Optical fiber sensor for measuring temperature

Abstract

The invention discloses an optical fiber sensor for measuring temperature, and particularly relates to the technical field of optical fiber sensing, and the optical fiber sensor comprises a double-core optical fiber structure which employs a special double-core optical fiber, one light beam serves as reference light, the other light beam serves as measuring light, and the special double-core optical fiber structure is used for measuring the temperature through double-core optical fiber interference; and a temperature sensitive material region. According to the invention, the measurement precision and interference resistance are realized, the dual-core optical fiber and the fluorescence lifetime are subjected to dual-mode weighting, the precision is improved by 30%-50%, 95% of baseline drift is inhibited by a reference optical path, and the strain-induced temperature error is reduced to be within 1% from 5% by field-induced calibration. The environment adaptability is high, the working temperature rise at 500 DEG C is less than 2 DEG C due to the heat dissipation fins, the wide-temperature measurement from-196 DEG C to 300 DEG C is realized through multi-layer packaging, and the impact resistance is 3 times of that of the traditional impact resistance due to the combination of the metal and the carbon nanotube layer. The intelligent and reliable performance is good, the microprocessor adjusts parameters adaptively, the response level is 10ms, and the fault self-diagnosis early warning accuracy rate exceeds 99%. Data security and transmission are excellent, multimode protocol switching efficiency is improved by 40%, consumption is reduced by 50%, three-level verification ensures low error codes, and hacker resistance reaches the national secret three-level standard.

Description

Technical Field

[0001] The present invention relates to the technical field of fiber optic sensing, and particularly to an optical fiber sensor for measuring temperature. Background Art

[0002] The measurement of temperature and pressure is crucial in the medical industry. With the popularization of minimally invasive therapies and the extremely narrow space parts to be detected, miniaturization has become important. The small size, corrosion resistance, insensitivity to electromagnetic radiation itself, and good biocompatibility of optical fibers make optical fiber sensors very attractive in the fabrication of devices in the fields of bioengineering, medical diagnosis and treatment, etc. due to their biological adaptability, small size, low cost, electrical insulation, and electrical interference. The research on optical fiber temperature sensors is one of the most active research directions in the field of fiber optic sensing;

[0003] Currently, the main optical fiber temperature sensing mechanisms include stimulated fluorescence, interference, light absorption, thermally induced optical radiation, light scattering, birefringence, etc. However, the only invasive temperature sensor that is relatively mature and has started to be practical is the F-P interferometric (FPI) optical fiber temperature sensor. Compared with optical fiber temperature sensing technology, the maturity and application of optical fiber pressure sensing technology are relatively insufficient, and most research is also based on FPI. Generally, microelectromechanical or micro-optoelectromechanical technology is used to form an FP cavity at the end of the optical fiber. Since the Young's moduli of quartz and silicon materials are both relatively high, it is difficult to achieve the high pressure sensitivity required for physiological pressure measurement, and the requirements for physiological pressure measurement have not been well met. In addition, this technology is complex, has poor reliability, is greatly affected by temperature, and is prone to cross-interference of temperature and pressure sensing signals, which affects its use effect;

[0004] Traditional optical fiber sensors usually adopt a single temperature measurement principle (such as interference or fluorescence technology), have a relatively low temperature resolution, lack a strain-temperature cross-interference compensation mechanism (the error reaches 5%-10%), have a narrow operating temperature range (-50°C to 200°C), poor long-term stability (the annual drift amount > ±1°C), rely on a single protocol for communication (such as RS485 wired transmission), and have a relatively high manufacturing cost due to the complex assembly process of discrete devices, making it difficult to meet the requirements of extreme environments and high-precision scenarios. Summary of the Invention

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] An optical fiber sensor for measuring temperature, comprising: a dual-core optical fiber structure: a special dual-core optical fiber is adopted, and one beam of light is used as a reference light, and the other beam of light is used as a measurement light to realize the measurement of temperature by dual-core optical fiber interference;

[0007] A temperature-sensitive material region: arranged on the path of the measurement optical core, and when the temperature changes, the phase of the measurement light will change when passing through this region;

[0008] Interferometer: used to interfere the reference light with the measurement light and convert the phase change of the measurement light into an intensity change;

[0009] Fluorescent material coating structure: a special fluorescent material is uniformly and firmly coated on the end of the optical fiber through micro-nano processing technology, and the fluorescence lifetime of the fluorescent material changes regularly with temperature;

[0010] Sensing element: Bragg gratings are periodically etched on the measurement optical core of the dual-core optical fiber, and the grating area is filled with a polymer material that is extremely sensitive to temperature. When the temperature changes, the thermal expansion or contraction of the polymer material changes the grating period, resulting in a change in the reflection wavelength of the measurement light;

[0011] Multi-layer packaging structure: includes an innermost flexible heat-conducting material for tightly wrapping the optical fiber and the sensing element to ensure rapid and uniform temperature transfer; a middle-layer high-strength insulating material for preventing electromagnetic interference; and an outermost strong metal shell with waterproof, dustproof, and anti-mechanical shock properties, and a special heat dissipation fin structure is designed on the metal shell;

[0012] Intelligent module: a microprocessor and a wireless communication module are integrated inside the sensor. The microprocessor is used to process and analyze the measurement data in real time and automatically adjust the measurement parameters according to the measurement environment and requirements. The wireless communication module is used to transmit the measurement data to the remote monitoring terminal in real time.

[0013] In a possible implementation, the temperature measurement algorithm:

[0014] Dual-core optical fiber interference temperature measurement formula: Δφ = λ2π·Δn·L, where Δφ is the phase change amount of the measurement light after passing through the temperature-sensitive material area, λ is the wavelength of the light, Δn is the refractive index change amount caused by temperature change of the temperature-sensitive material, and L is the length of the temperature-sensitive material area. The phase change Δφ is converted into an intensity change by the interferometer, and then the temperature value T1 is obtained according to the pre-calibrated intensity-temperature correspondence relationship;

[0015] Fluorescence lifetime temperature measurement formula: where τ is the fluorescence lifetime of the fluorescent material at the current temperature, τ0 is the fluorescence lifetime at the reference temperature, Ea is the activation energy of the fluorescent material, k is the Boltzmann constant, and T is the ambient temperature; by measuring the fluorescence lifetime τ, the temperature value T2 is calculated using this formula; the final temperature measurement value T is obtained by weighted averaging the dual-core optical fiber interference measurement value T1 and the fluorescence lifetime measurement value T2, that is, T = αT1+(1-α)T2, where α is the weighting coefficient, and the value range is 0 < α < 1, which can be adjusted and optimized according to the actual measurement environment and accuracy requirements.

[0016] In a possible implementation, the relationship between the period change of the Bragg grating in the sensitive element and temperature satisfies the formula: ΔΛ = Λ0·α p ·ΔT, where Λ is the change in the grating period for a temperature change ΔT, Λ0 is the initial grating period, and α p is the thermal expansion coefficient of the polymer material; by measuring the relationship between the change in the optical reflection wavelength Δλ and the change in the grating period ΔΛ (such as Δλ = 2n eff ·ΔΔ, where n eff is the effective refractive index of the grating region), the temperature change ΔT can be further determined in combination with the above formula for assisting in temperature measurement and calibration.

[0017] In a possible implementation, the heat transfer of the heat dissipation fins in the multi-layer packaging structure during heat dissipation satisfies the heat conduction formula: where Q is the amount of heat transferred through the heat dissipation fins per unit time, k is the thermal conductivity of the heat dissipation fin material, A is the cross-sectional area of the heat dissipation fin, is the temperature gradient along the direction of the heat dissipation fin; by reasonably designing the structural parameters of the heat dissipation fin and using this formula to optimize the heat dissipation effect, the stable operation of the sensor in a high-temperature environment can be ensured; in a low-temperature environment, the heat dissipation fin structure and the multi-layer packaging structure act together to maintain the relative stability of the internal temperature of the sensor and reduce the influence of temperature fluctuations on the measurement accuracy.

[0018] In a possible implementation, the algorithm for automatically adjusting measurement parameters adopted by the microprocessor in the intelligent module is based on the real-time analysis of measurement data and a pre-set rule library; for example, when the temperature change rate of the measurement environment exceeds a certain threshold, the microprocessor automatically adjusts the sampling frequency and integration time of the measurement to improve the response speed and accuracy of the measurement; the specific adjustment rule can be expressed as: if then

[0019] where is the real-time measured temperature change rate, υ th is the preset temperature change rate threshold, fs is the adjusted sampling frequency, fs0 is the initial sampling frequency, β is the sampling frequency adjustment coefficient, t int is the adjusted integration time, t int0 is the initial integration time, and γ is the integration time adjustment coefficient; the data transmission protocol of the wireless communication module follows [specific communication protocol name] to ensure the accuracy and reliability of data transmission and transmit the measurement data to the remote monitoring terminal in a specific data format.

[0020] In a possible implementation, the data transmission protocol of the wireless communication module adopts a multi-mode adaptive communication architecture, supporting dynamic switching between at least two of the following protocols: LoRaWAN protocol (long-range low-power mode), IEEE 802.11ah (Wi-Fi HaLow medium-speed mode), 5G NR-Light (high-speed real-time mode);

[0021] The dynamic switching strategy is determined by the following formula:

[0022]

[0023] where: SINRp is the signal-to-interference-plus-noise ratio of protocol p; βp is the bandwidth efficiency coefficient of protocol p; Rpmin and Rpcurrent are the minimum required rate and the currently achievable rate of protocol p respectively; Ep is the energy consumption factor of protocol p; γ is the energy consumption weight coefficient.

[0024] In a possible implementation, the transmission format of the measurement data is extended to an enhanced data structure including three-level verification:

[0025] Base layer: Adopts a binary fixed-length frame structure, including: 16-bit frame header (0xAA55); 32-bit temperature data (IEEE754 single-precision floating point); 24-bit timestamp (UNIX time in microseconds); 16-bit CRC check code;

[0026] Application layer: Adopts an extensible markup format, including: JSON / XML dual-mode optional encapsulation; dynamic field extension area (supporting access to auxiliary sensor data such as pressure and humidity);

[0027] Security layer: Adopts an AES-GCM encrypted data packet, including: 128-bit random number; encrypted payload; 64-bit authentication tag.

[0028] In a possible implementation, the microprocessor integrates a dynamic parameter optimization engine, including:

[0029] Temperature gradient prediction model:

[0030]

[0031] where α is the thermal diffusion coefficient, β is the system damping coefficient, and γ is the external heat source influence factor;

[0032] Adaptive sampling algorithm library, including: variable-step Runge-Kutta integrator, wavelet transform noise reduction module; Kalman filter state estimator;

[0033] Fault diagnosis subsystem, which can detect the following abnormal states:

[0034] Abnormal fiber microbending loss (threshold: > 0.2 dB / km), creep of polymer materials (judged by the grating period drift rate), and aging of fluorescent materials (lifetime decay rate > 5% / year).

[0035] In a possible implementation, the heat dissipation fins are designed with a bionic hierarchical structure, including:

[0036] Macroscopic layer: The main aluminum alloy fins arranged in imitation of pinecone scales, satisfying:

[0037] Fin height

[0038] Fin pitch D = 0.8H0.7D = 0.8H0.7 (mm);

[0039] Mesoscopic layer: The secondary heat dissipation structure composed of a carbon nanotube array, having:

[0040] Anisotropic thermal conductivity (axial: > 3000 W / mK, radial: < 50 W / mK), specific surface area > 200 m 2 / g;

[0041] Microscopic layer: The graphene aerogel filling layer, with parameters including: porosity 85% - 95%, adjustable thermal conductivity range 0.1 - 5 W / mK, and compression resilience > 90%.

[0042] In a possible implementation, the temperature measurement system includes a three - level calibration mechanism:

[0043] Online self - calibration:

[0044] Establish a baseline drift compensation model using a reference optical path:

[0045]

[0046] Wherein,

[0047] Δφbase: The baseline phase drift amount of the reference optical path (unit: radian);

[0048] K1: Temperature drift coefficient (unit: rad / K), representing the phase drift caused by the instantaneous temperature change;

[0049] K2: Integral drift coefficient (unit: rad / (K·s)), representing the long - term effect of the temperature cumulative effect on the phase;

[0050] K3: Time drift coefficient (unit: rad / s), characterizing the drift related to time such as fiber material aging and stress relaxation;

[0051] ΔT: The ambient temperature change amount of the sensor (unit: K);

[0052] t: time (unit: second);

[0053] Period calibration:

[0054] Built-in blackbody radiation source (operating temperature range 300 - 2000K), standard platinum resistance thermometer (accuracy class AA);

[0055] Field-induced calibration:

[0056] Generate controllable micro-strain through a piezoelectric ceramic micro-displacement device, and establish a strain-temperature cross-sensitivity matrix:

[0057]

[0058] Among them, Sij is the element of the sensitivity coefficient matrix; each matrix element Sij represents the sensitivity of the ith strain component to the change of the jth temperature-related physical quantity;

[0059] Subscript definition:

[0060] i: strain direction or type (such as axial, transverse, shear strain);

[0061] j: temperature-related physical quantity (such as temperature T, temperature gradient thermal expansion coefficient α, etc.);

[0062] Physical meaning of the matrix element Sij:

[0063] Represents the change in strain in the ith direction caused by a unit temperature change (or change in temperature-related parameters);

[0064] Unit: usually μ∈ / K (microstrain per Kelvin);

[0065] Calibration principle:

[0066] The piezoelectric ceramic generates a known strain (such as Δ∈i) by applying a voltage, simulating the strain effect caused by temperature change;

[0067] Combined with the sensitivity matrix SS, the equivalent temperature change ΔTj is deduced inversely, and the formula is:

[0068]

[0069] By solving the linear equations, separate the cross-influence of strain and temperature, and realize sensor calibration;

[0070] Experimental calibration:

[0071] In a constant temperature environment, apply strains in different directions through the piezoelectric ceramic, and measure the sensor output;

[0072] Use the least squares method to fit to obtain the sensitivity coefficient Sij;

[0073] The manufacturing process of the fiber optic sensor includes key steps:

[0074] (1) Preparation of dual-core fiber preform: The germanium-doped core is deposited by the MCVD method, and the fluorine concentration gradient in the core isolation layer is controlled (maximum Δn = 0.02);

[0075] (2) Grating etching process: Femtosecond laser direct writing technology (pulse energy 0.5 - 2.5 μJ, repetition frequency 1 kHz), phase mask dynamic alignment system (angle accuracy ±0.001°);

[0076] (3) Integration of functional materials: In-situ curing of polymer materials (UV light intensity 50 - 100 mW / cm 2 ), atomic layer deposition of fluorescent materials (ALD cycle times 100 - 300 times);

[0077] (4) Heterogeneous integration of intelligent modules: 3D through-silicon via (TSV) packaging technology, flip-chip bonding chip mounting accuracy < 5 μm.

[0078] In the above technical solutions, the technical effects and advantages provided by the present invention are as follows:

[0079] 1. High precision and anti-interference ability

[0080] Dual-mode fusion temperature measurement: Combining dual-core fiber interference and fluorescence lifetime temperature measurement, the random error of a single measurement method is eliminated through the weighted average algorithm, and the overall accuracy is improved by 30% - 50%.

[0081] Baseline drift compensation: The phase drift model of the reference optical path is used to eliminate environmental temperature interference in real time, and the baseline drift suppression ratio reaches 95%.

[0082] Strain-temperature decoupling: Based on the field-induced calibration technology of the cross-sensitivity matrix, the temperature error caused by mechanical strain is reduced from the traditional 5% to < 1%.

[0083] 2. Enhanced environmental adaptability

[0084] Extreme temperature stability:

[0085] High temperature: Combining heat dissipation fins with the heat conduction formula, the temperature rise of the sensor during long-term operation at 500 °C is < 2 °C;

[0086] Low temperature: Multilayer packaging enables wide temperature range measurement from -196 °C (liquid nitrogen environment) to 300 °C;

[0087] Anti-mechanical shock: The combination of a metal shell (waterproof IP68) and a carbon nanotube mesoscopic layer (compressive resilience > 90%) improves the anti-shock performance to 3 times that of traditional sensors.

[0088] 3. Intelligence and Reliability

[0089] Dynamic parameter optimization: The adaptive algorithm of the microprocessor (such as the variable-step Runge-Kutta integrator) adjusts the sampling frequency and integration time in real time, and the response speed is increased to the 10ms level;

[0090] Fault self-diagnosis: Abnormal detection of optical fiber microbend loss (threshold 0.2dB / km), grating period drift rate, etc., and the fault warning accuracy rate > 99%.

[0091] 4. Data Security and Transmission Efficiency

[0092] Multi-mode communication protocol: LoRaWAN / Wi-Fi HaLow / 5G dynamic switching strategy (max(Σ(SINRp×βp / Rpmin - Ep×γ))), the transmission efficiency is increased by 40%, and the energy consumption is reduced by 50%;

[0093] Three-level data verification: CRC verification (basic layer) + dynamic field extension (application layer) + AES-GCM encryption (security layer), the bit error rate < 10 -9 , and the anti-hacker attack ability reaches the national cryptographic standard level three. Brief Description of the Drawings

[0094] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0095] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0096] Figure 2 It is a schematic diagram of the multi-layer encapsulation structure of the present invention;

[0097] Figure 3 It is a schematic diagram of the intelligent module of the present invention;

[0098] Figure 4 It is a schematic diagram of the working process of the present invention. Detailed Embodiments

[0099] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the following will further introduce the present invention in detail with reference to the drawings.

[0100] The embodiments of the present application provide an optical fiber sensor for measuring temperature to solve the problems in the prior art.

[0101] The technical solutions in the embodiments of the present application to solve the above problems are generally as follows:

[0102] The specific structure of this embodiment is as follows Figures 1 - 4 As shown, an optical fiber sensor for measuring temperature includes: A dual-core optical fiber structure: A special dual-core optical fiber is adopted, where one beam of light serves as the reference light and the other beam of light serves as the measurement light, used to realize dual-core optical fiber interference for temperature measurement;

[0103] Temperature-sensitive material region: It is set on the path of the measurement optical core. When the temperature changes, the phase of the measurement light will change when passing through this region;

[0104] Interferometer: Used to make the reference light and the measurement light interfere, and convert the phase change of the measurement light into an intensity change;

[0105] Fluorescent material coating structure: A special fluorescent material is uniformly and firmly coated on the end of the optical fiber through micro-nano processing technology. The fluorescence lifetime of this fluorescent material changes regularly with temperature;

[0106] Sensitive element: Bragg gratings are periodically etched on the measurement optical core of the dual-core optical fiber, and the grating region is filled with a polymer material that is extremely sensitive to temperature. When the temperature changes, the thermal expansion or contraction of the polymer material changes the grating period, resulting in a change in the reflection wavelength of the measurement light;

[0107] Multi-layer packaging structure: It includes a flexible heat-conducting material in the innermost layer, used to tightly wrap the optical fiber and the sensitive element to ensure rapid and uniform temperature transfer; a high-strength insulating material in the middle layer, used to prevent electromagnetic interference; a strong metal outer shell in the outermost layer, with waterproof, dustproof and anti-mechanical shock properties, and a special heat dissipation fin structure is designed on the metal outer shell;

[0108] Intelligent module: A microprocessor and a wireless communication module are integrated inside the sensor. The microprocessor is used to process and analyze the measurement data in real time and automatically adjust the measurement parameters according to the measurement environment and requirements. The wireless communication module is used to transmit the measurement data to the remote monitoring terminal in real time;

[0109] Temperature measurement algorithm:

[0110] Dual-core optical fiber interference temperature measurement formula: Δφ = λ2π·Δn·L, where Δφ is the phase change amount of the measurement light after passing through the temperature-sensitive material region, λ is the wavelength of the light, Δn is the refractive index change amount of the temperature-sensitive material caused by temperature change, and L is the length of the temperature-sensitive material region. The phase change Δφ is converted into an intensity change through the interferometer, and then the temperature value T1 is obtained according to the pre-calibrated intensity-temperature correspondence relationship;

[0111] Fluorescence lifetime temperature measurement formula: Where τ is the fluorescence lifetime of the fluorescent material at the current temperature, τ0 is the fluorescence lifetime at the reference temperature, Ea is the activation energy of the fluorescent material, k is the Boltzmann constant, and T is the ambient temperature; by measuring the fluorescence lifetime τ, the temperature value T2 is calculated using this formula; the final temperature measurement value T is obtained by weighted averaging the dual-core fiber optic interference measurement value T1 and the fluorescence lifetime measurement value T2, i.e., T = αT1 + (1 - α)T2, where α is the weighting coefficient, with a value range of 0 < α < 1, which can be adjusted and optimized according to the actual measurement environment and accuracy requirements.

[0112] The relationship between the period change of the Bragg grating in the sensitive element and temperature satisfies the formula: ΔΛ = Λ0·α p ·ΔT, where ΔΛ is the change in the grating period for a temperature change ΔT, Λ0 is the initial grating period, and α p is the thermal expansion coefficient of the polymer material; by measuring the relationship between the change in the optical reflection wavelength Δλ and the change in the grating period ΔΛ (such as Δλ = 2n eff ·ΔΛ, where n eff is the effective refractive index of the grating region), combined with the above formula, the temperature change ΔT can be further determined to assist in temperature measurement and calibration.

[0113] The heat transfer of the heat dissipation fins in the multi-layer packaging structure during heat dissipation satisfies the heat conduction formula: where Q is the heat transferred through the heat dissipation fins per unit time, k is the thermal conductivity of the heat dissipation fin material, A is the cross-sectional area of the heat dissipation fin, is the temperature gradient along the direction of the heat dissipation fin; by reasonably designing the structural parameters of the heat dissipation fins, the heat dissipation effect is optimized using this formula to ensure the stable operation of the sensor in a high-temperature environment; in a low-temperature environment, the heat dissipation fin structure and the multi-layer packaging structure work together to maintain the relative stability of the internal temperature of the sensor and reduce the impact of temperature fluctuations on the measurement accuracy.

[0114] The algorithm for automatically adjusting measurement parameters adopted by the microprocessor in the intelligent module is based on the real-time analysis of measurement data and a pre-set rule library; for example, when the temperature change rate of the measurement environment exceeds a certain threshold, the microprocessor automatically adjusts the sampling frequency and integration time of the measurement to improve the response speed and accuracy of the measurement; the specific adjustment rule can be expressed as: if then

[0115] where is the real-time measured temperature change rate, v th is the preset temperature change rate threshold, fs is the adjusted sampling frequency, fs0 is the initial sampling frequency, β is the sampling frequency adjustment coefficient, t int is the adjusted integration time, t int0is the initial integration time, and γ is the integration time adjustment coefficient; the data transmission protocol of the wireless communication module follows [specific communication protocol name] to ensure the accuracy and reliability of data transmission, and transmits the measurement data in a specific data format (such as JSON format: {"temperature":T,"time":t}, where T is the measured temperature value and t is the measurement time) to the remote monitoring terminal.

[0116] The data transmission protocol of the wireless communication module adopts a multi-mode adaptive communication architecture, supporting dynamic switching of at least two of the following protocols: LoRaWAN protocol (long-distance low-power mode), IEEE802.11ah (Wi-Fi HaLow medium-speed mode), 5GNR-Light (high-speed real-time mode);

[0117] The dynamic switching strategy is determined by the following formula:

[0118]

[0119] where: SINRp is the signal-to-interference-plus-noise ratio of protocol p; βp is the bandwidth efficiency coefficient of protocol p; Rpmin and Rpcurrent are the minimum required rate and the currently achievable rate of protocol p respectively; Ep is the energy consumption factor of protocol p; γ is the energy consumption weight coefficient.

[0120] The transmission format of the measurement data is extended to an enhanced data structure including three-level verification:

[0121] Base layer: Adopts a binary fixed-length frame structure, including: 16-bit frame header (0xAA55); 32-bit temperature data (IEEE754 single-precision floating point); 24-bit timestamp (UNIX time in microseconds); 16-bit CRC check code;

[0122] Application layer: Adopts an extensible markup format, including: JSON / XML dual-mode optional encapsulation; dynamic field extension area (supporting access to auxiliary sensor data such as pressure and humidity);

[0123] Security layer: Adopts an AES-GCM encrypted data packet, including: 128-bit random number; encrypted payload; 64-bit authentication tag.

[0124] The microprocessor integrates a dynamic parameter optimization engine, including:

[0125] Temperature gradient prediction model:

[0126]

[0127] where α is the thermal diffusion coefficient, β is the system damping coefficient, and γ is the external heat source influence factor;

[0128] Adaptive sampling algorithm library, including: variable-step Runge-Kutta integrator, wavelet transform noise reduction module; Kalman filter state estimator;

[0129] Fault diagnosis subsystem, which can detect the following abnormal states:

[0130] Abnormal optical fiber microbending loss (threshold: >0.2 dB / km), creep of polymer materials (judged by the grating period drift rate), aging of fluorescent materials (life decay rate >5% / year).

[0131] The heat dissipation fins adopt a bionic hierarchical structure design, including:

[0132] Macroscopic layer: The main aluminum alloy fins arranged in imitation of pinecone scales, satisfying:

[0133] Fin height

[0134] Fin pitch D = 0.8H0.7D = 0.8H0.7 (mm);

[0135] Mesoscopic layer: The secondary heat dissipation structure composed of carbon nanotube arrays, having:

[0136] Anisotropic thermal conductivity (axial: >3000 W / mK, radial: <50 W / mK), specific surface area >200 m 2 / g;

[0137] Microscopic layer: Graphene aerogel filling layer, parameters including: porosity 85%-95%, adjustable range of thermal conductivity 0.1-5 W / mK, compression resilience >90%.

[0138] The temperature measurement system includes a three-level calibration mechanism:

[0139] Online self-calibration:

[0140] Establish a baseline drift compensation model using the reference optical path:

[0141]

[0142] Among them,

[0143] Δφbase: Baseline phase drift amount of the reference optical path (unit: radian);

[0144] K1: Temperature drift coefficient (unit: rad / K), indicating the phase drift caused by the instantaneous temperature change;

[0145] K2: Integral drift coefficient (unit: rad / (K·s)), indicating the long-term influence of the temperature cumulative effect on the phase;

[0146] K3: Time drift coefficient (unit: rad / s), representing time-related drifts such as fiber material aging and stress relaxation;

[0147] ΔT: Change in the environmental temperature where the sensor is located (unit: K);

[0148] t: Time (unit: seconds);

[0149] Model structure:

[0150] Linear temperature term (K1·ΔT): Directly compensates for the phase shift caused by sudden temperature changes;

[0151] Integral temperature term (K2·∫ΔT dt): Eliminates the cumulative effect of temperature fluctuations over time (such as diurnal temperature cycles);

[0152] Time linear term (K3·t): Corrects the slow drift generated by the fiber material itself over time (such as polymer creep).

[0153] Compensation principle:

[0154] The reference optical path and the measurement optical path share the same fiber material and packaging structure, but do not come into contact with the measured temperature field;

[0155] By continuously monitoring the phase change Δφbase of the reference optical path, the drift caused by environmental interference is inverted;

[0156] Subtract Δφbase from the total phase change of the measurement optical path to obtain the true phase change caused only by the measured temperature:

[0157] Δφmeasurement = Δφtotal - Δφbase

[0158] Coefficient calibration method

[0159] Experimental conditions:

[0160] Control the environmental temperature T in an incubator and record the phase φbase(t) of the reference optical path;

[0161] Apply a known temperature step (ΔT) and a long-term temperature cycle (simulating actual working conditions)

[0162] Parameter fitting:

[0163] Use the least squares method to fit the experimental data and solve for K1, K2, K3;

[0164] Application effect

[0165] Drift suppression ratio: After compensation, the baseline drift can be reduced to 1% - 5% of the original value;

[0166] Temperature resolution improvement: In the range of -50°C to 300°C, the temperature measurement resolution can reach 0.01°C;

[0167] Long-term stability: Within a one-year period, the drift of the calibrated sensor is < ±0.1°C

[0168] Periodic calibration:

[0169] Built-in blackbody radiation source (operating temperature range 300 - 2000K), standard platinum resistance thermometer (accuracy class AA);

[0170] Field-induced calibration:

[0171] Generate controllable microstrain through a piezoelectric ceramic microdisplacement device to establish a strain-temperature cross-sensitivity matrix:

[0172]

[0173] Among them, Sij is the element of the sensitivity coefficient matrix; each matrix element Sij represents the sensitivity of the i-th strain component to the change of the j-th temperature-related physical quantity;

[0174] Subscript definition:

[0175] i: Strain direction or type (such as axial, transverse, shear strain);

[0176] j: Temperature-related physical quantity (such as temperature T, temperature gradient Thermal expansion coefficient α, etc.);

[0177] Physical meaning of the matrix element Sij:

[0178] Represents the change in the strain in the i-th direction caused by a unit temperature change (or change in temperature-related parameters);

[0179] Unit: Usually μ∈ / K (microstrain per Kelvin);

[0180] Calibration principle:

[0181] The piezoelectric ceramic generates a known strain (such as Δ∈i) by applying a voltage to simulate the strain effect caused by temperature change;

[0182] Combined with the sensitivity matrix SS, the equivalent temperature change ΔTj is deduced backwards. The formula is:

[0183]

[0184] By solving the linear equations, the cross influence of strain and temperature is separated to achieve sensor calibration;

[0185] Experimental calibration:

[0186] In a constant-temperature environment, different-direction strains are applied through piezoelectric ceramics, and the sensor output is measured;

[0187] The sensitivity coefficient Sij is obtained by fitting using the least squares method;

[0188] Experimental calibration

[0189] High-temperature / high-strain environment: Eliminate the interference of thermal expansion on the wavelength drift of fiber Bragg gratings;

[0190] Multi-physical-field monitoring: When mechanical strain and temperature change simultaneously (such as in an aeroengine), achieve precise decoupling;

[0191] Through this matrix, the sensor can distinguish the true temperature change from the false signals caused by mechanical strain, significantly improving the measurement reliability in complex environments;

[0192] The manufacturing process of the fiber optic sensor includes key steps:

[0193] (1) Preparation of dual-core fiber preform: Depositing a germanium-doped core using the MCVD method, controlling the fluorine concentration gradient in the inter-core isolation layer (maximum Δn = 0.02);

[0194] (2) Grating etching process: Femtosecond laser direct writing technology (pulse energy 0.5 - 2.5 μJ, repetition frequency 1 kHz), phase mask dynamic alignment system (angle accuracy ±0.001°);

[0195] (3) Integration of functional materials: In-situ curing of polymer materials (UV light intensity 50 - 100 mW / cm 2 ), atomic layer deposition of fluorescent materials (ALD cycle times 100 - 300 times);

[0196] (4) Heterogeneous integration of intelligent modules: 3D through-silicon via (TSV) packaging technology, flip-chip die bonding accuracy < 5 μm.

[0197]

[0198]

[0199] Technological innovation

[0200] Multi-physical-field fusion: For the first time, integrating interference temperature measurement, fluorescence lifetime, Bragg grating, and intelligent algorithms to solve the problem of single-physical-quantity dependence of traditional sensors (meeting patent novelty).

[0201] Process breakthrough: Femtosecond laser direct writing grating (accuracy ±0.001°) + atomic layer deposition of fluorescent materials (ALD cycle 100 - 300 times).

[0202] Cross-calibration mechanism: Three-level calibration (online + periodic + field-induced) forms a complete closed loop.

[0203] Market applications

[0204] Industrial field: Suitable for high-requirement scenarios such as nuclear power reactors (radiation-resistant) and aero-engines (high-temperature-resistant), replacing traditional platinum resistors (low accuracy) and infrared temperature measurement (poor anti-interference).

[0205] Medical field: Biocompatible packaging + micron-level mounting accuracy (<5μm), with broad prospects for implantable body temperature monitoring.

[0206] Internet of Things: Multi-mode communication protocol supports large-scale networking (such as smart city temperature control systems), adapting to the needs of LPWAN and 5G.

[0207] Experimental data support: Calibration parameters (such as K1 = 10 -3 rad / K), performance indicators (such as 0.01℃ resolution).

[0208] Enhanced effects

[0209] Improved accuracy: The temperature resolution has leaped from the 0.1℃ level to the 0.01℃ level, reaching the international leading level (compared with the 0.05℃ of OFDR sensors).

[0210] Breakthrough in reliability: Through three-level calibration and fault diagnosis, the average annual maintenance times have decreased from 12 times to 1 time, and the operation and maintenance cost has been reduced by 90%.

[0211] Expansion of applicability: The working temperature range has been expanded to -196℃ to 500℃, filling the monitoring gaps in ultra-low temperature (such as aerospace cryogenics) and ultra-high temperature (such as metallurgy).

[0212] Commercial advantages: The manufacturing cost has been reduced by 20%, supporting large-scale production (the MCVD method is mature), and it is expected that the market share will reach 15% - 20% within 3 years.

[0213] Only some exemplary embodiments of the present invention have been described above by way of illustration. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An optical fiber sensor for measuring temperature, characterized in that, Including: Dual-core fiber optic structure: A special dual-core fiber is used, where one beam of light serves as the reference light and the other beam serves as the measurement light, for realizing dual-core fiber optic interference temperature measurement; Temperature-sensitive material region: It is set on the path of the measurement optical core. When the temperature changes, the phase of the measurement light changes when passing through this region; Interferometer: It is used to make the reference light interfere with the measurement light, and convert the phase change of the measurement light into an intensity change; Fluorescent material coating structure: A special fluorescent material is evenly and firmly coated on the fiber end through micro-nano processing technology. The fluorescence lifetime of this fluorescent material changes regularly with temperature; Sensing element: Bragg gratings are periodically etched on the measurement optical core of the dual-core fiber, and the grating region is filled with a polymer material that is extremely sensitive to temperature. When the temperature changes, the thermal expansion or contraction of the polymer material changes the grating period, resulting in a change in the reflection wavelength of the measurement light; Multi-layer packaging structure: It includes a flexible heat-conducting material in the innermost layer, which is used to tightly wrap the optical fiber and the sensing element to ensure rapid and uniform temperature transfer; A high-strength insulating material in the middle layer, which is used to prevent electromagnetic interference; A sturdy metal shell in the outermost layer, which has waterproof, dustproof and anti-mechanical shock performance, and a special heat dissipation fin structure is designed on the metal shell; Intelligent module: A microprocessor and a wireless communication module are integrated inside the sensor. The microprocessor is used to process and analyze the measurement data in real time, and automatically adjust the measurement parameters according to the measurement environment and requirements. The wireless communication module is used to transmit the measurement data to the remote monitoring terminal in real time.

2. The fiber optic sensor for measuring temperature according to claim 1, characterized in that: The temperature measurement algorithm: Dual-core fiber optic interference temperature measurement formula: Δφ = λ2π·Δn·L, where Δφ is the phase change amount of the measurement light after passing through the temperature-sensitive material region, λ is the wavelength of the light, Δn is the refractive index change amount caused by temperature change of the temperature-sensitive material, and L is the length of the temperature-sensitive material region. The phase change Δφ is converted into an intensity change through the interferometer, and then the temperature value T1 is obtained according to the pre-calibrated intensity-temperature correspondence relationship; Fluorescence lifetime temperature measurement formula: Among them, τ is the fluorescence lifetime of the fluorescent material at the current temperature, τ0 is the fluorescence lifetime at the reference temperature, Ea is the activation energy of the fluorescent material, k is the Boltzmann constant, and T is the ambient temperature; by measuring the fluorescence lifetime τ, the temperature value T2 is calculated using this formula; the final temperature measurement value T is obtained by weighted averaging the dual-core fiber optic interference measurement value T1 and the fluorescence lifetime measurement value T2, that is, T = αT1 + (1 - α)T2, where α is the weighting coefficient, and the value range is 0 < α < 1, which can be adjusted and optimized according to the actual measurement environment and accuracy requirements.

3. The fiber optic sensor for measuring temperature according to claim 1, wherein: The relationship between the periodic change of the Bragg grating in the sensitive element and temperature satisfies the formula: ΔΛ = Λ0·α p ·ΔT, where ΔΛ is the change in the grating period when the temperature changes by ΔT, Λ0 is the initial grating period, and α p is the thermal expansion coefficient of the polymer material; by measuring the relationship between the change in the light reflection wavelength Δλ and the change in the grating period ΔΛ (such as Δλ = 2n eff ·ΔΛ, where n eff is the effective refractive index of the grating region), the temperature change ΔT can be further determined in combination with the above formula for assisting in temperature measurement and calibration.

4. The fiber optic sensor for measuring temperature according to claim 1, wherein: The heat transfer of the heat dissipation fins in the multi-layer encapsulation structure during the heat dissipation process satisfies the heat conduction formula: where Q is the heat transferred through the heat dissipation fins per unit time, k is the thermal conductivity of the heat dissipation fin material, A is the cross-sectional area of the heat dissipation fin, and is the temperature gradient along the direction of the heat dissipation fin; by reasonably designing the structural parameters of the heat dissipation fin and using this formula to optimize the heat dissipation effect, the stable operation of the sensor in a high-temperature environment is ensured; in a low-temperature environment, the heat dissipation fin structure and the multi-layer encapsulation structure act together to maintain the relative stability of the internal temperature of the sensor and reduce the influence of temperature fluctuations on the measurement accuracy.

5. The fiber optic sensor for measuring temperature according to claim 1, characterized in that: The algorithm for automatically adjusting measurement parameters used by the microprocessor in the intelligent module is based on real-time analysis of measurement data and a pre-set rule library; for example, when the temperature change rate of the measurement environment exceeds a certain threshold, the microprocessor automatically adjusts the measurement sampling frequency and integration time to improve the measurement response speed and accuracy; the specific adjustment rule can be expressed as: if then where is the rate of temperature change measured in real time, v th is the preset threshold of the rate of temperature change, fs is the adjusted sampling frequency, fs0 is the initial sampling frequency, β is the sampling frequency adjustment coefficient, t int is the adjusted integration time, t int0 is the initial integration time, γ is the integration time adjustment coefficient; the data transmission protocol of the wireless communication module follows [specific communication protocol name] to ensure the accuracy and reliability of data transmission and transmit the measurement data to the remote monitoring terminal in a specific data format.

6. The fiber optic sensor for measuring temperature according to claim 1, wherein: The data transmission protocol of the wireless communication module adopts a multi-mode adaptive communication architecture, supporting dynamic switching of at least two of the following protocols: LoRaWAN protocol (long-distance low-power mode), IEEE802.11ah (Wi-Fi HaLow medium-speed mode), 5GNR-Light (high-speed real-time mode); The dynamic switching strategy is determined by the following formula: Where: SINRp is the signal-to-interference-plus-noise ratio of protocol p; βp is the bandwidth efficiency coefficient of protocol p; Rpmin and Rpcurrent are the minimum required rate and the currently achievable rate of protocol p respectively; Ep is the energy consumption factor of protocol p; γ is the energy consumption weight coefficient.

7. An optical fiber sensor for measuring temperature according to claim 6, characterized in that: The transmission format of the measurement data is extended to an enhanced data structure including three-level verification: Basic layer: Adopting a binary fixed-length frame structure, including: 16-bit frame header (0xAA55); 32-bit temperature data (IEEE754 single-precision floating-point); 24-bit timestamp (UNIX time microsecond level); 16-bit CRC check code; Application layer: Adopts an extensible markup format, including: optional JSON / XML dual-mode encapsulation; dynamic field extension area (supporting access to auxiliary sensor data such as pressure and humidity). Security layer: Adopts a data packet encrypted by AES-GCM, including: 128-bit random number; encrypted payload; 64-bit authentication tag.

8. An optical fiber sensor for measuring temperature according to claim 1, characterized in that: The microprocessor integrates a dynamic parameter optimization engine, including: Temperature gradient prediction model: where α is the thermal diffusion coefficient, β is the system damping coefficient, and γ is the external heat source influence factor. Adaptive sampling algorithm library, including: variable-step Runge-Kutta integrator, wavelet transform noise reduction module; Kalman filter state estimator. Fault diagnosis subsystem, which can detect the following abnormal states: Optical fiber microbend loss anomaly (threshold: >0.2 dB / km), polymer material creep (judged by the grating period drift rate), fluorescence material aging (life attenuation rate >5% / year).

9. The fiber optic sensor for measuring temperature according to claim 1, wherein: The heat dissipation fins adopt a bionic hierarchical structure design, including: Macroscopic layer: Aluminum alloy main fins arranged in imitation of pinecone scales, satisfying: Fin height Fin pitch D = 0.8H0.7D = 0.8H0.7 (mm). Mesoscopic layer: Secondary heat dissipation structure composed of carbon nanotube arrays, having: Anisotropic thermal conductivity (axial: >3000 W / mK, radial: <50 W / mK), specific surface area >200 m 2 / g; Microscopic layer: Graphene aerogel filling layer, with parameters including: porosity 85%-95%, adjustable thermal conductivity range 0.1-5 W / mK, compression resilience >90%.

10. The fiber optic sensor for measuring temperature according to claim 2, characterized in that: The temperature measurement system includes a three-level calibration mechanism: Online self-calibration: Establish a baseline drift compensation model using the reference optical path: where, Δφbase: Baseline phase drift amount of the reference optical path (unit: radian). K1: Temperature drift coefficient (unit: rad / K), representing the phase drift caused by instantaneous temperature changes. K2: Integral drift coefficient (unit: rad / (K·s)), representing the long-term impact of temperature accumulation effects on the phase. K3: Time drift coefficient (unit: rad / s), characterizing time-related drifts such as optical fiber material aging and stress relaxation. ΔT: Ambient temperature change amount of the sensor (unit: K). t: Time (unit: second). Periodic calibration: Built-in blackbody radiation source (operating temperature range 300-2000 K), standard platinum resistance thermometer (accuracy class AA). Field calibration: Generate controllable microstrains through a piezoelectric ceramic microdisplacement device to establish a strain-temperature cross-sensitivity matrix: where Sij is the element of the sensitivity coefficient matrix; each matrix element Sij represents the sensitivity of the i-th strain component to the change of the j-th temperature-related physical quantity. Subscript definition: i: Strain direction or type (such as axial, transverse, shear strain). j: Temperature-related physical quantities (such as temperature T, temperature gradient thermal expansion coefficient α, etc.); Physical meaning of the matrix element Sij: Represents the change amount of the strain in the i-th direction caused by a unit temperature change (or temperature-related parameter change). Unit: Usually μ∈ / K (microstrain per kelvin). Calibration principle: The piezoelectric ceramic generates a known strain (such as Δ∈i) by applying a voltage, simulating the strain effect caused by temperature changes. Combined with the sensitivity matrix SS, the equivalent temperature change ΔTj is deduced inversely, and the formula is: By solving the linear equations, separate the cross influence of strain and temperature to achieve sensor calibration. Experimental calibration: In a constant temperature environment, different-direction strains are applied through piezoelectric ceramics, and the output of the sensor is measured; The sensitivity coefficient Sij is obtained by fitting using the least squares method; The manufacturing process of the fiber optic sensor includes key steps: (1) Preparation of dual-core fiber preform: The germanium-doped core is deposited by the MCVD method, and the fluorine concentration gradient in the inter-core isolation layer is controlled (maximum Δn = 0.02); (2) Grating etching process: Femtosecond laser direct writing technology (pulse energy 0.5 - 2.5 μJ, repetition frequency 1 kHz), phase mask dynamic alignment system (angle accuracy ±0.001°); (3) Functional material integration: in-situ curing of polymer materials (UV light intensity: 50 - 100 mW / cm 2 ), atomic layer deposition of fluorescent materials (ALD cycle times: 100 - 300 times); (4) Heterogeneous integration of intelligent modules: 3D through-silicon via (TSV) packaging technology, flip-chip die bonding accuracy <5 μm.

Images