A method for manufacturing a fiber-optic gyroscope ring with reduced scale factor

Abstract

A method for manufacturing a fiber optic gyroscope ring to reduce the gyroscope scaling factor includes the following steps: If the expansion coefficient reaches a stabilization threshold, a scaling factor calibration system is activated. The scaling factor of the gyroscope is adjusted according to a preset calibration procedure. Based on the scaling factor calibration results, it is determined whether the measurement accuracy meets the preset accuracy requirements. Equivalent radius data is acquired over the entire temperature range, and the stability of the equivalent radius with temperature variation is analyzed. Based on the stability data of the equivalent radius, a performance stability evaluation model is established to describe the relationship between performance stability and equivalent radius stability. A preset performance optimization algorithm is used to adjust the winding process parameters so that the equivalent radius of the ring tends to stabilize over the entire temperature range. By monitoring the change trend of the equivalent radius in real time, it is determined whether the performance stability reaches the preset optimization threshold.

Description

Technical Field

[0001] This invention belongs to the field of fiber optic gyroscope ring manufacturing technology, and specifically relates to a method for manufacturing a fiber optic gyroscope ring that reduces the gyroscope scaling factor. Background Technology

[0002] In traditional fiber optic winding processes, factors such as imprecise temperature control, uneven curing agent distribution, and unstable expansion coefficients can easily lead to significant differences in the physical properties of different layers within the ring, thus affecting the scaling factor stability of the gyroscope and reducing measurement accuracy. Analysis reveals that the main reason is that if the temperature gradient inside and on the surface of the ring cannot be effectively controlled during fiber winding, different curing degrees among the layers will result, affecting the overall structural stability and mechanical strength of the ring. Therefore, a new method for manufacturing fiber optic gyroscope rings is urgently needed to solve these problems. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for manufacturing a fiber optic gyroscope ring that reduces the gyroscope scaling factor, optimizes the fiber optic winding process, and improves the consistency of the curing degree of each layer of the ring, thereby improving the overall performance and measurement accuracy of the fiber optic gyroscope.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0005] A method for manufacturing a fiber optic gyroscope ring to reduce the gyroscope scaling factor, comprising the following steps:

[0006] Preparation: Divide an optical fiber symmetrically into two equal parts at its midpoint and wind each part onto two fiber supply rollers. Mount the two fiber supply rollers on the front and rear winding mechanisms of the winding equipment, respectively, for clockwise and counter-clockwise fiber winding. The winding process is as follows:

[0007] S1. Obtain temperature distribution data during the optical fiber winding process, analyze the temperature gradient change trend inside and on the surface of the ring, and establish a mathematical model of the curing degree of each layer of the ring based on the temperature gradient change trend to describe the relationship between the curing degree and the temperature gradient.

[0008] S2. Using a preset temperature control algorithm, the heating power and cooling rate during the winding process are adjusted to make the temperature gradient of each layer of the ring more uniform. By monitoring the temperature changes of each layer of the ring in real time, it is determined whether the temperature gradient has reached the preset uniformity threshold.

[0009] S3. If the temperature gradient reaches the homogenization threshold, start the curing agent injection system and distribute the curing agent evenly to each layer of the ring according to the preset injection rate and pressure; adjust the curing time according to the distribution of the curing agent to make the curing degree of each layer of the ring tend to be consistent.

[0010] S4. Obtain the expansion coefficient data of each layer of the cured ring and analyze the distribution characteristics of the expansion coefficient; based on the distribution characteristics of the expansion coefficient, establish a relationship model between the equivalent radius of the ring and the expansion coefficient, and describe the changing trend of the equivalent radius with the expansion coefficient.

[0011] S5. A preset aging process algorithm is used to control the temperature and time parameters during the aging process, so that the expansion coefficients of each layer of the ring tend to be consistent. By monitoring the changes in the expansion coefficients of each layer of the ring in real time, it is determined whether the expansion coefficients have reached the preset stabilization threshold.

[0012] S6. If the expansion coefficient reaches the stabilization threshold, the scaling factor calibration system is activated, and the scaling factor of the gyroscope is adjusted according to the preset calibration procedure; based on the scaling factor calibration result, it is determined whether the measurement accuracy meets the preset accuracy requirements.

[0013] S7. Obtain equivalent radius data over the entire temperature range and analyze the stability of the equivalent radius as a function of temperature. Based on the stability data of the equivalent radius, establish a performance stability evaluation model to describe the relationship between performance stability and the stability of the equivalent radius.

[0014] S8 employs a preset performance optimization algorithm to adjust the winding process parameters, making the equivalent radius of the ring body tend to stabilize over the entire temperature range; by monitoring the change trend of the equivalent radius in real time, it determines whether the performance stability has reached the preset optimization threshold.

[0015] Preferably, the sub-step of S1 is as follows:

[0016] S1.1 During the fiber optic winding process, an infrared thermal imager is used to acquire temperature distribution data inside and on the surface of the ring, and a two-dimensional temperature distribution matrix at different time points is obtained;

[0017] S1.2 Preprocess the acquired temperature distribution data to remove outliers and noise, and obtain a smooth temperature distribution surface;

[0018] S1.3 The numerical differential method is used to calculate the temperature gradient in the radial and axial directions inside and on the surface of the ring, and the temperature gradient variation trend of each layer of the ring is obtained.

[0019] S1.4 Based on the thermophysical parameters and curing kinetics equations of the cyclic material, a mathematical model describing the relationship between the degree of curing of each layer of the cyclic material and the temperature gradient is established.

[0020] S1.5 The ring is divided into several discrete elements. Based on the temperature gradient change trend, the degree of solidification of each element is solved by the finite element method.

[0021] S1.6 The curing degree of each unit is interpolated and smoothed to obtain a continuous curing degree distribution surface, which is then compared and verified with the measured data.

[0022] S1.7 employs machine learning algorithms to establish a mapping relationship between temperature gradient and curing degree, enabling online prediction and optimized control of curing degree.

[0023] Preferably, in S1.7, the optical fiber ring is wound using a four-pole symmetrical winding method. After every two layers of winding, online ultraviolet curing is performed. The curing lamp is an LED ultraviolet curing light source, and the winding environment temperature is 25±3℃, until the entire optical fiber ring is completed.

[0024] Preferably, the sub-step of S2 is as follows:

[0025] S2.1 Obtain real-time temperature data for each layer of the ring, input the temperature data into the preset temperature control algorithm model for analysis and calculation, and obtain the temperature gradient value of each layer;

[0026] S2.2 Compare the temperature gradient value with the preset homogenization threshold. If the temperature gradient value is greater than the threshold, it is determined that the temperature gradient is not uniform and temperature control is required.

[0027] S2.3 If the temperature gradient value is less than or equal to the threshold, it is determined that the temperature gradient has become consistent and no adjustment is needed;

[0028] S2.4 When it is determined that temperature regulation is required, a temperature regulation model is established by using machine learning algorithms and combining historical process parameters with temperature gradient correlation data. The optimal heating power and cooling rate parameters are predicted by inputting the current temperature of each layer of the ring.

[0029] S2.5 sends the predicted optimal heating power and cooling rate parameters to the control system of the winding equipment, adjusts the power of the heating device and the cooling capacity of the cooling device during the winding process, and performs differentiated temperature regulation on each layer of the ring.

[0030] S2.6 During the temperature regulation process, real-time temperature data of each layer of the ring is continuously acquired, the temperature gradient value between each layer is calculated, and it is determined whether the temperature gradient has reached the homogenization threshold.

[0031] S2.7 If the temperature gradient value is less than or equal to the homogenization threshold for multiple consecutive judgments, it is considered that the temperature of each layer of the ring has become consistent, and temperature control is stopped. The current heating power and cooling rate are maintained until the winding process is completed.

[0032] S2.8 If, after a certain period of temperature adjustment, the temperature gradient values ​​of each layer of the ring are still greater than the homogenization threshold, an early warning message is triggered, the temperature control algorithm or homogenization threshold is manually adjusted, and the temperature control process is restarted until the requirements are met.

[0033] Preferably, in step S3, ultraviolet resin is selected as the adhesive and also as the curing agent for the optical fiber ring. The specific steps are as follows:

[0034] S3.1 Obtain temperature gradient data for each layer of the ring, determine whether the temperature gradient has reached the preset homogenization threshold, if the threshold is reached, start the curing agent injection system, if the threshold is not reached, continue to monitor the temperature gradient change.

[0035] S3.2 Based on the preset injection rate and injection pressure parameters, control the curing agent injection system to evenly distribute the curing agent to each layer of the ring. By adjusting the changes in injection rate and injection pressure, ensure the uniformity of curing agent distribution in each layer of the ring.

[0036] S3.3 Computer vision technology is used to detect and analyze the distribution of curing agent in each layer of the ring, and the distribution density data of curing agent in each layer of the ring is obtained. The distribution density data is compared with the preset uniform distribution threshold to determine whether the injection rate and injection pressure parameters need to be adjusted.

[0037] S3.4 Establish a correlation model between curing agent distribution density and curing time through machine learning algorithms. Based on the actual distribution of curing agent in each layer of the ring, dynamically adjust the curing time of each layer to make the curing degree of each layer tend to be consistent.

[0038] S3.5 uses infrared thermal imaging technology to monitor the curing process of each layer of the ring in real time, obtain temperature change data of each layer, and judge the curing degree of each layer by temperature change data. When the curing degree of each layer reaches the preset consistency threshold, the curing time is stopped and adjusted.

[0039] S3.6 During the curing process, data such as temperature and curing agent distribution density of each layer of the ring are continuously collected. Big data analysis technology is used to explore the correlation between data and dynamically optimize parameters such as injection rate, injection pressure and curing time to achieve dynamic balance of curing degree of each layer of the ring.

[0040] S3.7 Establish a three-dimensional visualization model of the ring curing process, map data such as temperature gradient, curing agent distribution and curing degree to the three-dimensional model, and display the curing state of each layer of the ring in real time through a visualization interface.

[0041] Preferably, the sub-step of S4 is as follows:

[0042] S4.1 Obtain the original expansion coefficient data of each layer of the cured ring, preprocess the data, remove outliers and noise data, and obtain an effective expansion coefficient dataset;

[0043] S4.2 Perform statistical analysis on the preprocessed expansion coefficient data, calculate the mean, variance and quantile of the expansion coefficient of each layer, and obtain the distribution characteristics of the expansion coefficient in each layer of the ring.

[0044] S4.3 Based on the distribution characteristics of the expansion coefficient, a regression analysis method is used to establish a relationship model between the equivalent radius of the ring and the expansion coefficient, where the equivalent radius is the dependent variable and the expansion coefficient is the independent variable. The regression equation is obtained by fitting and is used to describe the changing trend of the equivalent radius with the expansion coefficient.

[0045] S4.4 The established relationship model is evaluated and optimized. The cross-validation method is used to evaluate the goodness of fit and predictive performance of the model. Based on the evaluation results, the model is adjusted and optimized to obtain the optimal relationship model between the equivalent radius and the expansion coefficient.

[0046] S4.5 Using the optimized relational model, the equivalent radius of the new solidified ring sample is predicted. Based on the expansion coefficient data of the sample, the corresponding equivalent radius prediction value is calculated by the model to guide the design and manufacturing of the ring.

[0047] S4.6 Perform error analysis on the prediction results, calculate the error between the predicted value and the actual measured value, evaluate the prediction accuracy of the model, and if the error exceeds the preset threshold, it is necessary to further optimize the model or obtain more training data.

[0048] S4.7 Integrates the optimized model of the relationship between the equivalent radius and the coefficient of expansion into the design and manufacturing process of the ring. Based on the material properties and process parameters of the ring, the equivalent radius of each layer of the ring is predicted, which guides the structural design and process optimization of the ring and improves the accuracy and efficiency of the ring manufacturing.

[0049] Preferably, the sub-step of S5 is as follows:

[0050] S5.1 Based on the material properties of the ring, a three-dimensional mathematical model of aging treatment temperature-time-expansion coefficient is established to obtain the predicted values ​​of the expansion coefficient of each layer of the ring under different combinations of temperature and time.

[0051] S5.2 employs machine learning algorithms to train an aging treatment optimization model based on historical data, learning the optimal combination of temperature and time parameters to minimize the difference in expansion coefficients between layers;

[0052] S5.3 Place the ring in a temperature-controlled chamber and perform aging treatment according to the temperature-time parameters output by the optimized model;

[0053] S5.4 Simultaneously, strain sensors are installed in each layer of the ring to collect strain data of each layer in real time;

[0054] S5.5 Calculates the real-time expansion coefficients of each layer of the ring using strain data;

[0055] S5.6 compares the real-time expansion coefficient with the mathematical model prediction and dynamically adjusts the temperature and time parameters to make the actual expansion coefficient approach the prediction value.

[0056] S5.7 Perform statistical analysis on the expansion coefficients of each layer collected in real time, and calculate the mean and variance of the expansion coefficients of each layer;

[0057] S5.8 If the variance is less than the preset stabilization threshold, the aging process is considered complete and the expansion coefficients of each layer of the ring have become consistent.

[0058] S5.9 If, after a certain period of time, the variance of the expansion coefficient of each layer of the ring is still greater than the threshold, an alarm mechanism will be triggered to analyze the factors causing the difference in expansion coefficient, and if necessary, manual intervention will be used to adjust the aging treatment parameters.

[0059] After the S5.10 aging treatment is completed, the quality of each layer of the ring is tested. The mechanical properties of each layer are tested by sampling to verify the consistency of the properties of each layer and ensure that the aging treatment effect meets the design requirements.

[0060] Once the entire optical fiber is wound around the fiber, the curing temperature is 85±2℃.

[0061] Preferably, the sub-step of S6 is as follows:

[0062] S6.1 Acquire real-time data from the gyroscope, calculate the current expansion coefficient based on the data, compare the expansion coefficient with a preset stability threshold, and determine whether a stable state has been reached.

[0063] S6.2 If the expansion coefficient reaches the stable threshold, the scaling factor calibration system is activated, the preset scaling factor calibration program is called, and the scaling factor of the gyroscope is automatically adjusted according to the steps of the calibration program.

[0064] S6.3 During the scaling factor adjustment process, the Kalman filter algorithm is used to filter the gyroscope data to reduce noise interference and improve the accuracy of scaling factor adjustment;

[0065] S6.4 Using the least squares fitting algorithm, the optimal scaling factor calibration coefficient is fitted based on multiple sets of data before and after scaling factor adjustment, which is then used for subsequent scaling factor calibration.

[0066] S6.5 Write the calibrated scaling factor parameters into the gyroscope's calibration register to complete the scaling factor update, and save the calibration results to the system log;

[0067] S6.6 Based on the calibrated gyroscope data, the Bayesian estimation algorithm is used to calculate the current measurement accuracy, and the measurement accuracy is compared with the preset accuracy requirements to determine whether the calibrated measurement accuracy meets the requirements.

[0068] S6.7 If the measurement accuracy meets the preset requirements, the scaling factor calibration is completed. If the accuracy does not meet the requirements, the calibration parameters are adjusted and the scaling factor calibration is repeated until the measurement accuracy meets the requirements.

[0069] Preferably, the sub-step of S7 is as follows:

[0070] S7.1 Obtain equivalent radius measurement data over the entire temperature range and group the data according to temperature intervals;

[0071] S7.2 Calculate the mean and variance of the equivalent radius data within each temperature range to obtain the statistical characteristics of the equivalent radius at different temperatures;

[0072] S7.3 Based on the trend of the mean of the equivalent radius changing with temperature, the least squares method is used to perform curve fitting to obtain the equivalent radius-temperature relationship curve;

[0073] S7.4 Calculate the slope of each point on the equivalent radius-temperature curve. When the slope is less than the preset threshold, it is determined that the equivalent radius is stable within the temperature range.

[0074] S7.5 Acquire performance stability test data and extract the average value of performance indicators at different temperatures as a quantitative indicator of performance stability;

[0075] S7.6 Input the equivalent radius stability data and performance stability index into the support vector machine model for training to obtain the performance stability evaluation model;

[0076] S7.7 Utilizes a trained performance stability evaluation model to predict performance stability at any temperature, providing a basis for product performance optimization decisions.

[0077] Preferably, the sub-step of S8 is as follows:

[0078] S8.1 According to the preset optimization algorithm, obtain the initial winding parameter values, including the number of coil turns, wire diameter and winding tension, and determine these parameter values ​​as the input of the algorithm;

[0079] S8.2 Obtain the equivalent radius measurement data of the ring body at different temperatures. Using a temperature sensor and a high-precision displacement sensor, the radius data of the ring body at different temperatures is obtained. These data constitute an equivalent radius-temperature dataset.

[0080] S8.3 For the equivalent radius-temperature dataset, calculate the average value and standard deviation of the equivalent radius at each temperature point to evaluate the radius stability of the ring at different temperatures. If the standard deviation exceeds the preset stability threshold, the parameter adjustment step is required.

[0081] S8.4 uses a preset optimization algorithm to calculate new winding parameter values ​​based on the equivalent radius-temperature dataset and stability threshold. If the algorithm determines that the current parameter values ​​cannot stabilize the radius, the winding parameters are adjusted and the adjusted winding parameters are recorded.

[0082] S8.5 Based on the adjusted winding parameters, rewind the ring and obtain the equivalent radius measurement data of the new ring at different temperatures to form a new equivalent radius-temperature dataset for verifying the effect of parameter adjustment;

[0083] S8.6 For the new equivalent radius-temperature dataset, recalculate the average value and standard deviation of the equivalent radius for each temperature point, and determine whether the preset stability threshold has been reached. If not, return to step S8.3 for iterative optimization.

[0084] S8.7 If the standard deviation of the radius stability of the new dataset is less than or equal to the preset stability threshold, the current winding parameters are output, and it is determined that the equivalent radius of the ring body has reached a stable state in the full temperature range. The equivalent radius value in the stable state is then output.

[0085] The present invention can achieve the following beneficial effects:

[0086] 1. This invention employs temperature monitoring and control algorithms to ensure that the temperature gradient of each layer of the ring remains consistent during the winding process, thereby improving the curing quality.

[0087] 2. The present invention optimizes the curing agent injection strategy: a model of the relationship between curing agent distribution density and curing time is established through machine learning algorithm, and the curing parameters are dynamically adjusted according to the actual distribution to ensure uniform distribution of curing agent in each layer of the ring, so that the degree of curing tends to be consistent.

[0088] 3. This invention introduces expansion coefficient analysis and modeling technology, combined with aging treatment algorithm, to make the expansion coefficient of each layer of the ring tend to be consistent across the entire temperature range, reduce the influence of the equivalent radius changing with temperature, and thus improve the stability of the scaling factor.

[0089] 4. This invention optimizes the temperature and time parameters of the aging process through intelligent algorithms to ensure that the expansion coefficient of each layer of the ring reaches the preset stabilization threshold, thus guaranteeing reliability and accuracy during long-term use.

[0090] 5. This invention constructs a three-dimensional visualization model and utilizes big data analysis and machine learning technologies to monitor and dynamically optimize the entire manufacturing process in real time, providing intuitive decision support and improving the level of automation and efficiency of production. Attached Figure Description

[0091] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0092] Figure 1 This is a flowchart of the present invention;

[0093] Figure 2 This is a schematic diagram showing that each 2 / 4 layer is cured in this invention. Detailed Implementation

[0094] Preferred solutions include Figures 1 to 2 As shown, a method for manufacturing a fiber optic gyroscope ring to reduce the gyroscope scaling factor is presented.

[0095] Preparation: Divide an optical fiber symmetrically into two equal parts at its midpoint and wind each part onto two fiber supply rollers. Mount the two fiber supply rollers on the front and rear winding mechanisms of the winding equipment, respectively, for clockwise and counterclockwise fiber winding. The winding process is as follows:

[0096] S1. Obtain temperature distribution data during the optical fiber winding process, analyze the temperature gradient change trend inside and on the surface of the ring, and establish a mathematical model of the curing degree of each layer of the ring based on the temperature gradient change trend to describe the relationship between the curing degree and the temperature gradient.

[0097] S1.1 During the fiber optic winding process, an infrared thermal imager is used to acquire temperature distribution data inside and on the surface of the ring, and a two-dimensional temperature distribution matrix at different time points is obtained;

[0098] S1.2 Preprocess the acquired temperature distribution data to remove outliers and noise, and obtain a smooth temperature distribution surface;

[0099] S1.3 The numerical differential method is used to calculate the temperature gradient in the radial and axial directions inside and on the surface of the ring, and the temperature gradient variation trend of each layer of the ring is obtained.

[0100] S1.4 Based on the thermophysical parameters and curing kinetics equations of the cyclic material, a mathematical model describing the relationship between the degree of curing of each layer of the cyclic material and the temperature gradient is established.

[0101] For example: Curing reaction rate formula:

[0102] ;

[0103] Curing reaction rate, in units of Its value depends on temperature. T Frequency factor or pre-exponential factor, unit: This reflects the reaction rate at infinitely high temperatures.

[0104] Activation energy, unit: , which represents the minimum energy required to initiate a reaction.

[0105] R: Ideal gas constant, whose value is fixed. .

[0106] Formula for correction factor considering the effect of temperature gradient:

[0107]

[0108] k : Proportionality coefficient, unit is Its function is to quantify the degree of influence of temperature gradient on curing rate.

[0109] The magnitude of the temperature gradient, in units of .

[0110] Final curing degree expression:

[0111] ;

[0112] : Indicates spatial location And the degree of curing over time t; τ The integral variable represents time (unit: s).

[0113] S1.5 The ring is divided into several discrete elements. Based on the temperature gradient change trend, the degree of solidification of each element is solved by the finite element method.

[0114] S1.6 The curing degree of each unit is interpolated and smoothed to obtain a continuous curing degree distribution surface, which is then compared and verified with the measured data.

[0115] S1.7 Employs machine learning algorithms such as support vector machines or neural networks to establish a mapping relationship between temperature gradient and curing degree, enabling online prediction and optimized control of curing degree.

[0116] Preferably, in S1.7, the optical fiber ring is wound using a four-pole symmetrical winding method. After every two layers of winding, online ultraviolet curing is performed. Preferably, the curing lamp is an LED ultraviolet curing light source, and the winding environment temperature is 25±3℃, until the entire optical fiber ring is completed.

[0117] S2. Using a preset temperature control algorithm, the heating power and cooling rate during the winding process are adjusted to make the temperature gradient of each layer of the ring more uniform. By monitoring the temperature changes of each layer of the ring in real time, it is determined whether the temperature gradient has reached the preset uniformity threshold.

[0118] S2.1 Obtain real-time temperature data for each layer of the ring, input the temperature data into the preset temperature control algorithm model for analysis and calculation, and obtain the temperature gradient value of each layer;

[0119] S2.2 Compare the temperature gradient value with the preset homogenization threshold. If the temperature gradient value is greater than the threshold, it is determined that the temperature gradient is not uniform and temperature control is required.

[0120] S2.3 If the temperature gradient value is less than or equal to the threshold, it is determined that the temperature gradient has become consistent and no adjustment is needed;

[0121] S2.4 When it is determined that temperature regulation is required, a temperature regulation model is established by using machine learning algorithms and combining historical process parameters with temperature gradient correlation data. The optimal heating power and cooling rate parameters are predicted by inputting the current temperature of each layer of the ring.

[0122] S2.5 sends the predicted optimal heating power and cooling rate parameters to the control system of the winding equipment, adjusts the power of the heating device and the cooling capacity of the cooling device during the winding process, and performs differentiated temperature regulation on each layer of the ring.

[0123] S2.6 During the temperature regulation process, real-time temperature data of each layer of the ring is continuously acquired, the temperature gradient value between each layer is calculated, and it is determined whether the temperature gradient has reached the homogenization threshold.

[0124] S2.7 If the temperature gradient value is less than or equal to the homogenization threshold for multiple consecutive judgments, it is considered that the temperature of each layer of the ring has become consistent, and temperature control is stopped. The current heating power and cooling rate are maintained until the winding process is completed.

[0125] S2.8 If, after a certain period of temperature adjustment, the temperature gradient values ​​of each layer of the ring are still greater than the homogenization threshold, an early warning message will be triggered, manual intervention will be conducted to analyze the cause, and if necessary, the temperature control algorithm or homogenization threshold will be adjusted, and the temperature control process will be restarted until the requirements are met.

[0126] S3. If the temperature gradient reaches the homogenization threshold, start the curing agent injection system and distribute the curing agent evenly to each layer of the ring according to the preset injection rate and pressure; adjust the curing time according to the distribution of the curing agent to make the curing degree of each layer of the ring tend to be consistent.

[0127] UV resin is selected as the adhesive and also as the curing agent for the fiber optic ring.

[0128] The specific steps are as follows:

[0129] S3.1 Obtain the temperature gradient data of each layer of the annulus and determine whether the temperature gradient has reached the preset homogenization threshold:

[0130] During the fiber optic winding process, high-precision temperature sensors installed at key locations within the ring first acquire real-time temperature distribution data for both the interior and surface of the ring. These sensors provide accurate temperature readings, ensuring the capture of even minute temperature changes. Simultaneously, an infrared thermal imager performs non-contact temperature measurements on the ring, generating a two-dimensional temperature distribution image of the ring's surface. By analyzing this data, the temperature differences between different layers of the ring, i.e., the temperature gradient, can be calculated. Next, the system evaluates whether the current temperature gradient meets the requirements based on a preset homogenization threshold. If the temperature gradient at all measuring points is within the allowable range, it indicates that the temperature has been homogenized, and the curing agent injection system can be initiated. Conversely, if the temperature gradient does not meet the expected standard, the heating and cooling processes continue to be monitored and adjusted until the temperature gradient becomes uniform.

[0131] S3.2 Based on the preset injection rate and injection pressure parameters, control the curing agent injection system to evenly distribute the curing agent to each layer of the ring:

[0132] Once the temperature gradient is confirmed to have reached the preset homogenization threshold, the curing agent injection system is activated. This system operates according to pre-set parameters, including the curing agent injection rate (the amount of curing agent injected per unit time) and injection pressure (the force propelling the curing agent into the ring). To ensure uniform distribution of the curing agent across the ring layers, the system dynamically adjusts these two parameters. For example, higher injection rates or greater pressures may be required in certain areas to ensure sufficient curing agent penetration. Throughout the process, a visual inspection system monitors the curing agent's flow path to ensure it diffuses as intended, achieving uniform distribution.

[0133] S3.3 Computer vision technology is used to detect and analyze the distribution of curing agent in each layer of the ring.

[0134] To further verify whether the hardener is indeed uniformly distributed throughout the ring layers, advanced computer vision technology will be used for detection. Specifically, high-definition cameras will be used to capture images of each layer of the ring, and specially developed software algorithms will be used to analyze the distribution of the hardener in these images. The software can automatically identify and quantify information such as the coverage area and density of the hardener in each layer, generating a detailed distribution report. Then, this distribution data will be compared with preset standards to determine whether it is necessary to adjust the previous injection parameters. If there is any deviation, such as too little or too much hardener in a certain layer, the system will adjust the subsequent injection strategy accordingly to ensure the consistency of the final curing effect.

[0135] S3.4 Establish a correlation model between curing agent distribution density and curing time using machine learning algorithms;

[0136] Based on historical data and experimental results, a model was established using machine learning algorithms to model the relationship between curing agent distribution density and curing time. This model can help predict the optimal curing time under different conditions, ensuring consistent curing levels for each layer. When new curing agent distribution data is input into the model, it can recommend the best curing scheme based on existing conditions. For example, if the curing agent distribution in a particular layer is sparse, the model may suggest extending the curing time for that layer, and vice versa. The purpose of this is to ensure that the entire ring has the same physical properties after curing, avoiding quality problems caused by uneven curing.

[0137] S3.5 employs infrared thermal imaging technology to monitor the curing process of each layer of the ring in real time:

[0138] Infrared thermal imaging technology plays a crucial role in the curing process. By continuously scanning the temperature changes of each layer of the ring, the infrared thermal imager can track the curing progress in real time. Because curing is an exothermic reaction, the temperature of each layer rises as the reaction proceeds. By analyzing the temperature change trend, the degree of curing can be indirectly understood. When the temperature curves of all layers tend to stabilize and reach the preset consistency threshold, curing is considered complete, and the curing time can be stopped and adjusted. In addition, infrared thermal imaging provides extra safety assurance because it can detect any abnormal temperature rise at an early stage, allowing for timely measures to prevent potential risks.

[0139] S3.6 During the curing process, data such as temperature and curing agent distribution density of each layer of the ring are continuously collected:

[0140] To ensure the curing process remains optimal, it's crucial to continuously collect data on the temperature of each layer of the ring, the distribution density of the curing agent, and other relevant parameters. This data not only facilitates real-time monitoring of the curing progress but also provides rich material for subsequent big data analysis. Big data analytics can uncover patterns hidden within massive amounts of data, such as the subtle relationship between temperature changes and the degree of curing, and the impact of curing agent distribution patterns on the final product's performance. Using these findings, various parameters during the curing process, such as injection rate, injection pressure, and curing time, can be dynamically optimized to ensure that the curing degree of each layer is as close to the ideal as possible, thereby improving overall product quality.

[0141] S3.7 Establish a three-dimensional visualization model of the ring curing process:

[0142] To provide operators with a more intuitive understanding and support for decision-making, a 3D visualization model of the curing process was constructed. This model not only includes crucial information such as temperature gradient, curing agent distribution, and degree of curing, but also maps these elements into an easily understandable 3D space. Through this visual interface, operators can clearly see the curing status of each layer and even simulate different curing scenarios in a virtual environment. This not only improves operational transparency but also enhances control over the curing process. More importantly, such visualization tools provide strong support for intelligent control of the curing process, helping to better adjust parameters and optimize production processes.

[0143] S4. Obtain the expansion coefficient data of each layer of the cured ring and analyze the distribution characteristics of the expansion coefficient; based on the distribution characteristics of the expansion coefficient, establish a relationship model between the equivalent radius of the ring and the expansion coefficient, and describe the changing trend of the equivalent radius with the expansion coefficient.

[0144] S4.1 Obtain the original expansion coefficient data of each layer of the cured ring, preprocess the data, remove outliers and noise data, and obtain an effective expansion coefficient dataset;

[0145] S4.2 Perform statistical analysis on the preprocessed expansion coefficient data, calculate the statistical characteristics such as the mean, variance and quantile of the expansion coefficient of each layer, and obtain the distribution characteristics of the expansion coefficient in each layer of the ring.

[0146] S4.3 Based on the distribution characteristics of the expansion coefficient, a regression analysis method is used to establish a relationship model between the equivalent radius of the ring and the expansion coefficient, where the equivalent radius is the dependent variable and the expansion coefficient is the independent variable. The regression equation is obtained by fitting and is used to describe the changing trend of the equivalent radius with the expansion coefficient.

[0147] S4.4 The established relationship model is evaluated and optimized. Methods such as cross-validation are used to evaluate the goodness of fit and predictive performance of the model. Based on the evaluation results, the model is adjusted and optimized to obtain the optimal relationship model between the equivalent radius and the expansion coefficient.

[0148] S4.5 Using the optimized relational model, the equivalent radius of the new solidified ring sample is predicted. Based on the expansion coefficient data of the sample, the corresponding equivalent radius prediction value is calculated by the model to guide the design and manufacturing of the ring.

[0149] S4.6 Perform error analysis on the prediction results, calculate the error between the predicted value and the actual measured value, evaluate the prediction accuracy of the model, and if the error exceeds the preset threshold, it is necessary to further optimize the model or obtain more training data.

[0150] S4.7 Integrates the optimized model of the relationship between the equivalent radius and the coefficient of expansion into the design and manufacturing process of the ring. Based on the material properties and process parameters of the ring, the equivalent radius of each layer of the ring is predicted, which guides the structural design and process optimization of the ring and improves the accuracy and efficiency of the ring manufacturing.

[0151] S5. A preset aging process algorithm is used to control the temperature and time parameters during the aging process, so that the expansion coefficients of each layer of the ring tend to be consistent. By monitoring the changes in the expansion coefficients of each layer of the ring in real time, it is determined whether the expansion coefficients have reached the preset stabilization threshold.

[0152] S5.1 Based on the material properties of the ring, a three-dimensional mathematical model of aging treatment temperature-time-expansion coefficient is established to obtain the predicted values ​​of the expansion coefficient of each layer of the ring under different combinations of temperature and time.

[0153] S5.2 employs machine learning algorithms, such as support vector machines or neural networks, to train an aging treatment optimization model based on historical data, learning the optimal combination of temperature and time parameters to minimize the difference in expansion coefficients between layers.

[0154] S5.3 Place the ring in a temperature-controlled chamber and perform aging treatment according to the temperature-time parameters output by the optimized model;

[0155] S5.4 Simultaneously, strain sensors are installed in each layer of the ring to collect strain data of each layer in real time;

[0156] S5.5 Calculates the real-time expansion coefficients of each layer of the ring using strain data;

[0157] S5.6 compares the real-time expansion coefficient with the mathematical model prediction and dynamically adjusts the temperature and time parameters to make the actual expansion coefficient approach the prediction value.

[0158] S5.7 Perform statistical analysis on the expansion coefficients of each layer collected in real time, and calculate the mean and variance of the expansion coefficients of each layer;

[0159] S5.8 If the variance is less than the preset stabilization threshold, the aging process is considered complete and the expansion coefficients of each layer of the ring have become consistent.

[0160] S5.9 If, after a certain period of time, the variance of the expansion coefficient of each layer of the ring is still greater than the threshold, an alarm mechanism will be triggered to analyze the factors causing the difference in expansion coefficient, and if necessary, manual intervention will be used to adjust the aging treatment parameters.

[0161] After the S5.10 aging treatment is completed, the quality of each layer of the ring is tested. The mechanical properties of each layer are tested by sampling to verify the consistency of the properties of each layer and ensure that the aging treatment effect meets the design requirements.

[0162] After the entire optical fiber is wound, the optical fiber ring is aged at a certain temperature. Preferably, the curing temperature is 85±2℃.

[0163] S6. If the expansion coefficient reaches the stabilization threshold, the scaling factor calibration system is activated, and the scaling factor of the gyroscope is adjusted according to the preset calibration procedure; based on the scaling factor calibration result, it is determined whether the measurement accuracy meets the preset accuracy requirements.

[0164] S6.1 Acquire real-time data from the gyroscope, calculate the current expansion coefficient based on the data, compare the expansion coefficient with a preset stability threshold, and determine whether a stable state has been reached.

[0165] S6.2 If the expansion coefficient reaches the stable threshold, the scaling factor calibration system is activated, the preset scaling factor calibration program is called, and the scaling factor of the gyroscope is automatically adjusted according to the steps of the calibration program.

[0166] S6.3 During the scaling factor adjustment process, the Kalman filter algorithm is used to filter the gyroscope data to reduce noise interference and improve the accuracy of scaling factor adjustment;

[0167] S6.4 Using the least squares fitting algorithm, the optimal scaling factor calibration coefficient is fitted based on multiple sets of data before and after scaling factor adjustment, which is then used for subsequent scaling factor calibration.

[0168] S6.5 Write the calibrated scaling factor parameters into the gyroscope's calibration register to complete the scaling factor update, and save the calibration results to the system log;

[0169] S6.6 Based on the calibrated gyroscope data, the Bayesian estimation algorithm is used to calculate the current measurement accuracy, and the measurement accuracy is compared with the preset accuracy requirements to determine whether the calibrated measurement accuracy meets the requirements.

[0170] S6.7 If the measurement accuracy meets the preset requirements, the scaling factor calibration is completed. If the accuracy does not meet the requirements, the calibration parameters are adjusted and the scaling factor calibration is repeated until the measurement accuracy meets the requirements.

[0171] S7. Obtain equivalent radius data over the entire temperature range and analyze the stability of the equivalent radius as a function of temperature. Based on the stability data of the equivalent radius, establish a performance stability evaluation model to describe the relationship between performance stability and the stability of the equivalent radius.

[0172] S7.1 Obtain equivalent radius measurement data over the entire temperature range and group the data according to temperature intervals;

[0173] S7.2 Calculate the mean and variance of the equivalent radius data within each temperature range to obtain the statistical characteristics of the equivalent radius at different temperatures;

[0174] S7.3 Based on the trend of the mean of the equivalent radius changing with temperature, the least squares method is used to perform curve fitting to obtain the equivalent radius-temperature relationship curve;

[0175] S7.4 Calculate the slope of each point on the equivalent radius-temperature curve. When the slope is less than the preset threshold, it is determined that the equivalent radius is stable within the temperature range.

[0176] S7.5 Acquire performance stability test data and extract the average value of performance indicators at different temperatures as a quantitative indicator of performance stability;

[0177] S7.6 Input the equivalent radius stability data and performance stability index into the support vector machine model for training to obtain the performance stability evaluation model;

[0178] S7.7 Utilizes a trained performance stability evaluation model to predict performance stability at any temperature, providing a basis for product performance optimization decisions.

[0179] S8 employs a preset performance optimization algorithm to adjust the winding process parameters, stabilizing the equivalent radius of the ring across the entire temperature range. By monitoring the changing trend of the equivalent radius in real time, it determines whether the performance stability has reached a preset optimization threshold. Specifically:

[0180] S8.1 Based on the preset optimization algorithm, obtain the initial winding parameter values, including the number of coil turns, wire diameter, and winding tension, and determine these parameter values ​​as the input to the algorithm:

[0181] Before starting the optimization process, a set of initial winding parameters needs to be set based on past experience data or preliminary experimental results. These parameters include, but are not limited to, the number of turns of the coil (i.e., the number of turns of the fiber), the wire diameter (fiber diameter), and the winding tension (the force applied during winding). The selection of these parameters has a direct impact on the structure and performance of the final ring. These initial parameters are used as inputs to the optimization algorithm, providing a foundation for subsequent iterative optimization.

[0182] S8.2 Obtain the equivalent radius measurement data of the ring at different temperatures. Using a temperature sensor and a high-precision displacement sensor, the radius data of the ring at different temperatures is obtained. These data constitute an equivalent radius-temperature dataset.

[0183] To evaluate the performance of the ring under different temperature conditions, a high-precision temperature sensor was used to monitor temperature changes around the ring, and a high-resolution displacement sensor was used to measure the actual radius of the ring at each temperature point. In this way, a series of data points showing the equivalent radius changing with temperature were obtained, forming a complete equivalent radius-temperature dataset. This dataset not only contains the specific radius values ​​at different temperatures but also records the timestamps of each measurement, providing detailed information to support subsequent analysis.

[0184] S8.3 For the equivalent radius-temperature dataset, calculate the mean and standard deviation of the equivalent radius at each temperature point to evaluate the radius stability of the ring at different temperatures:

[0185] Based on the collected equivalent radius-temperature dataset, the mean and standard deviation of all measured samples at each temperature point are calculated. The mean reflects the center position of the equivalent radius of the ring under that temperature condition, while the standard deviation quantifies the dispersion of the data. If the standard deviation at a certain temperature point exceeds a preset stability threshold, it means that the radius of the ring changes significantly at that temperature and is not stable enough. In this case, it is necessary to enter the parameter adjustment stage to find a better combination of winding parameters to reduce this instability.

[0186] S8.4 uses a preset optimization algorithm to calculate new winding parameter values ​​based on the equivalent radius-temperature dataset and stability threshold. If the algorithm determines that the current parameter values ​​cannot stabilize the radius, the winding parameters are adjusted, and the adjusted winding parameters are recorded.

[0187] Once it is determined that the stability at certain temperature points is insufficient, an optimization algorithm is initiated to attempt to find new winding parameters that can improve stability. The optimization algorithm comprehensively considers existing equivalent radius-temperature datasets and preset stability thresholds, and recommends a set of potentially more effective winding parameters through simulation and evaluation of different parameter combinations. If, after multiple calculations, the algorithm still deems the current parameters insufficient to meet stability requirements, it continues to adjust the parameters until the optimal solution is found. Each parameter adjustment is meticulously recorded for subsequent analysis and traceability.

[0188] S8.5 Based on the adjusted winding parameters, rewind the ring and obtain the equivalent radius measurement data of the new ring at different temperatures to form a new equivalent radius-temperature dataset for verifying the effect of parameter adjustment:

[0189] Once the new winding parameters are determined, a new toroidal sample will be manufactured according to these parameters, and the previous measurement steps will be repeated to obtain the equivalent radius data of the new toroidal sample at different temperatures. This step is crucial because it directly verifies the effectiveness of the parameter adjustments. By comparing the old and new sets of data, the differences before and after the adjustments can be clearly seen, allowing for an assessment of whether the optimization measures have achieved the expected goals.

[0190] S8.6 For the new equivalent radius-temperature dataset, recalculate the mean and standard deviation of the equivalent radius for each temperature point, and determine whether the preset stability threshold has been reached. If not, return to step S8.3 for iterative optimization.

[0191] For the newly generated equivalent radius-temperature dataset, repeat the previous statistical analysis process to calculate the mean and standard deviation for each temperature point. The focus this time is to check whether the adjusted parameters have indeed improved the stability of the ring. If the standard deviation remains within the preset threshold at all temperature points, the optimization is successful; otherwise, if any temperature point still fails to meet the standard, return to step S8.3 and continue adjusting the parameters until the requirements are met.

[0192] S8.7 If the standard deviation of the radius stability of the new dataset is less than or equal to the preset stability threshold, output the current winding parameters, determine that the equivalent radius of the ring body has reached a stable state over the entire temperature range, and output the equivalent radius value under stable conditions.

[0193] Finally, when all temperature points exhibit good stability, meaning the standard deviation does not exceed the preset threshold, the optimization can be considered successful. At this point, the system will output the currently used winding parameters, which are considered the optimal choices to maintain the stability of the ring's equivalent radius across the entire temperature range. Simultaneously, the system will also output the equivalent radius value for each temperature point under steady-state conditions, providing important reference for subsequent production and quality control. Furthermore, these optimization results can be further used to improve future production processes, ensuring that every fiber optic gyroscope ring leaving the factory meets the highest performance standards.

[0194] Through this series of detailed steps, the present invention not only achieves effective optimization of winding process parameters, but also ensures the stability and consistency of the ring under various environmental conditions, significantly improving the overall performance and reliability of the fiber optic gyroscope.

[0195] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for manufacturing a fiber optic gyroscope ring to reduce the gyroscope scaling factor, characterized in that... Includes the following steps: Preparation: Divide an optical fiber symmetrically into two equal parts at its midpoint and wind each part onto two fiber supply rollers. Mount the two fiber supply rollers on the front and rear winding mechanisms of the winding equipment, respectively, for clockwise and counter-clockwise fiber winding. The winding process is as follows: S1. Obtain temperature distribution data during the optical fiber winding process, analyze the temperature gradient change trend inside and on the surface of the ring, and establish a mathematical model of the curing degree of each layer of the ring based on the temperature gradient change trend to describe the relationship between the curing degree and the temperature gradient. S2. Using a preset temperature control algorithm, the heating power and cooling rate during the winding process are adjusted to make the temperature gradient of each layer of the ring more uniform. By monitoring the temperature changes of each layer of the ring in real time, it is determined whether the temperature gradient has reached the preset uniformity threshold. S3. If the temperature gradient reaches the homogenization threshold, start the curing agent injection system and distribute the curing agent evenly to each layer of the ring according to the preset injection rate and pressure; adjust the curing time according to the distribution of the curing agent to make the curing degree of each layer of the ring tend to be consistent. S4. Obtain the expansion coefficient data of each layer of the cured ring and analyze the distribution characteristics of the expansion coefficient; Based on the distribution characteristics of the expansion coefficient, a relationship model between the equivalent radius of the ring and the expansion coefficient is established to describe the changing trend of the equivalent radius with the expansion coefficient. S5. A preset aging process algorithm is used to control the temperature and time parameters during the aging process, so that the expansion coefficients of each layer of the ring tend to be consistent. By monitoring the changes in the expansion coefficient of each layer of the ring in real time, it can be determined whether the expansion coefficient has reached the preset stabilization threshold. S6. If the expansion coefficient reaches the stabilization threshold, the scaling factor calibration system is activated, and the scaling factor of the gyroscope is adjusted according to the preset calibration procedure; based on the scaling factor calibration result, it is determined whether the measurement accuracy meets the preset accuracy requirements. S7. Obtain equivalent radius data over the entire temperature range and analyze the stability of the equivalent radius as a function of temperature. Based on the stability data of the equivalent radius, establish a performance stability evaluation model to describe the relationship between performance stability and the stability of the equivalent radius. S8 uses a preset performance optimization algorithm to adjust the winding process parameters so that the equivalent radius of the ring tends to be stable over the entire temperature range; By monitoring the changing trend of the equivalent radius in real time, it can be determined whether the performance stability has reached the preset optimization threshold.

2. The method for manufacturing a fiber optic gyroscope ring to reduce the gyroscope scaling factor according to claim 1, characterized in that: The sub-steps of S1 are as follows: S1.1 During the fiber optic winding process, an infrared thermal imager is used to acquire temperature distribution data inside and on the surface of the ring, and a two-dimensional temperature distribution matrix at different time points is obtained; S1.2 Preprocess the acquired temperature distribution data to remove outliers and noise, and obtain a smooth temperature distribution surface; S1.3 The numerical differential method is used to calculate the temperature gradient in the radial and axial directions inside and on the surface of the ring, and the temperature gradient variation trend of each layer of the ring is obtained. S1.4 Based on the thermophysical parameters and curing kinetics equations of the cyclic material, a mathematical model describing the relationship between the degree of curing of each layer of the cyclic material and the temperature gradient is established. S1.5 The ring is divided into several discrete elements. Based on the temperature gradient change trend, the degree of solidification of each element is solved by the finite element method. S1.6 The curing degree of each unit is interpolated and smoothed to obtain a continuous curing degree distribution surface, which is then compared and verified with the measured data. S1.7 employs machine learning algorithms to establish a mapping relationship between temperature gradient and curing degree, enabling online prediction and optimized control of curing degree.

3. The method for manufacturing a fiber optic gyroscope ring to reduce the gyroscope scaling factor according to claim 2, characterized in that: In S1.7, the optical fiber ring is wound using the four-pole symmetrical winding method. After every two layers of winding, online ultraviolet curing is performed. The curing lamp is an LED ultraviolet curing light source, and the winding environment temperature is 25±3℃, until the entire optical fiber ring is completed.

4. The method for manufacturing a fiber optic gyroscope ring to reduce the gyroscope scaling factor according to claim 1, characterized in that: The sub-steps of S2 are as follows: S2.1 Obtain real-time temperature data for each layer of the ring, input the temperature data into the preset temperature control algorithm model for analysis and calculation, and obtain the temperature gradient value of each layer; S2.2 Compare the temperature gradient value with the preset homogenization threshold. If the temperature gradient value is greater than the threshold, it is determined that the temperature gradient is not uniform and temperature control is required. S2.3 If the temperature gradient value is less than or equal to the threshold, it is determined that the temperature gradient has become consistent and no adjustment is needed; S2.4 When it is determined that temperature regulation is required, a temperature regulation model is established by using machine learning algorithms and combining historical process parameters with temperature gradient correlation data. The optimal heating power and cooling rate parameters are predicted by inputting the current temperature of each layer of the ring. S2.5 sends the predicted optimal heating power and cooling rate parameters to the control system of the winding equipment, adjusts the power of the heating device and the cooling capacity of the cooling device during the winding process, and performs differentiated temperature regulation on each layer of the ring. S2.6 During the temperature regulation process, real-time temperature data of each layer of the ring is continuously acquired, the temperature gradient value between each layer is calculated, and it is determined whether the temperature gradient has reached the homogenization threshold. S2.7 If the temperature gradient value is less than or equal to the homogenization threshold for multiple consecutive judgments, it is considered that the temperature of each layer of the ring has become consistent, and temperature control is stopped. The current heating power and cooling rate are maintained until the winding process is completed. S2.8 If, after a certain period of temperature adjustment, the temperature gradient values ​​of each layer of the ring are still greater than the homogenization threshold, an early warning message is triggered, the temperature control algorithm or homogenization threshold is manually adjusted, and the temperature control process is restarted until the requirements are met.

5. The method for manufacturing a fiber optic gyroscope ring with reduced gyroscope scaling factor according to claim 1, characterized in that: In S3, ultraviolet resin is selected as the adhesive and also as the curing agent for the fiber optic ring. The specific steps are as follows: S3.1 Obtain temperature gradient data for each layer of the ring, determine whether the temperature gradient has reached the preset homogenization threshold, if the threshold is reached, start the curing agent injection system, if the threshold is not reached, continue to monitor the temperature gradient change. S3.2 Based on the preset injection rate and injection pressure parameters, control the curing agent injection system to evenly distribute the curing agent to each layer of the ring. By adjusting the changes in injection rate and injection pressure, ensure the uniformity of curing agent distribution in each layer of the ring. S3.3 Computer vision technology is used to detect and analyze the distribution of curing agent in each layer of the ring, and the distribution density data of curing agent in each layer of the ring is obtained. The distribution density data is compared with the preset uniform distribution threshold to determine whether the injection rate and injection pressure parameters need to be adjusted. S3.4 Establish a correlation model between curing agent distribution density and curing time through machine learning algorithms. Based on the actual distribution of curing agent in each layer of the ring, dynamically adjust the curing time of each layer to make the curing degree of each layer tend to be consistent. S3.5 uses infrared thermal imaging technology to monitor the curing process of each layer of the ring in real time, obtain temperature change data of each layer, and judge the curing degree of each layer by temperature change data. When the curing degree of each layer reaches the preset consistency threshold, the curing time is stopped and adjusted. S3.6 During the curing process, temperature and curing agent distribution density data of each layer of the ring are continuously collected. Big data analysis technology is used to explore the correlation between the data and dynamically optimize the injection rate, injection pressure and curing time parameters to achieve a dynamic balance of the curing degree of each layer of the ring. S3.7 Establish a three-dimensional visualization model of the ring curing process, map the temperature gradient, curing agent distribution and curing degree data to the three-dimensional model, and display the curing state of each layer of the ring in real time through the visualization interface.

6. The method for manufacturing a fiber optic gyroscope ring with reduced gyroscope scaling factor according to claim 1, characterized in that: The sub-steps of S4 are as follows: S4.1 Obtain the original expansion coefficient data of each layer of the cured ring, preprocess the data, remove outliers and noise data, and obtain an effective expansion coefficient dataset; S4.2 Perform statistical analysis on the preprocessed expansion coefficient data, calculate the mean, variance and quantile of the expansion coefficient of each layer, and obtain the distribution characteristics of the expansion coefficient in each layer of the ring. S4.3 Based on the distribution characteristics of the expansion coefficient, a regression analysis method is used to establish a relationship model between the equivalent radius of the ring and the expansion coefficient, where the equivalent radius is the dependent variable and the expansion coefficient is the independent variable. The regression equation is obtained by fitting and is used to describe the changing trend of the equivalent radius with the expansion coefficient. S4.4 The established relationship model is evaluated and optimized. The cross-validation method is used to evaluate the goodness of fit and predictive performance of the model. Based on the evaluation results, the model is adjusted and optimized to obtain the optimal relationship model between the equivalent radius and the expansion coefficient. S4.5 Using the optimized relational model, the equivalent radius of the new solidified ring sample is predicted. Based on the expansion coefficient data of the sample, the corresponding equivalent radius prediction value is calculated by the model to guide the design and manufacturing of the ring. S4.6 Perform error analysis on the prediction results, calculate the error between the predicted value and the actual measured value, evaluate the prediction accuracy of the model, and if the error exceeds the preset threshold, it is necessary to further optimize the model or obtain more training data. S4.7 Integrates the optimized model of the relationship between the equivalent radius and the coefficient of expansion into the design and manufacturing process of the ring. Based on the material properties and process parameters of the ring, the equivalent radius of each layer of the ring is predicted, which guides the structural design and process optimization of the ring and improves the accuracy and efficiency of the ring manufacturing.

7. The method for manufacturing a fiber optic gyroscope ring with reduced gyroscope scaling factor according to claim 1, characterized in that: The sub-steps of S6 are: S6.1 Acquire real-time data from the gyroscope, calculate the current expansion coefficient based on the data, compare the expansion coefficient with a preset stability threshold, and determine whether a stable state has been reached. S6.2 If the expansion coefficient reaches the stable threshold, the scaling factor calibration system is activated, the preset scaling factor calibration program is called, and the scaling factor of the gyroscope is automatically adjusted according to the steps of the calibration program. S6.3 During the scaling factor adjustment process, the Kalman filter algorithm is used to filter the gyroscope data to reduce noise interference and improve the accuracy of scaling factor adjustment; S6.4 Using the least squares fitting algorithm, the optimal scaling factor calibration coefficient is fitted based on multiple sets of data before and after scaling factor adjustment, which is then used for subsequent scaling factor calibration. S6.5 Write the calibrated scaling factor parameters into the gyroscope's calibration register to complete the scaling factor update, and save the calibration results to the system log; S6.6 Based on the calibrated gyroscope data, the Bayesian estimation algorithm is used to calculate the current measurement accuracy, and the measurement accuracy is compared with the preset accuracy requirements to determine whether the calibrated measurement accuracy meets the requirements. S6.7 If the measurement accuracy meets the preset requirements, the scaling factor calibration is completed. If the accuracy does not meet the requirements, the calibration parameters are adjusted and the scaling factor calibration is repeated until the measurement accuracy meets the requirements.

8. The method for manufacturing a fiber optic gyroscope ring with reduced gyroscope scaling factor according to claim 1, characterized in that: The sub-steps of S7 are: S7.1 Obtain equivalent radius measurement data over the entire temperature range and group the data according to temperature intervals; S7.2 Calculate the mean and variance of the equivalent radius data within each temperature range to obtain the statistical characteristics of the equivalent radius at different temperatures; S7.3 Based on the trend of the mean of the equivalent radius changing with temperature, the least squares method is used to perform curve fitting to obtain the equivalent radius-temperature relationship curve; S7.4 Calculate the slope of each point on the equivalent radius-temperature curve. When the slope is less than the preset threshold, it is determined that the equivalent radius is stable within the temperature range. S7.5 Acquire performance stability test data and extract the average value of performance indicators at different temperatures as a quantitative indicator of performance stability; S7.6 Input the equivalent radius stability data and performance stability index into the support vector machine model for training to obtain the performance stability evaluation model; S7.7 Utilizes a trained performance stability evaluation model to predict performance stability at any temperature, providing a basis for product performance optimization decisions.

9. A method for manufacturing a fiber optic gyroscope ring to reduce the gyroscope scaling factor according to claim 1, characterized in that: The sub-steps of S8 are: S8.1 According to the preset optimization algorithm, obtain the initial winding parameter values, including the number of coil turns, wire diameter and winding tension, and determine these parameter values ​​as the input of the algorithm; S8.2 Obtain the equivalent radius measurement data of the ring body at different temperatures. Using a temperature sensor and a high-precision displacement sensor, the radius data of the ring body at different temperatures is obtained. These data constitute an equivalent radius-temperature dataset. S8.3 For the equivalent radius-temperature dataset, calculate the average value and standard deviation of the equivalent radius at each temperature point to evaluate the radius stability of the ring at different temperatures. If the standard deviation exceeds the preset stability threshold, the parameter adjustment step is required. S8.4 uses a preset optimization algorithm to calculate new winding parameter values ​​based on the equivalent radius-temperature dataset and stability threshold. If the algorithm determines that the current parameter values ​​cannot stabilize the radius, the winding parameters are adjusted and the adjusted winding parameters are recorded. S8.5 Based on the adjusted winding parameters, rewind the ring and obtain the equivalent radius measurement data of the new ring at different temperatures to form a new equivalent radius-temperature dataset for verifying the effect of parameter adjustment; S8.6 For the new equivalent radius-temperature dataset, recalculate the average value and standard deviation of the equivalent radius for each temperature point, and determine whether the preset stability threshold has been reached. If not, return to step S8.3 for iterative optimization. S8.7 If the standard deviation of the radius stability of the new dataset is less than or equal to the preset stability threshold, the current winding parameters are output, and it is determined that the equivalent radius of the ring body has reached a stable state in the full temperature range. The equivalent radius value in the stable state is then output.

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