Multi-core optical fiber temperature detection method and system fused with wavelength division multiplexing

Abstract

The invention provides a wavelength division multiplexing-fused multi-core optical fiber temperature detection method and system. Wherein the broadband light source is divided into multiple wavelength channels through wavelength division multiplexing, and a wavelength-fiber core mapping relation is established, so that each fiber core of the multi-core optical fiber transmits a single-wavelength optical signal. An optical fiber is integrated to the surface of an object according to a three-dimensional path, each fiber core covers a plurality of sensing sub-areas, the wavelength drift amount caused by temperature is extracted by detecting the attenuation characteristic of an optical signal at a bending position, the wavelength drift amount is converted into a temperature value in combination with a calibration curve, and a temperature gradient model bound with the fiber core position is constructed. And further performing spatial solution by using fiber core spacing, wavelength mapping and transmission time delay parameters, establishing a corresponding relationship between the coordinates of the sensing sub-regions and the temperature, and finally reconstructing three-dimensional temperature field distribution. According to the technical scheme provided by the invention, high-resolution dynamic reconstruction and gradient analysis of a three-dimensional temperature field on a complex surface are realized through wavelength-space two-dimensional decoupling.

Description

Technical Field

[0001] The present application relates to the technical field of wavelength division multiplexing, and in particular to a multi-core optical fiber temperature detection method and system integrating wavelength division multiplexing. Background Art

[0002] In fields such as high-temperature electronic equipment, energy and power systems, and biomedical hyperthermia, real-time three-dimensional temperature field monitoring is required for complex curved surfaces or densely integrated objects. This requires technology with multi-point simultaneous detection, millimeter-level spatial resolution, and electromagnetic interference immunity. Traditional solutions are limited to single-point measurement or two-dimensional plane coverage, making them difficult to meet the needs of dynamic temperature gradient analysis and complex spatial path integration.

[0003] Currently, the most advanced solution is the distributed temperature measurement system (DTS) based on Raman scattering. This system exploits spontaneous Raman scattering in optical fibers and measures temperature by demodulating the intensity ratio of anti-Stokes light to Stokes light. A typical system uses a single-core quartz fiber as the sensing medium and locates temperature changes through optical time-domain reflectometry (OTDR). The system achieves a spatial resolution of up to 1 meter and a temperature measurement accuracy of ±0.5°C.

[0004] Raman DTS systems face three key bottlenecks: First, due to their single-core fiber structure, spatial resolution is difficult to break through the centimeter level, making them incapable of monitoring precision components such as aircraft engine blades. Second, Raman signal strength is low, and the signal-to-noise ratio deteriorates dramatically in high-temperature environments, resulting in reduced temperature measurement accuracy. Third, the system's slow response speed (on the order of seconds) makes it difficult to capture rapid temperature transients on the millisecond scale. Furthermore, multiplexing technology introduces channel crosstalk, further reducing measurement reliability. These limitations have severely limited the technology's application in high-end equipment monitoring. Summary of the Invention

[0005] The present application provides a multi-core optical fiber temperature detection method and system integrating wavelength division multiplexing, which is used to solve the problems of low spatial resolution, large cross-sensitivity error and limited sensing density in single-core optical fiber three-dimensional temperature monitoring in the prior art.

[0006] In a first aspect, the present application provides a multi-core optical fiber temperature detection method integrating wavelength division multiplexing, comprising:

[0007] Based on wavelength division multiplexing technology, a broadband light source is divided into multiple wavelength channels, and the wavelength channels are guided to different core input ports of a multi-core optical fiber, so that each core can transmit an optical signal of a single wavelength, and a first wavelength-core mapping relationship is generated;

[0008] The multi-core optical fiber loaded with optical signals is integrated onto the surface of the object to be measured according to a preset three-dimensional spatial path, and each fiber core covers multiple sensing sub-areas;

[0009] After deployment is complete, the optical signals output by each fiber core are received. The wavelength drift caused by temperature changes is extracted based on the attenuation characteristics of the optical signals of each wavelength channel at the fiber core bend. Combined with the pre-stored nonlinear calibration curve, the wavelength drift is converted into the temperature value of each sensing sub-area to establish a temperature gradient model bound to the spatial position of the fiber core.

[0010] performing a wavelength-space solution on the optical signal based on the physical spacing between the cores in the multi-core optical fiber, the first mapping relationship, and the transmission delay of the optical signal in different cores to generate a second mapping relationship between the spatial coordinates and the temperature value in the sensing sub-area, and reconstructing a three-dimensional temperature field distribution topology on the surface of the object to be measured based on the second mapping relationship;

[0011] The temperature variation range of the surface of the object to be measured is determined according to the temperature gradient model and the three-dimensional temperature field distribution topology.

[0012] Optionally, determining the temperature variation range of the surface of the measured object according to the temperature gradient model and the three-dimensional temperature field distribution topology includes:

[0013] Dynamically associating and fusing the temperature gradient model with the three-dimensional temperature field distribution topology, and generating fused temperature field data by complementing the local temperature change characteristics of the temperature gradient model with the spatial thermal field distribution characteristics of the three-dimensional temperature field distribution topology;

[0014] Based on the fused temperature field data and the preset threshold, a spatial domain joint analysis is performed to locate the temperature abnormality area on the surface of the measured object, and the temperature variation range of the temperature abnormality area is calculated by inversion of the nonlinear calibration curve.

[0015] Optionally, extracting the wavelength drift caused by temperature change based on the attenuation characteristics of the optical signal of each wavelength channel at the fiber core bend includes:

[0016] For the optical signal of each wavelength channel, the initial light intensity value of the optical signal is obtained at each bend of the multi-core optical fiber, and the real-time light intensity value of the optical signal after passing through the bend is recorded;

[0017] Calculating the light intensity attenuation ratio of the optical signal at the bend according to the difference between the initial light intensity value and the real-time light intensity value;

[0018] By comparing the intensity attenuation ratio change trends of the optical signal of the same wavelength channel at different bends, the abnormal fluctuation range of the attenuation ratio caused by temperature change can be identified;

[0019] Within the abnormal fluctuation range of the attenuation ratio, locating a characteristic inflection point of the light intensity attenuation ratio changing with temperature, and measuring a wavelength offset distance corresponding to the characteristic inflection point;

[0020] The wavelength shift distance is matched with a pre-stored wavelength-temperature association table to obtain the wavelength drift caused by temperature change.

[0021] Optionally, based on the wavelength drift amount combined with a pre-stored nonlinear calibration curve, the wavelength drift amount is converted into a temperature value of each sensing sub-region to establish a temperature gradient model bound to the spatial position of the fiber core, including:

[0022] Accessing a nonlinear calibration curve pre-calibrated by experiments, wherein the nonlinear calibration curve stores a correspondence between a wavelength drift of each fiber core and a temperature change, and wherein the correspondence is defined in sections according to material properties of the fiber core and a curvature radius of a bending section;

[0023] For each curved segment corresponding to the sensing sub-region, searching for a curve segment matching the curved segment from the nonlinear calibration curve according to the identifier of the fiber core where the curved segment is located and the curvature radius of the curved segment;

[0024] Inputting the wavelength drift of the curved segment into the matching curve segment, obtaining the temperature change of the sensing sub-region where the curved segment is located by interpolation calculation, and determining the temperature value of the sensing sub-region in combination with the initial temperature value;

[0025] According to the arrangement order of each curved section on each fiber core in the transmission path, the temperature values ​​of multiple sensing sub-areas covered by the same fiber core are arranged in sequence according to the transmission direction of the optical signal, forming a temperature distribution sequence bound to the axial position of the fiber core;

[0026] Based on the physical spacing and three-dimensional spatial path coordinates of each fiber core in a multi-core optical fiber, the temperature value in the temperature distribution sequence of each fiber core is assigned to the corresponding three-dimensional spatial coordinate point, and the current temperature values ​​of all fiber cores are combined to form a temperature gradient model bound to the spatial coordinates.

[0027] Optionally, based on the physical spacing between the cores in the multi-core optical fiber, the first mapping relationship, and the transmission delay of the optical signal in different cores, wavelength-space resolution is performed on the optical signal to generate a second mapping relationship between the spatial coordinates and the temperature value in the sensing sub-area, and a three-dimensional temperature field distribution topology of the surface of the object to be measured is reconstructed based on the second mapping relationship, including:

[0028] The input port of the multi-core optical fiber is used as a reference point, and a core spatial distribution model is established according to the physical spacing of the cores; based on the first mapping relationship, an initial position of the core corresponding to each wavelength channel in the core spatial distribution model is determined;

[0029] By measuring the transmission delay of the optical signal of each wavelength channel from the input port to each sensing sub-area and combining it with the fiber core refractive index parameters, the actual propagation distance of the optical signal in each fiber core is calculated;

[0030] Determining the specific position coordinates of each sensing sub-region on the three-dimensional spatial path based on the actual propagation distance and the fiber core spatial distribution model; binding the temperature value detected by each sensing sub-region to the position coordinate corresponding to the sensing sub-region to form a second mapping relationship including spatial coordinates and temperature values;

[0031] Based on the position coordinates and temperature values ​​of all sensing sub-areas in the second mapping relationship, a continuous three-dimensional temperature field distribution topology of the surface of the object to be measured is constructed according to the three-dimensional spatial path arrangement rule of the multi-core optical fiber.

[0032] Optionally, dynamically associating and fusing the temperature gradient model with the three-dimensional temperature field distribution topology, and generating fused temperature field data by complementing the local temperature change characteristics of the temperature gradient model with the spatial thermal field distribution characteristics of the three-dimensional temperature field distribution topology, including:

[0033] Superimposing the temperature value of the three-dimensional grid node corresponding to each fiber core in the temperature gradient model with the temperature value of the same spatial coordinate point in the three-dimensional temperature field distribution topology, wherein during the superposition, a first weight is assigned to the temperature value of the temperature gradient model, and a second weight is assigned to the temperature value of the three-dimensional temperature field distribution topology, and the sum of the first weight and the second weight is a fixed value and is dynamically adjusted according to the distance between the fiber core and the spatial coordinate point;

[0034] In the superimposed temperature values, the steep temperature change features between adjacent sensing sub-areas in the temperature gradient model are identified, and the spatial smoothness features of the temperature values ​​in the same area in the three-dimensional temperature field distribution topology are extracted. The steep features and the smooth features are complementarily superimposed according to a preset ratio to form fused intermediate temperature field data;

[0035] For the temperature distribution gap area caused by the core spacing in the intermediate temperature field data, based on the temperature value change trend of the sensing sub-area around the gap area, the temperature value diffusion filling is performed along the three-dimensional spatial path extension direction of the multi-core optical fiber, so that the temperature values ​​of the adjacent core coverage areas are spatially continuous;

[0036] Based on the filled intermediate temperature field data, the area where the temperature value difference between the temperature gradient model and the three-dimensional temperature field distribution topology exceeds a preset threshold is marked as a to-be-corrected area, and the temperature value of the temperature gradient model is replaced by the temperature value of the three-dimensional temperature field distribution topology in the to-be-corrected area to generate fused temperature field data.

[0037] Optionally, performing a spatial domain joint analysis based on the fused temperature field data and a preset threshold to locate the temperature anomaly area on the surface of the measured object, and calculating the temperature variation range of the temperature anomaly area by inverting the nonlinear calibration curve, includes:

[0038] In the fused temperature field data, the difference between the temperature value of each spatial position and the preset threshold is compared point by point; the continuous spatial positions whose temperature values ​​exceed the preset threshold are marked as candidate abnormal areas;

[0039] For each candidate abnormal area, the number of sensor sub-areas covered by the candidate abnormal area and the corresponding wavelength drift are counted; based on the distribution characteristics of the wavelength drift in the candidate abnormal area, the candidate abnormal area that conforms to the temperature anomaly propagation law is screened as the final temperature anomaly area;

[0040] Extracting the wavelength drift of each sensing sub-region within the temperature anomaly region, and calculating the temperature deviation value of each sensing sub-region according to the corresponding relationship between the wavelength offset distance and the temperature in the nonlinear calibration curve;

[0041] The temperature variation range of the surface of the object being measured is determined based on the maximum value of the temperature deviation value and the spatial range of the temperature anomaly area.

[0042] In a second aspect, the present application provides a multi-core optical fiber temperature detection system integrating wavelength division multiplexing, comprising:

[0043] a first generation module, configured to divide a broadband light source into a plurality of wavelength channels based on wavelength division multiplexing technology, and guide the wavelength channels to different core input ports of a multi-core optical fiber so that each core can transmit an optical signal of a single wavelength, and generate a first wavelength-core mapping relationship;

[0044] An integration module is used to integrate the multi-core optical fiber loaded with optical signals onto the surface of the object to be measured according to a preset three-dimensional spatial path, with each fiber core covering multiple sensing sub-areas;

[0045] A processing module is configured to, after deployment, receive the optical signals output by each fiber core, extract the wavelength drift caused by temperature change based on the attenuation characteristics of the optical signals of each wavelength channel at the fiber core bend, and convert the wavelength drift into the temperature value of each sensing sub-area in combination with a pre-stored nonlinear calibration curve to establish a temperature gradient model bound to the spatial position of the fiber core;

[0046] a computing module, configured to perform a wavelength-space solution on the optical signal based on the physical spacing between the cores in the multi-core optical fiber, the first mapping relationship, and the transmission delay of the optical signal in different cores, so as to generate a second mapping relationship between the spatial coordinates and the temperature value in the sensing sub-area, and reconstruct a three-dimensional temperature field distribution topology on the surface of the object to be measured based on the second mapping relationship;

[0047] The second generating module is used to determine the temperature variation range of the surface of the measured object according to the temperature gradient model and the three-dimensional temperature field distribution topology.

[0048] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-core optical fiber temperature detection method integrating wavelength division multiplexing as described in the first aspect above.

[0049] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a multi-core optical fiber temperature detection method integrating wavelength division multiplexing as described in the first aspect.

[0050] In the embodiment of the present application, a broadband light source is divided into multiple wavelength channels based on wavelength division multiplexing technology, and the wavelength channels are guided to different core input ports of a multi-core optical fiber, so that each core can transmit a single wavelength optical signal and generate a first wavelength-core mapping relationship; the multi-core optical fiber loaded with the optical signal is integrated into the surface of the object to be measured according to a preset three-dimensional spatial path, and each core covers multiple sensing sub-areas; after the deployment is completed, the optical signal output by each core is received, and the wavelength drift caused by temperature change is extracted based on the attenuation characteristics of the optical signal of each wavelength channel at the core bend, and the wavelength drift is combined with the pre-stored nonlinear correction factor to obtain the wavelength drift caused by temperature change. A directrix curve is used to convert the wavelength drift into the temperature value of each sensing sub-area to establish a temperature gradient model bound to the spatial position of the fiber core; according to the physical spacing between the fiber cores in the multi-core optical fiber, the first mapping relationship and the transmission delay of the optical signal in different fiber cores, the wavelength space calculation is performed on the optical signal to generate a second mapping relationship between the spatial coordinates and the temperature value in the sensing sub-area, and the three-dimensional temperature field distribution topology of the surface of the object to be measured is reconstructed according to the second mapping relationship; according to the temperature gradient model and the three-dimensional temperature field distribution topology, the temperature change range of the surface of the object to be measured is determined.

[0051] The technical solution of this application has the following beneficial effects:

[0052] Through the collaborative design of multi-core optical fiber and wavelength division multiplexing, wavelength and space decoupling is achieved in the physical dimension, significantly improving the spatial resolution and anti-cross-interference capability of the three-dimensional temperature field; based on the extraction of bending attenuation characteristics and the fusion of dual mapping relationships, the sensing density limitation of single-core optical fiber is broken through, supporting dynamic temperature gradient monitoring of complex surfaces.

[0053] Furthermore, the temperature gradient model (local temperature variation) is dynamically correlated and fused with the three-dimensional temperature field distribution topology (global thermal field characteristics) to generate fused temperature field data. This data is then combined with preset thresholds and nonlinear calibration curve inversion calculations to accurately locate temperature anomaly areas and quantify their range. By complementing and enhancing local and global temperature characteristics, the lack of sensitivity of a single model to minor anomalies is addressed, improving the accuracy of temperature anomaly detection. Using nonlinear calibration inversion, environmental perturbation errors are further eliminated, achieving sub-degree Celsius precision calibration of the temperature variation range.

[0054] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] Figure 1 A flow chart of a multi-core optical fiber temperature detection method integrating wavelength division multiplexing provided by the present application is shown;

[0057] Figure 2 The present invention provides a schematic structural diagram of a multi-core optical fiber temperature detection system integrating wavelength division multiplexing;

[0058] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0060] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0061] Researchers have found that existing single-core optical fiber temperature detection solutions rely on single-core multi-wavelength multiplexing, resulting in insufficient accuracy in three-dimensional temperature field reconstruction, high decoupling errors caused by cross-sensitivity between temperature and strain, and poor scalability caused by strong coupling between sensing density and wavelength resources. Based on this, a multi-core optical fiber temperature detection method integrating wavelength division multiplexing is provided. This method separates signal transmission channels through wavelength-core mapping, combines three-dimensional spatial path integration with bending attenuation characteristic analysis, realizes core-level temperature gradient binding, and accurately reconstructs three-dimensional temperature field distribution through two-dimensional solution, significantly improving spatial resolution and anti-interference capability. The technical solution of this application can be applied to complex scenarios requiring high-precision three-dimensional temperature monitoring, such as heat dissipation monitoring of high-temperature electronic equipment, dynamic analysis of temperature fields of hot-end components of aircraft engines, and precise temperature control of biomedical hyperthermia areas.

[0062] The entire R&D process embodies the technical integration of multi-dimensional decoupling architecture design and dynamic perception algorithm collaboration: through the physical layer decoupling design of wavelength division multiplexing and multi-core optical fiber, wavelength-core mapping is used to realize independent signal channel allocation, breaking through the strong coupling limitation of wavelength resources and sensing density of single-core system; combining three-dimensional path integration and bending attenuation characteristic analysis, temperature-sensitive units and strain interference are isolated in the spatial domain, and cross-sensitivity errors are eliminated through nonlinear calibration; further, through the dynamic solution of wavelength-space dual mapping, a precise binding model of fiber core position and temperature gradient is constructed, and the real-time reconstruction of the three-dimensional temperature field is driven, forming a full-link closed loop from hardware architecture innovation (multi-core multiplexing), signal interference suppression (bending decoupling) to system dynamic modeling (dual mapping solution), and ultimately achieving a high-resolution, interference-resistant three-dimensional temperature field monitoring capability upgrade in complex curved surface scenarios.

[0063] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0064] Figure 1 A flow chart of a multi-core optical fiber temperature detection method integrating wavelength division multiplexing is provided for the embodiment of the present application. Figure 1 As shown, the method includes:

[0065] 101. Divide a broadband light source into multiple wavelength channels based on wavelength division multiplexing technology, and guide the wavelength channels to different core input ports of a multi-core optical fiber so that each core can transmit an optical signal of a single wavelength, and generate a first wavelength-core mapping relationship;

[0066] A wavelength channel is a separate spectral interval into which a broadband light source is divided using wavelength division multiplexing technology. Each interval carries a unique wavelength optical signal. For example, wavelength channels can be λ1 through λ7. The first mapping relationship is a logical relationship table that binds different wavelength channels to designated core ports of a multi-core optical fiber through hardware routing configuration, achieving decoupling of wavelength resources from the physical core channels.

[0067] In the embodiment of the present application, the broadband light source is first spectrally split by wavelength division multiplexing (WDM) to generate multiple equally spaced wavelength channels, such as λ1 to λ7; then, the wavelength channels are guided to different core input ports of the multi-core optical fiber through a fiber coupler, and each wavelength channel is directed to an independent core of the multi-core optical fiber, such as cores C1 to C7, to ensure single-core single-wavelength transmission; finally, the wavelength λ is recorded. i With core C j The corresponding relationship between wavelength and fiber core is generated to generate the first mapping relationship matrix of wavelength and fiber core that can be traced and queried, providing physical channel identification for subsequent signal processing.

[0068] In the thermal monitoring scenario of high-temperature electronic equipment, a high-precision arrayed waveguide grating (AWG) is used to split a broadband light source (e.g., with a bandwidth of 1530 to 1560 nm and an output power of 20 mW) into seven wavelength channels: λ1 = 1530.0 nm, λ2 = 1535.0 nm, …, λ7 = 1560.0 nm, with a 5 nm spacing. Using a polarization-independent fiber coupler with an insertion loss of <0.5 dB, each wavelength channel is coupled to a seven-core fiber (e.g., cores C1 to C7, each with a core diameter of 50 μm and a cladding of 125 μm). A wavelength-to-core mapping table is generated, for example, "λ3 to C3." This mapping table is stored in real time by an FPGA controller, providing a physical channel traceability foundation for subsequent signal processing. This ensures complete isolation of multi-wavelength signals between cores and prevents channel crosstalk.

[0069] 102. Integrate the multi-core optical fiber loaded with the optical signal onto the surface of the object to be measured according to a preset three-dimensional spatial path, with each fiber core covering multiple sensing sub-areas;

[0070] The preset three-dimensional spatial path is a fiber core layout trajectory pre-designed according to the surface curvature of the object being measured and the distribution of thermally sensitive areas; the sensing sub-area is an independent detection unit in which each fiber core is segmented and marked along the path, which is used to realize the discrete binding of spatial coordinates and temperature values.

[0071] In an embodiment of the present application, based on the three-dimensional model of the object to be measured, a path optimization algorithm, such as an ant colony algorithm, is used to generate a fiber core layout path to ensure coverage of key areas and minimize cross-interference; the multi-core optical fiber is flexibly adhered to the surface of the object through hot melt glue or a 3D printed fixture to keep the fiber core bending curvature consistent with the preset path; finally, the optical time domain reflectometer OTDR is used to measure the fiber core length, and the sub-area coordinates are marked according to the distance segment, and each fiber core covers multiple sensing sub-areas to complete the spatial deployment of the sensing unit.

[0072] Based on the three-dimensional surface model of the heat sink, the CAD modeling accuracy is ±0.1mm. The improved ant colony algorithm is used, and the convergence iteration number is ≤50 to plan the fiber core path. The optimization goal is to minimize the fiber core crossing and curvature mutation. Using a highly flexible polyimide-coated multi-core optical fiber, a customized 3D printed fixture (for example, material: high-temperature resistant nylon, melting point > 260°C) is used to adhere the optical fiber to the heat sink surface in a spiral path, ensuring that the core bending radius is >5mm to avoid modal leakage. After deployment, the fiber core length is measured by optical time domain reflectometry, and the sensing sub-area is marked at 5mm intervals, such as C3-S 12 It represents the sub-area at 60mm from the input end of the fiber core C3, and the path fitting error is verified to be less than 0.2mm by the laser displacement sensor, achieving full coverage of the heat-sensitive area on the heat sink surface.

[0073] 103. After deployment is complete, receive the optical signals output by each fiber core, extract the wavelength drift caused by temperature change based on the attenuation characteristics of the optical signals of each wavelength channel at the fiber core bend, and convert the wavelength drift into the temperature value of each sensing sub-area in combination with a pre-stored nonlinear calibration curve to establish a temperature gradient model bound to the spatial position of the fiber core;

[0074] The attenuation characteristic at the bend is the power loss of the optical signal caused by modal leakage in the bending area of ​​the fiber core. The attenuation is related to the bending radius and temperature. The nonlinear calibration curve is a nonlinear relationship function between the wavelength drift Δλ and the temperature change ΔT calibrated through experiments. It is used to eliminate strain interference and realize temperature value inversion.

[0075] In an embodiment of the present application, after deployment is completed, a phase-locked amplification technology is used to extract the optical power of the optical signal output by each fiber core, locate the optical signal of each wavelength channel in the fiber core bending area, and calculate the wavelength drift Δλ caused by the attenuation amplitude temperature change; the wavelength drift Δλ is analyzed by dense Fourier transform (DFT), and the temperature and strain effects are separated by combining the bending radius compensation algorithm; the wavelength drift Δλ is input into a pre-stored nonlinear calibration curve, such as a quadratic polynomial model, and converted into a temperature change ΔT value; finally, the temperature change ΔT is bound to the spatial coordinates of the fiber core sub-area to generate a temperature gradient distribution matrix.

[0076] The optical signal output from fiber core C5 was received using a lock-in amplifier with a bandwidth of 10kHz and a dynamic range of 120dB. The peak amplitude of the optical power attenuation in subregion S8, located 40mm from the input end, decreased by 3dB. Using a fast Fourier transform (FFT) with 4096 sampling points to analyze the spectral characteristics, the wavelength shift was calculated to be Δλ = 0.3nm with an accuracy of ±0.02nm. Combined with a pre-stored nonlinear calibration curve, for example, with a laboratory calibration range of 0 to 100°C, the fitting equation was ΔT = 0.5Δλ. 2 +2Δλ, R 2 = 0.998, and the inverted temperature change in this area is ΔT = 0.6°C. The ΔT value is bound to the local coordinates x = 10 mm, y = 5 mm, and the temperature gradient matrix is ​​constructed based on the OTDR marker data mapping in step 102. The rationality of the gradient distribution is verified using a thermal-mechanical coupled finite element model.

[0077] 104. Perform wavelength-space resolution on the optical signal based on the physical spacing between the cores in the multi-core optical fiber, the first mapping relationship, and the transmission delay of the optical signal in different cores to generate a second mapping relationship between the spatial coordinates and the temperature value in the sensing sub-region, and reconstruct a three-dimensional temperature field distribution topology on the surface of the object to be measured based on the second mapping relationship.

[0078] Transmission delay is the propagation time difference of the optical signal in different fiber cores due to the difference in path length, which is used to calculate the spatial coordinate offset; the second mapping relationship is a space-temperature mapping table that binds the ΔT value of the temperature gradient model to the three-dimensional coordinates (x, y, z) to reconstruct the continuous temperature field.

[0079] In the embodiment of the present application, the transmission delay τ of the optical signal in different fiber cores is measured based on the light pulse flight time ToF. i , combined with the physical spacing d between the cores in the multi-core optical fiber ij Calculate the path length difference ΔL = τ i *c (c is the speed of light); use the least squares method to convert the path length difference ΔL into the three-dimensional coordinates of the sub-region; bind the temperature value ΔT of each sub-region obtained in step 103, for example ΔT = 0.6°C, to the calculated three-dimensional coordinates (x, y, z) in the "coordinate-temperature" format to form a second mapping relationship; superimpose the wavelength-core mapping (first mapping relationship) and the coordinate-temperature mapping (second mapping relationship) to generate three-dimensional temperature field point cloud data; finally, interpolate the point cloud using the Delaunay triangulation algorithm to reconstruct the continuous temperature field distribution surface.

[0080] Based on the fiber core spacing, for example, the nominal value is 0.3 mm, the measured error is ±0.02 mm, and the optical pulse time of flight (ToF) measurement result C5 transmission delay τ = 8 ns, accuracy ±0.1 ns, the calculated path length difference ΔL = τ·c = 2.4 m, c = 3×108 m / s. Using the weighted least squares method, with the weight determined by the inverse of the core spacing error, the three-dimensional coordinates of the S8 sub-region are solved as x = 10.1 mm, y = 5.0 mm, and z = 2.2 mm. The temperature-space point cloud (density 200 points / cm) is generated by superimposing the data of all sub-regions. 3 ), and through the Delaunay triangulation algorithm, the MATLAB built-in function delaunayTriangulation reconstructed the continuous temperature field surface, showing that the maximum temperature in the edge area of ​​the heat sink reached 85.3℃, and the error compared with the infrared thermal imager was less than 0.5℃.

[0081] 105. Determine a temperature variation range of the surface of the object being measured based on the temperature gradient model and the three-dimensional temperature field distribution topology.

[0082] Dynamic correlation fusion refers to the process of jointly analyzing the temperature gradient model, such as the local temperature change trend, and the three-dimensional temperature field distribution topology, such as the global thermal field spatial characteristics, through data fusion algorithms to generate fused temperature field data with both local sensitivity and global consistency.

[0083] In an embodiment of the present application, the temperature gradient model is dynamically associated and fused with the three-dimensional temperature field distribution topology input Kalman filter to generate fused temperature field data; a preset safety threshold, such as T_max = 85°C, is compared through spatial domain convolution scanning to locate the coordinate cluster that exceeds the threshold; the nonlinear calibration curve is called to reversely calculate the wavelength drift Δλ deviation to quantify the temperature fluctuation range of the abnormal area, such as ΔT = ±2°C.

[0084] The temperature gradient model and three-dimensional temperature field topology data were input into an extended Kalman filter. For example, the state vector had a dimension of 6 and included temperature, coordinates, and gradient change rate. The dynamic temperature field distribution was predicted. A safety threshold, T_max, was set at 85°C. Based on the JEDEC solid-state device thermal standard, a sliding window convolution kernel (for example, a 3×3×3 cube-shaped scanned point cloud) was used to locate the temperature anomaly at coordinates x=15.2mm, y=8.1mm, and z=1.8mm, reaching 92.1°C. Using the calibration curve to infer Δλ=0.52nm, combined with Monte Carlo error analysis, the temperature in this area was determined to exceed the specified range, ΔT=+7.2°C to +9.0°C, with a 95% confidence level. This triggered a hierarchical alarm system, with a level 1 alarm (fan speed increase) and a level 2 alarm (load switching). An anomaly report was generated, including the spatial coordinates and the temperature fluctuation range.

[0085] This solution uses the physical decoupling architecture of wavelength division multiplexing and multi-core optical fiber to independently allocate wavelength channels to different fiber cores, breaking through the sensing density limitations of single-core systems. Combined with flexible integration of three-dimensional spatial paths, it achieves high-density coverage of complex curved surfaces and core-level spatial coordinate binding. Based on bend attenuation characteristic extraction and nonlinear calibration, it eliminates temperature-strain cross-sensitivity errors and improves detection accuracy. Using wavelength-space dual mapping solutions, it integrates core spacing and delay parameters to achieve dynamic reconstruction of the three-dimensional temperature field with millimeter-level resolution. Finally, by fusing temperature gradients with global distribution data, it accurately locates abnormal areas and quantifies the fluctuation range. This technology forms a closed loop from hardware architecture, signal processing, to algorithmic solution, significantly improving the anti-interference capability, spatial resolution, and dynamic response speed of three-dimensional temperature field monitoring in complex scenarios, providing a highly reliable solution for precise temperature control in industrial thermal management, biomedicine, and other fields.

[0086] In some embodiments, determining the temperature variation range of the surface of the measured object according to the temperature gradient model and the three-dimensional temperature field distribution topology includes:

[0087] 201. Dynamically associate and fuse the temperature gradient model with the three-dimensional temperature field distribution topology, and generate fused temperature field data by complementing the local temperature change characteristics of the temperature gradient model with the spatial thermal field distribution characteristics of the three-dimensional temperature field distribution topology;

[0088] The temperature gradient model refers to the data set of the local temperature change ΔT and its time gradient calculated by the wavelength drift Δλ of each sensing sub-area of ​​the multi-core optical fiber and the nonlinear calibration curve; the three-dimensional temperature field distribution topology refers to the mapping relationship between the three-dimensional coordinates and temperature values ​​generated by the fiber core spacing, transmission delay and spatial solution algorithm; dynamic correlation fusion is the process of complementary fusion of the local transient characteristics of the two, such as ΔT / Δt, and the global steady-state characteristics, such as the spatial thermal field distribution, through a weighted algorithm to generate fused temperature field data with both temporal and spatial resolution.

[0089] In an embodiment of the present application, the local temperature change ΔT value in the temperature gradient model, such as ΔT = 0.6°C / s of the fiber core C5-S8, is first aligned in time and space with the temperature value of the corresponding coordinate in the three-dimensional temperature field distribution topology, such as a temperature value of 85.3°C; then the extended Kalman filter EKF algorithm is adopted, with ΔT / Δt of the temperature gradient model as the state variable and the temperature value of the three-dimensional temperature field distribution topology as the observation variable, and the weight matrix is ​​iteratively updated to generate a fused temperature value, such as 85.3°C ± 0.2°C; finally, the uncertainty of the fusion result is analyzed based on the covariance matrix, and a confidence interval, such as ±0.3°C, is added to each temperature value to form a fused temperature field data set, which provides a temporally and spatially consistent data basis for anomaly analysis.

[0090] 202. Perform spatial domain joint analysis based on the fused temperature field data and a preset threshold value to locate the temperature anomaly area on the surface of the measured object, and calculate the temperature variation range of the temperature anomaly area by inverting the nonlinear calibration curve.

[0091] The preset threshold is the temperature safety limit set according to the material of the object being measured or the safety standard, which is used to trigger an alarm; spatial domain joint analysis is a technology that locates out-of-limit areas through scanning and clustering algorithms in the three-dimensional coordinate domain; temperature anomaly areas are coordinate clusters in the fused data where the temperature continuously exceeds the limit and is spatially connected; inversion calculation is the process of reversely inferring the temperature fluctuation range from the Δλ data in the anomaly area through a nonlinear calibration curve, and outputting quantitative results in combination with the error model.

[0092] In an embodiment of the present application, a 3×3×3 cubic sliding window convolution kernel is first used to traverse the fused temperature field data, marking all coordinate points exceeding a preset threshold, such as a temperature of 92.1°C at the coordinates (15.2, 8.1, 1.8); then, the density clustering algorithm DBSCAN is used to merge adjacent out-of-limit points to form a temperature anomaly area, such as a spherical cluster with a diameter of 3 mm, and isolated noise points are removed; finally, the wavelength drift Δλ data of the temperature anomaly area is extracted, such as the wavelength drift Δλ = 0.52 nm, and the nonlinear calibration curve is called to invert and calculate the temperature change ΔT = 8.1°C in the temperature anomaly area, and the fluctuation range of the temperature change is outputted in combination with the Monte Carlo simulation, such as +7.2°C to +9.0°C, to generate an anomaly report.

[0093] Here's a specific example:

[0094] In the surface temperature monitoring of aircraft engine turbine blades, the wavelength drift Δλ = 0.45nm was detected at the fiber core C3-S9, 120mm away from the blade root. The nonlinear calibration curve ΔT = 0.5Δλ 2The calculated local temperature variation, ΔT = 1.1°C, and the temporal gradient, ΔT / Δt = 0.6°C / s, were calculated using the extended Kalman filter (EKF) algorithm. Simultaneously, the temperature corresponding to the coordinates x = 25.3 mm, y = 12.1 mm, and z = 5.2 mm in the three-dimensional temperature field topology was calculated to be 950°C. This is based on a fiber core spacing of 0.3 mm and a transmission delay of τ = 10 ns. The combined data were then fused using an extended Kalman filter (EKF) to generate a fused temperature value of 950.5°C ± 2°C with a 95% confidence interval, covering 500 coordinate points on the blade surface. The scan revealed that the temperature at the coordinates x = 30.2mm, y = 15.5mm, z = 6.1mm reached 1010℃, exceeding the safety threshold T_max = 1000℃; DBSCAN clustering determined it to be an abnormal area with a diameter of 4mm, and inverse calculation of its Δλ = 0.8nm corresponding to ΔT = +3.2℃ was performed. Combined with the ±0.8℃ error, the output exceeded the standard range of +2.4℃ to +4.0℃, triggering the cooling system to increase the pressure to 120kPa and generating a maintenance work order, marking the abnormal coordinates and fluctuation range.

[0095] This solution enhances the spatiotemporal consistency of temperature field data through dynamic correlation and fusion, significantly improving the detection sensitivity of areas with minor anomalies. It accurately locates arbitrary-shaped temperature anomalies on complex surfaces based on intelligent spatial analysis, overcoming the geometric limitations of traditional threshold rules. Combining nonlinear inversion with error modeling, it provides high-precision quantification of temperature fluctuations for operational and maintenance decision-making. In demanding scenarios such as high-temperature equipment and aircraft engines, this solution enables an upgrade in intelligent thermal management from "passive warning" to "active prediction," supporting improved reliability and lifecycle cost optimization for industrial equipment.

[0096] In some embodiments, extracting the wavelength drift caused by temperature change based on the attenuation characteristics of the optical signal of each wavelength channel at the fiber core bend includes:

[0097] 301. For an optical signal of each wavelength channel, obtain an initial light intensity value of the optical signal at each bend of the multi-core optical fiber, and record a real-time light intensity value of the optical signal after the optical signal passes through the bend;

[0098] The initial optical intensity value refers to the baseline optical power value (unit: dBm) of the optical signal passing through the bend of the multi-core optical fiber under conditions without external interference. It is used as a reference for subsequent attenuation calculations. The real-time optical intensity value refers to the instantaneous power value of the optical signal when passing through the bend during actual monitoring, including loss changes caused by temperature or strain.

[0099] In an embodiment of the present application, during the system initialization phase, a high-precision optical power meter, such as Keysight N7744A, is used to measure the initial light intensity value of the optical signal of each wavelength channel at each bend in the multi-core optical fiber as the benchmark data under interference-free conditions. For example, the initial light intensity of the bending point 1 of the fiber core C3 is -20dBm, and that of the bending point 2 is -19.5dBm. In actual operation, a distributed light detector is used to capture the light intensity value of each bend in real time at a sampling rate of 1MHz. For example, the bending point 1 of the fiber core C3 is measured to be -23dBm at time t=10s. All data are synchronously stored in a time series database, such as InfluxDB, through an FPGA controller, and indexed by the fiber core number, bending position and timestamp to ensure the temporal and spatial consistency of subsequent analysis.

[0100] 302. Calculate the light intensity attenuation ratio of the optical signal at the bend according to the difference between the initial light intensity value and the real-time light intensity value;

[0101] Light intensity attenuation ratio: refers to the power loss ratio of the real-time light intensity value relative to the initial value. For example, the formula is: attenuation ratio = 1-real-time value / initial value. It is used to quantify the loss change at the bend.

[0102] In an embodiment of the present application, the initial light intensity and real-time light intensity data stored in the database are called, for example, the initial value of -20dBm and the real-time value of -23dBm of the bending point 1 of the fiber core C3 are calculated by the formula to obtain the light intensity attenuation ratio as 1 minus the ratio of the real-time light intensity to the initial light intensity, that is, 1-(-23) / (-20)=0.15. In order to suppress noise interference, the light intensity attenuation ratio in the continuous time window is subjected to sliding window mean filtering, for example, the window length is 10 milliseconds, the overlap rate is 50%, and the original data sequence [0.14, 0.16, 0.15] is smoothed to 0.15. Subsequently, the result is normalized to the range of 0 to 1, for example, the 15% attenuation ratio is mapped to 0.15, and a standardized light intensity attenuation sequence is generated for unified processing by subsequent algorithms.

[0103] 303. By comparing the intensity attenuation ratio change trends of the optical signal of the same wavelength channel at different bends, the abnormal fluctuation range of the attenuation ratio caused by temperature change can be identified;

[0104] The abnormal fluctuation range of attenuation ratio refers to the time period or spatial region where the attenuation ratio at different bends in the same wavelength channel significantly deviates from the normal range over time.

[0105] In an embodiment of the present application, a dynamic time warping algorithm (DTW) is used to compare the light intensity attenuation ratio sequences at different bends of the same wavelength channel, such as the data of the 10 bending points of the fiber core C3, to extract the common fluctuation pattern. For example, the analysis found that the attenuation ratio of bending points 1 to 9 was stable at 8% within 50 seconds to 60 seconds, while the attenuation ratio of bending point 5 suddenly increased to 15%. The isolation forest algorithm is used to detect abnormal fluctuation intervals that deviate from the common trend due to temperature changes. For example, the data of bending point 5 within 50 seconds to 60 seconds is marked as abnormal, and the spatiotemporal coordinates of the candidate temperature-sensitive areas are generated.

[0106] 304. Locate a characteristic inflection point of the light intensity attenuation ratio changing with temperature within the abnormal attenuation ratio fluctuation range, and measure a wavelength offset distance corresponding to the characteristic inflection point;

[0107] The characteristic inflection point is the extreme value of the first derivative of the attenuation ratio curve caused by a sudden temperature change, corresponding to the starting point of wavelength drift. The wavelength offset distance is the absolute value of the center wavelength shift of the spectrum before and after the characteristic inflection point.

[0108] In an embodiment of the present application, a first-order difference calculation is performed on the data marked as an abnormal fluctuation interval, such as the attenuation ratio curve of the fiber core C3 bending point 5 between 50 seconds and 60 seconds, to locate the derivative extreme point, that is, the characteristic inflection point where the light intensity attenuation ratio changes with temperature. For example, the characteristic inflection point is detected at time 53 seconds. Subsequently, the optical signal is intercepted in a 1-second time window before and after the characteristic inflection point, for example, from time 52 seconds to 54 seconds, and the corresponding wavelength offset distance is extracted by fast Fourier transform (FFT). For example, the initial wavelength of 1550nm shifts to 1550.2nm after the inflection point, and the calculated wavelength offset distance is the absolute difference between the two, 0.2nm.

[0109] 305. Match the wavelength offset distance with a pre-stored wavelength-temperature association table to obtain a wavelength drift caused by temperature change.

[0110] The wavelength-temperature correlation table refers to a table of correspondence between the wavelength drift Δλ and the temperature change ΔT calibrated through experiments.

[0111] In the embodiment of the present application, according to the temperature gradient model generated in step 103, the temperature change ΔT of each sensor sub-area is obtained, for example, the temperature change ΔT = 0.6°C. The temperature change ΔT is input into the pre-stored temperature-wavelength association table to find the corresponding wavelength drift Δλ. For example, if the wavelength-temperature association table shows that the wavelength drift Δλ = 0.1ΔT is a linear relationship, then ΔT = 0.6°C corresponds to Δλ = 0.06nm; if the wavelength-temperature association table shows a nonlinear relationship, for example, Δλ = 0.05ΔT 2 +0.2ΔT, then substitute into the calculated wavelength shift Δλ=0.05*(0.6)2 +0.2*0.6=0.138nm.

[0112] Here's a specific example:

[0113] In the surface temperature monitoring of aircraft engine turbine blades, the initial light intensity of the bending point 3 of the fiber core C5 was calibrated to -18dBm during the system initialization phase. During actual monitoring, the real-time light intensity at this point was measured to be -21dBm at 30 seconds, and the calculated attenuation ratio was 16.7%, which was corrected to 0.16 after sliding window filtering. The attenuation trend of the 10 bending points in the same wavelength channel was analyzed using the dynamic time warping algorithm. It was found that the attenuation ratio of bending point 3 continued to deviate from the normal range from 28 seconds to 32 seconds, and the isolation forest algorithm determined it to be an abnormal fluctuation interval. The first-order derivative of the data in this interval was calculated, and the characteristic inflection point was located at 30 seconds. The spectrum before and after the inflection point was intercepted and the central wavelength was extracted. The measured wavelength shifted from the initial value of 1545nm to 1545.3nm, and the wavelength shift distance was 0.3nm. According to the pre-stored nonlinear calibration curve ΔT = 0.5Δλ 2 +2Δλ, and the calculated temperature change ΔT = 0.645°C is substituted into the output, which is finally output to the turbine blade surface coordinates x = 50 mm, y = 20 mm. This triggers the control system to adjust the cooling airflow and generate an abnormality report, realizing full closed-loop management from data collection to operation and maintenance response.

[0114] This solution significantly improves the sensitivity and anti-interference ability of temperature change detection and effectively distinguishes between temperature and strain effects through the collaborative analysis of optical signal attenuation characteristics and wavelength-temperature correlation models. Based on dynamic data fusion and intelligent algorithms, it achieves precise positioning of tiny abnormal areas on complex surfaces, breaks through the geometric limitations of traditional threshold rules, and supports the recognition of temperature gradients of arbitrary shapes. The full-process automation architecture ensures seamless connection from data acquisition to temperature inversion, reduces manual intervention, and adapts to industrial scenarios with high dynamics and high reliability requirements. In applications such as high-temperature equipment monitoring, it promotes the upgrade of operation and maintenance modes from passive alarms to active predictions, providing high-precision and timely decision-making support for equipment life extension and safety management.

[0115] In some embodiments, based on the wavelength drift amount combined with a pre-stored nonlinear calibration curve, the wavelength drift amount is converted into a temperature value of each sensing sub-region to establish a temperature gradient model bound to the spatial position of the fiber core, including:

[0116] 401. Access a nonlinear calibration curve pre-calibrated by experiments, wherein the nonlinear calibration curve stores a correspondence between a wavelength drift and a temperature change of each fiber core, and the correspondence is defined in sections according to the material properties of the fiber core and the curvature radius of the bending section;

[0117] A nonlinear calibration curve is a pre-calibrated function that describes the relationship between wavelength drift and temperature change. This curve is segmented based on the core material properties and the curvature radius of the bend. For example, a quadratic polynomial fit is used for a standard quartz core in the 5mm to 10mm radius range, while an exponential function might be used for an erbium-doped core in the same curvature range.

[0118] In the embodiment of the present application, a pre-stored nonlinear calibration curve database is first loaded from a non-volatile memory. The nonlinear calibration curve database stores the correspondence between the wavelength drift and temperature change of each core according to the core type and curvature radius interval. For example, the nonlinear calibration curve of core C1 may include three segments: the curvature radius of 3mm to 5mm uses ΔT = 0.2Δλ 2 +1.8Δλ, 5mm to 8mm adopt ΔT=0.15Δλ 2 +2.1Δλ. The system automatically matches the corresponding curve group according to the currently detected fiber core number.

[0119] 402. For each curved segment corresponding to each sensing sub-region, search the nonlinear calibration curve for a curve segment matching the curved segment according to the identifier of the fiber core where the curved segment is located and the curvature radius of the curved segment.

[0120] Curve segment matching involves determining the curvature radius interval of the sensor core based on its physical location, thereby selecting the corresponding calibration curve segment. The curvature radius of each fiber core during layout is pre-recorded through 3D path planning.

[0121] In this embodiment of the present application, the core layout parameter database is first queried to obtain the curvature radius of the curved section where the current sensing sub-area is located. For example, the S5 sub-area of ​​the core C3 is located in a curved section with a curvature radius of 6.2 mm. Then, based on the core material type and the 5 mm to 8 mm interval to which 6.2 mm belongs, the corresponding nonlinear calibration curve segment ΔT = 0.15Δλ is selected from the nonlinear calibration curve. 2 +2.1Δλ. For boundary value cases, weighted interpolation of adjacent intervals is used.

[0122] 403. Input the wavelength drift of the curved segment into the matching curve segment, obtain the temperature change of the sensing sub-region where the curved segment is located by interpolation calculation, and determine the temperature value of the sensing sub-region in combination with the initial temperature value;

[0123] Temperature change calculation involves inputting the measured wavelength drift into the matching calibration curve segment and calculating or interpolating the temperature change. The initial temperature value refers to the baseline temperature recorded at system startup.

[0124] In the embodiment of the present application, the wavelength drift Δλ of the curved segment measured in step 302 is input into the selected curve segment. For example, the wavelength drift Δλ = 0.3nm is substituted into the temperature change ΔT = 0.15*(0.3) 2 +2.1*0.3=0.0135+0.63=0.6435° C. Combined with the initial temperature of 25° C. in the sensing sub-region where the bending section is located, the current temperature of 25.6435° C. is calculated by linear interpolation.

[0125] 404. Arrange the temperature values ​​of multiple sensing sub-areas covered by the same fiber core in sequence according to the arrangement order of the curved sections on each fiber core in the transmission path along the optical signal transmission direction to form a temperature distribution sequence bound to the axial position of the fiber core;

[0126] A temperature distribution sequence is an axial temperature curve formed by arranging the temperature values ​​of all sensor sub-areas on a single fiber core in the order of optical signal transmission. This sequence reflects the gradient of temperature along the length of the fiber core.

[0127] In this embodiment, the subregions on each fiber core are first sorted according to the order in which their bends are arranged in the optical transmission path, for example, C1-S1 to C1-S20. The temperature values ​​calculated for each sensing subregion are then sequentially filled in according to the direction of optical signal transmission, forming a sequence such as [25.1, 25.3, ..., 26.8]°C. Missing data points are interpolated using the mean of the adjacent points.

[0128] 405. Based on the physical spacing and three-dimensional spatial path coordinates of each fiber core in the multi-core optical fiber, the temperature value in the temperature distribution sequence of each fiber core is assigned to the corresponding three-dimensional spatial coordinate point, and the current temperature values ​​of all fiber cores are combined to form a temperature gradient model bound to the spatial coordinates.

[0129] The temperature gradient model maps the temperature distribution sequence of all cores of a multi-core optical fiber onto the surface of an object, based on the core spacing and three-dimensional path coordinates. This model contains both spatial coordinates and temperature value information.

[0130] In the embodiment of the present application, the physical spacing parameters of each core in the multi-core optical fiber are first read, for example, the center distance between cores C1 and C2 is 0.3 mm. Then, according to the three-dimensional space path coordinates, the temperature values ​​in the temperature distribution sequence of each core are assigned to the corresponding three-dimensional space coordinate point positions. For example, the temperature of C1-S5 is 25.6°C, which corresponds to the coordinates (10.2, 5.1, 2.3) mm. Finally, the current temperature values ​​of all cores are combined through Delaunay triangulation to generate a continuous three-dimensional temperature gradient model.

[0131] Here is a specific example:

[0132] In the aircraft engine turbine blade surface temperature monitoring scenario, the system first calls upon a pre-stored database of calibration curves for a 7-core optical fiber. Taking core C1 as an example, the calibration coefficients for this quartz optical fiber in the curvature radius range of 5.0 mm to 8.0 mm are a quadratic coefficient of 0.15 and a linear coefficient of 2.1. When the sensing subsystem detects a wavelength drift of 0.4 nanometers in the eighth sensor region of core C1, the system automatically matches the calibration curve segment corresponding to the 7.5 mm curvature radius where this region resides. Substituting this into a nonlinear calculation formula, the system calculates the temperature change in this region as 0.8992 degrees Celsius. Combined with the region's initial baseline temperature of 950 degrees Celsius, the system generates a current temperature value of 950.8992 degrees Celsius and binds this value to the three-dimensional coordinates of 120.5 mm on the X-axis, 32.7 mm on the Y-axis, and 8.2 mm on the Z-axis. The system then arranges the temperature data for all sensor regions on core C1 in sequence according to the direction of optical signal transmission to construct a complete axial temperature distribution sequence. The sequence clearly records the temperature gradient change from 950.1 degrees Celsius to 950.9 degrees Celsius. Based on these data, the system uses the Delaunay triangulation algorithm to reconstruct a three-dimensional temperature field model of the turbine blade surface. The temperature field model clearly shows that there is a local high-temperature area on the leading edge of the blade, with the highest temperature reaching 952 degrees Celsius, which is 12 degrees Celsius higher than the average temperature of the surrounding area. The system immediately activates the automatic response mechanism, increases the pressure level of the cooling system, and generates a detailed maintenance work order, which clearly marks the three-dimensional coordinates of the abnormal area and the specific temperature exceeding the standard value. The entire data processing and response process takes only 8 milliseconds, realizing a complete closed-loop control from data collection to decision execution.

[0133] This solution achieves high-precision dynamic monitoring of temperature distribution on complex surfaces through an innovative multi-core fiber optic sensing architecture and an intelligent temperature field reconstruction algorithm. Based on a wavelength-temperature nonlinear calibration model, the system effectively eliminates measurement errors caused by differences in material properties and bending deformation, significantly improving the consistency and reliability of temperature detection. Through three-dimensional spatial path solution and multi-physics field data fusion, a temperature gradient model with millimeter-level spatial resolution is constructed, which can accurately capture subtle changes in the surface temperature field. Dynamic correlation analysis technology enables early identification and precise positioning of temperature anomalies, providing a reliable basis for predictive maintenance of key equipment. The entire system demonstrates excellent anti-interference capabilities and real-time response characteristics, maintaining stable operation in harsh industrial environments such as high temperature and strong vibration, providing a breakthrough technical solution for intelligent thermal management in fields such as aircraft engines and power equipment.

[0134] In some embodiments, based on the physical spacing between the cores in the multi-core optical fiber, the first mapping relationship, and the transmission delay of the optical signal in different cores, the wavelength-space solution is performed on the optical signal to generate a second mapping relationship between the spatial coordinates and the temperature value in the sensing sub-area, and the three-dimensional temperature field distribution topology of the surface of the object to be measured is reconstructed based on the second mapping relationship, including:

[0135] 501. Using the input port of the multi-core optical fiber as a reference point, establish a core spatial distribution model according to the physical spacing of the cores; and determine, based on the first mapping relationship, an initial position of the core corresponding to each wavelength channel in the core spatial distribution model.

[0136] The core spatial distribution model is a geometric distribution model constructed based on the physical spacing between the cores, with the input port of a multi-core fiber as the reference point. This model describes the relative positions of the cores on the fiber cross-section, such as a hexagonal close-packed arrangement or a rectangular array. The initial position refers to the theoretical coordinates of the core corresponding to each wavelength channel in the spatial model, determined by the first mapping relationship.

[0137] In an embodiment of the present application, the physical spacing parameters of the cores of the multi-core optical fiber are first measured with the input port of the multi-core optical fiber as the reference point. For example, the spacing between the central core and the peripheral core is 0.3 mm. Then, a two-dimensional coordinate system is established according to the arrangement of the cores. For example, the central core C0 is set as the coordinate origin (0, 0), and the peripheral cores C1 to C6 are distributed at 60 degrees and equal angles on a circle with a radius of 0.3 mm. Then, according to the first mapping relationship, the core number corresponding to each wavelength channel is marked in the core space distribution model. For example, λ1 corresponds to C1, and its initial coordinates are (0.3 mm, 0).

[0138] 502. By measuring the transmission delay of the optical signal of each wavelength channel from the input port to each sensing sub-area and combining it with the fiber core refractive index parameter, the actual propagation distance of the optical signal in each fiber core is calculated;

[0139] Transmission delay is the difference in propagation time between an optical signal's input port and the sensor region, typically measured in nanoseconds. Actual propagation distance is the geometric path length of the optical signal within the fiber core, taking into account the core's refractive index. It is calculated by multiplying the delay by the group velocity.

[0140] In the embodiment of the present application, a high-precision time domain reflectometer is first used to measure the transmission delay of the optical signal of each wavelength channel from the input port to each sensing sub-area. For example, the delay of the fiber core C1 in the sub-area S8 is 8ns. Then, the material refractive index parameters of the fiber core are queried. For example, the group refractive index of the germanium-doped quartz fiber core at a wavelength of 1550nm is 1.46. Finally, the actual propagation distance of the optical signal in each fiber core is calculated using the formula L = τ·c / ng, where c is the speed of light and ng is the group refractive index. For example, an 8ns delay corresponds to a distance of 1.6m.

[0141] 503. Determine the specific position coordinates of each sensing sub-region on the three-dimensional spatial path based on the actual propagation distance and the fiber core spatial distribution model; bind the temperature value detected by each sensing sub-region to the position coordinate corresponding to the sensing sub-region to form a second mapping relationship including spatial coordinates and temperature values;

[0142] The spatial coordinates refer to the specific location of the sensing sub-region in three-dimensional space, which is calculated by the actual propagation distance along the preset path. The second mapping relationship is a data structure that binds the temperature value of each sub-region to its spatial coordinates, which is used for temperature field reconstruction.

[0143] In the embodiment of the present application, first, according to the fiber core spatial distribution model of step 501 and the actual propagation distance of step 502, coordinates are solved along the preset three-dimensional space path. For example, the S8 sub-area of ​​the fiber core C1 is 1.6m away from the input port, and the corresponding coordinates on the three-dimensional space path are (10.2mm, 5.1mm, 2.3mm). Then, the position coordinates corresponding to the sensing sub-area are bound to the temperature value of 25.6°C detected by each sensing sub-area in step 103 to form a mapping record. Repeat this process for all sub-areas to establish a complete second mapping relationship table.

[0144] 504. Based on the position coordinates and temperature values ​​of all sensing sub-areas in the second mapping relationship, and in accordance with the three-dimensional spatial path arrangement rule of the multi-core optical fiber, a continuous three-dimensional temperature field distribution topology of the surface of the object to be measured is constructed.

[0145] The three-dimensional temperature field distribution topology refers to the temperature distribution model formed by continuousizing the discrete sub-region temperature values ​​on the surface of an object, which can intuitively display temperature gradient changes and abnormal areas.

[0146] In the embodiment of the present application, the position coordinates and temperature values ​​of all the sensing sub-areas in the second mapping relationship are first read. Then, according to the layout path of the three-dimensional spatial path arrangement rule of the multi-core optical fiber, an improved radial basis function interpolation algorithm is used for spatial interpolation. For example, using the Gaussian kernel function φ(r) = exp(-(r / 0.5) 2) for weighted averaging. Finally, the Marching Cubes algorithm is used to generate isothermal surfaces, and the temperature gradient is rendered in different colors to form a visual temperature field. This allows the construction of a continuous three-dimensional temperature field distribution topology on the surface of the object to be measured.

[0147] Here's a specific example:

[0148] In an aircraft engine combustion chamber monitoring scenario, the system first establishes a spatial distribution model based on the physical structure of a seven-core optical fiber. The central fiber core, C0, is designated as the coordinate origin, with the six outer cores evenly spaced 0.3 mm apart. Using optical time-domain reflectometry, the signal transmission delay at sensor sub-area 12 on core C3 is measured to be 12 nanoseconds. Combined with the refractive index of the fiber core material, the actual propagation distance is calculated to be 2.4 meters. The system then calculates the spatial coordinates corresponding to this distance along a pre-set three-dimensional spiral path: 25 mm on the X axis, 10 mm on the Y axis, and 5 mm on the Z axis. These coordinates are then bound to the temperature sensor reading of 950.8 degrees Celsius and stored. After repeating this process for 500 sensor sub-areas on the combustion chamber surface, the system uses an improved radial basis function interpolation algorithm to generate a continuous temperature field. This clearly reveals a localized high-temperature zone, approximately 3 mm in diameter, at the combustion chamber head, significantly higher than the surrounding area. Based on this three-dimensional temperature field, the system automatically marks the abnormal area and generates a maintenance alert.

[0149] This solution achieves three-dimensional visual reconstruction of the temperature field of complex surfaces through precise calculation of the spatial coordinates of multi-core optical fibers. The system innovatively combines optical fiber time delay measurement with spatial geometric modeling, effectively solving the problem of inaccurate spatial positioning in traditional temperature monitoring. The temperature field interpolation method based on intelligent algorithms can truly restore the temperature distribution characteristics of the surface of the measured object and accurately identify local abnormal temperature areas. In harsh industrial environments such as high temperature and strong vibration, the system demonstrates excellent stability and reliability, providing a new technical means for temperature monitoring and fault warning of key equipment, significantly improving the safety of equipment operation and maintenance efficiency. This solution is particularly suitable for advanced manufacturing and energy equipment fields that require high-precision temperature field analysis.

[0150] In some embodiments, the temperature gradient model is dynamically associated and fused with the three-dimensional temperature field distribution topology to generate fused temperature field data by complementing the local temperature change characteristics of the temperature gradient model with the spatial thermal field distribution characteristics of the three-dimensional temperature field distribution topology, including:

[0151] 601. Superimpose the temperature value of the three-dimensional grid node corresponding to each fiber core in the temperature gradient model with the temperature value of the same spatial coordinate point in the three-dimensional temperature field distribution topology, wherein during the superposition, a first weight is assigned to the temperature value of the temperature gradient model, and a second weight is assigned to the temperature value of the three-dimensional temperature field distribution topology, wherein the sum of the first weight and the second weight is a fixed value and is dynamically adjusted according to the distance between the fiber core and the spatial coordinate point;

[0152] The first weight refers to the contribution coefficient of the temperature gradient model's temperature value in the superposition calculation, reflecting the reliability of local temperature changes. The second weight refers to the contribution coefficient of the topological temperature value of the three-dimensional temperature field, reflecting the integrity of the spatial distribution. Fixed value constraints ensure energy conservation in the fusion results, and a dynamic adjustment mechanism optimizes weight distribution based on measurement confidence.

[0153] In the embodiment of the present application, a three-dimensional grid node coordinate system is first established, a first weight is assigned to the temperature value of the temperature gradient model, and a second weight is assigned to the temperature value of the three-dimensional temperature field distribution topology; the three-dimensional grid node temperature value corresponding to each fiber core in the temperature gradient model (such as 25.6°C for fiber core C3-S8) is aligned with the temperature value of the corresponding coordinate point in the three-dimensional temperature field distribution topology (such as 25.3°C). Then the Euclidean distance from each node to the fiber core is calculated. For example, when the distance is 0.2mm, the first weight is 0.7 and the second weight is 0.3. Finally, weighted superposition is performed: 25.6×0.7+25.3×0.3=25.51°C to form a preliminary fusion node temperature, and the sum of the first weight and the second weight is a fixed value and is dynamically adjusted according to the distance between the fiber core and the spatial coordinate point.

[0154] 602. Identify, from the superimposed temperature values, steep temperature change features between adjacent sensing sub-regions in the temperature gradient model, and simultaneously extract spatially smooth temperature values ​​within the same region in the three-dimensional temperature field distribution topology. Complementarily superimpose the steep features and the smooth features according to a preset ratio to form fused intermediate temperature field data.

[0155] A steep temperature change feature refers to a rapid temperature jump between adjacent subregions in a temperature gradient model, reflecting a sudden change in local heat flow. A spatially smooth feature refers to a gradual change in temperature values ​​within a three-dimensional temperature field topology, reflecting the laws of heat conduction. The preset ratio is set based on the thermal conductivity of the material. For example, a steep feature weight of 60% is assigned to metal components, while a weight of 40% is assigned to non-metal components.

[0156] In the embodiment of the present application, the steep temperature change characteristics between adjacent sensing sub-areas in the temperature gradient model are first detected and identified, for example, the temperature difference between adjacent sub-areas on the fiber core C1 is as high as 10°C / mm. At the same time, the spatial smoothness characteristics of the temperature values ​​in the corresponding area in the three-dimensional temperature field distribution topology are extracted, such as a gradual gradient of 5°C / mm. Then, according to the material properties, the steep features and the smooth features are fused at a preset ratio: the metal area takes a synthetic gradient of 0.6×10+0.4×5=8°C / mm to form the fused intermediate temperature field data.

[0157] 603. For the temperature distribution gap area caused by the core spacing in the intermediate temperature field data, based on the temperature value change trend of the sensing sub-area surrounding the gap area, perform temperature value diffusion and filling along the three-dimensional spatial path extension direction of the multi-core optical fiber, so that the temperature values ​​of the adjacent core coverage areas are spatially continuous;

[0158] Temperature gaps are areas not directly measured due to the fiber core spacing. These gaps require inference from surrounding data using a diffusion algorithm. The three-dimensional path extension direction refers to the orientation of the multi-core fiber during installation and determines the primary direction of temperature diffusion.

[0159] In the embodiment of the present application, the temperature distribution gap region caused by the core spacing in the intermediate temperature field data is first identified, such as the 0.3mm unmeasured band between cores C2 and C3. The temperature value variation trend of the sensing sub-region surrounding the gap region is then extracted, such as 20°C for C2-S5 and 22°C for C3-S5. Anisotropic diffusion is performed along the three-dimensional spatial path extension direction of the multi-core optical fiber to generate a filling value of 21°C, so that the temperature values ​​of the adjacent core coverage areas are spatially continuous. The finite element method is used to ensure the continuity of heat flow.

[0160] 604. Based on the filled intermediate temperature field data, the area where the temperature value difference between the temperature gradient model and the three-dimensional temperature field distribution topology exceeds a preset threshold is marked as a to-be-corrected area, and the temperature value of the temperature gradient model is replaced by the temperature value of the three-dimensional temperature field distribution topology in the to-be-corrected area to generate fused temperature field data.

[0161] The area to be corrected is where the temperature difference between the two models exceeds the reliability threshold, indicating a possible measurement anomaly. The replacement mechanism prioritizes preserving the overall distribution characteristics of the three-dimensional temperature field to ensure physical rationality.

[0162] In the embodiment of the present application, the difference threshold is first set to 3°C. Based on the detection of the intermediate temperature field data after filling, it is found that the temperature gradient model at the coordinate (15mm, 8mm) reports 35°C, and the temperature value of the three-dimensional temperature field distribution topology shows 30°C. The area where the temperature value difference between the temperature gradient model and the three-dimensional temperature field distribution topology exceeds the preset threshold is marked as the area to be corrected and triggers replacement. The temperature values ​​of the temperature gradient model in the 3×3 grid of the area are uniformly replaced with the temperature values ​​of the three-dimensional temperature field distribution topology to generate the final fused temperature field data.

[0163] Here's a specific example:

[0164] In the aircraft engine turbine blade monitoring scenario, the system first fuses the temperature data of the leading edge area of ​​the blade. The temperature gradient model shows that there is a sharp temperature rise in the sensor area of ​​the third fiber core at 15 mm on the X-axis, with a measured temperature of 1025 degrees Celsius, while the temperature of the corresponding coordinate point in the three-dimensional temperature field topology is 995 degrees Celsius. Based on the material properties of the nickel-based alloy in this area, the system assigns a weight of 0.7 to the temperature gradient model and a weight of 0.3 to the three-dimensional temperature field. After weighted calculation, the fusion temperature value is 1013 degrees Celsius. The system then identifies the presence of a steep temperature change feature of 12 degrees Celsius per millimeter in this area, and performs a weighted fusion of 7 to 3 with the smooth feature of 6 degrees Celsius per millimeter in the three-dimensional temperature field, ultimately forming an optimized temperature gradient of 10 degrees Celsius per millimeter. For the core gap area, the system performs temperature diffusion filling along the chord length of the blade to ensure that the temperature transition between adjacent cores remains continuous. Finally, the system detected and corrected three abnormal data differences caused by airflow disturbances in the cooling holes. The generated high-precision fusion temperature field clearly showed an 8 mm diameter high-temperature area caused by local peeling of the leading edge thermal barrier coating, providing a key basis for engine maintenance decisions.

[0165] This solution achieves the organic unity of local precise measurement and global temperature distribution through an innovative dynamic fusion algorithm. The system's intelligent weight distribution mechanism effectively balances the advantages of different data sources, retaining the subtle temperature change characteristics of key parts while ensuring the physical rationality of the overall temperature field. Advanced gap filling and anomaly correction technologies significantly improve the integrity and reliability of temperature monitoring on complex surfaces. Under extreme working conditions such as high temperature and high pressure, the solution demonstrates excellent anti-interference ability and environmental adaptability, and can accurately identify local temperature anomalies that are difficult to detect with traditional methods, providing a breakthrough technical means for fault warning and health management of high-end equipment. Its adaptive optimization characteristics enable the system to automatically adjust the fusion strategy for different materials and working conditions, and it has a wide range of engineering application value.

[0166] In some embodiments, performing a spatial domain joint analysis based on the fused temperature field data and a preset threshold to locate the temperature anomaly area on the surface of the measured object, and calculating the temperature variation range of the temperature anomaly area by inverting the nonlinear calibration curve includes:

[0167] 701. In the fused temperature field data, compare the difference between the temperature value of each spatial position and a preset threshold point by point; mark the continuous spatial positions where the temperature value exceeds the preset threshold as candidate abnormal areas;

[0168] Candidate anomaly regions are continuous spatial regions in the fused temperature field data where the temperature exceeds a preset safety threshold. This threshold is determined based on the material properties of the object being measured and the operating environment, reflecting the safe operating limits of the equipment.

[0169] In this embodiment of the present application, the system first reads all temperature values ​​in the fused temperature field data and compares the temperature value at each spatial location with a preset threshold, point by point. For example, in gas turbine blade monitoring, the safety threshold for nickel-based alloys is set at 980 degrees Celsius. The system uses a three-dimensional connected domain analysis algorithm to cluster temperature points in consecutive spatial locations whose adjacent temperature values ​​exceed the preset threshold, forming candidate anomaly region outlines and marking them as candidate anomaly regions. Each outline region records the set of spatial coordinate points it contains. For example, a candidate region containing 15 consecutive points is detected.

[0170] 702. For each candidate abnormal region, count the number of sensing sub-regions covered by the candidate abnormal region and the corresponding wavelength drift; and select candidate abnormal regions that meet the temperature anomaly propagation law as final temperature anomaly regions based on the distribution characteristics of the wavelength drift within the candidate abnormal region;

[0171] The propagation patterns of temperature anomalies refer to the physical characteristics that a true anomaly region should exhibit, including the spatial continuity of wavelength drift and the rationality of gradient changes. This step eliminates false alarms by analyzing the consistency of data from the sensing subregions.

[0172] In an embodiment of the present application, for each candidate area, the number of sensing sub-areas covered by the candidate abnormal area is counted, for example, a candidate area contains 8 sub-areas; the wavelength drift of each sensing sub-area is extracted, and the spatial distribution is checked to see whether it conforms to the law of heat conduction based on the distribution characteristics of the wavelength drift in the candidate abnormal area; the Mahalanobis distance test is used to exclude isolated abnormal points; and the candidate abnormal areas that pass the verification are retained, for example, 5 final abnormal areas that meet the requirements are screened out.

[0173] 703. Extract the wavelength drift of each sensing sub-region within the temperature anomaly region, and calculate the temperature deviation value of each sensing sub-region according to the corresponding relationship between the wavelength offset distance and the temperature in the nonlinear calibration curve;

[0174] The temperature deviation value is the specific value at which the temperature at each point in the abnormal area exceeds the safety threshold. It is obtained by inverting the wavelength drift using a nonlinear calibration curve. This value reflects the severity of the abnormality.

[0175] In the embodiment of the present application, the system queries the wavelength drift of each sub-region in the temperature anomaly region, for example, Δλ = 0.45nm; the corresponding relationship between the wavelength offset distance and temperature in the corresponding nonlinear calibration curve is matched according to the core type, for example, ΔT = 0.5Δλ 2 +2Δλ; calculate the temperature deviation value of each sensing sub-area, for example, ΔT = 0.5*(0.45) 2 +2*0.45=1.00125°C; generate a temperature deviation distribution map of the abnormal area.

[0176] 704. Determine the temperature variation range of the surface of the measured object based on the maximum value of the temperature deviation value and the spatial range of the temperature anomaly area.

[0177] The temperature variation range is a comprehensive quantitative description of the abnormal area, including the maximum deviation value and the size of the affected area. This parameter provides a quantitative basis for operation and maintenance decisions.

[0178] In an embodiment of the present application, the system finds the maximum temperature deviation value in the temperature anomaly area, for example, 1.8°C; calculates the equivalent diameter of the temperature anomaly area, for example, 4.2 mm; uses the maximum value of the temperature deviation value as a benchmark, combines the equivalent diameter and the thermal diffusion coefficient of the material to evaluate the impact depth; and outputs a complete description of the temperature change range, such as a maximum excess of 1.8°C in a 4.2 mm diameter area.

[0179] Here's a specific example:

[0180] During the monitoring of aircraft engine turbine blades, the system detected temperature anomalies in the leading edge area: the fusion temperature field data showed that the temperature of 15 consecutive spatial points within the range of 120-125mm on the X-axis and 50-55mm on the Y-axis exceeded the safety threshold of 1000°C, with the highest reaching 1025°C. The system verified the candidate abnormal area and confirmed that it covered 8 sensing sub-areas. The wavelength drift Δλ of each sub-area was between 0.42-0.48nm, showing a reasonable distribution that decreased from the center to the edge. By matching the calibration curve ΔT=0.5Δλ of the nickel-based alloy in this area 2 +2Δλ, the calculated temperature deviation was 2.1°C at the center and 1.6°C at the edge. Combined with analysis of the regional geometry, the abnormal area was ultimately determined to be a quasi-circular area with a diameter of 6mm, with a maximum over-limit temperature of 2.1°C. The system immediately triggered a secondary alarm and marked the leading edge of the blade for priority inspection.

[0181] This solution significantly improves the accuracy of temperature anomaly identification through multi-dimensional data verification and physical law testing. An innovative spatial domain joint analysis method effectively distinguishes true anomalies from measurement noise, ensuring the reliability of alarm information. Temperature deviation calculation based on nonlinear calibration provides a precise quantitative basis for anomaly severity assessment. The system outputs a temperature variation range description that encompasses key parameter indicators while maintaining engineering practicality, providing strong support for equipment maintenance decisions. This technology is particularly suitable for high-end equipment monitoring scenarios with stringent requirements for false alarm rates.

[0182] Figure 2 The present invention provides a schematic diagram of a multi-core optical fiber temperature detection system integrating wavelength division multiplexing. Figure 2 As shown, the system includes:

[0183] A first generating module 21 is configured to divide a broadband light source into a plurality of wavelength channels based on wavelength division multiplexing technology, and guide the wavelength channels to different core input ports of a multi-core optical fiber so that each core can transmit an optical signal of a single wavelength, and generate a first wavelength-core mapping relationship;

[0184] Integration module 22, used to integrate the multi-core optical fiber loaded with optical signals onto the surface of the object to be measured according to a preset three-dimensional spatial path, with each fiber core covering multiple sensing sub-areas;

[0185] The processing module 23 is configured to, after deployment, receive the optical signals output by each fiber core, extract the wavelength drift caused by temperature change based on the attenuation characteristics of the optical signals of each wavelength channel at the fiber core bend, and convert the wavelength drift into the temperature value of each sensing sub-area in combination with a pre-stored nonlinear calibration curve to establish a temperature gradient model bound to the spatial position of the fiber core;

[0186] a computing module 24 for performing a wavelength-space solution on the optical signal based on the physical spacing between the cores in the multi-core optical fiber, the first mapping relationship, and the transmission delay of the optical signal in different cores, so as to generate a second mapping relationship between the spatial coordinates and the temperature value in the sensing sub-area, and reconstructing a three-dimensional temperature field distribution topology on the surface of the object to be measured based on the second mapping relationship;

[0187] The second generating module 25 is configured to determine the temperature variation range of the surface of the object under test according to the temperature gradient model and the three-dimensional temperature field distribution topology.

[0188] Figure 2 The multi-core optical fiber temperature detection system integrating wavelength division multiplexing can be implemented Figure 1The implementation principle and technical effects of the multi-core optical fiber temperature detection method integrated with wavelength division multiplexing described in the illustrated embodiment are not further described. The specific manner in which each module and unit performs operations in the multi-core optical fiber temperature detection system integrated with wavelength division multiplexing in the above embodiment has been described in detail in the embodiments of the method and will not be elaborated on here.

[0189] In one possible design, Figure 2 The multi-core optical fiber temperature detection system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0190] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0191] The processing component 32 is used for the above Figure 1 The embodiment provides a multi-core optical fiber temperature detection method integrating wavelength division multiplexing.

[0192] The processing component 32 may include one or more processors to execute computer instructions to perform all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0193] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0194] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0195] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0196] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0197] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0198] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a multi-core optical fiber temperature detection method integrating wavelength division multiplexing.

[0199] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0200] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0201] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0202] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-core optical fiber temperature detection method integrating wavelength division multiplexing, characterized in that: include: Based on wavelength division multiplexing technology, a broadband light source is divided into multiple wavelength channels, and the wavelength channels are guided to different core input ports of a multi-core optical fiber, so that each core can transmit an optical signal of a single wavelength, and a first wavelength-core mapping relationship is generated; The multi-core optical fiber loaded with optical signals is integrated onto the surface of the object to be measured according to a preset three-dimensional spatial path, and each fiber core covers multiple sensing sub-areas; After deployment is complete, the optical signals output by each fiber core are received. The wavelength drift caused by temperature changes is extracted based on the attenuation characteristics of the optical signals of each wavelength channel at the fiber core bend. Combined with the pre-stored nonlinear calibration curve, the wavelength drift is converted into the temperature value of each sensing sub-area to establish a temperature gradient model bound to the spatial position of the fiber core. performing a wavelength-space solution on the optical signal based on the physical spacing between the cores in the multi-core optical fiber, the first mapping relationship, and the transmission delay of the optical signal in different cores to generate a second mapping relationship between the spatial coordinates and the temperature value in the sensing sub-area, and reconstructing a three-dimensional temperature field distribution topology on the surface of the object to be measured based on the second mapping relationship; The temperature variation range of the surface of the measured object is determined according to the temperature gradient model and the three-dimensional temperature field distribution topology.

2. The method according to claim 1, characterized in that Determining a temperature variation range of a surface of a measured object according to the temperature gradient model and the three-dimensional temperature field distribution topology includes: Dynamically associating and fusing the temperature gradient model with the three-dimensional temperature field distribution topology, and generating fused temperature field data by complementing the local temperature change characteristics of the temperature gradient model with the spatial thermal field distribution characteristics of the three-dimensional temperature field distribution topology; Based on the fused temperature field data and the preset threshold, a spatial domain joint analysis is performed to locate the temperature abnormality area on the surface of the measured object, and the temperature variation range of the temperature abnormality area is calculated by inversion of the nonlinear calibration curve.

3. The method according to claim 1, characterized in that The wavelength drift caused by temperature change is extracted based on the attenuation characteristics of the optical signal of each wavelength channel at the fiber core bend, including: For the optical signal of each wavelength channel, the initial light intensity value of the optical signal is obtained at each bend of the multi-core optical fiber, and the real-time light intensity value of the optical signal after passing through the bend is recorded; Calculating the light intensity attenuation ratio of the optical signal at the bend according to the difference between the initial light intensity value and the real-time light intensity value; By comparing the intensity attenuation ratio change trends of the optical signal of the same wavelength channel at different bends, the abnormal fluctuation range of the attenuation ratio caused by temperature change can be identified; Within the abnormal fluctuation range of the attenuation ratio, locating a characteristic inflection point of the light intensity attenuation ratio changing with temperature, and measuring a wavelength offset distance corresponding to the characteristic inflection point; The wavelength shift distance is matched with a pre-stored wavelength-temperature association table to obtain the wavelength drift caused by temperature change.

4. The method according to claim 1, wherein Based on the wavelength drift and a pre-stored nonlinear calibration curve, the wavelength drift is converted into a temperature value of each sensing sub-region to establish a temperature gradient model bound to the spatial position of the fiber core, including: Accessing a nonlinear calibration curve pre-calibrated by experiments, wherein the nonlinear calibration curve stores a correspondence between a wavelength drift of each fiber core and a temperature change, and wherein the correspondence is defined in sections according to material properties of the fiber core and a curvature radius of a bending section; For each curved segment corresponding to the sensing sub-region, searching the nonlinear calibration curve for a curve segment matching the curved segment according to the identifier of the fiber core where the curved segment is located and the curvature radius of the curved segment; Inputting the wavelength drift of the curved segment into the matching curve segment, obtaining the temperature change of the sensing sub-region where the curved segment is located by interpolation calculation, and determining the temperature value of the sensing sub-region in combination with the initial temperature value; According to the arrangement order of each curved section on each fiber core in the transmission path, the temperature values ​​of multiple sensing sub-areas covered by the same fiber core are arranged in sequence according to the transmission direction of the optical signal, forming a temperature distribution sequence bound to the axial position of the fiber core; Based on the physical spacing and three-dimensional spatial path coordinates of each fiber core in a multi-core optical fiber, the temperature value in the temperature distribution sequence of each fiber core is assigned to the corresponding three-dimensional spatial coordinate point, and the current temperature values ​​of all fiber cores are combined to form a temperature gradient model bound to the spatial coordinates.

5. The method according to claim 1, wherein The method includes performing wavelength-space resolution on the optical signal based on the physical spacing between the cores in the multi-core optical fiber, the first mapping relationship, and the transmission delay of the optical signal in different cores to generate a second mapping relationship between the spatial coordinates and the temperature value in the sensing sub-area, and reconstructing the three-dimensional temperature field distribution topology of the surface of the object to be measured based on the second mapping relationship, including: The input port of the multi-core optical fiber is used as a reference point, and a core spatial distribution model is established according to the physical spacing of the cores; based on the first mapping relationship, an initial position of the core corresponding to each wavelength channel in the core spatial distribution model is determined; By measuring the transmission delay of the optical signal of each wavelength channel from the input port to each sensing sub-area and combining it with the fiber core refractive index parameters, the actual propagation distance of the optical signal in each fiber core is calculated; Determining the specific position coordinates of each sensing sub-region on the three-dimensional spatial path based on the actual propagation distance and the fiber core spatial distribution model; binding the temperature value detected by each sensing sub-region to the position coordinate corresponding to the sensing sub-region to form a second mapping relationship including spatial coordinates and temperature values; Based on the position coordinates and temperature values ​​of all sensing sub-areas in the second mapping relationship, a continuous three-dimensional temperature field distribution topology of the surface of the object to be measured is constructed according to the three-dimensional spatial path arrangement rule of the multi-core optical fiber.

6. The method according to claim 2, characterized in that Dynamically associating and fusing the temperature gradient model with the three-dimensional temperature field distribution topology, and generating fused temperature field data by complementing the local temperature change characteristics of the temperature gradient model with the spatial thermal field distribution characteristics of the three-dimensional temperature field distribution topology, including: Superimposing the temperature value of the three-dimensional grid node corresponding to each fiber core in the temperature gradient model with the temperature value of the same spatial coordinate point in the three-dimensional temperature field distribution topology, wherein during the superposition, a first weight is assigned to the temperature value of the temperature gradient model, and a second weight is assigned to the temperature value of the three-dimensional temperature field distribution topology, and the sum of the first weight and the second weight is a fixed value and is dynamically adjusted according to the distance between the fiber core and the spatial coordinate point; In the superimposed temperature values, the steep temperature change features between adjacent sensing sub-areas in the temperature gradient model are identified, and the spatial smoothness features of the temperature values ​​in the same area in the three-dimensional temperature field distribution topology are extracted. The steep features and the smooth features are complementarily superimposed according to a preset ratio to form fused intermediate temperature field data; For the temperature distribution gap area caused by the core spacing in the intermediate temperature field data, based on the temperature value change trend of the sensing sub-area around the gap area, the temperature value diffusion filling is performed along the three-dimensional spatial path extension direction of the multi-core optical fiber, so that the temperature values ​​of the adjacent core coverage areas are spatially continuous; Based on the filled intermediate temperature field data, the area where the temperature value difference between the temperature gradient model and the three-dimensional temperature field distribution topology exceeds a preset threshold is marked as a to-be-corrected area, and the temperature value of the temperature gradient model is replaced by the temperature value of the three-dimensional temperature field distribution topology in the to-be-corrected area to generate fused temperature field data.

7. The method according to claim 2, characterized in that Performing a spatial domain joint analysis based on the fused temperature field data and a preset threshold value to locate the temperature abnormality area on the surface of the measured object, and calculating the temperature variation range of the temperature abnormality area by inverting the nonlinear calibration curve, including: In the fused temperature field data, the difference between the temperature value of each spatial position and the preset threshold is compared point by point; the continuous spatial positions whose temperature values ​​exceed the preset threshold are marked as candidate abnormal areas; For each candidate abnormal area, the number of sensor sub-areas covered by the candidate abnormal area and the corresponding wavelength drift are counted; based on the distribution characteristics of the wavelength drift in the candidate abnormal area, the candidate abnormal area that conforms to the temperature anomaly propagation law is screened as the final temperature anomaly area; Extracting the wavelength drift of each sensing sub-region within the temperature anomaly region, and calculating the temperature deviation value of each sensing sub-region according to the corresponding relationship between the wavelength offset distance and the temperature in the nonlinear calibration curve; The temperature variation range of the surface of the object being measured is determined based on the maximum value of the temperature deviation value and the spatial range of the temperature anomaly area.

8. A multi-core optical fiber temperature detection system integrating wavelength division multiplexing, characterized in that: include: a first generation module, configured to divide a broadband light source into a plurality of wavelength channels based on wavelength division multiplexing technology, and guide the wavelength channels to different core input ports of a multi-core optical fiber so that each core can transmit an optical signal of a single wavelength, and generate a first wavelength-core mapping relationship; An integration module is used to integrate the multi-core optical fiber loaded with optical signals onto the surface of the object to be measured according to a preset three-dimensional spatial path, with each fiber core covering multiple sensing sub-areas; A processing module is configured to, after deployment, receive the optical signals output by each fiber core, extract the wavelength drift caused by temperature change based on the attenuation characteristics of the optical signals of each wavelength channel at the fiber core bend, and convert the wavelength drift into the temperature value of each sensing sub-area in combination with a pre-stored nonlinear calibration curve to establish a temperature gradient model bound to the spatial position of the fiber core; a computing module, configured to perform a wavelength-space solution on the optical signal based on the physical spacing between the cores in the multi-core optical fiber, the first mapping relationship, and the transmission delay of the optical signal in different cores, so as to generate a second mapping relationship between the spatial coordinates and the temperature value in the sensing sub-area, and reconstruct a three-dimensional temperature field distribution topology on the surface of the object to be measured based on the second mapping relationship; The second generating module is used to determine the temperature variation range of the surface of the measured object according to the temperature gradient model and the three-dimensional temperature field distribution topology.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-core optical fiber temperature detection method integrating wavelength division multiplexing as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the temperature detection method of a multi-core optical fiber integrated with wavelength division multiplexing according to any one of claims 1 to 7 is implemented.

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