Method for automatically measuring and adjusting refractive index of cross-section of optical fiber

Abstract

Disclosed in the present invention is a method for automatically measuring and adjusting the refractive index of the cross-section of an optical fiber. The method comprises the following steps: S1, optical fiber alignment; S2, detection of optical signals; S3, automatic compensation for optical fiber fusion splicing; S4, analysis of the optical signals; and S5, automatic adjustment and compensation. Compared with the conventional related art, the automatic compensation technique for optical fiber fusion splicing in the present invention has advantages such as higher quality stability, reduced manual intervention, precise parameter adjustment, rapid response capability and improved production efficiency, and is applicable to production and application in the field of optical fiber communications.

Description

A method for automatically measuring and adjusting the refractive index of an optical fiber cross section Technical Field

[0001] The invention relates to a method for automatically measuring and adjusting the refractive index of an optical fiber cross section, and belongs to the technical field of optical fiber fusion splicing methods. Background Art

[0002] When there is a difference in the core diameter or refractive index of the two optical fibers to be connected, the optical signal transmission at the fusion point will be affected, thereby reducing the quality of the connection.

[0003] Traditional manual splicing methods are inefficient and difficult to guarantee the signal quality of the spliced ​​optical fiber transmission. However, the introduction of automatic compensation technology for the optical fiber refractive index can more effectively solve this problem.

[0004] In the automatic measurement and compensation technology of optical fiber cross-sectional refractive index, there are still some technical problems to be solved, including:

[0005] The cross-section of the optical fiber splicing point is complex: In actual applications, the optical fiber diameter may change slightly due to factors such as temperature. This will cause the optical fiber diameter to change during the splicing process, affecting the splicing quality and thus the optical signal transmission quality at the splicing point.

[0006] Adaptability to different optical fiber materials: There are different optical fiber materials on the market, such as silicon-based optical fiber, ultra-high purity quartz optical fiber, plastic optical fiber, etc. The refractive index of optical fiber changes under different optical fiber materials, temperatures and other factors, and the degree of refraction of light when propagating in the optical fiber changes. Therefore, when welding two optical fibers of different materials, it is necessary to accurately measure and adjust the optical fiber cross-sectional refractive index in real time to improve the adaptability of the refractive index of the two optical fibers and thus improve the transmission quality of the optical signal.

[0007] Real-time data feedback and control requirements: Since the diameter or refractive index of the optical fiber cross section may change slightly at any time due to different optical fiber materials and various environmental factors, a real-time feedback and control system needs to be established based on the measurement results and parameter adjustment algorithm to ensure that the measurement parameters are transmitted in real time and accurately.

[0008] Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a method for automatically measuring and adjusting the refractive index of an optical fiber cross section, so as to solve the technical problems existing in the above-mentioned prior art.

[0010] The technical solution adopted by the present invention is: a method for automatically measuring and adjusting the refractive index of an optical fiber cross section, comprising the following steps:

[0011] S1, Fiber alignment: Place the two optical fibers to be spliced ​​into the fiber slots of the fiber fusion splicer and use the machine's alignment function to ensure that the end faces of the two fibers are accurately aligned. The fiber fusion splicer sends a light signal through one of the fibers and receives a light signal transmitted back from the other fiber.

[0012] S2, detecting optical signals: using a fiber fusion splicer to measure the core diameter and refractive index of the two optical fibers to be fused;

[0013] S3, automatic compensation for fiber fusion splicing: Based on the requirements of different types of optical fibers and working environments, a set of appropriate splicing parameters are pre-set on the fiber fusion splicer. Through the optimization algorithm, these parameters are optimized and adjusted to achieve the best splicing effect;

[0014] S4, Analyze Optical Signal: The fiber fusion splicer analyzes the received optical signal, compares the transmitted optical signal parameters with the set values, determines whether the optical fiber refractive index needs to be adjusted, and determines the required compensation measures based on the size and direction of the difference;

[0015] S5, automatic adjustment compensation: Based on the results of comparison and analysis, the system automatically adjusts the welding parameters of the welding machine, including welding temperature, welding time and welding energy, to achieve the best connection effect.

[0016] Preferably, in step S5, the inconsistency in the fiber core diameters is compensated by adjusting the electrode distance of the fusion splicer.

[0017] Preferably, in step S5, the inconsistency of the core refractive index is compensated by adjusting the discharge energy of the fusion splicer so as to achieve the best matching of the refractive index of the optical fiber.

[0018] Preferably, in step S2, appropriate sensors and digital image processing technology are used to accurately and quickly collect parameters such as optical fiber diameter and refractive index, and data processing and analysis are performed to extract useful information and features, including the precise value of optical fiber diameter, refractive index and change trend, spectral components of spectral characteristics, and abnormal data points.

[0019] Preferably, the data processing and analysis steps are as follows: first, the data is filtered and denoised, then the characteristic information of the optical fiber cross section is extracted using wavelet transform and Fourier transform, the data is classified and identified using a neural network, and statistical methods or anomaly detection algorithms based on machine learning are used to detect abnormal values ​​and irregular fluctuations in the optical fiber parameters, and finally, by analyzing the correlation between the data, the correlation between the optical fiber diameter and the refractive index is found; this correlation is mainly reflected by the numerical aperture of the optical fiber, which is an important parameter for measuring the transmission performance of the optical fiber and is usually defined as Where n1 is the refractive index of the optical fiber material, n2 is the refractive index of the sheath material or air surrounding the core material, and the numerical aperture can also be expressed as a function of the optical fiber cross-sectional diameter and the refractive index: NA = f(d, n), where d is the optical fiber diameter and n is the refractive index.

[0020] Preferably, parameter calculation is performed based on the collected data to find out the parameters that need to be compensated during the welding process, including temperature compensation parameters, tension compensation parameters, and time compensation parameters. The collected data is stored and analyzed, and a data model and an analysis model are established to identify the change trend of parameters such as optical fiber diameter and refractive index and analyze the correlation between different parameters.

[0021] The temperature compensation parameter calculation process is as follows: compare the actual measured fiber diameter with the standard diameter, calculate the deviation value of the fiber diameter, and calculate the temperature compensation parameter that needs to be adjusted based on the thermal expansion coefficient of the optical material and the impact of temperature changes on the fiber diameter. The calculation formula is: Temperature compensation parameter = frac(D-D0)×α×δt;

[0022] Where D is the actual diameter, D0 is the standard diameter, α is the thermal expansion coefficient, and δt is the temperature change;

[0023] The calculation process of tension compensation parameters is as follows:

[0024] The actual tension value measured by the tension sensor is compared with the standard value to calculate the tension deviation value; according to the mechanical properties of the optical fiber material, the tension compensation parameter that needs to be adjusted is calculated. The calculation formula is: tension compensation parameter = F-F0;

[0025] Among them, F is the actual tension, F0 is the standard tension;

[0026] The time compensation parameter calculation process is as follows:

[0027] Time compensation parameter: By comparing the actual welding time with the standard welding time, calculate the deviation value of the welding time and adjust the time compensation parameter. The calculation formula is: Time compensation parameter = T-T0;

[0028] Among them, T is the actual welding time, T0 is the standard welding time;

[0029] The data model includes: ① Real-time fiber parameter data: including fiber diameter, refractive index and other parameters; ② Fusion parameter record: recording the temperature, tension and fusion time parameters of each fusion process, as well as the corresponding compensation parameters; ③ Environmental factor data: recording environmental factors that affect fiber performance, such as temperature and humidity; ④ Abnormal data record: recording abnormal values, equipment failures, etc.

[0030] The analysis model includes: ① Trend analysis:

[0031] The change trend of optical fiber diameter = frac(δD / δt), where D is the optical fiber diameter and t is the splicing time;

[0032] The trend of refractive index change = frac(δn / δt), where n is the refractive index and t is the fusion time; The rate of change of optical fiber diameter = frac(δD / δt) = frac((D2-D1) / (t2-t1)); The rate of change of refractive index = frac(δn / δt) = frac((n2-n1) / (t2-t1)).

[0033] Preferably, in step S5, an adaptive compensation algorithm is designed, including a parameter calculation algorithm and a compensation algorithm, using mathematical models, statistical methods or machine learning techniques to improve the accuracy and robustness of the compensation algorithm; and compensation adjustment of welding parameters is achieved based on parameter calculation results and previous experience or models.

[0034] Preferably, the adaptive compensation algorithm first uses a PID control algorithm to adjust the welding temperature, tension or welding time according to the deviation between the actual measurement value and the standard value. After the PID algorithm, a fuzzy logic control algorithm is used to further fine-tune the welding parameters according to fuzzy rules to better match the expected fiber optic welding parameters; then a genetic algorithm is used to optimize the fiber optic welding parameters, continuously adjust the parameters according to different measurement data, and evaluate based on the fitness function to gradually optimize the fiber optic welding quality; finally, a neural network is established, and by training and learning historical data, the system can automatically adjust the welding parameters according to the measurement values ​​to improve the transmission quality of the fiber optic welding point.

[0035] Beneficial effects of the present invention: Compared with the prior art, the present invention has the following advantages:

[0036] 1. Improve quality stability: Automatic compensation technology can monitor and adjust welding parameters in real time to ensure that the temperature, arc and other key parameters during the welding process are in the optimal state, thereby improving the stability and consistency of welding quality.

[0037] 2. Reduced Human Intervention: Traditional fiber fusion splicing technology typically requires operators to set and adjust splicing parameters based on experience, which is susceptible to human error and operating skills, resulting in unstable splice quality. However, automatic compensation technology for fiber fusion splicing maintains optimal splicing performance by sensing fiber characteristics and environmental changes in real time and automatically adjusting splicing parameters based on a pre-set algorithm. This improves the stability and consistency of splicing quality. Whether splicing the same or different fiber types, automatic compensation technology accurately adjusts parameters based on real-time data, ensuring consistent splice quality.

[0038] 3. Precise Parameter Adjustment: Fiber fusion splicing automatic compensation technology utilizes high-precision sensors and data acquisition systems to sense key parameters such as fiber temperature, arcing, and tension in real time. Furthermore, using pre-set algorithms, this technology precisely adjusts splicing parameters to optimize splicing performance based on fiber characteristics, model, and operating environment. For example, different fiber types may require different heating temperatures and times during the splicing process. Fiber fusion automatic compensation technology accurately adjusts parameters based on real-time data to meet the needs of different fiber types and operating environments. This improves splice quality consistency and ensures the stability and reliability of fiber connections.

[0039] 4. Rapid Response: Fiber fusion splicing automatic compensation technology offers rapid response capabilities, enabling real-time detection and perception of changes in fiber characteristics. When unexpected changes or abnormalities occur, the system rapidly responds and makes corresponding adjustments, such as adjusting splice time, arc current, or heating temperature, to ensure the stability and reliability of splice quality. This rapid response improves the system's fault tolerance and adaptability, safeguarding the quality and stability of fiber connections at critical moments.

[0040] 5. Improved production efficiency: Automatic compensation technology optimizes welding parameters and realizes automated control of the welding process, improving production efficiency and capacity. At the same time, automatic compensation technology can reduce the welding failure rate caused by inconsistent manual operation, reduce secondary welding and scrap rate, and further improve production efficiency and economic benefits.

[0041] In summary, compared with previous related technologies, the automatic compensation technology for optical fiber fusion splicing has the advantages of higher quality stability, reduced manual intervention, precise parameter adjustment, rapid response capability and improved production efficiency. It is suitable for production and application in the field of optical fiber communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] FIG1 is an overall flow chart of the present invention.

[0043] Figure 2 is a flow chart of the PID control algorithm.

[0044] Figure 3 is a control flow chart of the fuzzy logic control algorithm. DETAILED DESCRIPTION

[0045] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0046] Example 1:

[0047] A method for automatically measuring and adjusting the refractive index of an optical fiber cross section, as shown in FIG1 , comprises the following steps:

[0048] S1, Fiber alignment: Place the two optical fibers to be spliced ​​into the fiber slots of the fiber fusion splicer and use the machine's alignment function to ensure that the end faces of the two fibers are accurately aligned. The fiber fusion splicer sends a light signal through one of the fibers and receives a light signal transmitted back from the other fiber.

[0049] S2, detecting optical signals: Using optical measurement sensors and digital image processing technology, parameters such as optical fiber diameter and refractive index are accurately and rapidly collected. Data processing and analysis are then performed to extract useful information and features, including the precise value of the optical fiber diameter, the refractive index and its changing trend, the spectral components of the spectral characteristics, and abnormal data points. The optical measurement sensor in this embodiment adopts the optoNCDT series, which utilizes the principle of laser triangulation to measure the diameter and shape of the optical fiber cross section with high precision.

[0050] The data processing and analysis steps are as follows:

[0051] First, the data is filtered and denoised, and then the characteristic information of the fiber cross section is extracted using wavelet transform and Fourier transform. The data is classified and identified using a neural network. Statistical methods or anomaly detection algorithms based on machine learning are used to detect abnormal values ​​and irregular fluctuations in the fiber parameters. Finally, by analyzing the correlation between the data, the relationship between the fiber diameter and the refractive index is found. This relationship is mainly reflected by the numerical aperture of the fiber, which is an important parameter for measuring the transmission performance of the optical fiber and is usually defined as Where n1 is the refractive index of the optical fiber material, n2 is the refractive index of the sheath material or air surrounding the core material, and the numerical aperture can also be expressed as a function of the optical fiber cross-sectional diameter and the refractive index: NA = f(d, n), where d is the optical fiber diameter and n is the refractive index.

[0052] Then, based on the collected data, parameter calculations are performed to find out the parameters that need to be compensated during the welding process, including temperature compensation parameters, tension compensation parameters, and time compensation parameters. The collected data are stored and analyzed, and data models and analysis models are established to identify the changing trends of parameters such as optical fiber diameter and refractive index, and analyze the correlation between different parameters.

[0053] The temperature compensation parameter calculation process is as follows: compare the actual measured fiber diameter with the standard diameter, calculate the deviation value of the fiber diameter, and calculate the temperature compensation parameter that needs to be adjusted based on the thermal expansion coefficient of the optical material and the impact of temperature changes on the fiber diameter. The calculation formula is: Temperature compensation parameter = frac(D-D0)×α×δt;

[0054] Where D is the actual diameter, D0 is the standard diameter, α is the thermal expansion coefficient, and δt is the temperature change;

[0055] The calculation process of tension compensation parameters is as follows:

[0056] The actual tension value measured by the tension sensor is compared with the standard value to calculate the tension deviation value; according to the mechanical properties of the optical fiber material, the tension compensation parameter that needs to be adjusted is calculated. The calculation formula is: tension compensation parameter = F-F0;

[0057] Among them, F is the actual tension, F0 is the standard tension;

[0058] The time compensation parameter calculation process is as follows:

[0059] Time compensation parameter: By comparing the actual welding time with the standard welding time, calculate the deviation value of the welding time and adjust the time compensation parameter. The calculation formula is: Time compensation parameter = T-T0;

[0060] Among them, T is the actual welding time, T0 is the standard welding time;

[0061] The data model includes: ① Real-time fiber parameter data: including fiber diameter, refractive index and other parameters. ② Fusion parameter record: record the temperature, tension and fusion time parameters of each fusion process, as well as the corresponding compensation parameters. ③ Environmental factor data: record environmental factors that affect fiber performance, such as temperature and humidity. ④ Abnormal data record: record abnormal values, equipment failures, etc.

[0062] The analysis model includes: ① Trend analysis:

[0063] The change trend of optical fiber diameter = frac(δD / δt), where D is the optical fiber diameter and t is the splicing time;

[0064] Refractive index change trend = frac(δn / δt), where n is the refractive index and t is the splicing time; Fiber diameter change rate = frac(δD / δt) = frac((D2-D1) / (t2-t1)); Refractive index change rate = frac(δn / δt) = frac((n2-n1) / (t2-t1));

[0065] S3, automatic compensation for fiber fusion splicing: Based on the requirements of different types of optical fibers and working environments, a set of appropriate splicing parameters are pre-set on the fiber fusion splicer. These parameters include heating temperature, heating time, arc current, etc. Through the optimization algorithm, these parameters are optimized and adjusted to achieve the best splicing effect;

[0066] The optimization algorithm includes an adaptive compensation algorithm: first, the PID control algorithm is used to adjust the welding temperature, tension or welding time according to the deviation between the actual measurement value and the standard value. After the PID algorithm, the fuzzy logic control algorithm is used to further fine-tune the welding parameters according to fuzzy rules to better match the expected fiber optic welding parameters; then the genetic algorithm is used to optimize the fiber optic welding parameters, continuously adjust the parameters according to different measurement data, and evaluate based on the fitness function to gradually optimize the fiber optic welding quality; finally, a neural network is established to enable the system to automatically adjust the welding parameters according to the measurement values ​​through training and learning historical data to improve the transmission quality of the fiber optic welding point.

[0067] Figure 2 shows the control flow chart for the PID control algorithm. This control model is based on the classic PID control regulator structure, with a fuzzy controller added. The input variables are the temperature deviation e(t) and the rate of change of the deviation de(t) / dt, and the output variables are the PID controller's three control parameters, KP, KI, and KD. By combining fuzzy control theory with traditional control, fuzzy rules are proposed to infer and judge the control system, which are then output to the traditional PID regulator, thus achieving fuzzy control of temperature, tension, and other parameters.

[0068] The control flow chart of the fuzzy logic control algorithm is shown in Figure 3. The input variables are the input error E and the output error change rate EC, and the control variable is the next state input U. E, EC, and U are collectively referred to as fuzzy variables.

[0069] S4, Analyze Optical Signal: The fiber fusion splicer analyzes the received optical signal, compares the transmitted optical signal parameters with the set values, determines whether the optical fiber refractive index needs to be adjusted, and determines the required compensation measures based on the size and direction of the difference;

[0070] S5, automatic adjustment and compensation: Based on the results of comparison and analysis, the system automatically adjusts the welding parameters of the welding machine, including welding temperature, welding time and welding energy, to achieve the best connection effect; for inconsistent fiber core diameters, it compensates by adjusting the electrode distance of the welding machine; for inconsistent fiber core refractive index, it compensates by adjusting the discharge energy of the welding machine to achieve the best match of the refractive index of the optical fiber.

[0071] In order to ensure the stability and consistency of the welding process, it is necessary to monitor and adjust parameters such as welding time, arc current, and heating temperature in real time. This is because too short a welding time may lead to incomplete welding, while too long a welding time may cause excessive diffusion of the welding area, thereby affecting the performance of the optical fiber. The appropriate arc current can provide sufficient energy to make the welding area reach the required temperature and shape, thereby ensuring the uniformity and stability of the welding. Too high a heating temperature may cause damage to the fiber core, while too low a heating temperature may not completely melt the outer cladding of the optical fiber, thereby affecting the quality and strength of the welding.

[0072] Therefore, the optimization algorithm in this embodiment also includes a perception algorithm (an algorithm that uses sensors and data processing technology to perform real-time monitoring, data collection, analysis, and feedback control of a specific system or environment). This perception algorithm is used to achieve real-time perception and control of the fiber splicing process. The perception algorithm can automatically identify and adjust splicing parameters based on fiber characteristics and environmental changes, and feed this information back to the compensation system to achieve real-time, dynamic parameter adjustment and compensation: ① Characteristic Identification: The perception algorithm analyzes fiber characteristics, such as diameter and refractive index. This can be achieved by using sensors to acquire real-time data and then combining it with previous models or experience to identify the current state of the fiber characteristics. ② Environmental Monitoring: The perception algorithm monitors environmental changes, such as temperature and humidity, as well as the operating status of the equipment in real time. These environmental variables are acquired through sensors and compared with preset thresholds or standards. ③ Parameter Adjustment: Based on the identification of fiber characteristics and environmental changes, the perception algorithm can automatically adjust splicing parameters, such as splicing time, arc current, and heating temperature. ④ Feedback to the compensation system: The adjusted parameter values ​​are fed back to the compensation system. This can be achieved by transmitting the new parameter values ​​to the fiber fusion splicer via a real-time communication protocol, such as a data bus or network communication, to achieve real-time parameter adjustment. By effectively exchanging data and information between the monitoring equipment and the compensation system, the stability and consistency of the fusion splice quality are ensured.

[0073] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for automatically measuring and adjusting the refractive index of an optical fiber cross section, characterized in that: The following steps are involved: S1, fiber alignment: put the two optical fibers to be spliced ​​into the fiber alignment slots of the fiber splicer respectively, and use the alignment function of the machine to ensure that the end faces of the two optical fibers are accurately aligned; the fiber splicer sends a light signal through one of the optical fibers and receives the light signal transmitted back by the other optical fiber; S2, detecting optical signals: using an optical fiber splicer to measure the core diameter and refractive index of the two optical fibers to be spliced; S3, automatic compensation for fiber fusion: According to the requirements of different types of optical fibers and working environments, a set of appropriate fusion parameters are pre-set on the fiber fusion splicer. Through the optimization algorithm, these parameters are optimized and adjusted to obtain the best fusion effect; S4, Analyze optical signal: The fiber fusion splicer analyzes the received optical signal, compares the difference between the transmitted optical signal parameters and the set values, determines whether the optical fiber refractive index needs to be adjusted, and determines the required compensation measures based on the size and direction of the difference; S5, automatic adjustment compensation: Based on the results of comparison and analysis, the system automatically adjusts the welding parameters of the welding machine, including welding temperature, welding time and welding energy, to achieve the best connection effect.

2. A method for automatically measuring and adjusting the refractive index of an optical fiber cross section according to claim 1, characterized in that: In step S5, the inconsistency of the fiber core diameters is compensated by adjusting the electrode distance of the fusion splicer.

3. The method for automatically measuring and adjusting the refractive index of an optical fiber cross section according to claim 1, characterized in that: In step S5, the inconsistency of the core refractive index is compensated by adjusting the discharge energy of the fusion splicer to achieve the best match of the refractive index of the optical fiber.

4. The method for automatically measuring and adjusting the refractive index of an optical fiber cross section according to claim 1, characterized in that: In step S2, appropriate sensors and digital image processing technology are used to accurately and quickly collect parameters such as optical fiber diameter and refractive index, and data processing and analysis are performed to extract useful information and features, including the precise value of optical fiber diameter, refractive index and change trend, spectral components of spectral characteristics, and abnormal data points.

5. A method for automatically measuring and adjusting the refractive index of an optical fiber cross section according to claim 4, characterized in that: The steps of data processing and analysis are as follows: first, filter and reduce noise on the data, then use wavelet transform and Fourier transform to extract characteristic information of the fiber cross section, use neural network to classify and identify the data, use statistical methods or machine learning-based anomaly detection algorithms to detect abnormal values ​​and irregular fluctuations in fiber parameters, and finally analyze the correlation between the data to find the correlation between the fiber diameter and the refractive index; this correlation is mainly reflected by the numerical aperture of the fiber, which is an important parameter for measuring the transmission performance of the optical fiber and is usually defined as Where n1 is the refractive index of the optical fiber material, n2 is the refractive index of the sheath material or air surrounding the core material, and the numerical aperture can also be expressed as a function of the optical fiber cross-sectional diameter and the refractive index: NA = f(d, n), where d is the optical fiber diameter and n is the refractive index.

6. A method for automatically measuring and adjusting the refractive index of an optical fiber cross section according to claim 5, characterized in that: According to the collected data, parameter calculation is performed to find out the parameters that need to be compensated in the welding process, including temperature compensation parameters, tension compensation parameters, and time compensation parameters. The collected data is stored and analyzed, and data models and analysis models are established to identify the change trends of parameters such as optical fiber diameter and refractive index, and analyze the correlation between different parameters; The temperature compensation parameter calculation process is as follows: compare the actual measured fiber diameter with the standard diameter, calculate the fiber diameter deviation value, and adjust the fiber diameter according to the thermal expansion coefficient of the optical material and the temperature change. The influence of fiber diameter is used to calculate the temperature compensation parameters that need to be adjusted. The calculation formula is: Temperature compensation parameter = frac(D-D0)×α×δt; Where D is the actual diameter, D0 is the standard diameter, α is the thermal expansion coefficient, and δt is the temperature change; The calculation process of tension compensation parameters is as follows: The actual tension value measured by the tension sensor is compared with the standard value to calculate the deviation value of the tension; according to the mechanical properties of the optical fiber material, the tension compensation parameter that needs to be adjusted is calculated. The calculation formula is: Tension compensation parameter = F-F0; Among them, F is the actual tension, F0 is the standard tension; The time compensation parameter calculation process is as follows: Time compensation parameters: By comparing the actual welding time with the standard welding time, calculate the deviation value of the welding time and adjust the time compensation parameters. The calculation formula is: Time compensation parameter = T-T0; Among them, T is the actual welding time, T0 is the standard welding time; The data model includes: ① Real-time fiber parameter data: including fiber diameter, refractive index and other parameters; ② Fusion parameter record: record the temperature, tension and fusion time parameters of each fusion process, as well as the corresponding compensation parameters; ③ Environmental factor data: record environmental factors that affect the performance of optical fiber, such as temperature and humidity; ④ Abnormal data record: record abnormal values, equipment failures, etc. The analysis model includes: ① Trend analysis: The change trend of optical fiber diameter = frac(δD / δt), where D is the optical fiber diameter and t is the fusion time; Refractive index change trend = frac(δn / δt), where n is the refractive index and t is the welding time; Fiber diameter change rate = frac(δD / δt) = frac((D2-D1) / (t2-t1)); The rate of change of refractive index = frac(δn / δt) = frac((n2-n1) / (t2-t1)).

7. A method for automatically measuring and adjusting the refractive index of an optical fiber cross section according to claim 6, characterized in that: In step S3, an adaptive compensation algorithm is designed, including a parameter calculation algorithm and a compensation algorithm, and mathematical models, statistical methods or machine learning techniques are used to improve the accuracy and robustness of the compensation algorithm; compensation adjustment of welding parameters is achieved based on parameter calculation results and previous experience or models.

8. A method for automatically measuring and adjusting the refractive index of an optical fiber cross section according to claim 7, characterized in that: The adaptive compensation algorithm first uses the PID control algorithm to adjust the welding temperature, tension or welding time according to the deviation between the actual measurement value and the standard value. After the PID algorithm, the fuzzy logic control algorithm is used to further fine-tune the welding parameters according to fuzzy rules to better match the expected fiber optic welding parameters; then the genetic algorithm is used to optimize the fiber optic welding parameters, and the parameters are continuously adjusted according to different measurement data, and evaluated based on the fitness function to gradually optimize the fiber optic welding quality; finally, a neural network is established to enable the system to automatically adjust the welding parameters according to the measurement values ​​through training and learning historical data to improve the transmission quality of the fiber optic welding point.

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