for subsequent data fusion, prediction and optimization tasks. By extracting the equipment sensor data of the production line sensor data to be analyzed, the key parameters related to the equipment operation status can be accurately identified, thereby providing detailed data support for the operation monitoring of the production equipment. These data can help us further analyze the health status, operation efficiency and potential failure risks of the equipment, and timely warn and make adjustments. Further time-series arrangement of equipment sensor data points enables the operation data of all equipment to be arranged in chronological order, forming accurate production equipment operation time-series data. These time-series data can not only show the operation status of the equipment, but also reveal possible bottlenecks, inefficient links and collaboration problems between equipment in the production process, providing data support for subsequent optimization plans. Combining these equipment operation time-series data with production process data to conduct production line topology analysis can clearly depict the relationship between each link in the production line and their interaction mode, and establish a sophisticated microphone production topology model. This model not only helps to fully understand each link in the production process, but also provides a scientific basis for optimizing the production process and adjusting equipment and process parameters.

[0018] Optionally, step S15 is specifically:

[0019] Step S151: dividing the production links based on the production process data to be analyzed, thereby obtaining production process link data;

[0020] Step S152: extracting production equipment description features from the production process data to be analyzed, thereby obtaining production equipment description data;

[0021] Step S153: performing equipment-link association docking on the production process link data and the production equipment timing data according to the production equipment description data, thereby obtaining link-equipment docking data;

[0022] Step S154: integrating the production link connection relationship based on the production process link data, thereby obtaining the production link connection relationship data, and mapping the production link connection relationship data according to the link-equipment docking data, thereby obtaining the production process link relationship data;

[0023] Step S155: Perform production line topology analysis based on production process link relationship data to obtain a microphone production topology model.

[0024] The present invention can effectively improve the automation, intelligence and flexibility of the production line through a detailed analysis of the production process and equipment of the microphone, and solve the problems of low efficiency and inaccurate quality control in the traditional production process. First, the production link division is carried out based on the production process data to be analyzed, and the production process can be disassembled into multiple process links with clear responsibilities and goals, providing a clear framework for subsequent production process optimization, resource allocation and efficiency improvement. The individual analysis of each production link can help identify which links are efficiency bottlenecks and which links may have quality control problems, thereby providing a specific direction for process optimization and improvement. Extracting the production equipment description features of the production process data to be analyzed can provide an in-depth understanding of the functions, performance and working characteristics of each equipment in the production line. These equipment description data can not only provide a basis for the selection, scheduling and optimization of production equipment, but also help the production system to better monitor the equipment status, determine whether the equipment meets the production requirements, and identify potential equipment failures or performance degradation problems in advance, and provide data support for the maintenance and maintenance of the equipment. By connecting the production equipment description data with the production process link data for equipment-link association docking, the interaction and dependency between the equipment and each process link can be clarified. Each link in the production process can be accurately matched with the corresponding equipment to ensure the reasonable configuration and maximum utilization of the equipment in the production process. Through this association docking, it is possible to identify which equipment can efficiently support specific process links, thereby avoiding waste and inefficient use of equipment resources and improving the overall efficiency of the production line. Based on the production process link data, the connection relationship of the production links is integrated, the correlation between the production links can be clearly expressed, and the connection relationship of the production links can be mapped according to the link-equipment docking data, further strengthening the collaboration and cooperation between the links in the process. The optimization of the production link is not limited to the improvement of a single link, but from a global perspective, it optimizes the collaborative work of all links and equipment in the production process to improve the smoothness and efficiency of the overall production line. Combined with the production process link relationship data, the production line topology structure analysis can form a complete and systematic microphone production topology structure model. This model not only reflects the coordination of each production link and equipment, but also provides a scientific basis for the optimization of production processes, equipment scheduling and process improvement by simulating and analyzing the effects of different production configurations and adjustment plans.

[0025] Optionally, step S2 specifically includes:

[0026] Step S21: integrating the sensor spatial deployment of the microphone production line sensor data, thereby obtaining sensor spatial deployment data;

[0027] Step S22: performing spatiotemporal fusion of production line sensor data on the microphone production topology model and the microphone production line sensor data according to the sensor spatial deployment data, thereby obtaining a microphone production line sensor network;

[0028] Step S23: acquiring microphone historical production data, and performing microphone production mode recognition on the microphone historical production data, thereby obtaining microphone historical production mode data;

[0029] Step S24: integrating the production status of the production line based on the microphone production line sensor network, thereby obtaining real-time production status data of the microphone production line;

[0030] Step S25: Perform production demand forecasting based on the historical production mode data of the microphone and the real-time production status data of the microphone production line, so as to obtain a microphone production demand forecasting data set.

[0031] The present invention can greatly improve the automation level, precision and efficiency of the production process by systematically optimizing the microphone production line, thereby realizing efficient production management. By integrating the sensor spatial deployment of the microphone production line sensor data, various sensor data on the production line can be efficiently collected and integrated to ensure that the sensor distribution is reasonable and covers the key links in the production process. This integration process not only helps to improve the comprehensiveness and accuracy of data collection, but also can better reflect the changes in the production environment, and provide strong support for subsequent data analysis and production process optimization. Combining the sensor spatial deployment data with the microphone production topology model to carry out spatiotemporal fusion of production line sensor data, the real-time data collected by each sensor in the production process can be more accurately spatiotemporally docked with the production process. This fusion not only improves the timeliness and integrity of the production data, but also enhances the adaptability of the production line to emergencies and changes through dynamic real-time feedback of the production environment, and provides data basis for timely decision-making in the production process. By obtaining the historical production data of the microphone and performing production pattern recognition on it, the typical patterns of different historical production stages can be identified and compared with the current production needs. This pattern recognition can not only provide valuable historical experience data for the production line, but also reveal the potential differences and changing trends between different production stages, thus providing a benchmark and guidance for optimization in the production process. Based on the microphone production line sensor network, the production line production status integration can fully integrate and analyze the real-time data of the production line, accurately reflecting the actual operation status of the production process. This not only improves the transparency and monitorability of the production process, but also can timely discover problems in the production process, timely adjust the production plan or improve the process, and avoid potential production interruptions or quality problems. Through this real-time production status monitoring, the production line can maintain efficient operation under changing demand and environmental conditions. According to the microphone historical production pattern data and real-time production status data, production demand forecasting can accurately predict future production demand through in-depth analysis of past production data and current production status. This forecast can help the production line rationally plan production resources, adjust production strategies, and ensure the accurate satisfaction of production needs, thereby maximizing production efficiency, reducing production costs, and further improving product quality.

[0032] Optionally, step S25 is specifically:

[0033] Step S251: performing microphone production process comparison on the microphone historical production data and the production process data to be analyzed, thereby obtaining microphone production process difference data, and quantifying the impact of the production process efficiency based on the microphone production process difference data, thereby obtaining a production efficiency impact factor;

[0034] Step S252: performing periodic production pattern recognition on the microphone historical production pattern data, thereby obtaining periodic production pattern data;

[0035] Step S253: performing microphone production mode recognition according to the real-time production status data of the microphone production line, thereby obtaining the real-time production mode data of the microphone;

[0036] Step S254: Adaptively adjust the historical production mode efficiency of the periodic production mode data according to the production efficiency influencing factor, thereby obtaining the historical periodic production mode data;

[0037] Step S255: performing production pattern matching on the historical periodic production pattern data and the real-time production pattern data of the microphone, so as to obtain production pattern similarity data;

[0038] Step S256: Perform production demand prediction based on the production mode similarity data, thereby obtaining a microphone production demand prediction data set.

[0039] The present invention can significantly improve the accuracy, flexibility and efficiency of the production line by intelligently analyzing the production process and production mode of microphones. In the process of comparing the historical production data of microphones and the production process data to be analyzed, the difference between the historical process and the current production process can be identified in detail. This difference analysis not only helps to understand the advantages and disadvantages of the traditional production mode, but also provides a scientific basis for improving the process. By quantifying the impact of the production process efficiency based on the process difference data, it is possible to more accurately evaluate the impact of different production links on the overall production efficiency, and further reveal which links have a greater impact on the production efficiency under different processes, thereby providing key optimization factors for optimizing the production links and improving production efficiency. Through the identification of periodic production modes, it is helpful to deeply analyze the production characteristics in different time periods during the production process and extract periodic production mode data. This process can reveal the fluctuation trend and periodic law of the production line, provide a time basis for production demand forecasting and resource allocation, and make the production plan more accurate and efficient. Combined with the real-time production status data of the microphone production line, real-time identification of the production mode helps to capture the immediate changes in production, further improve the accuracy and real-time performance of production mode identification, ensure that the production process can be flexibly adjusted according to the current production status, and avoid production bottlenecks or waste of resources. Using the production efficiency influencing factors to make adaptive adjustments to the historical production mode efficiency of the periodic production mode data helps to match the historical data with the efficiency requirements of the current production process. Through this adjustment, the historical periodic production mode can be better adapted to the modern production environment, process requirements and real-time production conditions, thereby effectively improving production efficiency and reducing production costs. This adjustment strategy can help the production line better adapt to changes in production demand and improve production flexibility and stability. By matching the historical periodic production mode data with the real-time production mode data, it can help identify the similarities between different production modes, thereby providing a basis for the adjustment and optimization of the production plan. Through this matching process, future production demand can be accurately predicted, avoiding the problem of overproduction or insufficient resources, providing a more scientific and reasonable basis for production scheduling and resource allocation, and further ensuring that various resources in the production process are optimally utilized and improving overall production efficiency. Production demand forecasting based on production pattern similarity data can accurately grasp production trend changes, optimize production scheduling and resource allocation, and ensure efficient execution of production plans. This intelligent demand forecasting not only improves production efficiency, but also ensures quality consistency during the production process, reduces human intervention, and improves the automation level of the production process.

[0040] Optionally, step S3 specifically includes:

[0041] Step S31: Calculating the production efficiency of the microphone production topology model according to the real-time production status data of the microphone production line, thereby obtaining the production efficiency data of the microphone production link;

[0042] Step S32: Calculating the expected production efficiency of microphones for the microphone production demand prediction data set, thereby obtaining the expected production efficiency data of microphones;

[0043] Step S33: performing a production efficiency comparison on the production efficiency data of the microphone production link and the expected production efficiency data of the microphone, so as to obtain expected production efficiency difference data;

[0044] Step S34: extracting microphone process link parameters according to the microphone production topology structure model, thereby obtaining microphone process link parameter data;

[0045] Step S35: Optimize the microphone process parameter data according to the expected production efficiency difference data, so as to obtain a process optimization data set, and transmit it to the microphone production control platform to execute the microphone production process parameter adjustment task.

[0046] The present invention can quantify the actual efficiency of each production link by calculating the efficiency of the production link based on the real-time production status data of the microphone production line. This provides a direct basis for identifying the bottleneck links and potential efficiency improvement points in the production process, and is helpful to formulate targeted optimization strategies. By calculating the expected production efficiency in combination with the production demand forecast data set, an idealized efficiency target can be established, thereby providing a quantitative standard for the actual production process to be compared with the current production efficiency. By comparing the actual efficiency of the production link with the expected production efficiency, the gap in the production process can be revealed, which helps to identify which links have low efficiency, thereby providing a direction for subsequent optimization. Based on the microphone production topology structure model, the process link parameters are extracted, and the key parameters and operating conditions of each link in the production process can be analyzed in detail, which provides data support for further optimizing the production process. By optimizing the process link parameters according to the expected production efficiency difference data, the key parameters of each process link can be accurately adjusted, so that the production process is more efficient, stable, and close to the expected efficiency. This optimization can not only improve the overall efficiency of the production line, but also improve economic benefits by reducing energy consumption, reducing production costs, etc. By transmitting the optimized process parameters to the production control platform, the working status and production rhythm of the production equipment can be adjusted and optimized in a timely manner. This real-time dynamic parameter adjustment capability improves the flexibility and adaptability of the production line, allowing the production process to respond efficiently according to actual needs, thereby avoiding waste and stagnation in production.

[0047] Optionally, step S35 is specifically:

[0048] Step S351: estimating the bottleneck of the microphone process step on the parameter data of the microphone process step, thereby obtaining the bottleneck data of the microphone process step;

[0049] Step S352: classifying the real-time production status data of the microphone production line into process links according to the bottleneck data of the microphone process links, thereby obtaining the near-bottleneck process link data and the far-bottleneck process link data;

[0050] Step S353: performing process link parameter association on the near-bottleneck process link data and the far-bottleneck process link data respectively according to the microphone process link parameter data, thereby obtaining the near-bottleneck process link parameter data and the far-bottleneck process link parameter data;

[0051] Step S354: optimizing the production cycle of the process link for the parameter data of the near-bottleneck process link according to the expected production efficiency difference data, thereby obtaining the first process link optimization parameter data; optimizing the process equipment utilization rate for the parameter data of the far-bottleneck process link according to the expected production efficiency difference data, thereby obtaining the second process link optimization parameter data;

[0052] Step S355: Couple the first process link optimization parameter data and the second process link optimization parameter data with the production process link parameters to obtain a process optimization data set, and transmit it to the microphone production control platform to execute the microphone production process parameter adjustment task.

[0053] The present invention can accurately identify the bottleneck link in the production process by estimating the bottleneck of the microphone microphone process link parameter data. This process helps to find the key link that affects the overall efficiency and quality in production, and provides a clear direction for subsequent optimization. Further, the process link classification of the real-time status of the production line according to the process link bottleneck data helps to distinguish the process links close to the bottleneck and far from the bottleneck, and then take targeted optimization measures for different links. By classifying and processing the process link parameter data of the near bottleneck and the far bottleneck, the characteristics of each link can be more clearly identified, thereby providing a basis for refined management and production scheduling. The process parameter correlation analysis is performed for the near bottleneck and the far bottleneck link, and the key parameters of the bottleneck link can be effectively connected with the data of other process links, thereby improving the synergy of the entire production process. In particular, the expected production efficiency difference data is optimized, and the key parameters of each link can be accurately adjusted by optimizing the production cycle of the near bottleneck link and optimizing the equipment utilization of the far bottleneck link, so as to improve the production efficiency and maximize the equipment utilization. Through this refined optimization, the production cycle can be significantly shortened and the working efficiency of the equipment can be improved, thereby reducing production costs, improving output value and product quality. By coupling the optimized parameter data of the first process link and the second process link, the mutual influence of each link can be comprehensively considered to ensure the balance and stability of the entire production process. Through this coupling process, the optimization strategy can not only improve the performance of local links, but also ensure the improvement of overall production efficiency and quality, and avoid the imbalance of the system caused by the optimization of a single link. Transmitting the optimized data set to the production control platform to achieve real-time parameter adjustment can ensure that the production process receives efficient feedback and adjustment in actual operation, thereby further improving the flexibility, intelligence level and overall production efficiency of the production line.

[0054] Optionally, step S4 specifically includes:

[0055] Step S41: collecting real-time production line sensor data based on the microphone production line sensor network, thereby obtaining real-time microphone production line sensor data;

[0056] Step S42: extracting sensor features from the real-time microphone production line sensor data, thereby obtaining production line visual sensor data and production line vibration sensor data;

[0057] Step S43: performing product image edge detection of the process link of the production line according to the visual sensing data of the production line, thereby obtaining product edge data of the process link, and performing product contour integration on the product edge data of the process link, thereby obtaining product contour data of the process link;

[0058] Step S44: performing product defect identification based on the process link product profile data, thereby obtaining process link product defect data;

[0059] Step S45: performing process link vibration mode recognition on the production line vibration sensor data, thereby obtaining process link vibration mode data, and performing vibration signal abnormality analysis based on the process link vibration mode data, thereby obtaining process link abnormal vibration data;

[0060] Step S46: performing a process link intersection operation on the process link product defect data and the process link abnormal vibration data, thereby obtaining low-quality process link data;

[0061] Step S47: Associating the process optimization data set with the low-quality process link data to obtain a quality inspection data set.

[0062] The present invention can monitor the operation status of the production line in real time and obtain real-time information about various data in the production process by performing real-time data collection based on the microphone production line sensor network, which provides first-hand basis for subsequent quality control and production optimization. Feature extraction of real-time sensor data can analyze the visual and vibration data in the production line separately, providing basic data for accurate quality inspection and production link analysis. By extracting visual sensor data and vibration sensor data, different types of production problems can be effectively paid attention to. For example, visual data can help identify product appearance problems, while vibration data can reveal the operation status and potential faults of the equipment. In terms of image processing, by edge detection and contour integration of products in the production line process, the appearance features of the products can be accurately extracted, thereby providing a clear visual basis for defect identification. This process can identify the appearance defects that may occur in the product during the production process, and provide direct data support for subsequent production adjustments and product screening. Further product defect identification can not only find significant appearance problems, but also provide direction for production process optimization by analyzing the defect type and location, and avoid unqualified products from flowing into subsequent links. Through pattern recognition and abnormal analysis of vibration data, the operating status of equipment in the production process can be effectively monitored, and potential risks caused by equipment failure or abnormal operation can be discovered in time. By identifying abnormal vibration data in the process link, hidden faults in the production process can be discovered in time to avoid these problems from having a greater impact on product quality and production progress. By combining product defect data and abnormal vibration data for intersection operations, low-quality process links can be identified, helping production line managers to accurately locate which links in the production process have quality problems, so as to prescribe the right medicine and avoid unnecessary waste of resources. Associating low-quality process link data with process optimization data sets provides a scientific basis for optimizing production links and improving product quality. Through this association, the specific causes of low-quality links can be analyzed and identified at the data level, thereby providing valuable support for subsequent process improvements, equipment adjustments, and production process optimization, thereby ensuring that the entire production process is more intelligent, precise, and automated, and improving production efficiency and product qualification rate.

[0063] Optionally, step S5 specifically includes:

[0064] Step S51: Calculating the low quality frequency of the process link according to the quality inspection data set, thereby obtaining the low quality frequency data of the process link;

[0065] Step S52: quantifying the impact of quality fluctuations on the low-quality frequency data of the process link and the microphone production demand forecast data set, thereby obtaining the equipment load impact factor;

[0066] Step S53: dividing the equipment load of the microphone production topology structure model into process links according to the equipment load influencing factor, thereby obtaining the equipment data of high load in the process link and the equipment data of low load in the process link;

[0067] Step S54: Based on the high-load equipment data of the process link and the low-load equipment data of the process link, the process link equipment load is balanced to obtain the equipment scheduling optimization data set, and transmit it to the microphone production control platform to execute the equipment scheduling task.

[0068] The present invention calculates the low-quality frequency of the process link according to the quality detection data set, which can effectively evaluate the quality fluctuation of each process link and identify the link where quality problems repeatedly occur in the production process. This process can help production managers to discover potential production bottlenecks in a timely manner and provide data support for subsequent improvements. Low-quality frequency data helps to identify weak links in the process, thereby providing a targeted basis for production optimization and equipment adjustment. By combining low-quality frequency data with production demand forecast data to quantify the impact of quality fluctuations, the impact of different production demands on the quality fluctuations in the production process can be accurately understood, thereby providing a scientific basis for production scheduling, equipment load management and resource allocation. This process helps to identify which links' production loads may cause quality fluctuations under demand fluctuations, further improving production stability. The calculation of equipment load impact factors can help to deeply understand the direct impact of equipment load on product quality and production efficiency in the production process. According to this factor, various types of equipment in the production process can be accurately divided into loads, and it can be identified which process links require high-load equipment and which are suitable for low-load equipment. This process provides specific guidance for optimizing equipment efficiency, reducing production costs, and reducing equipment failures. By reasonably dividing the equipment load of the process link and implementing equipment load balancing, it is possible to avoid overloading of certain equipment under high load, thereby reducing production stagnation or product quality problems caused by equipment overload, and at the same time effectively extending the service life of the equipment. The generation of the equipment scheduling optimization data set provides a detailed scheduling plan for the production control platform, which can dynamically adjust the equipment load according to the production load and production demand to ensure the efficient and stable operation of the production process. This process not only optimizes the allocation of production resources and improves production efficiency, but also ensures the continuous high-quality output of the process link, which helps to improve the adaptability and flexible response capabilities of the overall production line.

[0069] Optionally, the present specification also provides a microphone production line intelligent control system, which is used to execute the microphone production line intelligent control method as described above, and the microphone production line intelligent control system includes:

[0070] A production structure analysis module is used to obtain microphone production line sensor data and microphone production process data, and perform microphone production topology structure analysis based on the microphone production process data, so as to obtain a microphone production topology structure model;

[0071] The production demand prediction module is used to perform production line sensor data fusion on the microphone production topology structure model and the microphone production line sensor data, so as to obtain the microphone production line sensor network; the production demand prediction is performed based on the microphone production line sensor network, so as to obtain the microphone production demand prediction data set;

[0072] The production process optimization module is used to optimize the production process parameters of the microphone production topology structure model according to the microphone production demand prediction data set, so as to obtain the process optimization data set and transmit it to the microphone production control platform to perform the microphone production process parameter adjustment task;

[0073] A product quality inspection module is used to collect real-time production line sensor data based on a microphone production line sensor network, thereby obtaining real-time microphone production line sensor data, and to inspect product quality in the production process based on the real-time microphone production line sensor data and a process optimization data set, thereby obtaining a quality inspection data set;

[0074] The production line equipment scheduling optimization module is used to optimize the production line equipment scheduling of the microphone production topology structure model according to the quality inspection data set and the microphone production demand prediction data set, so as to obtain the equipment scheduling optimization data set and transmit it to the microphone production control platform to perform equipment scheduling tasks.

[0075] The intelligent control system for the microphone production line of the present invention can implement any intelligent control method for the microphone production line of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the intelligent control method for the microphone production line. The internal modules of the system cooperate with each other, thereby improving the production efficiency, product quality and production flexibility of the microphone production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0077] Figure 1 This is a schematic diagram of the steps of the intelligent control method for microphone production line of the present invention;

[0078] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0079] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0080] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0081] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0082] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0083] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0084] To achieve this, please refer to Figures 1 to 3 The present invention provides an intelligent control method for a microphone production line, the method comprising the following steps:

[0085] Step S1: acquiring sensor data of a microphone production line and microphone production process data, and performing microphone production topology structure analysis according to the microphone production process data, thereby obtaining a microphone production topology structure model;

[0086] In this embodiment, it is necessary to obtain various sensor data and production process data of the microphone production line. Specifically, the sensor data includes temperature, humidity, vibration, sound, pressure, etc., which are collected in real time by sensors arranged at various key positions on the production line. For example, the temperature sensor is located in the welding area, the humidity sensor is deployed in the assembly area, and the vibration sensor is installed near the mechanical arm and the main equipment. The production process data involves data from each production link from raw material processing, component assembly to final product testing, including the production time, equipment status, process parameters, etc. of each link. By analyzing these data, the topological structure analysis of the production line of the microphone can be performed according to the production process of the microphone, and then a production topological structure model that fully reflects the production process and equipment connection relationship can be constructed. This model provides a basis for subsequent production data fusion and optimized scheduling by identifying the key nodes of each production link and their interrelationships.

[0087] Step S2: performing production line sensor data fusion on the microphone production topology model and the microphone production line sensor data, thereby obtaining the microphone production line sensor network; performing production demand prediction based on the microphone production line sensor network, thereby obtaining a microphone production demand prediction data set;

[0088] In this embodiment, data fusion is achieved by combining the microphone production topology model with the production line sensor data. The sensor data includes multi-dimensional data from different production links, such as image data from the visual system, vibration frequency data from the vibration sensor, etc. After unified processing, these data are input into the data fusion algorithm, and the various data sources are integrated into a comprehensive production line sensor network through spatiotemporal association and multi-level data fusion. The network can reflect the working status, equipment performance, etc. of each link in the production process in real time. Based on this production line sensor network, production demand can be predicted. For example, by real-time monitoring of equipment status, possible equipment failures or production bottlenecks can be predicted, so as to adjust production strategies or arrange maintenance in advance to avoid production line shutdowns. The production demand prediction data set includes estimates of the production capacity, resource requirements, etc. of each link, which helps optimize resource allocation and production scheduling.

[0089] Step S3: Optimize the production process parameters of the microphone production topology model according to the microphone production demand prediction data set, so as to obtain a process optimization data set, and transmit it to the microphone production control platform to perform the microphone production process parameter adjustment task;

[0090] In this embodiment, based on the microphone production demand prediction data set, the production process link parameters are optimized in combination with the production topology model. The process link parameter optimization is mainly adjusted for the working status of the equipment, the utilization efficiency of materials, the optimization of the production cycle, etc. For example, in the production process, if the equipment running speed of a certain link is lower than expected, it may be because the equipment is overloaded or the process parameters are inappropriate. By analyzing the production demand prediction data set and combining the feedback from the production topology model, it is determined which process parameters need to be adjusted, such as adjusting the power of the welding equipment, improving the operation time of the assembly process, etc., so as to improve the overall production efficiency. The optimized data set will be transmitted to the microphone production control platform, and the platform will automatically adjust the production process according to these data to ensure that the operating efficiency and product quality of each link meet expectations.

[0091] Step S4: performing real-time production line sensor data collection based on the microphone production line sensor network to obtain real-time microphone production line sensor data, and performing product quality inspection in the production link based on the real-time microphone production line sensor data and the process optimization data set to obtain a quality inspection data set;

[0092] In this embodiment, quality inspection of the production process is performed through real-time data collection based on the production line sensor network. In this process, the production line sensors on the production line sensor network will monitor the key data of each link in the production process in real time and transmit it to the quality inspection system. For example, the vibration sensor detects whether there is abnormal vibration in the equipment, and the visual sensor performs image detection on the product. By comparing the image data with the standard product, possible defect areas are identified. Based on real-time sensor data and process optimization data sets, product quality can be intelligently detected to automatically determine whether the product meets the quality standards. The quality inspection results will generate a quality inspection data set, covering information such as the defect type, location, and severity of the defect of the product, providing a basis for subsequent production process adjustments and optimization.

[0093] Step S5: Optimize the production line equipment scheduling of the microphone production topology model according to the quality inspection data set and the microphone production demand prediction data set, so as to obtain the equipment scheduling optimization data set, and transmit it to the microphone production control platform to execute the equipment scheduling task.

[0094] In this embodiment, the production topology model is optimized for equipment scheduling. The quality inspection data set provides feedback on quality issues in each process link, while the production demand forecast data set reflects the changing trend of production demand. Combining these two data sets, it is possible to analyze which equipment needs to adjust its workload and which equipment may have bottlenecks, and optimize equipment scheduling accordingly. For example, if a certain equipment plays a key role in multiple process links and has quality problems, the work priority of the equipment is automatically adjusted, or the equipment with heavier loads is scheduled to idle time periods to avoid affecting the overall production rhythm. The optimized equipment scheduling plan will be executed through the production control platform to ensure the efficient operation of the production line and the stability of product quality.

[0095] Optionally, step S1 specifically includes:

[0096] Step S11: Acquire microphone production line sensor data and microphone production process data;

[0097] In this embodiment, obtaining the sensor data and production process data of the microphone production line involves collecting data from sensors in various production links and integrating them with information from the production process. Specifically, sensor data includes temperature, humidity, vibration, pressure, image, etc., which are collected in real time by different types of sensors arranged in various production links. For example, vibration sensors are installed on welding equipment, temperature sensors are located in the key links of the assembly line, and visual sensors are used to detect whether there are defects on the surface of the product. At the same time, production process data is obtained through the microphone production control platform, covering data from all links from raw materials to product completion, such as equipment status, working hours, process parameters, etc. of each production link. By collecting these data, comprehensive production information can be obtained to provide data support for subsequent analysis and optimization.

[0098] Step S12: performing data preprocessing on the microphone production line sensor data and the microphone production process data respectively, so as to obtain the production line sensor data to be analyzed and the production process data to be analyzed;

[0099] In this embodiment, data preprocessing is performed on the sensor data of the microphone production line and the production process data. For the sensor data, preprocessing includes noise removal, missing data filling, outlier detection and correction, etc. For example, high-frequency noise in the sensor data is removed by a filtering algorithm, and interpolation is used to fill in the missing data caused by equipment failure or sensor problems. For the production process data, preprocessing includes standardization and normalization processing, so that data of different dimensions can be compared and analyzed under the same standard. The preprocessed data is organized into production line sensor data to be analyzed and production process data to be analyzed, which provide clear and standardized input for the next step of analysis.

[0100] Step S13: extracting production line equipment sensor data from the production line sensor data to be analyzed, thereby obtaining production line equipment sensor data;

[0101] In this embodiment, when processing the production line sensor data to be analyzed, the key sensor data of the production line equipment is extracted. Specifically, data points related to the operating status of the production equipment are extracted from the sensor network, such as the frequency changes captured by the vibration sensor, the pressure value measured by the pressure sensor, and the temperature changes fed back by the temperature sensor. Different algorithms are used to extract relevant data according to different types of sensors to ensure that the extracted data accurately reflects the operating status of the equipment. For example, vibration data can help determine whether the equipment has overload or wear problems, while temperature data can reveal the risk of equipment overheating or cooling system failure. The extracted equipment sensor data provides a data basis for subsequent equipment timing analysis.

[0102] Step S14: Arranging the equipment sensor data points in time sequence according to the production line equipment sensor data, thereby obtaining the production equipment operation time sequence data;

[0103] In this embodiment, the operation timing data of the production equipment can be obtained by arranging the time series of the sensor data of the production line equipment. Specifically, the equipment sensor data points are sorted according to the timestamp to form the operation record of each device in a specific time period. Through the time series data analysis technology, these data are sorted and synchronized to ensure that the operation status of all devices are aligned on the same time axis. For example, if the vibration sensor of a device is abnormal at a certain moment, the timestamp of that moment will be recorded and synchronized with the data of other devices. In this way, by analyzing the operation timing data of the equipment, the operation status of the equipment can be accurately judged, and possible problems in the production process can be identified, such as coordination problems between equipment or production bottlenecks.

[0104] Step S15: Perform production line topology analysis based on the production process data to be analyzed and the production equipment operation timing data, so as to obtain a microphone production topology model.

[0105] In this embodiment, a topological structure analysis of the production line is performed based on the production process data to be analyzed and the operating timing data of the production equipment. By analyzing the timing relationship between different devices and their positions in the production process, a topological structure model that reflects the connection relationship between the production process and the equipment can be constructed. For example, by performing a timing analysis on the sensor data of the equipment, the relative order of each device in the production process and the dependencies between them are determined. The topological structure model will clearly show how each device collaborates with other devices, how the various links in the production line are connected, and which devices may become bottlenecks. Through this analysis, the overall layout and equipment configuration of the production line can be optimized, and the bottlenecks and potential problems of the production process can also be identified in advance, providing a basis for subsequent optimization.

[0106] Optionally, step S15 is specifically:

[0107] Step S151: dividing the production links based on the production process data to be analyzed, thereby obtaining production process link data;

[0108] In this embodiment, the entire microphone production process is decomposed into multiple key process links by dividing the production links based on the production process data to be analyzed. Specifically, the production process data includes every production step from material preparation to final product packaging, and different process links such as assembly, testing, welding, and debugging are determined based on these data. The basis for dividing each link includes production time, process requirements, equipment usage, etc., to ensure that each link can operate independently and meet the overall production goals. For example, in the production process, the two links of automated assembly and manual inspection have different process requirements. These links are divided independently, and specific operating standards and equipment requirements are specified for each link.

[0109] Step S152: extracting production equipment description features from the production process data to be analyzed, thereby obtaining production equipment description data;

[0110] In this embodiment, the production process data to be analyzed is subjected to production equipment description feature extraction, specifically including extracting the working status, performance indicators and operating characteristics of the equipment from the sensor data related to the production equipment. For example, the descriptive features of the equipment can be reflected by sensor data, such as temperature, rotation speed, pressure, etc., and integrated with the specific functions of the equipment. If a certain device is used for assembly operations, the device description will include parameters such as its maximum load capacity, working accuracy, and operating time, while if it is a test device, its description will include test accuracy, response time, etc. Through these device description data, the performance of the production line equipment can be fully understood, thereby preparing for the subsequent links and equipment matching.

[0111] Step S153: performing equipment-link association docking on the production process link data and the production equipment timing data according to the production equipment description data, thereby obtaining link-equipment docking data;

[0112] In this embodiment, based on the production equipment description data, the production process link data is associated with the production equipment timing data. This process is carried out by analyzing the equipment's operating timing data and matching it with the requirements of each process link. Specifically, it identifies which equipment belongs to a specific process link, and then matches the link data based on the equipment's operating timing data to determine the specific equipment used in each process link during the production process. For example, the assembly link requires automated assembly equipment, while the testing link requires testing instruments. Through this association and docking, it can ensure that the equipment used in each production link can be effectively switched and scheduled according to the timing requirements, avoiding equipment resource conflicts or idle phenomena.

[0113] Step S154: integrating the production link connection relationship based on the production process link data, thereby obtaining the production link connection relationship data, and mapping the production link connection relationship data according to the link-equipment docking data, thereby obtaining the production process link relationship data;

[0114] In this embodiment, the connection relationship of the production links is integrated based on the production process link data. By constructing the logical connection relationship between each link, the flow mode of the entire production process can be clearly displayed. Specifically, the link connection relationship includes the use of technical means such as graph theory to reflect the dependency relationship between each link and other links, the resource flow path, and the scheduling strategy. For example, the welding link depends on the completion of the assembly link, and the testing link is started after all assemblies are completed. According to the link-equipment docking data, these link connection relationships are further mapped, and the collaborative relationship between each link and the equipment is analyzed, thereby optimizing the coordination and efficiency between the links in the production process. In this process, the link connection relationship is not only a theoretical dependence, but also adjusted in combination with the actual status of the equipment to ensure the smoothness of the production line and the maximum utilization of the equipment.

[0115] Step S155: Perform production line topology analysis based on production process link relationship data to obtain a microphone production topology model.

[0116] In this embodiment, the production line topology analysis is performed based on the relationship data of the production process links. The goal of the topology analysis is to layout and optimize all production links and equipment according to certain rules to form a clear production line model. Specifically, by analyzing the relationship data of the production links, the key nodes and connection paths in the production line are identified, and the topology structure is integrated using algorithms such as graph theory. For example, according to the time requirements of each process link, the load capacity of the equipment, and the order of the production process, the layout location of the equipment and the order of the production steps are determined to ensure smooth flow of materials and information and avoid the occurrence of bottlenecks. Through this topology analysis, the layout and process of the production line are reasonably integrated, which improves production efficiency and reduces the idle time of equipment.

[0117] Optionally, step S2 specifically includes:

[0118] Step S21: integrating the sensor spatial deployment of the microphone production line sensor data, thereby obtaining sensor spatial deployment data;

[0119] In this embodiment, the sensor spatial deployment and integration of the sensor data of the microphone production line is carried out. First, it is necessary to determine the sensor distribution area of ​​the production line and select appropriate sensor types, such as temperature sensors, pressure sensors, vibration sensors, image acquisition sensors, etc., and deploy them according to the specific needs of the production line. For example, each workstation on the production line is equipped with a vibration sensor to monitor the operating status of the equipment, visual sensors are used for image acquisition and defect detection, and pressure sensors monitor the working pressure of pneumatic equipment. After acquiring the sensor data, it is processed into a unified data structure through spatial integration technology to ensure that all sensor data can synchronously and accurately reflect the status of the production line. In this process, the distribution density of the sensors also needs to be considered to ensure that each production link and equipment has sufficient sensor support to provide accurate data support.

[0120] Step S22: performing spatiotemporal fusion of production line sensor data on the microphone production topology model and the microphone production line sensor data according to the sensor spatial deployment data, thereby obtaining a microphone production line sensor network;

[0121] In this embodiment, the spatiotemporal fusion of production line sensor data is performed based on the sensor spatial deployment data, and the data of each sensor is combined with the microphone production topology model according to the location and function to form a multi-dimensional data set. For example, the time series data of the temperature sensor and the vibration sensor need to be combined with the working status data, and the image data is spatiotemporally associated with the process requirements corresponding to the production link. Through the spatiotemporal fusion algorithm, data from different times and spaces can be combined to form a comprehensive production line sensor network. This fused sensor network can not only provide real-time monitoring, but also predict and warn abnormal situations in the production process through deep learning algorithms, such as equipment failures, abnormal material flow, etc., thereby providing support for production scheduling and decision-making.

[0122] Step S23: acquiring microphone historical production data, and performing microphone production mode recognition on the microphone historical production data, thereby obtaining microphone historical production mode data;

[0123] In this embodiment, the historical production data of the microphone is obtained through the microphone production control platform, and the microphone production pattern recognition is performed on it. This recognition process relies on a machine learning algorithm, and pattern recognition is performed by analyzing historical production data (such as production volume, yield, equipment failure history, operating environment, etc.) to extract common patterns in the production process. For example, it is possible to identify patterns with low production efficiency under certain specific production conditions (such as aging equipment, inexperienced operators, etc.), and it is also possible to identify the best production operation mode. By combining historical data with these production patterns, the key factors affecting production efficiency can be effectively identified, and a basis for optimization can be provided for future production processes.

[0124] Step S24: integrating the production status of the production line based on the microphone production line sensor network, thereby obtaining real-time production status data of the microphone production line;

[0125] In this embodiment, the production status of the production line is integrated based on the microphone production line sensor network, and the data collected from the sensor network is processed and comprehensively analyzed in real time to obtain the current operating status of the production line. This includes various indicators of the production process, such as equipment operating status, process parameters, material flow conditions, etc. For example, by real-time monitoring of the data of the vibration sensor, it is possible to determine whether the production equipment is in normal working condition or whether there is a risk of imminent failure; through the data of the temperature sensor, it is possible to evaluate whether the production environment meets the process requirements. The integrated real-time production status data can be displayed on the production control platform to provide production management personnel with real-time production status updates.

[0126] Step S25: Perform production demand forecasting based on the historical production mode data of the microphone and the real-time production status data of the microphone production line, so as to obtain a microphone production demand forecasting data set.

[0127] In this embodiment, production demand forecasting is performed based on the historical production mode data of the microphone and the real-time production status data of the production line. By combining the historical production mode with the real-time production status data, demand forecasting is performed using data mining and prediction models (such as regression analysis, time series analysis, deep learning, etc.). For example, the average production demand under a certain production mode is obtained based on historical data, and adjustments are made based on the operating status of the current production line (such as equipment load, production line efficiency, process changes, etc.) to predict the production demand in the future. These prediction results can help production managers make more accurate production scheduling decisions, optimize resource allocation, and avoid overcapacity or shortages.

[0128] Optionally, step S25 is specifically:

[0129] Step S251: performing microphone production process comparison on the microphone historical production data and the production process data to be analyzed, thereby obtaining microphone production process difference data, and quantifying the impact of the production process efficiency based on the microphone production process difference data, thereby obtaining a production efficiency impact factor;

[0130] In this embodiment, a detailed comparison is made between the historical production data of the microphone and the production process data to be analyzed. The historical production data includes the process parameters of each production batch, the equipment operation data during the production process, the work records of the operators, etc., while the production process data to be analyzed includes the specific process flow and equipment configuration under the current production environment. Through comparative analysis, it is possible to find the process differences between different production cycles and different production batches, such as some batches using different welding processes or testing methods, resulting in different production efficiency. Based on these process difference data, a mathematical model is established to quantify the impact of these differences on production efficiency. For example, the adjustment of a specific process link will increase the production efficiency by 10%, and the corresponding production efficiency impact factor is +10%. The obtained production efficiency impact factor can provide data support for subsequent production optimization.

[0131] Step S252: performing periodic production pattern recognition on the microphone historical production pattern data, thereby obtaining periodic production pattern data;

[0132] In this embodiment, the periodic production pattern is identified by analyzing the historical production pattern data of the microphone using time series analysis technology. Specifically, the historical production data records information such as the production output, the status of the production equipment, and the changes in the production environment in each cycle. By clustering these data, some common production cycle patterns can be identified. For example, the monthly production volume is relatively stable under certain production modes, while production fluctuations occur in certain time periods under other production modes. The identification of periodic production patterns can not only help analyze the production efficiency under different production cycles, but also provide data support for predicting future production needs.

[0133] Step S253: performing microphone production mode recognition according to the real-time production status data of the microphone production line, thereby obtaining the real-time production mode data of the microphone;

[0134] In this embodiment, real-time production mode recognition is performed based on the real-time collected microphone production line data. Real-time production status data includes information such as the equipment status, production rhythm, material flow speed, etc. of each link in the production process. Machine learning algorithms, such as support vector machines (SVM) or neural networks, are used to perform real-time classification and pattern recognition on these real-time data. By analyzing the real-time production mode, it is possible to determine whether the current production process meets the expected production mode and detect abnormal situations in a timely manner. For example, if the production rhythm is too slow or the equipment is frequently shut down, it will be marked as an abnormal production mode and an alarm will be issued to the production management personnel.

[0135] Step S254: Adaptively adjust the historical production mode efficiency of the periodic production mode data according to the production efficiency influencing factor, thereby obtaining the historical periodic production mode data;

[0136] In this embodiment, the obtained production efficiency influencing factors are used to adaptively adjust the historical production mode efficiency of the periodic production mode data. For example, a certain historical production mode data shows that the production efficiency is negatively affected by certain process links. By combining the production efficiency influencing factors with the historical production mode data, the efficiency of each periodic production mode can be adjusted. For example, if the welding link in a certain production cycle reduces the production efficiency due to equipment failure, different optimization schemes (such as replacing equipment or adjusting the production process) will be simulated by adjusting the efficiency factor, and finally more adaptable and efficient production mode data will be obtained.

[0137] Step S255: performing production pattern matching on the historical periodic production pattern data and the real-time production pattern data of the microphone, so as to obtain production pattern similarity data;

[0138] In this embodiment, the historical periodic production mode data is matched with the real-time production mode data, and the similarity of the production mode is calculated. Specifically, a distance-based measurement method (such as Euclidean distance or cosine similarity) is used to compare historical data and real-time data. For example, if the equipment operation status in the current production process is similar to the equipment status of a certain historical mode, it is considered that the current production mode has a high similarity with the historical mode. Through this matching analysis, it is possible to determine whether the current production deviates from the optimal production mode, thereby providing targeted adjustment suggestions. The historical production mode is compared with the current real-time production mode. If the similarity between the two is high, it is considered that the current production process is similar to the performance of the historical production cycle, and the future production demand trend can be predicted. If the similarity is low, it will be marked that the current production mode may deviate from the expected production mode, and further adjustments are required. Through this matching analysis of production modes, the system can effectively monitor the status of the production line and ensure the stability of the production process.

[0139] Step S256: Perform production demand prediction based on the production mode similarity data, thereby obtaining a microphone production demand prediction data set.

[0140] In this embodiment, production demand forecasting is performed based on production pattern similarity data. Statistical analysis or machine learning models (such as regression analysis, time series forecasting) are used to predict production demand for a period of time in the future. For example, if the current production pattern is similar to the historical pattern, and historical data shows that the production volume is stable under this pattern, the production demand for a certain period of time in the future will be predicted. The forecast results will provide an important basis for production scheduling, resource allocation, etc., so that production management can foresee production peaks or troughs, thereby optimizing resource allocation and avoiding overcapacity or shortages. If the current production pattern is similar to the historical pattern and the production demand under the historical pattern is relatively stable, the stability of future production demand can be predicted, thereby helping production management to allocate resources and plan production capacity. This forecasting capability helps to avoid overproduction or underproduction, optimize resource allocation, and improve production efficiency.

[0141] Optionally, step S3 specifically includes:

[0142] Step S31: Calculating the production efficiency of the microphone production topology model according to the real-time production status data of the microphone production line, thereby obtaining the production efficiency data of the microphone production link;

[0143] In this embodiment, the real-time production status data of the microphone production line, including the equipment operation status, production rhythm, output, etc. of each production link, will be collected in real time and sent to the computing cloud platform. By analyzing these data, the actual production efficiency of each production link can be calculated based on the microphone production topology model. Taking the assembly link as an example, the efficiency value of the link is calculated through data such as equipment operation time, production output and equipment utilization rate, such as the production quantity of a certain production link in a unit time and the ratio of the actual equipment operation time, so as to obtain the efficiency data of the production link. These data will help analyze the bottlenecks and optimization space of each production link.

[0144] Step S32: Calculating the expected production efficiency of microphones for the microphone production demand prediction data set, thereby obtaining the expected production efficiency data of microphones;

[0145] In this embodiment, the expected production efficiency is calculated based on the microphone production demand prediction data set. Specifically, the expected production efficiency of each production link in the future is predicted based on historical production data, current order demand, production capacity, production rhythm and other information. For example, assuming that the current production task requires the completion of 500 microphone production tasks within a certain period of time, the ideal production efficiency value that each production link should achieve under these conditions is calculated based on the historical production mode, output demand and production line capacity. This data is the expected production efficiency data.

[0146] Step S33: performing a production efficiency comparison on the production efficiency data of the microphone production link and the expected production efficiency data of the microphone, so as to obtain expected production efficiency difference data;

[0147] In this embodiment, the actual production efficiency data of the microphone production link is compared with the expected production efficiency data to analyze the difference. Specifically, the efficiency difference of each production link is calculated, that is, the difference between the expected production efficiency and the actual production efficiency. For example, in the assembly link, if the expected production efficiency is 50 microphones per hour, and the actual production efficiency is 45 per hour, then the expected production efficiency difference is 5 microphones. Through this comparison, it is possible to find out which parts of the production link have failed to achieve the expected efficiency, providing a basis for subsequent optimization work.

[0148] Step S34: extracting microphone process link parameters according to the microphone production topology structure model, thereby obtaining microphone process link parameter data;

[0149] In this embodiment, key process parameters are extracted from each process link according to the microphone production topology model. For example, for the coating link, the key process parameters include the type of coating, coating thickness, temperature control, spraying speed, etc. By combining these process parameters with the real-time data of the production link, the process data used in each link in actual production can be extracted and integrated into process link parameter data. This process can be achieved through the collection of sensor data of the equipment, monitoring of the production process, and historical process data, thereby laying the foundation for subsequent optimization work.

[0150] Step S35: Optimize the microphone process parameter data according to the expected production efficiency difference data, so as to obtain a process optimization data set, and transmit it to the microphone production control platform to execute the microphone production process parameter adjustment task.

[0151] In this embodiment, the parameters of the process link are optimized according to the expected production efficiency difference data. Specifically, according to the efficiency difference of each production link, combined with the parameter data of the process link, optimization and adjustment are performed. For example, if the efficiency difference of a certain process link is too large, the operating parameters of the equipment, the control process of the process or the addition of automated equipment will be adjusted to improve the efficiency of the link. After optimization, the generated process optimization data set will be transmitted to the microphone microphone production control platform, and the control platform will adjust the production process in real time according to these data to ensure that the overall efficiency of the production line is improved, thereby maximizing production capacity and reducing waste. For example, if the production efficiency of the coating link does not reach the expected value, the spraying frequency, paint flow or temperature control of the sprayer can be adjusted, or the production speed can be increased by introducing more efficient equipment. The optimized process data will form a data set, and the parameter adjustment task will be performed through the production control platform to ensure that each link can be produced according to the predetermined efficient target, thereby improving the overall production efficiency and product quality.

[0152] Optionally, step S35 is specifically:

[0153] Step S351: estimating the bottleneck of the microphone process step on the parameter data of the microphone process step, thereby obtaining the bottleneck data of the microphone process step;

[0154] In this embodiment, the parameter data of the microphone microphone process link is used for bottleneck estimation. In order to achieve this, the production load and time requirements of each process link are calculated based on historical production data, microphone microphone process link parameter data and real-time sensor data. For example, for the painting link, the time consumption, machine operation status and material consumption of each painting step are analyzed to evaluate whether the link has reached the production bottleneck. If the production time of a certain link far exceeds that of other links, and there is a situation where the equipment load is too high, then this link is the bottleneck link. Based on these data, the bottleneck data of each process link is calculated and determined to provide a basis for subsequent optimization.

[0155] Step S352: classifying the real-time production status data of the microphone production line into process links according to the bottleneck data of the microphone process links, thereby obtaining the near-bottleneck process link data and the far-bottleneck process link data;

[0156] In this embodiment, the process links are classified according to the bottleneck data of the process links and the real-time production status data of the microphone production line. By analyzing the impact of the process bottleneck link on the production line, the process links in the production line are divided into "near bottleneck" and "far bottleneck" links. For example, assuming that the production capacity of the assembly link and the testing link is relatively strong, but a bottleneck occurs in the assembly link, resulting in a decrease in the work efficiency of its subsequent links, the assembly link is regarded as a "near bottleneck" process link, and the subsequent testing and packaging links are "far bottleneck" process links. After classification, more targeted optimization can be carried out, more resources can be allocated or the process flow can be adjusted.

[0157] Step S353: performing process link parameter association on the near-bottleneck process link data and the far-bottleneck process link data respectively according to the microphone process link parameter data, thereby obtaining the near-bottleneck process link parameter data and the far-bottleneck process link parameter data;

[0158] In this embodiment, according to the microphone microphone process link parameter data, the near-bottleneck process link data and the far-bottleneck process link data are parameter-associated. The parameters of each process link are analyzed in detail. For example, the process parameters of the assembly link (such as part docking accuracy and assembly speed) will affect the subsequent detection link (such as the test cycle and failure rate of the detection equipment). By analyzing the mutual influence between the near-bottleneck and far-bottleneck links, it is ensured that the parameters of each link are optimized within a certain range, thereby improving the overall production efficiency. Specifically, the equipment operation status, process parameters, production progress and other information of each process link will be analyzed in detail based on real-time data. For the near-bottleneck link, for example, if the equipment operation load is detected to be high in the assembly link, it will be associated with the data of other links, such as the time consumption of the parts inspection link before assembly, the waiting time of the product quality inspection link after assembly, etc., to optimize the operation efficiency of the assembly link. At the same time, the far-bottleneck link will also be analyzed. For example, if there is a situation where the equipment idle time is too long in the packaging link, it will be combined with the data of subsequent links, such as the storage time or shipping time after product packaging, to perform parameter association optimization. In this way, it is ensured that the optimization measures of each process link can effectively improve the overall production efficiency and equipment utilization while taking into account their mutual influence.

[0159] Step S354: optimizing the production cycle of the process link for the parameter data of the near-bottleneck process link according to the expected production efficiency difference data, thereby obtaining the first process link optimization parameter data; optimizing the process equipment utilization rate for the parameter data of the far-bottleneck process link according to the expected production efficiency difference data, thereby obtaining the second process link optimization parameter data;

[0160] In this embodiment, the data of the near-bottleneck and far-bottleneck process links are optimized respectively according to the expected production efficiency difference data. In the near-bottleneck process link, the process link production cycle optimization method is adopted, for example, by reducing unnecessary operating steps, improving equipment working efficiency or increasing the degree of automation, shortening the production cycle, and ensuring that the production line operates in an efficient state. For example, if the production cycle of the assembly link is too long, the cycle can be shortened by optimizing the operation sequence, reducing manual intervention, and improving the level of equipment automation. In the far-bottleneck link, the utilization rate of the process equipment is optimized, and the comprehensive utilization rate of the equipment is improved by increasing the frequency of equipment use, improving equipment maintenance plans, adjusting work shifts, etc. For example, if the idle time of the detection equipment is too long, the utilization rate can be improved by adjusting the working time and frequency of the detection equipment, thereby effectively reducing the impact of production bottlenecks.

[0161] Step S355: Couple the first process link optimization parameter data and the second process link optimization parameter data with the production process link parameters to obtain a process optimization data set, and transmit it to the microphone production control platform to execute the microphone production process parameter adjustment task.

[0162] In this embodiment, the optimization parameter data of the first process link is coupled with the optimization parameter data of the second process link to ensure the coordination and integrity of the optimization scheme. Through parameter coupling, the optimization effects between different links are analyzed to ensure that each optimization measure will not have a negative impact on other links. For example, adjusting the operating mode of the assembly link affects the workload of the subsequent links. The optimization effect of each link is comprehensively considered to ensure the balance and smooth operation of the production line. The generated process optimization data set will be transmitted to the microphone production control platform to perform the process parameter adjustment task to ensure that the optimization measures of each link in the production process are adjusted and implemented in real time, thereby improving the overall production efficiency and quality.

[0163] Optionally, step S4 specifically includes:

[0164] Step S41: collecting real-time production line sensor data based on the microphone production line sensor network, thereby obtaining real-time microphone production line sensor data;

[0165] In this embodiment, real-time data collection is performed through a sensor network deployed in each process link of the microphone production line. These sensors may include temperature sensors, pressure sensors, vibration sensors, visual sensors, etc., which monitor the status of production equipment and the physical characteristics of products respectively. For example, visual sensors can monitor changes in product appearance in the process link by collecting image data in real time; vibration sensors can detect the operating status of production equipment, such as whether excessive mechanical vibration occurs, so as to perform necessary equipment maintenance and adjustments. The data collected by the sensor will be transmitted to the data processing center in real time to ensure that problems in the production process can be discovered and handled in a timely manner.

[0166] Step S42: extracting sensor features from the real-time microphone production line sensor data, thereby obtaining production line visual sensor data and production line vibration sensor data;

[0167] In this embodiment, feature extraction is performed on the real-time collected production line sensor data, and valuable signals can be extracted from it. For visual sensor data, image processing algorithms can be used to extract edge features in the image, such as the outline and size of the product; and for vibration sensor data, spectrum analysis methods are used to extract key frequency components from the vibration signal to identify possible equipment anomalies in the production line. Through this data processing method, key features related to the production process can be effectively distinguished and extracted, thereby providing a reliable data basis for subsequent quality inspection and process optimization.

[0168] Step S43: performing product image edge detection of the process link of the production line according to the visual sensing data of the production line, thereby obtaining product edge data of the process link, and performing product contour integration on the product edge data of the process link, thereby obtaining product contour data of the process link;

[0169] In this embodiment, the contour of the product in the process is identified by performing edge detection on the product image of the production line visual sensor data. For example, the Canny edge detection algorithm is used to process the image collected by the sensor to obtain the edge data of the product, and the edge data is further integrated through algorithms such as Hough transform and edge connection to form complete product contour data. This ensures that any appearance defects caused by the production process can be identified early during the product production process, and provides a basis for subsequent defect analysis and correction.

[0170] Step S44: performing product defect identification based on the process link product profile data, thereby obtaining process link product defect data;

[0171] In this embodiment, defect recognition is performed based on the product profile data of the process link. Using a deep learning model, such as a convolutional neural network (CNN), it is possible to automatically identify various defects that occur in the production process of microphone products, including surface cracks, flaws, uneven colors, etc. During the model training process, by inputting a large number of images of normal products and defective products, their features are learned, and any minor defects can be automatically detected during the production process. For example, small scratches or color deviations on the surface of the product will be identified as defect data, and the specific defect location and type will be marked.

[0172] Step S45: performing process link vibration mode recognition on the production line vibration sensor data, thereby obtaining process link vibration mode data, and performing vibration signal abnormality analysis based on the process link vibration mode data, thereby obtaining process link abnormal vibration data;

[0173] In this embodiment, vibration mode recognition of the process link is performed based on the vibration sensor data of the production line. By combining time domain analysis and frequency domain analysis, it is possible to monitor whether the equipment is operating within the normal vibration frequency range. If abnormal fluctuations appear in the vibration data, these abnormal modes will be identified, and early warnings will be issued in time to indicate possible failures of the production equipment, such as bearing wear or loose parts. By identifying these problems in advance, the impact of equipment failures on production quality and efficiency can be effectively prevented. By performing process link vibration mode recognition on the vibration sensor data of the production line, the working status of the production equipment can be further analyzed. By using frequency domain analysis and time domain analysis, it is possible to identify whether the equipment has abnormal vibration modes, such as excessive frequency fluctuations or periodic mechanical anomalies. These abnormal vibration signals usually indicate that there is a problem with the equipment, such as bearing wear, loose mechanical parts, etc. Through abnormal analysis of vibration modes, early warnings can be issued to prevent equipment failures from affecting production efficiency.

[0174] Step S46: performing a process link intersection operation on the process link product defect data and the process link abnormal vibration data, thereby obtaining low-quality process link data;

[0175] In this embodiment, the intersection operation is performed on the process link product defect data and the vibration signal abnormal data to identify the low-quality process link. For example, when a certain production link has obvious product appearance defects, and the vibration data of this link also shows abnormalities, it indicates that there is a problem with the equipment in this link, resulting in a decline in product quality. By analyzing this correlation, the key low-quality links can be found, providing important quality improvement directions for production managers.

[0176] Step S47: Associating the process optimization data set with the low-quality process link data to obtain a quality inspection data set.

[0177] In this embodiment, the process optimization data set is associated with the low-quality links based on the low-quality process link data, so as to identify the quality bottleneck link. For example, if a specific process link is found to have frequent low-quality problems, it will be analyzed based on historical data to evaluate the production parameters of the link and identify the optimization space. By associating the low-quality process link with the process optimization data set, it is possible to provide targeted improvement plans, transmit the optimization results to the microphone production control platform, automatically adjust the production process, and further improve the overall production efficiency and product quality.

[0178] Optionally, step S5 specifically includes:

[0179] Step S51: Calculating the low quality frequency of the process link according to the quality inspection data set, thereby obtaining the low quality frequency data of the process link;

[0180] In this embodiment, the low-quality frequency of the process link is calculated based on the quality inspection data set, which requires the collection and analysis of quality inspection data of each production process link, which includes the defect type, number of defects and frequency of occurrence of each process link. For example, in the assembly link of microphones, sensors and visual inspection technology are used to capture the product quality information of each link in real time, and the low-quality frequency data of each process link is calculated by counting the number of low-quality products in each link. Through this data, it is possible to identify which process links have caused more quality problems in the production process, providing a basis for subsequent quality control and process optimization.

[0181] Step S52: quantifying the impact of quality fluctuations on the low-quality frequency data of the process link and the microphone production demand forecast data set, thereby obtaining the equipment load impact factor;

[0182] In this embodiment, the quality fluctuations of the low-quality frequency data of the process links and the microphone production demand forecast data set are quantified. It is necessary to count the low-quality frequencies obtained in the quality inspection data and identify which links have large frequency fluctuations, resulting in unstable production quality. Combined with the microphone production demand forecast data, the impact of quality fluctuations in different process links on the load of production equipment under different production demands and production plans is analyzed. For example, if the low-quality frequency of a certain industrial link increases during the peak demand period, the equipment will be overloaded, thereby affecting the long-term operating efficiency of the equipment. The equipment load influencing factor can be obtained through quantitative analysis, which can reflect the specific degree of impact of quality fluctuations on the equipment load.

[0183] Step S53: dividing the equipment load of the microphone production topology structure model into process links according to the equipment load influencing factor, thereby obtaining the equipment data of high load in the process link and the equipment data of low load in the process link;

[0184] In this embodiment, the microphone production topology model is divided into process link equipment loads according to the equipment load influencing factors. By analyzing the equipment load influencing factors, it is determined which equipment in the process link needs to operate at a high load and which can be allocated to low-load equipment. For example, in the production process, if it is found that a certain link (such as the testing link) has a high equipment load due to the frequent occurrence of low-quality products, it needs to upgrade or optimize the equipment, while other links (such as the packaging link) have a lighter load. Based on this, combined with the production topology, the process links with heavier loads can be allocated to equipment with stronger load capacity, while the links with lower loads can be allocated to equipment with lighter loads. Through such a division, it is possible to avoid equipment overload or waste of resources while ensuring production efficiency.

[0185] Step S54: Based on the high-load equipment data of the process link and the low-load equipment data of the process link, the process link equipment load is balanced to obtain the equipment scheduling optimization data set, and transmit it to the microphone production control platform to execute the equipment scheduling task.

[0186] In this embodiment, the load balancing of the process link equipment is performed based on the data of high-load equipment in the process link and the data of low-load equipment in the process link. By analyzing the load situation of each process link, it is determined whether the high-load equipment is close to its load limit and whether the low-load equipment has excess production capacity. Using a load balancing algorithm, such as a load balancer or a heuristic scheduling algorithm, the allocation plan of the equipment is optimized, and the burden of the overloaded process link is appropriately transferred to the equipment with a lower load. For example, if a high-load equipment is overloaded due to excessive quality fluctuations, it will automatically adjust and transfer part of the process link to other equipment for processing to avoid equipment failure or production delays. These optimized data sets (i.e., equipment scheduling optimization data sets) will be transmitted to the microphone production control platform, and the platform will readjust the work plan of the production line equipment according to the optimization plan, thereby achieving balanced scheduling of equipment and improving production efficiency.

[0187] Optionally, the present specification also provides a microphone production line intelligent control system, which is used to execute the microphone production line intelligent control method as described above, and the microphone production line intelligent control system includes:

[0188] A production structure analysis module is used to obtain microphone production line sensor data and microphone production process data, and perform microphone production topology structure analysis based on the microphone production process data, so as to obtain a microphone production topology structure model;

[0189] The production demand prediction module is used to perform production line sensor data fusion on the microphone production topology structure model and the microphone production line sensor data, so as to obtain the microphone production line sensor network; the production demand prediction is performed based on the microphone production line sensor network, so as to obtain the microphone production demand prediction data set;

[0190] The production process optimization module is used to optimize the production process parameters of the microphone production topology structure model according to the microphone production demand prediction data set, so as to obtain the process optimization data set and transmit it to the microphone production control platform to perform the microphone production process parameter adjustment task;

[0191] A product quality inspection module is used to collect real-time production line sensor data based on a microphone production line sensor network, thereby obtaining real-time microphone production line sensor data, and to inspect product quality in the production process based on the real-time microphone production line sensor data and a process optimization data set, thereby obtaining a quality inspection data set;

[0192] The production line equipment scheduling optimization module is used to optimize the production line equipment scheduling of the microphone production topology structure model according to the quality inspection data set and the microphone production demand prediction data set, so as to obtain the equipment scheduling optimization data set and transmit it to the microphone production control platform to perform equipment scheduling tasks.

[0193] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0194] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for intelligent control of a microphone production line, characterized in that: The following steps are involved: Step S1: Acquire sensor data of the microphone production line and microphone production process data, and perform microphone production topology structure analysis based on the microphone production process data to obtain a microphone production topology structure model; Step S1 is specifically as follows: Step S11: Acquire microphone production line sensor data and microphone production process data; Step S12: performing data preprocessing on the microphone production line sensor data and the microphone production process data respectively, so as to obtain the production line sensor data to be analyzed and the production process data to be analyzed; Step S13: extracting production line equipment sensor data from the production line sensor data to be analyzed, thereby obtaining production line equipment sensor data; Step S14: Arranging the equipment sensor data points in time sequence according to the production line equipment sensor data, thereby obtaining the production equipment operation time sequence data; Step S15: Perform production line topology analysis based on the production process data to be analyzed and the production equipment operation timing data, so as to obtain a microphone production topology model; Step S15 is specifically as follows: Step S151: dividing the production links based on the production process data to be analyzed, thereby obtaining production process link data; Step S152: extracting production equipment description features from the production process data to be analyzed, thereby obtaining production equipment description data; Step S153: performing equipment-link association docking on the production process link data and the production equipment operation sequence data according to the production equipment description data, thereby obtaining equipment-link docking data; Step S154: integrating the production link connection relationship based on the production process link data, thereby obtaining the production link connection relationship data, and mapping the production link connection relationship data according to the equipment-link docking data, thereby obtaining the production process link relationship data; Step S155: performing a production line topology analysis based on the production process link relationship data, thereby obtaining a microphone production topology model; Step S2: performing production line sensor data fusion on the microphone production topology model and the microphone production line sensor data, thereby obtaining the microphone production line sensor network; Production demand forecasting is performed based on the microphone production line sensor network, thereby obtaining a microphone production demand forecasting data set; Step S3: Optimize the production process parameters of the microphone production topology model according to the microphone production demand prediction data set, so as to obtain a process optimization data set, and transmit it to the microphone production control platform to perform the microphone production process parameter adjustment task; Step S4: Based on the microphone production line sensor network, real-time production line sensor data is collected to obtain real-time microphone production line sensor data, and product quality inspection of the production link is performed according to the real-time microphone production line sensor data and the process optimization data set to obtain a quality inspection data set; Step S4 is specifically: Step S41: collecting real-time production line sensor data based on the microphone production line sensor network, thereby obtaining real-time microphone production line sensor data; Step S42: extracting sensor features from the real-time microphone production line sensor data, thereby obtaining production line visual sensor data and production line vibration sensor data; Step S43: performing product image edge detection of the process link of the production line according to the visual sensing data of the production line, thereby obtaining product edge data of the process link, and performing product contour integration on the product edge data of the process link, thereby obtaining product contour data of the process link; Step S44: performing product defect identification based on the process link product profile data, thereby obtaining process link product defect data; Step S45: performing process link vibration mode recognition on the production line vibration sensor data, thereby obtaining process link vibration mode data, and performing vibration signal abnormality analysis based on the process link vibration mode data, thereby obtaining process link abnormal vibration data; Step S46: performing a process link intersection operation on the process link product defect data and the process link abnormal vibration data, thereby obtaining low-quality process link data; Step S47: Associating the process optimization data set with the low-quality process link data to obtain a quality inspection data set; Step S5: Optimize the production line equipment scheduling of the microphone production topology model according to the quality inspection data set and the microphone production demand prediction data set, so as to obtain the equipment scheduling optimization data set, and transmit it to the microphone production control platform to execute the equipment scheduling task.

2. The intelligent control method for microphone production line according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: integrating the sensor spatial deployment of the microphone production line sensor data, thereby obtaining sensor spatial deployment data; Step S22: performing spatiotemporal fusion of production line sensor data on the microphone production topology model and the microphone production line sensor data according to the sensor spatial deployment data, thereby obtaining a microphone production line sensor network; Step S23: acquiring microphone historical production data, and performing microphone production mode recognition on the microphone historical production data, thereby obtaining microphone historical production mode data; Step S24: integrating the production status of the production line based on the microphone production line sensor network, thereby obtaining real-time production status data of the microphone production line; Step S25: Perform production demand forecasting based on the historical production mode data of the microphone and the real-time production status data of the microphone production line, so as to obtain a microphone production demand forecasting data set.

3. The intelligent control method for microphone production line according to claim 2, characterized in that: Step S25 is specifically as follows: Step S251: performing microphone production process comparison on the microphone historical production data and the production process data to be analyzed, thereby obtaining microphone production process difference data, and quantifying the impact of the production process efficiency based on the microphone production process difference data, thereby obtaining a production efficiency impact factor; Step S252: performing periodic production pattern recognition on the microphone historical production pattern data, thereby obtaining periodic production pattern data; Step S253: performing microphone production mode recognition according to the real-time production status data of the microphone production line, thereby obtaining the real-time production mode data of the microphone; Step S254: Adaptively adjust the historical production mode efficiency of the periodic production mode data according to the production efficiency influencing factor, thereby obtaining the historical periodic production mode data; Step S255: performing production pattern matching on the historical periodic production pattern data and the real-time production pattern data of the microphone, so as to obtain production pattern similarity data; Step S256: Perform production demand prediction based on the production pattern similarity data, thereby obtaining a microphone production demand prediction data set.

4. The intelligent control method for microphone production line according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: Calculating the production efficiency of the microphone production topology model according to the real-time production status data of the microphone production line, thereby obtaining the production efficiency data of the microphone production link; Step S32: Calculating the expected production efficiency of microphones for the microphone production demand prediction data set, thereby obtaining the expected production efficiency data of microphones; Step S33: performing a production efficiency comparison on the production efficiency data of the microphone production link and the expected production efficiency data of the microphone, so as to obtain expected production efficiency difference data; Step S34: extracting microphone process link parameters according to the microphone production topology structure model, thereby obtaining microphone process link parameter data; Step S35: Optimize the microphone process parameter data according to the expected production efficiency difference data, so as to obtain a process optimization data set, and transmit it to the microphone production control platform to execute the microphone production process parameter adjustment task.

5. The intelligent control method for microphone production line according to claim 4, characterized in that: Step S35 is specifically as follows: Step S351: estimating the bottleneck of the microphone process step on the parameter data of the microphone process step, thereby obtaining the bottleneck data of the microphone process step; Step S352: classifying the real-time production status data of the microphone production line into process links according to the bottleneck data of the microphone process links, thereby obtaining the near-bottleneck process link data and the far-bottleneck process link data; Step S353: performing process link parameter association on the near-bottleneck process link data and the far-bottleneck process link data respectively according to the microphone process link parameter data, thereby obtaining the near-bottleneck process link parameter data and the far-bottleneck process link parameter data; Step S354: optimizing the production cycle of the process link according to the expected production efficiency difference data for the parameter data of the process link near the bottleneck, thereby obtaining the optimized parameter data of the first process link; Optimize the process equipment utilization rate of the process link parameter data far from the bottleneck according to the expected production efficiency difference data, so as to obtain the optimized parameter data of the second process link; Step S355: Couple the first process link optimization parameter data and the second process link optimization parameter data with the production process link parameters to obtain a process optimization data set, and transmit it to the microphone production control platform to execute the microphone production process parameter adjustment task.

6. The intelligent control method for microphone production line according to claim 1, characterized in that: Step S5 is specifically as follows: Step S51: Calculating the low quality frequency of the process link according to the quality inspection data set, thereby obtaining the low quality frequency data of the process link; Step S52: quantifying the impact of quality fluctuations on the low-quality frequency data of the process link and the microphone production demand forecast data set, thereby obtaining the equipment load impact factor; Step S53: dividing the equipment load of the microphone production topology structure model into process links according to the equipment load influencing factor, thereby obtaining the equipment data of high load in the process link and the equipment data of low load in the process link; Step S54: Based on the high-load equipment data of the process link and the low-load equipment data of the process link, the process link equipment load is balanced to obtain the equipment scheduling optimization data set, and transmit it to the microphone production control platform to execute the equipment scheduling task.

7. An intelligent control system for a microphone production line, characterized in that: Used to execute the intelligent control method for microphone production line according to claim 1, the intelligent control system for microphone production line comprises: A production structure analysis module is used to obtain microphone production line sensor data and microphone production process data, and perform microphone production topology structure analysis based on the microphone production process data, so as to obtain a microphone production topology structure model; The production demand prediction module is used to perform production line sensor data fusion on the microphone production topology structure model and the microphone production line sensor data, so as to obtain the microphone production line sensor network; the production demand prediction is performed based on the microphone production line sensor network, so as to obtain the microphone production demand prediction data set; The production process optimization module is used to optimize the production process parameters of the microphone production topology structure model according to the microphone production demand prediction data set, so as to obtain the process optimization data set and transmit it to the microphone production control platform to perform the microphone production process parameter adjustment task; A product quality inspection module is used to collect real-time production line sensor data based on a microphone production line sensor network, thereby obtaining real-time microphone production line sensor data, and to inspect product quality in the production process based on the real-time microphone production line sensor data and a process optimization data set, thereby obtaining a quality inspection data set; The production line equipment scheduling optimization module is used to optimize the production line equipment scheduling of the microphone production topology structure model according to the quality inspection data set and the microphone production demand prediction data set, so as to obtain the equipment scheduling optimization data set and transmit it to the microphone production control platform to perform equipment scheduling tasks.

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