An optical fiber production equipment collaborative control method and system based on an internet of things

Abstract

The application discloses a kind of based on Internet of Things's optical fiber production equipment collaborative control method and system, it is related to optical fiber production equipment control field, real-time acquisition temperature distribution data and preform material parameter, determine real-time temperature gradient and material change index, then combine distributed computing framework analysis temperature gradient deviation and influence between equipment, obtain temperature gradient deviation value and the influence coefficient between adjacent wire drawing equipment.When temperature gradient deviation is out of limit, neural network model predicts the influence of material change on temperature distribution, generates heating power parameter and wire drawing speed adjustment parameter, implements collaborative control to optimize temperature distribution, to generate production line temperature distribution optimization scheme, to the production speed of wire drawing equipment is collaboratively controlled, significantly improve production stability and optical fiber quality, maintain the overall stability of production line temperature gradient and wire drawing speed.

Description

Technical Field

[0001] This invention relates to the field of optical fiber production equipment control technology, and in particular to a collaborative control method and system for optical fiber production equipment based on the Internet of Things. Background Technology

[0002] As a pillar industry in modern communication and information technology, optical fiber manufacturing directly impacts the stability and efficiency of data transmission. With the surge in global demand for high-speed networks, the refinement and large-scale production of optical fibers have become crucial for the industry's development. However, existing production equipment control methods often struggle to achieve dynamic coordination among multiple devices in complex production environments, leading to fluctuations in product quality, particularly when faced with changes in raw materials or environmental disturbances, resulting in insufficient production stability. This deficiency not only increases the scrap rate but may also affect the performance of optical fibers in extreme environments, becoming a bottleneck restricting the industry's efficient development. During the optical fiber drawing process, temperature control is a core element in ensuring the uniformity of fiber diameter and stable performance.

[0003] Traditional methods typically rely on the independent adjustment of a single device, lacking the ability to coordinate responses between multiple devices. When the material of the preform changes or the external ambient temperature fluctuates, it is difficult to maintain a consistent temperature distribution across the drawing furnaces. This inconsistency can lead to an imbalance in the temperature gradient, causing some devices to overheat or undercool, thus affecting the matching of drawing speeds. The mismatch between temperature and speed can directly cause minute deviations in the fiber diameter, which can lead to signal attenuation in high-speed communication, severely impacting product quality. A deeper challenge lies in the fact that the operating states of multiple drawing devices on the production line influence each other, but current technology lacks mechanisms for real-time monitoring and dynamic adjustment. Uneven temperature distribution stems not only from insufficient control precision of individual devices but also from the lack of coordinated control and linkage between devices.

[0004] For example, if a drawing furnace experiences an abnormal temperature rise due to a change in material, and adjacent equipment fails to adjust its heating power or drawing speed in time, the temperature gradient of the entire production line will deviate from the optimal range. This chain reaction makes it difficult for the production line to maintain stable process parameters, resulting in inconsistent fiber quality.

[0005] Therefore, how to dynamically adapt to changes in preform material and fluctuations in ambient temperature through real-time monitoring and multi-device coordinated control, and maintain the overall stability of the temperature gradient and drawing speed of the production line, has become a key issue in the field of optical fiber production equipment control. Summary of the Invention

[0006] This invention provides a collaborative control method and system for optical fiber production equipment based on the Internet of Things, which enables dynamic adaptation to changes in preform material and fluctuations in ambient temperature through real-time monitoring and collaborative control of multiple devices, thereby maintaining the overall stability of the production line temperature gradient and drawing speed.

[0007] This invention provides a collaborative control method for optical fiber production equipment based on the Internet of Things, executed by a computer, comprising:

[0008] The real-time temperature gradient is determined based on the temperature distribution data of each wire drawing equipment, and the material change index is determined based on the preform material parameter information of each wire drawing equipment.

[0009] The temperature status of the current production line is determined based on the real-time temperature gradient, and the material adaptation requirement value is determined based on the material change index.

[0010] Based on the temperature conditions and material adaptation requirements, a distributed computing framework is used to process the temperature data correlation between each drawing device, and to determine the temperature gradient deviation value and the influence coefficient between adjacent drawing devices.

[0011] If the temperature gradient deviation value exceeds the preset deviation threshold, then based on the influence coefficient, the potential impact of material change on temperature distribution is predicted by a neural network model to obtain heating power parameters and wire drawing speed correction parameters.

[0012] Based on the heating power parameters and the drawing speed correction parameters, collaborative control and optimization are performed on each drawing device to obtain an optimized temperature distribution scheme for the production line.

[0013] Based on the temperature distribution optimization scheme of the production line, the linkage control logic between each wire drawing equipment is updated to coordinate the production speed of the wire drawing equipment.

[0014] According to the IoT-based collaborative control method for optical fiber production equipment of the present invention, if the temperature gradient deviation value exceeds a preset deviation threshold, then based on the influence coefficient, a neural network model is used to predict the potential impact of material changes on the temperature distribution, thereby obtaining heating power parameters and drawing speed correction parameters, including:

[0015] If the temperature gradient deviation exceeds a preset deviation threshold, the temperature gradient deviation and the material parameter information of the preform are analyzed by a neural network model to obtain the temperature distribution prediction result.

[0016] Based on the temperature distribution prediction results, the heating power parameters and the adjustment range of the heating power parameters are determined;

[0017] Based on the adjustment range, an initial correction value for the wire drawing speed is determined;

[0018] If the initial correction value does not meet the preset wire drawing speed value required for stable production, the wire drawing speed is adjusted through an iterative optimization algorithm to determine the wire drawing speed correction parameter.

[0019] According to the IoT-based collaborative control method for optical fiber production equipment of the present invention, the step of performing collaborative regulation and optimization on each drawing device based on the heating power parameter and the drawing speed correction parameter to obtain an optimized temperature distribution scheme for the production line includes:

[0020] Based on the heating power parameters and the wire drawing speed correction parameters, the operating data of each wire drawing device is collected in real time to obtain the status of the wire drawing furnace.

[0021] The state of the drawing furnace is compared with the preset production line temperature consistency requirements to identify abnormal drawing equipment with abnormal temperature.

[0022] Based on the abnormal wire drawing equipment, generate collaborative control commands;

[0023] Based on the aforementioned coordinated control command, the heating power parameter and the drawing speed correction parameter are adjusted to obtain a process parameter adjustment scheme.

[0024] Based on the process parameter adjustment scheme, the coordinated control command is sent to each wire drawing equipment to obtain the control execution efficiency and the production line temperature data corresponding to the control execution efficiency.

[0025] Based on the temperature data of the production line, a temperature distribution optimization scheme is generated.

[0026] According to the IoT-based collaborative control method for optical fiber production equipment of the present invention, the step of updating the linkage control logic between each drawing device based on the temperature distribution optimization scheme of the production line to collaboratively control the production speed of the drawing device includes:

[0027] Based on the temperature distribution optimization scheme of the production line, the linkage control logic between each wire drawing equipment is updated to determine the dynamic adjustment range of speed matching;

[0028] If the dynamic adjustment range is inconsistent, a support vector machine algorithm is used to analyze the interference factors of environmental fluctuations on speed matching and determine the speed synchronization compensation value between the wire drawing equipment.

[0029] The production line speed is calibrated as a whole based on the speed synchronization compensation value, the calibrated diameter measurement data is obtained, and the uniformity index of the fiber diameter is determined based on the diameter measurement data.

[0030] The effectiveness of multi-device collaborative response is verified based on the uniformity index, and the production stability confirmation result is obtained.

[0031] If the production stability confirmation result is passed, the linkage control logic between each wire drawing device is updated to coordinate the production speed of the wire drawing device.

[0032] According to the IoT-based collaborative control method for optical fiber production equipment of the present invention, the step of updating the linkage control logic between each drawing device based on the temperature distribution optimization scheme of the production line and determining the dynamic adjustment range of speed matching includes:

[0033] Based on the production line temperature distribution optimization scheme, real-time temperature monitoring data is obtained to determine the ambient temperature value.

[0034] If the ambient temperature value exceeds the preset temperature threshold, the operating speed of the equipment is adjusted through control logic to obtain speed adjustment parameters;

[0035] Based on the speed adjustment parameters, a distributed control algorithm is used to update the device linkage status and determine the cooperative operation mode;

[0036] Based on the aforementioned collaborative operation mode, real-time feedback data between the wire drawing devices is obtained;

[0037] If the data consistency index of the real-time feedback data is lower than the preset standard index, the temperature monitoring data is corrected by the data processing module to obtain the corrected temperature data.

[0038] Based on the corrected temperature data, a dynamic programming algorithm is used to optimize the speed matching range and determine the dynamic adjustment range.

[0039] According to the IoT-based collaborative control method for optical fiber production equipment of the present invention, if the dynamic adjustment range shows inconsistency, a support vector machine algorithm is used to analyze the interference factors of environmental fluctuations on speed matching and determine the speed synchronization compensation value between drawing equipment, including:

[0040] If the dynamic adjustment range is inconsistent, environmental fluctuation data is collected to obtain an environmental variable dataset, wherein the environmental variable dataset includes real-time values ​​of temperature, humidity, and vibration frequency.

[0041] Based on the environmental variable dataset, the mapping relationship between environmental fluctuations and speed synchronization deviation is determined by the support vector machine algorithm, and the prediction model and the output deviation of the prediction model are obtained.

[0042] If the output deviation exceeds a preset deviation threshold, the feature weights of the prediction model are adjusted to obtain optimized model parameters.

[0043] Based on the prediction model corresponding to the optimized model parameters, the interference of real-time environmental variables on speed matching is analyzed, and the weight distribution of interference factors is obtained.

[0044] Based on the weight distribution of the interference factors, the speed synchronization compensation value between the wire drawing equipment is determined.

[0045] This invention also proposes a collaborative control system for optical fiber production equipment based on the Internet of Things, comprising:

[0046] The index determination module is used to determine the real-time temperature gradient based on the temperature distribution data of each wire drawing equipment, and to determine the material change index based on the preform material parameter information of each wire drawing equipment.

[0047] The status determination module is used to determine the temperature status of the current production line based on the real-time temperature gradient, and to determine the material adaptation requirement value based on the material change index.

[0048] The influence coefficient determination module is used to process the temperature data correlation between each drawing device based on the temperature state and material adaptation requirements, and to determine the temperature gradient deviation value and the influence coefficient between adjacent drawing devices using a distributed computing framework.

[0049] The parameter determination module is used to predict the potential impact of material changes on temperature distribution based on the influence coefficient and a neural network model if the temperature gradient deviation value exceeds a preset deviation threshold, thereby obtaining heating power parameters and wire drawing speed correction parameters.

[0050] The control and optimization module is used to perform coordinated control and optimization on each wire drawing equipment based on the heating power parameters and the wire drawing speed correction parameters, so as to obtain an optimized temperature distribution scheme for the production line.

[0051] The collaborative control module is used to update the linkage control logic between each wire drawing device based on the temperature distribution optimization scheme of the production line, so as to collaboratively control the production speed of the wire drawing device.

[0052] This invention provides an IoT-based collaborative control method and system for optical fiber production equipment. Addressing the issue of uneven temperature distribution, insufficient material adaptability, and speed synchronization deviations between equipment leading to fiber diameter uniformity problems, the method collects real-time temperature distribution data and preform material parameters to determine real-time temperature gradients and material change indicators. Then, it uses a distributed computing framework to analyze temperature gradient deviations and inter-equipment influences, obtaining temperature gradient deviation values ​​and influence coefficients between adjacent drawing equipment. This allows for accurate assessment of production line stability through temperature gradient deviations and influence coefficients. When the temperature gradient deviation exceeds the limit, a neural network model predicts the impact of material changes on temperature distribution, generating heating power parameters and drawing speed adjustment parameters. Collaborative control is then implemented to optimize temperature distribution, generating a production line temperature distribution optimization scheme. Finally, through the optimized production line temperature distribution scheme, the linkage control logic between each drawing equipment is updated to collaboratively control the production speed of the drawing equipment, significantly improving production stability and optical fiber quality. This solves the problems of complex and inefficient control in traditional production lines, achieving dynamic adaptation to preform material changes and environmental temperature fluctuations through real-time monitoring and multi-equipment collaborative control, maintaining the overall stability of the production line temperature gradient and drawing speed. Attached Figure Description

[0053] Figure 1 This is one of the flowcharts illustrating a collaborative control method for optical fiber production equipment based on the Internet of Things provided in this embodiment of the invention;

[0054] Figure 2 This is a second schematic flowchart of a collaborative control method for optical fiber production equipment based on the Internet of Things provided in this embodiment of the invention;

[0055] Figure 3 This is the third flowchart illustrating a collaborative control method for optical fiber production equipment based on the Internet of Things provided in this embodiment of the invention;

[0056] Figure 4 This is the fourth flowchart illustrating a collaborative control method for optical fiber production equipment based on the Internet of Things, provided in an embodiment of the present invention.

[0057] Figure 5 This is the fifth flowchart illustrating a collaborative control method for optical fiber production equipment based on the Internet of Things, provided in an embodiment of the present invention.

[0058] Figure 6 This is the sixth flowchart illustrating a collaborative control method for optical fiber production equipment based on the Internet of Things, provided in this embodiment of the invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Reference Figure 1 This invention provides a collaborative control method for optical fiber production equipment based on the Internet of Things, comprising the following steps:

[0061] Step 100: Determine the real-time temperature gradient based on the temperature distribution data of each wire drawing equipment, and determine the material change index based on the preform material parameter information of each wire drawing equipment.

[0062] A collaborative analysis and dynamic evaluation of the temperature status of the wire drawing equipment and the material properties of the preforms are conducted to determine the real-time temperature gradient of the wire drawing equipment and to identify material change indicators. Specifically, firstly, distributed temperature sensors deployed on each wire drawing machine continuously collect multi-dimensional, high-density temperature field data within the heating furnace, obtaining temperature distribution data. This temperature distribution data is used for panoramic temperature sensing of the production environment. For example, temperature distribution data is aggregated and fused through an IoT platform to construct a dynamic temperature distribution model that reflects the changing patterns of the thermal field within the wire drawing furnace. Based on this dynamic temperature distribution model, the current temperature distribution data of the wire drawing equipment is obtained, and the rate of temperature difference change between adjacent sensing areas or along the preform axis is calculated to obtain the real-time temperature gradient. The real-time temperature gradient not only characterizes the temperature distribution characteristics of the heating area.

[0063] Simultaneously, the material parameter information of the preform from the material management unit is processed in parallel. This material parameter information can include key attributes such as glass system composition, dopant ion concentration, geometric tolerances, and refractive index profile. Therefore, by establishing a mapping relationship between material parameters and the ideal drawing state, a material variation index can be constructed. This index quantifies the degree of matching between the current physicochemical properties of the preform and the standard process window, and reflects the instability risk in the drawing process caused by material fluctuations.

[0064] Step 200: Determine the temperature status of the current production line based on the real-time temperature gradient, and determine the material adaptation requirement value based on the material change index;

[0065] After obtaining the real-time temperature gradient and material change indicators, a pre-defined thermodynamic knowledge model is used to comprehensively determine and classify the temperature state of the entire production line based on the real-time temperature gradient, thus obtaining the current temperature state of the production line. The temperature state of the production line includes the working state of each drawing device and the heating state of the internal optical fiber. Therefore, this temperature state determination is a multi-dimensional evaluation process used to describe the health and stability of the drawing device. For example, if the temperature gradient is gentle and stable, the current working state of the drawing device may be judged as an ideal forming state, indicating that the optical fiber is undergoing a uniform and controlled cooling process and the internal structural integrity of the optical fiber is good. If the temperature gradient has local drastic changes or deviates from the baseline, the current working state of the drawing device may be identified as a state of thermal stress risk or a state of suboptimal energy consumption. Therefore, this generation of temperature state transforms the raw physical quantity data into the internal temperature state of the drawing device that can directly guide production, thus providing a clear target and direction for adjusting the control strategy.

[0066] Simultaneously, the material change index is analyzed to further determine the material adaptation requirement value. The calculation of this material adaptation requirement value is a dynamic mapping process, transforming static indicators characterizing material properties into dynamic control commands that guide the drawing equipment. Therefore, the material change index indicates specific material properties, such as thermal response sensitivity or viscoelastic threshold. The material adaptation requirement value is essentially a quantitative control driving force, clearly indicating the adaptive changes made by the current production line to preform material variations in parameters such as temperature setting, traction speed, or cooling rate. For example, when the material change index indicates a high doping concentration in the preform core layer, the system may output a corresponding temperature rise adaptation requirement value, instructing the heating furnace to provide more energy to overcome the increase in the material's softening point.

[0067] Step 300: Based on the temperature state and material adaptation requirements, a distributed computing framework is used to process the temperature data correlation between each drawing device, and to determine the temperature gradient deviation value and the influence coefficient between adjacent drawing devices.

[0068] After completing the diagnosis of the overall temperature status of the production line and the quantification of material adaptation requirements, in order to handle the massive data and dynamic correlations across equipment, a distributed computing framework is adopted to process the temperature status and material adaptation requirements. It can be understood that the distributed computing framework can be understood as the digital nervous system of the production line. While the edge computing nodes of each wire drawing equipment process data, they continuously interact with the central coordination unit through efficient communication protocols to achieve continuous information exchange and task collaboration, thereby optimizing the computing load and improving the response speed.

[0069] The distributed computing framework is essentially used to deeply mine and quantify the correlation of temperature data among various wire drawing machines. By analyzing the spatiotemporal variation sequences of the temperature fields of adjacent or even the entire wire drawing line, it can accurately identify how temperature fluctuations caused by process parameter adjustments or internal disturbances of a particular wire drawing machine have a conductive impact on the temperature stability of its upstream, downstream, or neighboring machines through media such as thermal radiation, conduction, or shared environmental media. Therefore, based on the deep correlation analysis of the distributed computing framework, a temperature gradient deviation value is generated. It should be noted that this temperature gradient deviation value reflects the degree of deviation of each wire drawing machine's real-time temperature gradient from its optimal gradient within the collaborative production line, under the current material adaptation requirements and overall temperature conditions. Therefore, the generated temperature gradient deviation value can accurately locate weak points in the process consistency of the wire drawing machines on the production line.

[0070] Furthermore, the distributed computing framework can also calculate the influence coefficient between adjacent wire drawing equipment based on temperature conditions and material adaptation requirements. For example, it can use complex correlation algorithms and historical operating data models to calculate this influence coefficient. This influence coefficient is a dynamically changing weighting factor that quantifies the intensity and direction of the mutual influence between any two adjacent devices. For instance, when the heating power of a wire drawing device changes significantly, the impact amplitude and phase delay of this change on the temperature field of its downstream wire drawing equipment can be accurately predicted based on the pre-calculated influence coefficient.

[0071] Step 400: If the temperature gradient deviation value exceeds the preset deviation threshold, then based on the influence coefficient, the potential impact of material change on temperature distribution is predicted by a neural network model to obtain heating power parameters and wire drawing speed correction parameters.

[0072] When the temperature gradient deviation of a certain wire drawing equipment exceeds the preset deviation threshold, it indicates that the current production state deviates from the ideal process window. Conventional local fine-tuning is insufficient to cope with the complex operating conditions caused by the combined effects of material changes and inter-equipment coupling. Therefore, the inter-equipment influence coefficient determined in the previous steps is used as one of the key inputs. Since the influence coefficient characterizes the path and intensity of the propagation of disturbances at any node on the production line, it provides a causal relationship diagram for understanding this "chain reaction." Therefore, by calling a neural network model trained on massive amounts of historical process data, it can handle highly nonlinear relationships that are difficult to model accurately in traditional control methods. This neural network model can simulate and predict how material changes interact with the temperature field in the future based on the currently captured material change indicators and deeply integrate the dynamic correlations between equipment described by the influence coefficient. This predicts the impact of material changes on the temperature distribution of the wire drawing equipment on the production line, and the output prediction results include heating power parameters and wire drawing speed correction parameters.

[0073] Among them, the heating power parameter and the drawing speed correction parameter are the result of multi-dimensional optimization and trade-offs performed by the neural network model in a vast solution space. For example, in order to compensate for the accelerated thermal process caused by the decrease in the material softening point, it is not only necessary to appropriately reduce the heating power of the current problematic equipment to curb the overheating trend, but also to coordinately fine-tune the drawing speed of its upstream and downstream equipment to balance the fiber tension changes that may be caused by the power adjustment, thereby ensuring that the stability of the fiber diameter and the integrity of the structure are maintained while correcting the temperature gradient.

[0074] Step 500: Based on the heating power parameters and the drawing speed correction parameters, perform coordinated control optimization on each drawing device to obtain an optimized temperature distribution scheme for the production line;

[0075] After generating the heating power parameters and drawing speed correction parameters, to ensure that the parameter adjustments for a single drawing device can not only accurately correct its own temperature gradient deviation but also achieve positive synergy with the operating status of other devices on the production line, a multi-objective optimization algorithm can be used. This algorithm focuses on improving the uniformity of temperature distribution across the entire line, ensuring product quality consistency, and optimizing overall energy consumption. The heating power parameters and drawing speed correction parameters are used as inputs, while simultaneously considering the dynamic coupling relationship between devices described by the influence coefficient. By simulating the temperature distribution response under different parameter combinations, an optimized temperature distribution scheme for the production line is calculated. This optimized scheme includes the optimal power setting and optimal drawing speed values ​​that each drawing device should execute, i.e., the target heating power and the target drawing speed. Therefore, by moderately adjusting the drawing speed of upstream devices to match changes in the heating power of downstream devices, the stability of fiber tension can be maintained. Alternatively, while correcting the power of problematic devices, the power of adjacent devices can be slightly pre-adjusted as compensation to proactively offset known inter-device influences and prevent corrective intervention from causing new secondary disturbances.

[0076] It should be noted that the target heating power and target drawing speed are a set of parameters calculated through global optimization, systematically integrated and encapsulated to form a complete and executable production line temperature distribution optimization scheme. Furthermore, this production line temperature distribution optimization scheme can be a dynamic, interconnected set of control instructions, which includes how each drawing device on the production line should adjust its behavior in a synchronized manner. This allows the production line to intelligently and efficiently return to the optimal process trajectory from non-ideal states caused by material fluctuations or external interference, ensuring that optical fiber products continuously meet the required physical and optical performance indicators.

[0077] Step 600: Based on the temperature distribution optimization scheme of the production line, update the linkage control logic between each wire drawing device to coordinate the production speed of the wire drawing device.

[0078] After generating a global production line temperature distribution optimization scheme, this scheme includes parameter settings for each drawing machine on the production line. Then, the new power and speed matching relationships between the drawing machines, extracted from the optimization scheme, are refined into updated collaborative rules and response strategies. These collaborative rules are injected in real-time into the control nodes of each device within the IoT architecture, thereby refreshing their interconnection logic and enabling collaborative control of the production speed of each drawing machine on the production line. For example, the new logic might specify that when a drawing machine in the production line needs to increase its heating power due to a change in the material of its preform, the two upstream adjacent machines must pre-adjust their speeds slightly according to a certain proportion of their current drawing speed to cope with the potential change in glass viscosity caused by the temperature increase, thus jointly maintaining constant fiber tension.

[0079] Ultimately, the updated linkage control logic is used for precise coordinated control of the production speed of all drawing equipment. Based on this updated logic, the control system controls the heating power of each drawing machine, more accurately coordinating their drawing speeds to ensure the balance of the entire production line. This transforms the production line from a passively executing set of mechanical components into a whole capable of actively maintaining internal balance and dynamically damping external disturbances. By updating the linkage control logic in real time based on material and temperature changes, the optical fiber production system acquires a self-optimizing intelligence, enabling it to consistently anchor the process state within the optimal range under varying production conditions.

[0080] This invention provides an IoT-based collaborative control method for optical fiber production equipment. Addressing the issue of uneven temperature distribution, insufficient material adaptability, and speed synchronization deviations between equipment leading to fiber diameter uniformity problems, the method collects real-time temperature distribution data and preform material parameters to determine real-time temperature gradients and material change indicators. Then, it uses a distributed computing framework to analyze temperature gradient deviations and inter-equipment influences, obtaining temperature gradient deviation values ​​and influence coefficients between adjacent drawing equipment. This allows for accurate assessment of production line stability through temperature gradient deviations and influence coefficients. When the temperature gradient deviation exceeds the limit, a neural network model predicts the impact of material changes on temperature distribution, generating heating power parameters and drawing speed adjustment parameters. Collaborative control is then implemented to optimize temperature distribution, generating a production line temperature distribution optimization scheme. Finally, through this optimized scheme, the linkage control logic between drawing equipment is updated to collaboratively control the production speed, significantly improving production stability and fiber quality. This solves the problems of complex and inefficient control in traditional production lines, achieving dynamic adaptation to preform material changes and environmental temperature fluctuations through real-time monitoring and multi-equipment collaborative control, maintaining the overall stability of the production line temperature gradient and drawing speed.

[0081] In one embodiment, based on the temperature state and material adaptation requirements, a distributed computing framework is used to process the temperature data correlation between each drawing device, and to determine the temperature gradient deviation value and the influence coefficient between adjacent drawing devices.

[0082] The purpose of this step is to transform the raw, heterogeneous temperature data into high-value information that can accurately guide collaborative control. First, the temperature distribution data collected from various wire drawing equipment is standardized and preprocessed to eliminate the problem of inconsistent dimensions and benchmarks caused by individual differences in sensors or local environmental fluctuations, thereby forming a clean and comparable standardized temperature dataset.

[0083] Subsequently, leveraging the computing power of a distributed computing framework, the generated standardized dataset is efficiently processed in parallel across partitions. This step does not treat the entire production line as a chaotic whole, but rather intelligently divides it into multiple logical temperature-affected zones, and performs parallel computing on the real-time temperature differences between devices within each zone. By analyzing these spatial differences, a temperature gradient distribution map is constructed that accurately depicts the steepness of thermal field changes. This temperature gradient distribution map reveals the flow trends and concentration of heat energy on the production line, providing intuitive data visualization for identifying unstable areas in the process.

[0084] When the temperature gradient distribution in a certain area exceeds a preset safety threshold, it indicates a potential risk of thermal stress concentration or energy waste. To handle this anomaly, a weighted averaging algorithm is used to dynamically adjust the weights of each temperature data point in the calculation, considering its historical stability and spatial importance. This automatically suppresses data points affected by transient noise, thus enhancing the data reflecting the true process trend. By adjusting the weights of each temperature gradient using the weighted averaging algorithm, a more reliable corrected temperature gradient value is output, filtered for noise and enhanced for trend, significantly improving the robustness of state awareness.

[0085] Finally, using the corrected temperature gradient value as input, an in-depth analysis of the synchronicity and directionality of temperature changes between any adjacent wire drawing equipment is conducted. This allows for the quantification of the extent to which the temperature fluctuations of one piece of equipment can be explained by the fluctuations of its neighboring equipment, thereby accurately determining the influence coefficient between the equipment. It should be noted that this influence coefficient is a key indicator that is dynamically calculated based on real-time data and accurately reflects the thermodynamic coupling strength between the equipment.

[0086] For example, in a production line, temperature data for each piece of equipment is collected in real time using IoT sensors. For instance, equipment A has a temperature of 85.2 degrees Celsius, equipment B has 78.5 degrees Celsius, and equipment C has 92.1 degrees Celsius. This data reflects the distribution of thermal stress under high-temperature processing conditions. Simultaneously, material adaptability requirements are considered; for example, the heat resistance threshold for aluminum alloy is 90 degrees Celsius, and for steel, it is 95 degrees Celsius. A threshold comparison algorithm determines that the temperatures of equipment A and C are approaching or exceeding the aluminum alloy's limit, requiring priority adjustment to avoid material deformation. The temperature gradient deviation is calculated using the finite difference method, as shown in the following formula:

[0087]

[0088] in, For equipment spacing, when When the gradient is 1 meter, the calculated gradient from A to B is 6.7 degrees / meter, and the gradient from B to C is -13.6 degrees / meter. The deviation is calculated to be 8.4 degrees / meter using the root mean square error compared to the ideal gradient of 3 degrees / meter, revealing the risk of local overheating. Simultaneously, principal component analysis is used to extract the influence coefficients of adjacent equipment, decomposing the correlation matrix into principal components. The influence coefficient of equipment C on A is 0.65, and the influence coefficient of A on B is 0.42, quantifying the impact of heat conduction to optimize the cooling strategy. Finally, a stability index model is constructed based on these parameters, and the weighted summation method is used for calculation. The formula is as follows:

[0089]

[0090] Among them, weight =0.6、 =0.4, the calculated stability index is =0.72, indicating that the overall stability is at a medium level. It is necessary to activate the automatic air cooling system to reduce the temperature of C to 88 degrees to increase S to 0.85 and ensure that the production line runs continuously without interruption.

[0091] In this embodiment, the global complex computing task is decomposed into distributed parallel processing, which greatly improves the processing efficiency of massive temperature data and the real-time response speed of the system. Furthermore, correlation analysis is dynamically applied to the quantification of the influence coefficients between devices, enabling the collaborative control strategy to be constructed based on the real, time-varying interaction relationships between devices, thereby improving the adaptive and collaborative optimization capabilities of the entire production line in dealing with complex working conditions.

[0092] In one embodiment, please refer to Figure 2 If the temperature gradient deviation value exceeds a preset deviation threshold, then based on the influence coefficient, a neural network model is used to predict the potential impact of material changes on the temperature distribution, obtaining heating power parameters and wire drawing speed correction parameters, including:

[0093] Step 401: If the temperature gradient deviation exceeds a preset deviation threshold, the temperature gradient deviation and the material parameter information of the preform are analyzed by a neural network model to obtain a temperature distribution prediction result.

[0094] Step 402: Based on the temperature distribution prediction results, determine the heating power parameters and the adjustment range of the heating power parameters;

[0095] Step 403: Based on the adjustment range, determine the initial correction value for the wire drawing speed;

[0096] Step 404: If the initial correction value does not meet the preset wire drawing speed value required for stable production, the wire drawing speed is adjusted by an iterative optimization algorithm to determine the wire drawing speed correction parameter.

[0097] When the temperature gradient deviation is determined to exceed a preset threshold, a deep fusion prediction and optimization intelligent control process is initiated. First, the temperature gradient deviation, representing the current abnormal operating state, along with the preform material parameters, is input into a fully trained neural network model. Leveraging its powerful nonlinear mapping capabilities, the neural network model can simulate how the current temperature deviation, under existing material conditions, will affect the temperature distribution of the entire production line over future periods, thus generating a high-precision temperature distribution prediction. Next, based on the temperature distribution prediction, the parameter decision stage begins. According to the difference between the predicted temperature distribution and the ideal process window, the heating power parameters of each drawing device required to guide the thermal field back to stability are calculated. Simultaneously, the adjustment range of the power parameters is determined. This adjustment range is not a fixed value but a dynamic variable that comprehensively considers the urgency of the correction effect, equipment response characteristics, and energy efficiency.

[0098] After determining the adjustment range of the heating power parameter, considering the strong inherent coupling between temperature and speed in the fiber drawing process, changes in heating power will inevitably affect the viscosity and forming process of the optical fiber. Therefore, based on the previously calculated power adjustment range, an initial correction value for the drawing speed corresponding to the new thermal field state is derived through the embedded process knowledge model. However, this initial value may only be based on the perspective of temperature balance and may not meet the stringent requirements for the overall dynamic stability of the production line. Therefore, a key verification and optimization step is introduced: if the initial correction value of the drawing speed deviates from the preset speed range required to ensure production stability, an iterative optimization algorithm is initiated. Starting from the initial correction value, it can quickly search and weigh multiple constraints such as temperature control objectives, constant fiber tension, and equipment physical limits, and finally output a globally optimal drawing speed correction parameter. This drawing speed correction parameter can unify the requirements of temperature control and production speed stability.

[0099] In this embodiment, a neural network model is used to anticipate and avoid potential quality risks. Furthermore, an iterative optimization algorithm is used to make coordinated decisions on two key parameters, heating power and drawing speed, ensuring that any control command is the optimal solution under multiple production objectives. This enhances the robustness and adaptability of the production line in the face of material fluctuations and improves the quality consistency of high-end optical fiber products.

[0100] In one embodiment, please refer to Figure 3 The step of performing coordinated control and optimization on each wire drawing device based on the heating power parameters and the wire drawing speed correction parameters to obtain an optimized temperature distribution scheme for the production line includes:

[0101] Step 501: Based on the heating power parameters and the drawing speed correction parameters, collect the operating data of each drawing device in real time to obtain the drawing furnace status;

[0102] Step 502: Compare the state of the drawing furnace with the preset production line temperature consistency requirements to identify abnormal drawing equipment with abnormal temperature.

[0103] Step 503: Generate a coordinated control command based on the abnormal wire drawing device;

[0104] Step 504: Based on the coordinated control command, adjust the heating power parameter and the drawing speed correction parameter to obtain a process parameter adjustment scheme;

[0105] Step 505: Based on the process parameter adjustment scheme, issue the coordinated control command to each wire drawing equipment, and obtain the control execution efficiency and the production line temperature data corresponding to the control execution efficiency;

[0106] Step 506: Based on the production line temperature data, generate a temperature distribution optimization scheme.

[0107] Based on the determined heating power parameters and fiber drawing speed correction parameters, real-time operating data from all fiber drawing equipment is collected, including but not limited to the current power of heating elements, the real-time fiber drawing speed, furnace pressure, and key temperature points. This multi-source data is fused and processed to determine the furnace status. Then, this real-time furnace status is precisely compared with the preset production line temperature consistency requirements. This comparison process is a pattern recognition process based on process rules, used to locate abnormal fiber drawing equipment that deviates from expectations and may cause quality problems within the temperature field.

[0108] Based on the identified abnormal wire drawing equipment, the system does not adjust it in isolation. Instead, it generates corresponding coordinated control commands according to the collaborative relationships within the production network. These commands integrate previously determined inter-equipment influence coefficients, ensuring that the adjustment strategy for a single piece of equipment has already considered its cascading effects on upstream and downstream equipment. Subsequently, based on the coordinated control commands, the initial heating power parameters and wire drawing speed correction parameters are further optimized and fine-tuned, ultimately forming a comprehensive and executable process parameter adjustment plan. This plan can restore overall temperature consistency with minimal system disturbance. As the process parameter adjustment plan is issued, the execution efficiency of the commands is acquired in real time, and the resulting changes in production line temperature data are collected. Based on this real-time production line temperature data reflecting the control effect, a new round of analysis and optimization is performed, dynamically generating and iterating its temperature distribution optimization plan, thereby driving the entire control system into a self-learning, continuously improving intelligent cycle.

[0109] Specifically, on the wire drawing production line, temperature sensor data and power meter readings for each drawing furnace are first collected through a real-time monitoring system. A PID control algorithm is then used to calculate the target heating power. For example, based on the current production line speed set at 15 m / min, historical data analysis reveals temperature fluctuations of up to 2.5 degrees Celsius. Therefore, the proportional gain Kp is adjusted to 0.8, the integral time Ti to 120 seconds, and the derivative time Td to 25 seconds. Iterative calculation yields an optimized power of 85 kW to ensure uniform heating of the molten glass. Subsequently, to obtain the wire drawing speed correction parameters, machine learning models such as support vector machines are used to perform regression analysis on the correlation between speed and tension. The input dataset includes speed deviation values ​​from the past 1000 production cycles. After model training, the predicted correction coefficient is 1.12. When the actual speed deviates from the target by 2 m / min, the correction formula is calculated using the least squares method. The corrected speed calculation formula is as follows:

[0110]

[0111] The corrected speed, calculated using the above formula, is 17.12 m / min, thus stabilizing the fiber diameter within the range of 125 μm ± 0.5 μm. Next, using the obtained heating power target of 85 kW and the drawing speed correction parameter of 1.12, coordinated control commands are executed for each drawing furnace. The commands are broadcast to furnaces 1 through 5 using a distributed control system. The algorithm integrates a genetic algorithm to optimize a multi-objective function, as follows:

[0112] Objective function =

[0113] in, The heating temperature, For wire drawing speed, With a weight of 0.7 and an initial population size of 50, after 20 generations of iterations to converge, the calculated furnace temperatures were set at 1450°C, 1448°C, 1452°C, 1449°C, and 1451°C, respectively, ensuring that the thermal gradient did not exceed 1°C / meter. Finally, based on the results of coordinated control, a unified production line temperature distribution optimization scheme was obtained. The heat flux distribution was analyzed using finite element simulation software, with the input boundary conditions being optimized temperature and velocity. The simulation showed that the overall temperature uniformity improved by 15%, and the standard deviation decreased from 3.2°C to 0.8°C.

[0114] In this embodiment, an adaptive optimization process based on real-time performance feedback is proposed, which enables precise location of anomaly sources. Furthermore, by introducing real-time evaluation of the control execution efficiency, the ability to verify and continuously optimize the effectiveness of the control strategy is realized, thereby improving the quality uniformity and production efficiency of optical fiber manufacturing.

[0115] In one embodiment, please refer to Figure 4 The step of updating the linkage control logic between each wire drawing device based on the temperature distribution optimization scheme of the production line, in order to coordinately control the production speed of the wire drawing device, includes:

[0116] Step 601: Based on the production line temperature distribution optimization scheme, update the linkage control logic between each wire drawing device and determine the dynamic adjustment range of speed matching;

[0117] Step 602: If the dynamic adjustment range is inconsistent, the support vector machine algorithm is used to analyze the interference factors of environmental fluctuations on speed matching and determine the speed synchronization compensation value between the wire drawing equipment.

[0118] Step 603: Perform overall calibration of the production line speed based on the speed synchronization compensation value, obtain the calibrated diameter measurement data, and determine the uniformity index of the fiber diameter based on the diameter measurement data.

[0119] Step 604: Verify the effectiveness of multi-device collaborative response based on the uniformity index to obtain the production stability confirmation result;

[0120] Step 605: If the production stability confirmation result is passed, the linkage control logic between each wire drawing device is updated to coordinate the production speed of the wire drawing device.

[0121] Based on the production line temperature distribution optimization scheme, dynamic updates to the linkage control logic are initiated, transforming the macro-level temperature control strategy into precise guidance for production speed coordination. First, based on the implicit thermal coupling relationships between equipment in the production line temperature distribution optimization scheme, the dynamic adjustment range for speed coordination of each wire drawing device is recalculated and defined. This dynamic adjustment range is not a fixed value, but rather a working interval that fluctuates with the process status of the wire drawing equipment. It provides clear boundaries and space for fine-tuning the speed of the production line while maintaining stability.

[0122] When a mismatch is detected in the actual speed matching relationship between devices, i.e., falling into or approaching the edge of this dynamic range, it indicates the possible presence of unmodeled environmental fluctuation interference. To address this, a support vector machine pattern recognition algorithm is introduced to analyze the collected environmental parameters and speed deviation data. This algorithm can accurately identify the specific impact patterns of nonlinear factors such as ambient temperature fluctuations and cooling airflow disturbances on speed synchronization, quantifying the speed synchronization compensation values ​​required to offset these interferences for each pair of adjacent devices. Subsequently, based on the calculated speed synchronization compensation values, a comprehensive and precise calibration of the fiber drawing speed on the production line is performed. The calibration effect can be verified using a high-precision online diameter measuring instrument. The diameter measurement data of the calibrated optical fiber is obtained, and the uniformity index of the optical fiber diameter, a core characteristic of product quality, is extracted from the diameter measurement data. The uniformity index reflects the collaborative control effect of the production line, and its quality directly reflects the effectiveness of the coordinated control of speed and temperature.

[0123] Subsequently, this uniformity index is used to finally verify the effectiveness of the aforementioned multi-device coordinated response. By analyzing the degree of improvement of the uniformity index before and after speed calibration, it is possible to objectively assess whether this coordinated control has truly achieved the expected goal and generate a quantitative confirmation result of production stability. If the confirmation result is successful, it indicates that the entire coordinated control logic is effective. Therefore, the speed matching rules and compensation mechanisms that have been successfully verified are solidified and updated into the linkage control logic between each wire drawing device.

[0124] In this embodiment, an adaptive intelligent scheme that can actively identify and compensate for unknown environmental disturbances is proposed, which significantly enhances the robustness and stability of the production line under complex working conditions.

[0125] In one embodiment, please refer to Figure 5 The step of updating the linkage control logic between each wire drawing device based on the temperature distribution optimization scheme of the production line and determining the dynamic adjustment range of speed matching includes:

[0126] Step 6011: Based on the production line temperature distribution optimization scheme, obtain real-time temperature monitoring data to determine the ambient temperature value;

[0127] Step 6012: If the ambient temperature value exceeds the preset temperature threshold, the operating speed of the device is adjusted through control logic to obtain speed adjustment parameters;

[0128] Step 6013: Based on the speed adjustment parameters, update the device linkage status using a distributed control algorithm to determine the cooperative operation mode;

[0129] Step 6014: Based on the collaborative operation mode, obtain real-time feedback data between the wire drawing devices;

[0130] Step 6015: If the data consistency index of the real-time feedback data is lower than the preset standard index, the temperature monitoring data is corrected by the data processing module to obtain the corrected temperature data.

[0131] Step 6016: Based on the corrected temperature data, a dynamic programming algorithm is used to optimize the speed matching range and determine the dynamic adjustment range.

[0132] Firstly, based on the established production line temperature distribution optimization scheme, high-frequency real-time temperature monitoring data is continuously acquired. This real-time temperature monitoring data reflects the heating status inside the equipment and is used to accurately perceive the macroscopic temperature conditions of the production environment. This allows for the determination of ambient temperature values ​​that may potentially affect the wire drawing process. When comparison reveals that the ambient temperature value exceeds the preset process safety threshold, it indicates that the external environment constitutes a quantifiable disturbance to production stability. Therefore, through its embedded control logic, a pre-adaptive adjustment to the equipment's operating speed is initiated, generating preliminary speed regulation parameters. This aims to compensate for the impact of environmental temperature changes on the thermodynamic processes of the materials from the source.

[0133] Subsequently, a distributed control algorithm is employed to handle the cascading effects of speed adjustments. This algorithm uses the generated speed adjustment parameters as input and efficiently negotiates and redistributes tasks among the control nodes of each device, thereby dynamically updating the linkage status of the entire equipment group and establishing a coordinated operation mode. This coordinated operation mode ensures that the production line can respond as a whole to environmental disturbances, rather than exhibiting isolated behavior from individual devices.

[0134] To verify the effectiveness of this collaborative mode, real-time feedback data from each wire drawing device was collected. When the data analysis module detected that the data consistency index of the real-time feedback data was lower than the preset accuracy standard, it indicated that there might be noise or bias in the monitoring system. At this time, the advanced data processing module was activated to filter and correct the raw temperature monitoring data, remove outliers, compensate for system errors, and finally output more accurate and reliable corrected temperature data.

[0135] Finally, based on this batch of high-confidence corrected temperature data, dynamic programming algorithm is used for optimization calculation. Taking into account equipment performance constraints and overall process objectives, among multiple possible speed configuration schemes, a path that can ensure long-term stability and optimal efficiency is found, thereby accurately defining the dynamic adjustment range that each piece of equipment should follow in the next stage to maintain coordination.

[0136] For example, in a smart home system, the linkage control logic between multiple devices is optimized using IoT protocols such as MQTT. First, real-time status data from air conditioners, heaters, and sensor nodes are collected. A device dependency graph is constructed using graph theory algorithms, where nodes represent devices and edges represent interaction relationships. For instance, a strong dependency is represented by a weight of 0.8 between an air conditioner node and a temperature sensor node. Then, Dijkstra's algorithm is applied to calculate the shortest path delay, ensuring a linkage response time of less than 50 milliseconds. When a user sets the living room temperature to 24.5℃, the system automatically evaluates the path and adjusts the air conditioner fan speed to medium to match the heater's output power, avoiding approximately 15% energy waste. Based on this, real-time temperature monitoring data is obtained by deploying multiple nodes in a wireless sensor network. Each node is equipped with a DHT22 sensor sampling temperature at a frequency of 1Hz. The data is fused using a Kalman filter algorithm. For example, an initial measurement of 25.2℃ with a noise covariance of 0.1 is filtered to estimate 24.8℃, reducing the error to 0.2℃. A linear regression model is then used to analyze trends. ,in, The system predicts the temperature deviation over the next 5 minutes, using time in minutes. If the deviation exceeds 0.5℃, an alarm is triggered and the data is uploaded to a cloud database for remote monitoring. This then determines the dynamic adjustment range for speed matching and, combined with a PID control algorithm, calculates the actuator speed, such as the proportional gain. =2.0, integral =0.5, differential =0.1, for fan speed Adjustments are made, and the adjustment formula is as follows:

[0137]

[0138] The error e(t) = 24.5 - 24.8 = -0.3℃. After iteration, the speed dynamically increases from the initial 1200 rpm to 1350 rpm, with the range limited to 1000 to 1500 rpm to match the heat load changes. Simulation analysis shows that this adjustment can control the steady-state error within 0.1℃, ensuring a 12% improvement in system energy efficiency. This forms a closed-loop control chain from optimized linkage to real-time monitoring and then to dynamic adjustment, achieving high efficiency and energy saving.

[0139] In this embodiment, external environmental fluctuations are used as a key input variable into the speed collaborative control, enabling proactive compensation capabilities to counteract environmental disturbances. At the same time, by introducing data consistency indicators and correction mechanisms, the reliability of the decision-making basis is ensured.

[0140] In one embodiment, please refer to Figure 6 If the dynamic adjustment range shows inconsistency, a support vector machine algorithm is used to analyze the interference factors of environmental fluctuations on speed matching, and to determine the speed synchronization compensation value between the wire drawing equipment, including:

[0141] Step 6021: If the dynamic adjustment range is inconsistent, then collect environmental fluctuation data to obtain an environmental variable dataset, wherein the environmental variable dataset includes real-time values ​​of temperature, humidity, and vibration frequency.

[0142] Step 6022: Based on the environmental variable dataset, determine the mapping relationship between environmental fluctuations and speed synchronization deviation using the support vector machine algorithm, and obtain the prediction model and the output deviation of the prediction model;

[0143] Step 6023: If the output deviation exceeds a preset deviation threshold, adjust the feature weights of the prediction model to obtain optimized model parameters.

[0144] Step 6024: Based on the prediction model corresponding to the optimized model parameters, analyze the interference of real-time environmental variables on speed matching and obtain the weight distribution of interference factors.

[0145] Step 6025: Determine the speed synchronization compensation value between the wire drawing devices based on the weight distribution of the interference factors.

[0146] When an inconsistency in the dynamic adjustment range is detected, a refined environmental interference analysis and compensation process is initiated. First, comprehensive multidimensional fluctuation data from the production environment is collected to construct an environmental variable dataset containing key parameters such as temperature, humidity, and vibration frequency, thereby achieving a panoramic characterization of external conditions affecting equipment synchronization. Based on the environmental variable dataset, a support vector machine (SVM) algorithm is used to reveal the complex nonlinear mapping relationship between environmental fluctuations and equipment speed synchronization deviations. This SVM algorithm, through the construction and segmentation of a high-dimensional feature space, can accurately extract the core patterns affecting synchronization accuracy from seemingly disordered environmental variables, thereby training a highly reliable prediction model. Simultaneously, the output deviation of this prediction model is continuously monitored; this output deviation is a key indicator for evaluating the adaptability of the prediction model in the actual operating environment.

[0147] When the model output deviation exceeds the preset fault tolerance threshold, it indicates a mismatch between the initial model and the current actual process conditions. The model's online self-learning mechanism is then activated, dynamically adjusting its feature weights to fine-tune and optimize the model. This process enables the prediction model to adapt to changes in environmental fluctuations, ultimately generating a set of optimized model parameters. Using the optimized prediction model, real-time collected environmental variables are analyzed to quantify the contribution of various interference factors to speed matching, thus obtaining a weight distribution map of the interference factors. Finally, based on the generated weight distribution, a speed synchronization compensation value is calculated and assigned for each wire drawing machine. This speed synchronization compensation value is not a uniform adjustment but a customized instruction based on the intensity and characteristics of the interference experienced by each machine, thereby achieving precise interference cancellation and speed synchronization recovery at the system level.

[0148] In this embodiment, a differentiated compensation mechanism based on weight distribution is proposed, which enables speed synchronization control to enter a precise on-demand allocation mode, effectively improving the resilience and adaptability of high-end optical fibers in the face of complex environmental disturbances during the production process.

[0149] The following describes the IoT-based collaborative control system for optical fiber production equipment provided by the present invention. The IoT-based collaborative control system for optical fiber production equipment described below can be referred to in correspondence with the IoT-based collaborative control method for optical fiber production equipment described above.

[0150] This invention also provides a collaborative control system for optical fiber production equipment based on the Internet of Things, comprising:

[0151] The index determination module is used to determine the real-time temperature gradient based on the temperature distribution data of each wire drawing equipment, and to determine the material change index based on the preform material parameter information of each wire drawing equipment.

[0152] The status determination module is used to determine the temperature status of the current production line based on the real-time temperature gradient, and to determine the material adaptation requirement value based on the material change index.

[0153] The influence coefficient determination module is used to process the temperature data correlation between each drawing device based on the temperature state and material adaptation requirements, and to determine the temperature gradient deviation value and the influence coefficient between adjacent drawing devices using a distributed computing framework.

[0154] The parameter determination module is used to predict the potential impact of material changes on temperature distribution based on the influence coefficient and a neural network model if the temperature gradient deviation value exceeds a preset deviation threshold, thereby obtaining heating power parameters and wire drawing speed correction parameters.

[0155] The control and optimization module is used to perform coordinated control and optimization on each wire drawing equipment based on the heating power parameters and the wire drawing speed correction parameters, so as to obtain an optimized temperature distribution scheme for the production line.

[0156] The collaborative control module is used to update the linkage control logic between each wire drawing device based on the temperature distribution optimization scheme of the production line, so as to collaboratively control the production speed of the wire drawing device.

[0157] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A collaborative control method for optical fiber production equipment based on the Internet of Things, characterized in that, Executed by a computer, including: The real-time temperature gradient is determined based on the temperature distribution data of each wire drawing equipment, and the material change index is determined based on the preform material parameter information of each wire drawing equipment. The temperature status of the current production line is determined based on the real-time temperature gradient, and the material adaptation requirement value is determined based on the material change index. Based on the temperature conditions and material adaptation requirements, a distributed computing framework is used to process the temperature data correlation between each drawing device, and to determine the temperature gradient deviation value and the influence coefficient between adjacent drawing devices. If the temperature gradient deviation value exceeds the preset deviation threshold, then based on the influence coefficient, the potential impact of material change on temperature distribution is predicted by a neural network model to obtain heating power parameters and wire drawing speed correction parameters. Based on the heating power parameters and the drawing speed correction parameters, collaborative control and optimization are performed on each drawing device to obtain an optimized temperature distribution scheme for the production line. Based on the temperature distribution optimization scheme of the production line, the linkage control logic between each wire drawing equipment is updated to coordinate the production speed of the wire drawing equipment.

2. The collaborative control method for optical fiber production equipment based on the Internet of Things according to claim 1, characterized in that, If the temperature gradient deviation value exceeds a preset deviation threshold, then based on the influence coefficient, a neural network model is used to predict the potential impact of material changes on the temperature distribution, and heating power parameters and drawing speed correction parameters are obtained, including: If the temperature gradient deviation exceeds a preset deviation threshold, the temperature gradient deviation and the material parameter information of the preform are analyzed by a neural network model to obtain the temperature distribution prediction result. Based on the temperature distribution prediction results, the heating power parameters and the adjustment range of the heating power parameters are determined; Based on the adjustment range, an initial correction value for the wire drawing speed is determined; If the initial correction value does not meet the preset wire drawing speed value required for stable production, the wire drawing speed is adjusted through an iterative optimization algorithm to determine the wire drawing speed correction parameter.

3. The collaborative control method for optical fiber production equipment based on the Internet of Things according to claim 1, characterized in that, The process of performing coordinated control and optimization on each wire drawing device based on the heating power parameters and the wire drawing speed correction parameters yields an optimized temperature distribution scheme for the production line, including: Based on the heating power parameters and the wire drawing speed correction parameters, the operating data of each wire drawing device is collected in real time to obtain the status of the wire drawing furnace. The state of the drawing furnace is compared with the preset production line temperature consistency requirements to identify abnormal drawing equipment with abnormal temperature. Based on the abnormal wire drawing equipment, generate collaborative control commands; Based on the aforementioned coordinated control command, the heating power parameter and the drawing speed correction parameter are adjusted to obtain a process parameter adjustment scheme. Based on the process parameter adjustment scheme, the coordinated control command is sent to each wire drawing equipment to obtain the control execution efficiency and the production line temperature data corresponding to the control execution efficiency. Based on the temperature data of the production line, a temperature distribution optimization scheme is generated.

4. The collaborative control method for optical fiber production equipment based on the Internet of Things according to claim 1, characterized in that, The step of updating the linkage control logic between each wire drawing device based on the production line temperature distribution optimization scheme to coordinate the production speed of the wire drawing device includes: Based on the temperature distribution optimization scheme of the production line, the linkage control logic between each wire drawing equipment is updated to determine the dynamic adjustment range of speed matching; If the dynamic adjustment range is inconsistent, a support vector machine algorithm is used to analyze the interference factors of environmental fluctuations on speed matching and determine the speed synchronization compensation value between the wire drawing equipment. The production line speed is calibrated as a whole based on the speed synchronization compensation value, the calibrated diameter measurement data is obtained, and the uniformity index of the fiber diameter is determined based on the diameter measurement data. The effectiveness of the multi-device collaborative response is verified based on the uniformity index, and the production stability confirmation result is obtained. If the production stability confirmation result is passed, the linkage control logic between each wire drawing device is updated to coordinate the production speed of the wire drawing device.

5. The collaborative control method for optical fiber production equipment based on the Internet of Things according to claim 4, characterized in that, The step of updating the linkage control logic between each wire drawing device based on the production line temperature distribution optimization scheme and determining the dynamic adjustment range of speed matching includes: Based on the production line temperature distribution optimization scheme, real-time temperature monitoring data is obtained to determine the ambient temperature value. If the ambient temperature value exceeds the preset temperature threshold, the operating speed of the equipment is adjusted through control logic to obtain speed adjustment parameters; Based on the speed adjustment parameters, a distributed control algorithm is used to update the device linkage status and determine the cooperative operation mode; Based on the aforementioned collaborative operation mode, real-time feedback data between the wire drawing devices is obtained; If the data consistency index of the real-time feedback data is lower than the preset standard index, the temperature monitoring data is corrected by the data processing module to obtain the corrected temperature data. Based on the corrected temperature data, a dynamic programming algorithm is used to optimize the speed matching range and determine the dynamic adjustment range.

6. The collaborative control method for optical fiber production equipment based on the Internet of Things according to claim 4, characterized in that, If the dynamic adjustment range shows inconsistency, a support vector machine algorithm is used to analyze the interference factors of environmental fluctuations on speed matching, and to determine the speed synchronization compensation value between the wire drawing equipment, including: If the dynamic adjustment range is inconsistent, environmental fluctuation data is collected to obtain an environmental variable dataset, wherein the environmental variable dataset includes real-time values ​​of temperature, humidity, and vibration frequency. Based on the environmental variable dataset, the mapping relationship between environmental fluctuations and speed synchronization deviation is determined by the support vector machine algorithm, and the prediction model and the output deviation of the prediction model are obtained. If the output deviation exceeds a preset deviation threshold, the feature weights of the prediction model are adjusted to obtain optimized model parameters. Based on the prediction model corresponding to the optimized model parameters, the interference of real-time environmental variables on speed matching is analyzed, and the weight distribution of interference factors is obtained. Based on the weight distribution of the interference factors, the speed synchronization compensation value between the wire drawing equipment is determined.

7. A collaborative control system for fiber optic production equipment based on the Internet of Things, characterized in that, include: The index determination module is used to determine the real-time temperature gradient based on the temperature distribution data of each wire drawing equipment, and to determine the material change index based on the preform material parameter information of each wire drawing equipment. The status determination module is used to determine the temperature status of the current production line based on the real-time temperature gradient, and to determine the material adaptation requirement value based on the material change index. The influence coefficient determination module is used to process the temperature data correlation between each drawing device based on the temperature state and material adaptation requirements, and to determine the temperature gradient deviation value and the influence coefficient between adjacent drawing devices using a distributed computing framework. The parameter determination module is used to predict the potential impact of material changes on temperature distribution based on the influence coefficient and a neural network model if the temperature gradient deviation value exceeds a preset deviation threshold, thereby obtaining heating power parameters and wire drawing speed correction parameters. The control and optimization module is used to perform coordinated control and optimization on each wire drawing equipment based on the heating power parameters and the wire drawing speed correction parameters, so as to obtain an optimized temperature distribution scheme for the production line. The collaborative control module is used to update the linkage control logic between each wire drawing device based on the temperature distribution optimization scheme of the production line, so as to collaboratively control the production speed of the wire drawing device.

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