Optical fiber gyroscope ring body manufacturing method for reducing gyroscope scale factor

Abstract

An optical fiber gyroscope ring body manufacturing method for reducing a gyroscope scale factor comprises the following steps: if an expansion coefficient reaches a stabilization threshold value, starting a scale factor calibration system, adjusting the scale factor of a gyroscope according to a preset calibration program, and judging whether the measurement precision reaches a preset precision requirement or not according to a calibration result of the scale factor; acquiring equivalent radius data in a full temperature range, analyzing the stability of the equivalent radius along with the temperature change, establishing a performance stability evaluation model according to the stability data of the equivalent radius, and describing the relationship between the performance stability and the equivalent radius stability; a preset performance optimization algorithm is adopted, winding process parameters are adjusted, the equivalent radius of the ring body in the full-temperature range tends to be stable, and whether the performance stability reaches a preset optimization threshold value or not is judged by monitoring the change trend of the equivalent radius in real time.

Description

Technical Field

[0001] The present invention belongs to the technical field of the manufacturing of the ring body of a fiber optic gyroscope, and particularly relates to a method for manufacturing the ring body of a fiber optic gyroscope for reducing the gyro scale factor. Background Art

[0002] In the traditional optical fiber winding process, due to factors such as inaccurate temperature control, uneven distribution of curing agents, and unstable expansion coefficients, it is easy to cause large differences in the physical properties between layers of the ring body, thereby affecting the scale factor stability of the gyroscope and reducing the measurement accuracy. Analyzing the main reasons shows that: during the optical fiber winding process, if the temperature gradient inside and on the surface of the ring body cannot be effectively controlled, it will lead to different curing degrees of each layer, affecting the overall structural stability and mechanical strength of the ring body. Therefore, it is urgent to propose a new method for manufacturing the ring body of a fiber optic gyroscope to solve the above problems. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for manufacturing the ring body of a fiber optic gyroscope for reducing the gyro scale factor, optimize the optical fiber winding process, improve the consistency of the curing degrees of each layer of the ring body, and thus enhance the overall performance and measurement accuracy of the fiber optic gyroscope.

[0004] To solve the above technical problems, the technical solution adopted by the present invention is: A method for manufacturing the ring body of a fiber optic gyroscope for reducing the gyro scale factor, the steps are as follows: Preparation for production: Divide an optical fiber symmetrically at the midpoint and equally into two parts and wind them around two fiber supply wheels respectively. Install the two fiber supply wheels on the front and rear winding mechanisms of the winding equipment and use them for clockwise winding and counterclockwise optical fiber winding respectively; the winding process is as follows: S1. Obtain the temperature distribution data during the optical fiber winding process, analyze the changing trend of the temperature gradient inside and on the surface of the ring body, and establish a mathematical model of the curing degree of each layer of the ring body according to the changing trend of the temperature gradient to describe the relationship between the curing degree and the temperature gradient; S2. Adopt a preset temperature control algorithm to adjust the heating power and cooling rate during the winding process to make the temperature gradients of each layer of the ring body tend to be consistent, and judge whether the temperature gradient reaches a preset homogenization threshold by real-time monitoring of the temperature changes of each layer of the ring body; 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 body according to the preset injection rate and pressure; adjust the curing time according to the distribution of the curing agent to make the curing degrees of each layer of the ring body tend to be consistent; S4. Obtain the expansion coefficient data of each layer of the cured ring body, and analyze the distribution characteristics of the expansion coefficient; according to the distribution characteristics of the expansion coefficient, establish a relationship model between the equivalent radius of the ring body and the expansion coefficient to describe the change trend of the equivalent radius with the expansion coefficient; S5. Adopt a preset aging treatment algorithm to control the temperature and time parameters during the aging process, so that the expansion coefficients of each layer of the ring body tend to be consistent; judge whether the expansion coefficient reaches the preset stabilization threshold by real-time monitoring of the change of the expansion coefficient of each layer of the ring body; S6. If the expansion coefficient reaches the stabilization threshold, start the scale factor calibration system, and adjust the scale factor of the gyroscope according to the preset calibration procedure; judge whether the measurement accuracy meets the preset accuracy requirements according to the calibration result of the scale factor; S7. Obtain the equivalent radius data within the full temperature range, and analyze the stability of the equivalent radius with temperature change; according to the stability data of the equivalent radius, establish a performance stability evaluation model to describe the relationship between performance stability and equivalent radius stability; S8. Adopt a preset performance optimization algorithm to adjust the winding process parameters to make the equivalent radius of the ring body stable within the full temperature range; judge whether the performance stability reaches the preset optimization threshold by real-time monitoring of the change trend of the equivalent radius.

[0005] Preferably, the sub-steps of S1 are as follows: S1.1 During the fiber winding process, use an infrared thermal imager to obtain the temperature distribution data inside and on the surface of the ring body to obtain a two-dimensional temperature distribution matrix at different time points; S1.2 Preprocess the obtained temperature distribution data to remove outliers and noise to obtain a smooth temperature distribution surface; S1.3 Use the numerical differentiation method to calculate the temperature gradients in the radial and axial directions inside and on the surface of the ring body to obtain the change trend of the temperature gradients of each layer of the ring body; S1.4 According to the thermal physical properties of the ring body material and the curing kinetics equation, establish a mathematical model describing the relationship between the curing degree of each layer of the ring body and the temperature gradient; S1.5 Divide the ring body into several discrete units, and use the finite element method to solve the curing degree of each unit according to the change trend of the temperature gradient; S1.6 Interpolate and smooth the curing degree of each unit to obtain a continuous curing degree distribution surface, and compare and verify it with the measured data; S1.7 Use a machine learning algorithm to establish a mapping relationship between the temperature gradient and the curing degree to realize the online prediction and optimal control of the curing degree.

[0006] Preferably, in S1.7, the fiber optic ring is wound according to a quadrupole symmetric winding method, and after every two layers of winding, on-line ultraviolet curing is carried out. The curing lamp is an LED ultraviolet curing light source, and the winding ambient temperature is 25±3°C until the entire fiber optic ring winding is completed.

[0007] Preferably, the sub-steps of S2 are as follows: S2.1 Obtain the real-time temperature data of each layer of the ring body, input the temperature data into a 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 a preset homogenization threshold. If the temperature gradient value is greater than the threshold, it is determined that the temperature gradient is uneven and temperature regulation 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 tended to be consistent and no regulation is required; S2.4 When it is determined that temperature regulation is required, through a machine learning algorithm, combined with the correlation data between historical process parameters and temperature gradients, establish a temperature regulation model, input the current temperature of each layer of the ring body, and predict the optimal heating power and cooling rate parameters; S2.5 Send the predicted optimal heating power and cooling rate parameters to the control system of the winding equipment, adjust the power of the heating device and the refrigerating capacity of the cooling device during the winding process, and perform differential temperature regulation on each layer of the ring body; S2.6 During the temperature regulation process, continuously obtain the real-time temperature data of each layer of the ring body, calculate the temperature gradient value between each layer, and judge whether the temperature gradient reaches the homogenization threshold; S2.7 If it is continuously determined that the temperature gradient value is less than or equal to the homogenization threshold for multiple times, it is considered that the temperature of each layer of the ring body has tended to be consistent, stop temperature regulation, and maintain the current heating power and cooling rate until the winding process is completed; S2.8 If after a certain period of temperature regulation, the temperature gradient value of each layer of the ring body is still greater than the homogenization threshold, trigger a warning message, manually adjust the temperature control algorithm or the homogenization threshold, and restart the temperature regulation process until the requirements are met.

[0008] Preferably, in S3, an ultraviolet resin is selected as the adhesive and also the curing agent for the fiber optic ring. The specific steps are as follows: S3.1 Obtain the temperature gradient data of each layer of the ring body, judge whether the temperature gradient reaches a preset homogenization threshold. If it reaches the threshold, start the curing agent injection system. If it does not reach the threshold, continue to monitor the change of the temperature gradient; S3.2 According to 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 body, and ensure the uniformity of the curing agent distribution in each layer of the ring body by adjusting the changes of the injection rate and injection pressure; S3.3 Use computer vision technology to detect and analyze the distribution of the curing agent in each layer of the ring body, obtain the distribution density data of the curing agent in each layer of the ring body, compare the distribution density data with the preset uniform distribution threshold, and determine whether it is necessary to adjust the injection rate and injection pressure parameters; S3.4 Establish a correlation model between the curing agent distribution density and the curing time through a machine learning algorithm, and dynamically adjust the curing time of each layer according to the actual distribution of the curing agent in each layer of the ring body so that the curing degree of each layer tends to be consistent; S3.5 Use infrared thermal imaging technology to monitor the curing process of each layer of the ring in real time, obtain the temperature change data of each layer, and judge the curing degree of each layer based on the temperature change data. When the curing degree of each layer reaches the preset consistency threshold, stop adjusting the curing time; S3.6 During the curing process, continuously collect data such as the temperature and curing agent distribution density of each layer of the ring body, and use big data analysis technology to explore the correlation between the data, dynamically optimize parameters such as injection rate, injection pressure and curing time, and achieve a dynamic balance of the curing degree of each layer of the ring body; S3.7 Establish a three-dimensional visualization model of the ring body curing process, map data such as temperature gradient, curing agent distribution and curing degree with the three-dimensional model, and display the curing status of each layer of the ring body in real time through a visualization interface.

[0009] Preferably, the sub-steps of S4 are as follows: S4.1 Obtain the original expansion coefficient data of each layer of the ring body after curing, pre-process the data, remove abnormal values ​​and noise data, and obtain a valid expansion coefficient data set; S4.2 Statistically analyze 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 body; 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, in which the equivalent radius is the dependent variable and the expansion coefficient is the independent variable. The regression equation is obtained by fitting to describe the change trend of the equivalent radius with the expansion coefficient; S4.4 Evaluate and optimize the established relationship model, use the cross-validation method to evaluate the goodness of fit and prediction performance of the model, adjust and optimize the model based on the evaluation results, and obtain the optimal equivalent radius and expansion coefficient relationship model; S4.5 Use the optimized relationship model to predict the equivalent radius of the new cured ring sample. According to the expansion coefficient data of the sample, the corresponding equivalent radius prediction value is calculated through the model to guide the design and manufacturing of the ring; S4.6 Conduct 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. If the error exceeds the preset threshold, it is necessary to further optimize the model or obtain more training data; S4.7 Integrate the optimized relationship model between the equivalent radius and the expansion coefficient into the ring body design and manufacturing process. According to the material properties and process parameters of the ring body, predict the equivalent radius of each layer of the ring body, guide the ring body structure design and process optimization, and improve the accuracy and efficiency of ring body manufacturing.

[0010] Preferably, the sub-steps of S5 are as follows: S5.1 Establish a three-dimensional mathematical model of aging treatment temperature-time-expansion coefficient based on the material properties of the ring body, and obtain the predicted values of the expansion coefficients of each layer of the ring body under different temperature and time combinations; S5.2 Use machine learning algorithms to train the aging treatment optimization model based on historical data, and learn the optimal temperature and time parameter combinations to minimize the difference in expansion coefficients of each layer; S5.3 Place the ring body in a temperature-controlled box and perform aging treatment according to the temperature-time parameters output by the optimization model; S5.4 At the same time, install strain sensors on each layer of the ring body to collect strain data of each layer in real time; S5.5 Calculate the real-time expansion coefficients of each layer of the ring body through the strain data; S5.6 Compare the real-time expansion coefficients with the predicted values of the mathematical model, and dynamically adjust the temperature and time parameters to make the actual expansion coefficients approach the predicted values; S5.7 Conduct statistical analysis on the real-time collected expansion coefficients of each layer, and calculate the mean and variance of the expansion coefficients of each layer; S5.8 If the variance is less than the preset stabilization threshold, it is determined that the aging treatment is completed, and the expansion coefficients of each layer of the ring body have tended to be consistent; S5.9 If after a certain period of time, the variance of the expansion coefficients of each layer of the ring body is still greater than the threshold, trigger the alarm mechanism, analyze the factors causing the difference in expansion coefficients, and manually intervene to adjust the aging treatment parameters if necessary; S5.10 After the aging treatment is completed, conduct quality inspection on each layer of the ring body, test the mechanical properties of each layer by sampling, verify the consistency of the properties of each layer, and ensure that the aging treatment effect meets the design requirements; When the entire optical fiber winding is completed, the curing temperature is 85±2°C.

[0011] Preferably, the sub-steps of S6 are: S6.1 Obtain the data collected by the gyroscope in real time, calculate the current expansion coefficient according to the data, compare the expansion coefficient with the preset stability threshold, and judge whether it reaches the stable state; S6.2 If the expansion coefficient reaches the stable threshold, start the scale factor calibration system, call the preset scale factor calibration program, and automatically adjust the scale factor of the gyroscope according to the steps of the calibration program; S6.3 During the scale factor adjustment process, use the Kalman filter algorithm to filter the gyroscope data, reduce noise interference, and improve the accuracy of scale factor adjustment; S6.4 Through the least squares fitting algorithm, based on multiple sets of data before and after scale factor adjustment, fit the optimal scale factor calibration coefficient for subsequent scale factor calibration; S6.5 Write the calibrated scale factor parameter into the calibration register of the gyroscope, complete the update of the scale factor, and save the calibration result to the system log; S6.6 According to the calibrated gyroscope data, use the Bayesian estimation algorithm to calculate the current measurement accuracy, compare the measurement accuracy with the preset accuracy requirement, and determine whether the calibrated measurement accuracy meets the requirement; S6.7 If the measurement accuracy meets the preset requirement, complete the current scale factor calibration. If the accuracy does not meet the requirement, adjust the calibration parameters and re - perform the scale factor calibration until the measurement accuracy reaches the standard.

[0012] Preferably, the sub - steps of S7 are: S7.1 Obtain the equivalent radius measurement data within the full temperature range, and group and classify the data according to temperature intervals; S7.2 Calculate the mean and variance of the equivalent radius data within each temperature interval to obtain the statistical characteristics of the equivalent radius at different temperatures; S7.3 According to the changing trend of the equivalent radius mean with temperature, use the least squares method for 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 this temperature interval; S7.5 Obtain the performance stability test data, and extract the mean of the performance indicators at different temperatures as the performance stability quantification index; 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 Use the trained performance stability evaluation model to predict the performance stability at any temperature, providing a decision - making basis for product performance optimization.

[0013] Preferably, 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. Use a temperature sensor and a high-precision displacement sensor to obtain the radius data of the ring body at different temperatures, and these data form an equivalent radius-temperature data set. S8.3 For the equivalent radius-temperature data set, calculate the average value and standard deviation of the equivalent radius at each temperature point to evaluate the radius stability of the ring body at different temperatures. If the standard deviation exceeds the preset stability threshold, it is necessary to enter the parameter adjustment step. S8.4 Use a preset optimization algorithm. According to the equivalent radius-temperature data set and the stability threshold, calculate the new winding parameter values. If the algorithm determines that the current parameter values cannot stabilize the radius, adjust the winding parameters and record the adjusted winding parameters. S8.5 According to the adjusted winding parameters, rewind the ring body and obtain the equivalent radius measurement data of the new ring body at different temperatures to form a new equivalent radius-temperature data set for verifying the effect of parameter adjustment. S8.6 For the new equivalent radius-temperature data set, calculate the average value and standard deviation of the equivalent radius at each temperature point again, and determine whether the preset stability threshold is reached. If not, return to step S8.3 for iterative optimization. S8.7 If the standard deviation of the radius stability of the new data set is less than or equal to the preset stability threshold, output the current winding parameters and determine that the equivalent radius of the ring body has reached a stable state within the full temperature range, and output the equivalent radius value in the stable state.

[0014] The present invention can achieve the following beneficial effects: 1. The present invention adopts a temperature monitoring and control algorithm to ensure that the temperature gradient of each layer of the ring body remains consistent during the winding process, improving the curing quality.

[0015] 2. The present invention optimizes the curing agent injection strategy: establish a relationship model between the curing agent distribution density and the curing time through a machine learning algorithm, and dynamically adjust the curing parameters according to the actual distribution situation to ensure the uniform distribution of the curing agent between each layer of the ring body, making the curing degree tend to be consistent.

[0016] 3. The present invention introduces expansion coefficient analysis and modeling technology, combined with an aging treatment algorithm, to make the expansion coefficients of each layer of the ring body tend to be consistent within the full temperature range, reducing the influence of the equivalent radius change with temperature, thereby improving the stability of the scale factor.

[0017] 4. The present invention optimizes the temperature and time parameters of the aging treatment through an intelligent algorithm to ensure that the expansion coefficients of each layer of the ring body reach the preset stabilization threshold, ensuring the reliability and accuracy during long-term use.

[0018] 5. The present invention constructs a three-dimensional visualization model, uses big data analysis and machine learning technologies to monitor the entire manufacturing process in real time and optimize it dynamically, provides intuitive decision-making support, and improves the automation level and efficiency of production. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the drawings and embodiments: Figure 1 is the flowchart of the present invention; Figure 2 is the schematic diagram of curing every 2 / 4 layers in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The preferred solution is as Figures 1 to 2 shown, a method for manufacturing a fiber optic gyroscope ring body for reducing the gyro scale factor, Preparation work: A fiber optic is symmetrically divided into two equal parts according to the midpoint and wound around two fiber supply wheels respectively. The two fiber supply wheels are respectively installed on the front and rear winding mechanisms of the winding equipment and are respectively used for clockwise winding and counterclockwise fiber winding. The winding process is as follows: S1. Obtain the temperature distribution data during the fiber optic winding process, analyze the temperature gradient change trend inside and on the surface of the ring body, and establish a mathematical model for the curing degree of each layer of the ring body according to the temperature gradient change trend to describe the relationship between the curing degree and the temperature gradient; S1.1 During the fiber optic winding process, use an infrared thermal imager to obtain the temperature distribution data inside and on the surface of the ring body to obtain a two-dimensional temperature distribution matrix at different time points; S1.2 Preprocess the obtained temperature distribution data to remove outliers and noise to obtain a smooth temperature distribution surface; S1.3 Use the numerical differentiation method to calculate the temperature gradients in the radial and axial directions inside and on the surface of the ring body to obtain the temperature gradient change trend of each layer of the ring body; S1.4 According to the thermal physical properties of the ring body material and the curing kinetics equation, establish a mathematical model describing the relationship between the curing degree of each layer of the ring body and the temperature gradient; For example: the curing reaction rate formula: ; : the curing reaction rate, with the unit of , and its value depends on the temperature T : the frequency factor or pre-exponential factor, with the unit of , which reflects the reaction rate situation at infinitely high temperature.

[0021] : the activation energy, with the unit of , representing the minimum energy required to initiate the reaction.

[0022] R: The ideal gas constant, whose value is fixed at .

[0023] Correction factor formula considering the influence of temperature gradient:

[0024] k : Proportionality coefficient, with the unit of , which serves to quantify the influence degree of temperature gradient on the curing rate.

[0025] : Magnitude of the temperature gradient, with the unit of .

[0026] Final curing degree expression: ; : Represents the curing degree at the spatial position and time t; τ is the integration variable representing time (unit: s).

[0027] S1.5 Divide the ring body into several discrete units, and according to the variation trend of the temperature gradient, use the finite element method to solve the curing degree of each unit; S1.6 Interpolate and smooth the curing degree of each unit to obtain a continuous curing degree distribution surface, and compare and verify it with the measured data; S1.7 Adopt machine learning algorithms such as support vector machines or neural networks to establish the mapping relationship between the temperature gradient and the curing degree, and realize the online prediction and optimal control of the curing degree.

[0028] Preferably, in S1.7, wind the fiber optic ring in a quadrupole symmetric winding method, perform on-line ultraviolet curing after every 2 layers of winding. Preferably, the curing lamp is an LED ultraviolet curing light source, and the winding environment temperature is 25 ± 3 °C until the entire fiber optic ring winding is completed.

[0029] S2. Adopt a preset temperature control algorithm to adjust the heating power and cooling rate during the winding process, so that the temperature gradients of each layer of the ring body tend to be consistent, and judge whether the temperature gradient reaches the preset homogenization threshold by real-time monitoring the temperature changes of each layer of the ring body; S2.1 Obtain the real-time temperature data of each layer of the ring body, input the temperature data into the preset temperature control algorithm model for analysis and calculation, and obtain the temperature gradient values of each layer; S2.2 Compare the temperature gradient value with a preset homogenization threshold. If the temperature gradient value is greater than the threshold, it is determined that the temperature gradient is uneven 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 control is required, a temperature control model is established through a machine learning algorithm, combining the historical process parameters and the associated data of the temperature gradient. The current temperatures of each layer of the ring body are input, and the optimal heating power and cooling rate parameters are predicted. S2.5 Send the predicted optimal heating power and cooling rate parameters to the control system of the winding equipment, adjust the power of the heating device and the refrigerating capacity of the cooling device during the winding process, and perform differential temperature adjustment on each layer of the ring body. S2.6 During the temperature adjustment process, continuously obtain the real-time temperature data of each layer of the ring body, calculate the temperature gradient value between each layer, and determine whether the temperature gradient reaches the homogenization threshold. S2.7 If it is continuously determined that the temperature gradient value is less than or equal to the homogenization threshold for multiple times, it is considered that the temperatures of each layer of the ring body have become consistent, stop the temperature control, and maintain the current heating power and cooling rate until the winding process is completed. S2.8 If after a certain period of temperature adjustment, the temperature gradient value of each layer of the ring body is still greater than the homogenization threshold, trigger a warning message, manually intervene to analyze the reason, and adjust the temperature control algorithm or the homogenization threshold if necessary, and start the temperature control process again until the requirements are met.

[0030] 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 body 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 body tend to be consistent. Select ultraviolet resin as the adhesive, which is also the curing agent for the fiber optic ring.

[0031] The specific steps are as follows: S3.1 Obtain the temperature gradient data of each layer of the ring body, and determine whether the temperature gradient reaches the preset homogenization threshold: During the process of fiber optic coil winding, first, high-precision temperature sensors installed at various key positions of the coil body are used to obtain the temperature distribution inside and on the surface of the coil body in real time. These sensors can provide accurate temperature readings to ensure that subtle temperature changes can be captured. At the same time, an infrared thermal imager is used to perform non-contact temperature measurement on the coil body to generate a two-dimensional temperature distribution image of the coil body surface. By analyzing this data, the temperature difference, that is, the temperature gradient, between different layers of the coil body can be calculated. Next, the system will evaluate whether the current temperature gradient meets the requirements according to the preset homogenization threshold. If the temperature gradients at all measurement points are within the allowable range, it indicates that the temperature has been homogenized and the curing agent injection system can be activated; otherwise, if the temperature gradient does not reach the expected standard, continue to monitor and adjust the heating and cooling processes until the temperature gradient tends to be consistent.

[0032] S3.2 Control the curing agent injection system to evenly distribute the curing agent to each layer of the coil body according to the preset injection rate and injection pressure parameters: Once it is confirmed that the temperature gradient has reached the preset homogenization threshold, the curing agent injection system will be activated. This system operates according to the preset parameters, including the injection rate of the curing agent (the amount of curing agent injected per unit time) and the injection pressure (the force to push the curing agent into the coil body). To ensure that the curing agent can be evenly distributed among the layers of the coil body, the system will dynamically adjust these two parameters. For example, in some areas, a higher injection rate or greater pressure may be required to ensure that the curing agent can fully penetrate. Throughout the process, the flow path of the curing agent will also be monitored by a vision detection system to ensure that it spreads in the expected manner, thus achieving uniform distribution.

[0033] S3.3 Detect and analyze the distribution of the curing agent in each layer of the coil body using computer vision technology To further verify whether the curing agent is indeed evenly distributed in each layer of the coil body, advanced computer vision technology will be used for detection. Specifically, high-definition cameras are used to take pictures of each layer of the coil body, and the distribution of the curing agent in these images is analyzed with the help of specially developed software algorithms. The software can automatically identify and quantify information such as the coverage area and density of the curing agent in each layer to form a detailed distribution report. Then, these distribution data are compared with the preset standards to determine whether it is necessary to adjust the previous injection parameters. If there are any deviations, such as too little or too much curing agent in a certain layer, the system will correspondingly adjust the subsequent injection strategy to ensure the consistency of the final curing effect.

[0034] S3.4 Establish an association model between the curing agent distribution density and the curing time through machine learning algorithms; Based on historical data and experimental results, a relationship model between the curing agent distribution density and the curing time was established using machine learning algorithms. This model can help predict the most suitable curing time under different conditions, ensuring that the curing degree of each layer remains consistent. When new curing agent distribution data is input into the model, it can recommend the best curing solution according to the existing conditions. For example, if the curing agent distribution in a specific layer is relatively 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 body has the same physical properties after curing, avoiding quality problems caused by uneven curing.

[0035] S3.5 Use infrared thermal imaging technology to monitor the curing process of each layer of the ring body in real time: During the curing process, infrared thermal imaging technology plays a crucial role. By continuously scanning the temperature changes of each layer of the ring body, the infrared thermal imager can track the curing progress in real time. Since curing is an exothermic reaction, the temperature of each layer will increase as the reaction proceeds. By analyzing the trend of temperature changes, the change in the curing degree can be indirectly understood. When the temperature curves of all layers tend to be stable and reach the preset consistency threshold, it indicates that the curing is complete, and at this time, the adjustment of the curing time can be stopped. In addition, infrared thermal imaging also provides additional safety protection because it can detect any abnormal temperature rise at an early stage and take timely measures to prevent potential risks.

[0036] S3.6 During the curing process, continuously collect data such as the temperature and curing agent distribution density of each layer of the ring body: To ensure that the curing process is always in the optimal state, it is necessary to continuously collect data on the temperature, curing agent distribution density of each layer of the ring body, and other relevant parameters. These data not only help monitor the curing progress in real time but also provide rich materials for subsequent big data analysis. Through big data analysis techniques, the laws hidden behind a large amount of data can be mined, such as the subtle relationship between temperature changes and the curing degree, and the impact of the curing agent distribution pattern on the performance of the final product. Using these findings, various parameters in the curing process, such as the 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 state as possible, thereby improving the overall product quality.

[0037] S3.7 Establish a three-dimensional visualization model of the ring body curing process: In order to provide operators with a more intuitive understanding and support decision-making, a 3D visualization model of the ring curing process was constructed. In this model, not only important information such as temperature gradient, curing agent distribution and curing degree are included, but also they are mapped into an easy-to-understand 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 the transparency of the operation, but also enhances the control over the curing process. More importantly, such visualization tools provide strong support for the intelligent control of the curing process, helping to better adjust parameters and optimize the production process.

[0038] S4, obtaining the expansion coefficient data of each layer of the ring body after curing, and analyzing the distribution characteristics of the expansion coefficient; according to the distribution characteristics of the expansion coefficient, establishing a relationship model between the equivalent radius of the ring body and the expansion coefficient, and describing the change trend of the equivalent radius with the expansion coefficient; S4.1 Obtain the original expansion coefficient data of each layer of the ring body after curing, pre-process the data, remove abnormal values ​​and noise data, and obtain a valid expansion coefficient data set; S4.2 Statistically analyze the preprocessed expansion coefficient data, calculate the statistical characteristics of the expansion coefficient of each layer, such as the mean, variance and quantile, and obtain the distribution characteristics of the expansion coefficient in each layer of the ring body; 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, in which the equivalent radius is the dependent variable and the expansion coefficient is the independent variable. The regression equation is obtained by fitting to describe the change trend of the equivalent radius with the expansion coefficient; S4.4 Evaluate and optimize the established relationship model, use methods such as cross-validation to evaluate the model's goodness of fit and prediction performance, adjust and optimize the model based on the evaluation results, and obtain the optimal equivalent radius and expansion coefficient relationship model; S4.5 Use the optimized relationship model to predict the equivalent radius of the new cured ring sample. According to the expansion coefficient data of the sample, the corresponding equivalent radius prediction value is calculated through 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, and evaluate the prediction accuracy of the model. If the error exceeds the preset threshold, it is necessary to further optimize the model or obtain more training data; S4.7 Integrate the optimized equivalent radius and expansion coefficient relationship model into the ring design and manufacturing process. According to the material properties and process parameters of the ring, predict the equivalent radius of each layer of the ring, guide the ring structure design and process optimization, and improve the accuracy and efficiency of ring manufacturing.

[0039] S5. Use a preset aging treatment algorithm to control the temperature and time parameters during the aging process, making the expansion coefficients of each layer of the ring body tend to be consistent; judge whether the expansion coefficient reaches the preset stabilization threshold by monitoring the change of the expansion coefficient of each layer of the ring body in real time; S5.1 Based on the material characteristics of the ring body, establish a three-dimensional mathematical model of aging treatment temperature-time-expansion coefficient to obtain the predicted values of the expansion coefficients of each layer of the ring body under different temperature and time combinations; S5.2 Use machine learning algorithms, such as support vector machines or neural networks, to train an aging treatment optimization model based on historical data, and learn the optimal combination of temperature and time parameters to minimize the difference in expansion coefficients of each layer; S5.3 Place the ring body in a temperature-controlled box and perform aging treatment according to the temperature-time parameters output by the optimization model; S5.4 At the same time, install strain sensors on each layer of the ring body to collect the strain data of each layer in real time; S5.5 Calculate the real-time expansion coefficients of each layer of the ring body through the strain data; S5.6 Compare the real-time expansion coefficient with the predicted value of the mathematical model, and dynamically adjust the temperature and time parameters to make the actual expansion coefficient approach the predicted value; S5.7 Conduct 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; S5.8 If the variance is less than the preset stabilization threshold, it is determined that the aging treatment is completed, and the expansion coefficients of each layer of the ring body have tended to be consistent; S5.9 If after a certain period of time, the variance of the expansion coefficients of each layer of the ring body is still greater than the threshold, trigger the alarm mechanism, analyze the factors causing the difference in expansion coefficients, and manually intervene to adjust the aging treatment parameters if necessary; S5.10 After the aging treatment is completed, conduct quality inspection on each layer of the ring body, test the mechanical properties of each layer by sampling, verify the consistency of the performance of each layer, and ensure that the aging treatment effect meets the design requirements.

[0040] After the entire optical fiber ring is wound, age the optical fiber ring at a certain temperature. Preferably, the curing and forming temperature is 85 ± 2 °C.

[0041] S6. If the expansion coefficient reaches the stabilization threshold, start the scale factor calibration system, and adjust the scale factor of the gyroscope according to the preset calibration procedure; judge whether the measurement accuracy meets the preset accuracy requirements according to the calibration result of the scale factor; S6.1 Obtain the data collected by the gyroscope in real time, calculate the current expansion coefficient based on the data, compare the expansion coefficient with the preset stable threshold, and judge whether it reaches the stable state; S6.2 If the expansion coefficient reaches the stable threshold, start the scale factor calibration system, call the preset scale factor calibration program, and automatically adjust the scale factor of the gyroscope according to the steps of the calibration program; S6.3 During the scale factor adjustment process, use the Kalman filter algorithm to filter the gyroscope data, reduce noise interference, and improve the accuracy of scale factor adjustment; S6.4 Through the least squares fitting algorithm, based on multiple sets of data before and after scale factor adjustment, fit the optimal scale factor calibration coefficient for subsequent scale factor calibration; S6.5 Write the calibrated scale factor parameter into the calibration register of the gyroscope, complete the update of the scale factor, and save the calibration result to the system log; S6.6 According to the calibrated gyroscope data, use the Bayesian estimation algorithm to calculate the current measurement accuracy, compare the measurement accuracy with the preset accuracy requirement, and determine whether the calibrated measurement accuracy meets the requirement; S6.7 If the measurement accuracy meets the preset requirement, complete the current scale factor calibration. If the accuracy does not meet the requirement, adjust the calibration parameters and re-perform the scale factor calibration until the measurement accuracy reaches the standard.

[0042] S7. Obtain the equivalent radius data within the full temperature range and analyze the stability of the equivalent radius with respect to temperature change; based on the stability data of the equivalent radius, establish a performance stability evaluation model to describe the relationship between performance stability and equivalent radius stability; S7.1 Obtain the equivalent radius measurement data within the full temperature range and group and classify the data according to temperature intervals; S7.2 Calculate the mean and variance of the equivalent radius data within each temperature interval to obtain the statistical characteristics of the equivalent radius at different temperatures; S7.3 According to the changing trend of the equivalent radius mean with respect to temperature, use the least squares method for 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, determine that the equivalent radius is stable within this temperature interval; S7.5 Obtain the performance stability test data and extract the mean of the performance indicators at different temperatures as the performance stability quantification index; S7.6 Input the equivalent radius stability data and performance stability indicators into the support vector machine model for training to obtain the performance stability evaluation model; S7.7 Use the trained performance stability evaluation model to predict the performance stability at any temperature, providing a decision-making basis for product performance optimization.

[0043] S8 adopts a preset performance optimization algorithm to adjust the winding process parameters, making the equivalent radius of the ring body tend to be stable within the full temperature range; by monitoring the change trend of the equivalent radius in real time, it judges whether the performance stability reaches the preset optimization threshold, specifically: S8.1 According to the preset optimization algorithm, obtain the initial winding parameter values, including the number of turns of the coil, wire diameter, and winding tension, and determine these parameter values as the input of the algorithm: Before starting the optimization process, first, a set of initial winding parameters need to be set according to past empirical 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 optical fiber winding), wire diameter (optical fiber diameter), and winding tension (the force applied during the winding process). The selection of these parameters has a direct impact on the structure and performance of the final ring body. Taking these initial parameters as the input of the optimization algorithm provides a basis for subsequent iterative optimization.

[0044] 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, obtain the radius data of the ring body at different temperatures, and these data form an equivalent radius - temperature data set: To evaluate the performance of the ring body under different temperature conditions, use a high-precision temperature sensor to monitor the temperature change around the ring body, and combine a high-resolution displacement sensor to measure the actual radius of the ring body at each temperature point. In this way, a series of data points of the equivalent radius changing with temperature can be obtained, forming a complete equivalent radius - temperature data set. This data set not only contains the specific radius values at different temperatures but also records the time stamp of each measurement, providing detailed information support for subsequent analysis.

[0045] S8.3 For the equivalent radius - temperature data set, calculate the average value and standard deviation of the equivalent radius at each temperature point to evaluate the radius stability of the ring body at different temperatures: Based on the collected equivalent radius - temperature data set, calculate the average value and standard deviation of all measurement samples at each temperature point. The average value reflects the central position of the equivalent radius of the ring body under this temperature condition, while the standard deviation quantifies the degree of data dispersion. If the standard deviation of a certain temperature point exceeds the preset stability threshold, it means that the radius of the ring body changes greatly and is not stable enough at this temperature. At this time, it is necessary to enter the parameter adjustment link to find a better combination of winding parameters to reduce this instability.

[0046] S8.4 Adopt the preset optimization algorithm. According to the equivalent radius - temperature data set and the stability threshold, calculate the new winding parameter values. If the algorithm determines that the current parameter values cannot stabilize the radius, adjust the winding parameters and record the adjusted winding parameters: Once the instability of certain temperature points is determined, an optimization algorithm is initiated to attempt to find new winding parameters that can improve stability. The optimization algorithm comprehensively considers the existing equivalent radius-temperature data set and the preset stability threshold. By simulating and evaluating different parameter combinations, it recommends a set of potentially more effective winding parameters. If, after multiple calculations, the algorithm still believes that the current parameters cannot meet the stability requirements, the parameters are continuously adjusted until the optimal solution is found. Each adjusted parameter is recorded in detail for subsequent analysis and traceability.

[0047] S8.5 According to the adjusted winding parameters, rewind the toroid and obtain the equivalent radius measurement data of the new toroid at different temperatures to form a new equivalent radius-temperature data set for verifying the effect of parameter adjustment: When the new winding parameters are determined, a toroid sample will be remanufactured according to these parameters, and the previous measurement steps will be repeated to obtain the equivalent radius data of the new toroid at different temperatures. This step is crucial because it directly verifies the effect of parameter adjustment. By comparing the two sets of old and new data, the differences before and after adjustment can be intuitively seen, and whether the optimization measures have achieved the expected goals can be evaluated.

[0048] S8.6 For the new equivalent radius-temperature data set, recalculate the average value and standard deviation of the equivalent radius at each temperature point to determine whether the preset stability threshold is reached. If not, return to step S8.3 for iterative optimization: For the newly generated equivalent radius-temperature data set, repeat the previous statistical analysis process to calculate the average value and standard deviation at each temperature point. The focus this time is to check whether the adjusted parameters have indeed improved the stability of the toroid. If the standard deviation can be kept within the preset threshold at all temperature points, it indicates that the optimization is successful; otherwise, if any temperature point still does not meet the standard, return to step S8.3 to continue adjusting the parameters until the requirements are met.

[0049] S8.7 If the standard deviation of the radius stability of the new data set is less than or equal to the preset stability threshold, output the current winding parameters and determine that the equivalent radius of the toroid has reached a stable state within the full temperature range, and output the equivalent radius value in the stable state Finally, when all temperature points exhibit good stability, that is, the standard deviation does not exceed the preset threshold, it can be confirmed that the optimization has been successful. At this time, the system will output the current winding parameters used, which are considered to be the optimal choices that can maintain the stability of the equivalent radius of the ring body within the full temperature range. At the same time, the system will also output the equivalent radius values of each temperature point in the stable state, providing important references for subsequent production and quality control. In addition, these optimization results can be further used to improve future production processes, ensuring that each fiber optic gyroscope ring body leaving the factory can meet the highest performance standards.

[0050] Through this series of detailed steps, the present invention not only effectively optimizes the winding process parameters, but also ensures the stability and consistency of the ring body under various environmental conditions, significantly improving the overall performance and reliability of the fiber optic gyroscope.

[0051] The above embodiments are only the preferred technical solutions of the present invention and should not be regarded as limitations on the present invention. The protection scope of the present invention should be the technical solutions recorded in the claims, including the equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, the equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for manufacturing a fiber optic gyroscope ring body for reducing a gyroscope scale factor, characterized in that The following steps are involved: Preparation work: Divide an optical fiber into two equal parts symmetrically at the midpoint and wind them on two fiber supply wheels respectively. Install the two fiber supply wheels on the front and rear winding mechanisms of the winding equipment and use them for clockwise and counterclockwise fiber winding respectively. The winding process is as follows: S1. Obtain the temperature distribution data during the 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 according to the temperature gradient change trend to describe the relationship between the curing degree and the temperature gradient; S2. Using a preset temperature control algorithm, adjust the heating power and cooling rate during the winding process to make the temperature gradient of each layer of the ring body tend to be consistent, and determine whether the temperature gradient reaches the preset uniformity threshold by real-time monitoring the temperature changes of each layer of the ring body; S3. If the temperature gradient reaches the homogenization threshold, the curing agent injection system is started to evenly distribute the curing agent to each layer of the ring body according to the preset injection rate and pressure; the curing time is adjusted according to the distribution of the curing agent so that the curing degree of each layer of the ring body tends to be consistent; S4, obtaining the expansion coefficient data of each layer of the ring body after curing, and analyzing the distribution characteristics of the expansion coefficient; According to 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 variation trend of the equivalent radius with the expansion coefficient. S5. Using a preset aging treatment algorithm, control the temperature and time parameters during the aging process to make the expansion coefficients of the various layers of the ring body tend to be consistent; By real-time monitoring of the expansion coefficient changes of each layer of the ring body, it is determined whether the expansion coefficient reaches a preset stabilization threshold; S6. If the expansion coefficient reaches the stabilization threshold, the scale factor calibration system is started, and the scale factor of the gyroscope is adjusted according to the preset calibration procedure; based on the calibration result of the scale factor, it is determined whether the measurement accuracy meets the preset accuracy requirement; S7. Obtain equivalent radius data within the full temperature range and analyze the stability of the equivalent radius as the temperature changes; establish a performance stability evaluation model based on the stability data of the equivalent radius to describe the relationship between performance stability and equivalent radius stability; S8 uses a preset performance optimization algorithm to adjust the winding process parameters so that the equivalent radius of the ring body tends to be stable within the full temperature range; By real-time monitoring of the changing trend of the equivalent radius, it is determined whether the performance stability has reached the preset optimization threshold.

2. The method for manufacturing a fiber optic gyroscope ring body for reducing a gyroscope scale 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 obtain the 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 to obtain a smooth temperature distribution surface; S1.3 Use the numerical differentiation method to calculate the radial and axial temperature gradients inside and on the surface of the ring body, and obtain the temperature gradient variation trend of each layer of the ring body; S1.4 Based on the thermophysical parameters of the ring material and the curing kinetic equation, a mathematical model describing the relationship between the curing degree of each layer of the ring and the temperature gradient is established; S1.5 Divide the ring into several discrete units, and use the finite element method to solve the curing degree of each unit according to the temperature gradient change trend; S1.6 Interpolate and smooth the curing degree of each unit to obtain a continuous curing degree distribution surface, and compare and verify it with the measured data; S1.7 uses machine learning algorithms to establish a mapping relationship between temperature gradient and curing degree, and realizes online prediction and optimization control of curing degree.

3. The method for manufacturing a fiber optic gyroscope ring body for reducing a gyroscope scale factor according to claim 2, characterized in that: In S1.7, the fiber ring is wound according to the quadrupole symmetrical winding method, and online UV curing is performed after every two layers of winding. The curing lamp is an LED UV curing light source, and the winding environment temperature is 25±3℃ until the entire fiber ring is completed.

4. The method for manufacturing a fiber optic gyroscope ring body for reducing a gyroscope scale factor according to claim 1, characterized in that: The sub-steps of S2 are as follows: S2.1 Obtain the real-time temperature data of each layer of the ring body, 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 compares 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 uneven and temperature control is required. S2.3 If the temperature gradient value is less than or equal to the threshold, it is judged that the temperature gradient has become consistent and no adjustment is required; S2.4 When it is determined that temperature control is required, a temperature control model is established by combining the historical process parameters and the temperature gradient correlation data through a machine learning algorithm, and the current temperature of each layer of the ring is input to predict the optimal heating power and cooling rate parameters; S2.5 Send the predicted optimal heating power and cooling rate parameters to the control system of the winding equipment, adjust the power of the heating device and the cooling capacity of the cooling device during the winding process, and perform differentiated temperature regulation on each layer of the ring body; S2.6 During the temperature adjustment process, continuously obtain the real-time temperature data of each layer of the ring body, calculate the temperature gradient value between each layer, and determine whether the temperature gradient reaches the homogenization threshold; S2.7 If the temperature gradient value is less than or equal to the homogenization threshold value for multiple consecutive times, it is considered that the temperatures of the various layers of the ring body have become consistent, and the temperature control is stopped, and 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 value of each layer of the ring body is still greater than the homogenization threshold, an early warning message is triggered, the temperature control algorithm or the homogenization threshold is manually adjusted, and the temperature control process is started again until the requirements are met.

5. The method for manufacturing a fiber optic gyroscope ring body for reducing a gyroscope scale factor according to claim 1, characterized in that: In S3, ultraviolet resin is selected as the adhesive, which is also the curing agent of the optical fiber ring. The specific steps are as follows: S3.1 Obtain the temperature gradient data of each layer of the ring body and determine whether the temperature gradient reaches 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 According to 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 body, and ensure the uniform distribution of the curing agent in each layer of the ring body by adjusting the injection rate and injection pressure; S3.3 Use computer vision technology to detect and analyze the distribution of the curing agent in each layer of the ring body, obtain the distribution density data of the curing agent in each layer of the ring body, compare the distribution density data with the preset uniform distribution threshold, and determine whether it is necessary to adjust the injection rate and injection pressure parameters; S3.4 Establish a correlation model between the curing agent distribution density and the curing time through a machine learning algorithm, and dynamically adjust the curing time of each layer according to the actual distribution of the curing agent in each layer of the ring body so that the curing degree of each layer tends to be consistent; S3.5 Use infrared thermal imaging technology to monitor the curing process of each layer of the ring in real time, obtain the temperature change data of each layer, and judge the curing degree of each layer based on the temperature change data. When the curing degree of each layer reaches the preset consistency threshold, stop adjusting the curing time; S3.6 During the curing process, continuously collect data such as the temperature and curing agent distribution density of each layer of the ring body, and use big data analysis technology to explore the correlation between the data, dynamically optimize parameters such as injection rate, injection pressure and curing time, and achieve a dynamic balance of the curing degree of each layer of the ring body; S3.7 Establish a three-dimensional visualization model of the ring body curing process, map data such as temperature gradient, curing agent distribution and curing degree with the three-dimensional model, and display the curing status of each layer of the ring body in real time through a visualization interface.

6. The method for manufacturing a fiber optic gyroscope ring body for reducing a gyroscope scale 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 ring body after curing, pre-process the data, remove abnormal values ​​and noise data, and obtain a valid expansion coefficient data set; S4.2 Statistically analyze 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 body; 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, in which the equivalent radius is the dependent variable and the expansion coefficient is the independent variable. The regression equation is obtained by fitting to describe the change trend of the equivalent radius with the expansion coefficient; S4.4 Evaluate and optimize the established relationship model, use the cross-validation method to evaluate the goodness of fit and prediction performance of the model, adjust and optimize the model based on the evaluation results, and obtain the optimal equivalent radius and expansion coefficient relationship model; S4.5 Use the optimized relationship model to predict the equivalent radius of the new cured ring sample. According to the expansion coefficient data of the sample, the corresponding equivalent radius prediction value is calculated through 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, and evaluate the prediction accuracy of the model. If the error exceeds the preset threshold, it is necessary to further optimize the model or obtain more training data; S4.7 Integrate the optimized equivalent radius and expansion coefficient relationship model into the ring design and manufacturing process. According to the material properties and process parameters of the ring, predict the equivalent radius of each layer of the ring, guide the ring structure design and process optimization, and improve the accuracy and efficiency of ring manufacturing.

7. The method for manufacturing a fiber optic gyroscope ring body for reducing a gyroscope scale factor according to claim 1, characterized in that: The sub-steps of S5 are as follows: S5.1 According to the material characteristics of the ring body, a three-dimensional mathematical model of temperature-time-expansion coefficient for aging treatment is established to obtain the predicted values ​​of the expansion coefficient of each layer of the ring body under different temperature and time combinations; S5.2 Use machine learning algorithms to train the aging treatment optimization model based on historical data to learn the optimal combination of temperature and time parameters to minimize the difference in expansion coefficients of each layer; S5.3 Place the ring body in a controllable temperature box and perform aging treatment according to the temperature-time parameters output by the optimization model; S5.4 At the same time, strain sensors are installed on each layer of the ring body to collect strain data of each layer in real time; S5.5 Calculate the real-time expansion coefficient of each layer of the ring body through the strain data; S5.6 Compare the real-time expansion coefficient with the predicted value of the mathematical model, and dynamically adjust the temperature and time parameters so that the actual expansion coefficient approaches the predicted value; 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; S5.8 If the variance is less than the preset stabilization threshold, it is determined that the aging process is complete and the expansion coefficients of the various layers of the ring body have become consistent; S5.9 If after a certain period of time, the variance of the expansion coefficients of the ring layers is still greater than the threshold, the alarm mechanism is triggered, the factors causing the difference in expansion coefficients are analyzed, and if necessary, manual intervention is performed to adjust the aging treatment parameters; S5.10 After the aging treatment is completed, the quality of each layer of the ring body is tested, and the mechanical properties of each layer are tested by sampling to verify the consistency of the performance of each layer to ensure that the aging treatment effect meets the design requirements; When the entire optical fiber winding is completed, the curing temperature is 85±2℃.

8. The method for manufacturing a fiber optic gyroscope ring body for reducing a gyroscope scale factor according to claim 1, characterized in that: The sub-steps of S6 are: S6.1 obtains data collected by the gyroscope in real time, calculates the current expansion coefficient based on the data, compares the expansion coefficient with a preset stability threshold, and determines whether a stable state is reached; S6.2 If the expansion coefficient reaches the stability threshold, the scale factor calibration system is started, a preset scale factor calibration procedure is called, and the scale factor of the gyroscope is automatically adjusted according to the steps of the calibration procedure; S6.3 During the scale factor adjustment process, the Kalman filter algorithm is used to filter the gyroscope data to reduce noise interference and improve the accuracy of scale factor adjustment; S6.4 fitting the optimal scale factor calibration coefficients based on the data before and after the multiple scale factor adjustments by the least squares fitting algorithm for subsequent scale factor calibration; S6.5 writes the calibrated scale factor parameter into the calibration register of the gyroscope, completes the update of the scale factor, and saves the calibration result into the system log; S6.6 Calculate the current measurement accuracy using the Bayesian estimation algorithm based on the calibrated gyroscope data, compare the measurement accuracy with the preset accuracy requirement, and determine whether the calibrated measurement accuracy meets the requirement; S6.7 If the measurement accuracy meets the preset requirements, the scale factor calibration is completed. If the accuracy does not meet the requirements, the calibration parameters are adjusted and the scale factor calibration is performed again until the measurement accuracy meets the requirements.

9. The method for manufacturing a fiber optic gyroscope ring body for reducing a gyroscope scale factor according to claim 1, characterized in that: The sub-steps of S7 are: S7.1 Obtain equivalent radius measurement data over the full temperature range and group the data by temperature range; S7.2 Calculate the mean and variance of the equivalent radius data in each temperature range to obtain the statistical characteristics of the equivalent radius at different temperatures; S7.3 Based on the variation trend of the mean value of the equivalent radius with temperature, the least square 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, and when the slope is less than a preset threshold, determine that the equivalent radius is stable within the temperature range; S7.5 Obtain performance stability test data and extract the mean of performance indicators at different temperatures as a quantitative indicator of performance stability; S7.6 Input the equivalent radius stability data and the performance stability index into a support vector machine model for training to obtain a performance stability evaluation model; S7.7 uses the trained performance stability evaluation model to predict the performance stability at any temperature and provide a decision-making basis for product performance optimization.

10. The method for manufacturing a fiber optic gyroscope ring body for reducing a gyroscope scale factor according to claim 1, characterized in that: The sub-steps of S8 are: S8.1 obtaining initial winding parameter values, including the number of coil turns, wire diameter, and winding tension, according to a preset optimization algorithm, and determining these parameter values ​​as inputs to 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 to obtain the radius data of the ring body at different temperatures, which constitute an equivalent radius-temperature data set; S8.3 For the equivalent radius-temperature data set, calculate the mean 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, it is necessary to enter the parameter adjustment stage; S8.4 uses a preset optimization algorithm to calculate new winding parameter values ​​based on the equivalent radius-temperature data set and the stability threshold. If the algorithm determines that the current parameter value cannot stabilize the radius, the winding parameter is adjusted and the adjusted winding parameter is recorded; S8.5 Rewind the ring body according to the adjusted winding parameters, and obtain the equivalent radius measurement data of the new ring body at different temperatures to form a new equivalent radius-temperature data set for verifying the effect of parameter adjustment; S8.6 For the new equivalent radius-temperature data set, the equivalent radius mean value and standard deviation of each temperature point are calculated again to determine whether the preset stability threshold is reached. If not, return to step S8.3 for iterative optimization; S8.7 If the radius stability standard deviation of the new data set 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 in the full temperature range has reached a stable state, and the equivalent radius value in the stable state is output.

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