A multi-core optical fiber temperature detection method and system based on fusion wavelength division multiplexing

Abstract

The application provides a kind of multi-core optical fiber temperature detection method and system of fusion wavelength division multiplexing. Wherein, the broadband light source is divided into multiple wavelength channels by wavelength division multiplexing, and the wavelength-core mapping relationship is established, so that each core of the multi-core optical fiber transmits a single wavelength optical signal. The optical fiber is integrated into the surface of the object according to the three-dimensional path, each core covers multiple sensing sub-regions, the wavelength drift caused by temperature is extracted by detecting the attenuation characteristics of the optical signal at the bending part, and the temperature value is converted by combining the calibration curve, and a temperature gradient model bound to the core position is constructed. Further, the spatial solution is carried out by using the core spacing, wavelength mapping and transmission delay parameters, the corresponding relationship between the sensing sub-region coordinates and the temperature is established, and finally the three-dimensional temperature field distribution is reconstructed. The technical scheme provided by the application realizes high-resolution dynamic reconstruction and gradient analysis of complex surface three-dimensional temperature field by wavelength-space two-dimensional decoupling.

Description

TECHNICAL FIELD

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

[0002] In the fields of high-temperature electronic equipment, energy power system, and biological medical heat therapy, real-time monitoring of three-dimensional temperature field on the surface of complex curved surfaces or high-density integrated objects is required, and the technology is required to have multi-point synchronous detection, millimeter-level spatial resolution, and anti-electromagnetic interference capability. The traditional scheme is limited by single-point measurement or two-dimensional plane coverage, and it is difficult to meet the needs of dynamic temperature gradient analysis and complex spatial path integration.

[0003] The most advanced solution at present is a distributed optical fiber temperature measurement system (DTS) based on Raman scattering. The system utilizes the spontaneous Raman scattering effect in the optical fiber to realize temperature measurement by demodulating the intensity ratio of anti-Stokes light to Stokes light. A typical system uses a single-core quartz optical fiber as a sensing medium, and locates the temperature change point through optical time domain reflectometry (OTDR), with a spatial resolution of 1 meter and a temperature measurement accuracy of ±0.5℃.

[0004] The Raman DTS system has three key bottlenecks: first, the spatial resolution is difficult to break through centimeter level due to the limitation of single-core optical fiber structure, which cannot meet the monitoring needs of precision components such as aircraft engine blades; second, the Raman signal intensity is low, and the signal-to-noise ratio deteriorates sharply in high-temperature environments, resulting in a decrease in actual temperature measurement accuracy; third, the system response speed is slow (seconds), which makes it difficult to capture millisecond-level rapid temperature transients. In addition, the multiplexing technology introduces channel crosstalk, further reducing the measurement reliability. These defects seriously restrict the application effect of the technology in the field of high-end equipment monitoring. SUMMARY

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

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

[0007] Based on the 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 a single wavelength of optical signal, and a first mapping relationship of wavelength-core is generated;

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

[0009] After deployment, light signals received from each core are used to extract the wavelength shift caused by temperature changes based on the attenuation characteristics of the light signals of each wavelength channel at the core bends, and the wavelength shift is converted into temperature values of each sensing sub-region by combining the pre-stored nonlinear calibration curve, to establish a temperature gradient model bound to the spatial position of the core;

[0010] According to the physical spacing between the cores in the multicore optical fiber, the first mapping relationship, and the transmission delay of the optical signals in different cores, the wavelength space of the optical signals is calculated to generate a second mapping relationship between the spatial coordinates and temperature values in the sensing sub-region, and a three-dimensional temperature field distribution topology of the surface of the object to be measured is reconstructed according to the second mapping relationship;

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

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

[0013] The temperature gradient model and the three-dimensional temperature field distribution topology are dynamically associated and fused to generate fused temperature field data by complementing the local temperature variation characteristics of the temperature gradient model and the spatial thermal field distribution characteristics of the three-dimensional temperature field distribution topology.

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

[0015] Optionally, the wavelength shift caused by temperature changes is extracted based on the attenuation characteristics of the light signals of each wavelength channel at the core bends, including:

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

[0017] According to the difference between the initial light intensity value and the real-time light intensity value, the light intensity attenuation ratio of the light signal at the bend is calculated.

[0018] By comparing the light intensity attenuation ratio change trend of the light signal of the same wavelength channel at different bends, an abnormal fluctuation range of the attenuation ratio caused by temperature changes is identified.

[0019] In the abnormal fluctuation range of the attenuation ratio, a characteristic inflection point of the light intensity attenuation ratio with temperature change is located, and a wavelength offset distance corresponding to the characteristic inflection point is measured.

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

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

[0022] A nonlinear calibration curve calibrated in advance through experiments is accessed, the nonlinear calibration curve stores a corresponding relationship between a wavelength shift amount of each fiber core and a temperature change, and the corresponding relationship is defined in segments according to material characteristics of the fiber core and a curvature radius of the bending section;

[0023] For each bending section corresponding to a sensing sub-region, a curve segment matched with the bending section is searched from the nonlinear calibration curve according to an identifier of a fiber core where the bending section is located and the curvature radius of the bending section;

[0024] The wavelength shift amount of the bending section is input into the matched curve segment, an amount of temperature change of the sensing sub-region where the bending section is located is calculated through interpolation, and a temperature value of the sensing sub-region is determined in combination with an initial temperature value;

[0025] According to an arrangement order of each bending section on each fiber core in a transmission path, temperature values of a plurality of sensing sub-regions covered by a same fiber core are sequentially arranged in an optical signal transmission direction to form a temperature distribution sequence bound to an axial position of the fiber core;

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

[0027] Optionally, according to a physical spacing between fiber cores in the multicore optical fiber, the first mapping relationship and a transmission time delay of the optical signal in different fiber cores, wavelength space calculation is performed on the optical signal to generate a second mapping relationship between a space coordinate and a temperature value in the sensing sub-region, and a three-dimensional temperature field distribution topology of a surface of an object to be measured is reconstructed according to the second mapping relationship, including:

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

[0029] By measuring a transmission time delay of the optical signal from the input port to each sensing sub-region of each wavelength channel, a real propagation distance of the optical signal in each fiber core is calculated in combination with a refractive index parameter of the fiber core;

[0030] According to the actual propagation distance and the core spatial distribution model, the specific position coordinates of each sensing sub-region on the three-dimensional space path are determined; the temperature value detected by each sensing sub-region is bound with the position coordinates corresponding to the sensing sub-region to form a second mapping relationship containing spatial coordinates and temperature values;

[0031] Based on the position coordinates and temperature values of all sensing sub-regions 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 arrangement rule of the three-dimensional space path of the multi-core optical fiber.

[0032] Optionally, the temperature gradient model and the three-dimensional temperature field distribution topology are dynamically associated and fused, and a fused temperature field data is generated by complementing the local temperature change characteristics of the temperature gradient model and the spatial heat field distribution characteristics of the three-dimensional temperature field distribution topology, including:

[0033] The three-dimensional grid node temperature value corresponding to each core in the temperature gradient model is superimposed with the temperature value of the same spatial coordinate point in the three-dimensional temperature field distribution topology, wherein the temperature value of the temperature gradient model is given a first weight and the temperature value of the three-dimensional temperature field distribution topology is given a second weight when superimposed, 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 core and the spatial coordinate point.

[0034] In the superimposed temperature value, the steep temperature change characteristics between adjacent sensing sub-regions in the temperature gradient model are identified, and the spatial smoothness characteristics of the temperature values in the same region in the three-dimensional temperature field distribution topology are extracted, and the steep characteristics and the smooth characteristics are complementarily superimposed according to a preset proportion to form a fused intermediate temperature field data.

[0035] For the temperature distribution gap region caused by the core spacing in the intermediate temperature field data, according to the temperature value change trend of the sensing sub-regions around the gap region, the temperature value is diffused and filled along the extension direction of the three-dimensional space path of the multi-core optical fiber, so that the temperature values of the adjacent core coverage regions are continuously connected in space.

[0036] Based on the filled intermediate temperature field data, the region 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 region, and the temperature value of the three-dimensional temperature field distribution topology is used to replace the temperature value of the temperature gradient model in the to-be-corrected region to generate a fused temperature field data.

[0037] Optionally, based on the spatial domain joint analysis of the fused temperature field data and a preset threshold, the temperature abnormal region on the surface of the measured object is located, and the temperature change range of the temperature abnormal region is calculated by inverse calculation of the nonlinear calibration curve, including:

[0038] In the fusion temperature field data, the temperature values of each spatial position are compared with the difference of the preset threshold value point by point; the continuous spatial position whose temperature value exceeds the preset threshold value is marked as a candidate abnormal area;

[0039] For each candidate abnormal area, the number of sensing sub-regions covered by the candidate abnormal area and the corresponding wavelength drift amount are counted; according to the distribution characteristics of the wavelength drift amount in the candidate abnormal area, the candidate abnormal area meeting the temperature abnormal propagation rule is screened out as the final temperature abnormal area;

[0040] The wavelength drift amount of each sensing sub-region in the temperature abnormal area is extracted, and the temperature deviation value of each sensing sub-region is calculated according to the corresponding relationship between the wavelength offset distance and the temperature in the nonlinear calibration curve;

[0041] Taking the maximum value of the temperature deviation value as a reference, the temperature change range of the measured object surface is determined in combination with the spatial range of the temperature abnormal area.

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

[0043] A first generation module 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 a single wavelength of optical signal, and generate a first mapping relationship between wavelength and core;

[0044] An integration module is configured to integrate the multi-core optical fiber loaded with optical signals to the surface of a measured object along a preset three-dimensional space path, and each core covers a plurality of sensing sub-regions;

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

[0046] An operation module is configured to perform wavelength space calculation on the optical signals according to the physical distance between the cores in the multi-core optical fiber, the first mapping relationship and the transmission time delay of the optical signals in different cores, to generate a second mapping relationship between the spatial coordinates and the temperature values in the sensing sub-regions, and reconstruct the three-dimensional temperature field distribution topology of the surface of the measured object according to the second mapping relationship;

[0047] A second generation module is configured to determine the temperature change 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, the embodiments of the present application provide 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, so as to realize the multi-core optical fiber temperature detection method based on wavelength division multiplexing as described in the first aspect.

[0049] In a fourth aspect, the embodiments of the present application provide a computer storage medium, which stores a computer program; when the computer program is executed by a computer, a multi-core optical fiber temperature detection method based on wavelength division multiplexing as described in the first aspect is realized.

[0050] In the embodiments 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 optical signals of a single wavelength, and a first mapping relationship of wavelength-core is generated; the multi-core optical fiber loaded with optical signals is integrated on the surface of a measured object according to a preset three-dimensional space path, and each core covers multiple sensing sub-regions; after deployment, the optical signals output by each core are received, the wavelength drift caused by temperature change is extracted based on the attenuation characteristics of the optical signals of each wavelength channel at the core bending part, and the wavelength drift is converted into the temperature value of each sensing sub-region in combination with a pre-stored nonlinear calibration curve, so as to establish a temperature gradient model bound with the spatial position of the core; the wavelength space of the optical signals is calculated based on the physical distance between the cores in the multi-core optical fiber, the first mapping relationship and the transmission delay of the optical signals in different cores, so as to generate a second mapping relationship of the spatial coordinates and the temperature value in the sensing sub-region, and reconstruct the three-dimensional temperature field distribution topology of the surface of the measured object according to the second mapping relationship; the temperature change range of the surface of the measured object is determined according to the temperature gradient model and the three-dimensional temperature field distribution topology.

[0051] The technical scheme of the present application has the following beneficial effects:

[0052] Through the cooperative design of the multi-core optical fiber and the wavelength division multiplexing, the wavelength and the space are decoupled in the physical dimension, so that the spatial resolution of the three-dimensional temperature field and the anti-cross interference ability are significantly improved; based on the extraction of the bending attenuation characteristics and the fusion of the double mapping relationships, the limitation of the sensing density of the single-core optical fiber is broken, and the dynamic temperature gradient monitoring of the complex curved surface is supported.

[0053] Further, the temperature gradient model (local temperature change) is dynamically associated and fused with the three-dimensional temperature field distribution topology (global thermal field characteristics) to generate fused temperature field data, and the preset threshold and nonlinear calibration curve inversion calculation are combined to accurately locate the temperature anomaly area and quantify the change range. Through the complementary enhancement of local and global temperature characteristics, the problem of insufficient sensitivity of a single model to small anomalies is solved, and the accuracy of temperature anomaly detection is improved; using nonlinear calibration inversion, further eliminate environmental disturbance error, realize the sub-degree Celsius level precision calibration of temperature change range.

[0054] These aspects or other aspects of the present application will be more apparent in the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

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

[0057] Figure 2 A structural schematic diagram of a fusion wavelength division multiplexing multi-core optical fiber temperature detection system provided by the present application is shown;

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

[0059] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application.

[0060] In some of the descriptions in the specification and claims of the present application and the above-mentioned drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that these operations can be executed or in parallel without the order appearing in this text. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and the operations can be executed in sequence or in parallel. It should be noted that the "first", "second", etc. in this text are used to distinguish different messages, devices, modules, etc., and do not represent the order. Also, "first" and "second" are not of different types.

[0061] The researchers found that the existing single-core optical fiber temperature detection scheme has the problems of insufficient three-dimensional temperature field reconstruction accuracy, high decoupling error caused by the cross-sensitivity of temperature and strain, and poor scalability caused by the strong coupling of sensing density and wavelength resources due to the dependence on single-core multi-wavelength multiplexing. Based on this, a multi-core optical fiber temperature detection method is provided, which separates the signal transmission channel through wavelength-core mapping, combines three-dimensional space path integration and bending attenuation characteristic analysis, realizes core-level temperature gradient binding, and accurately reconstructs the three-dimensional temperature field distribution through two-dimensional solution, significantly improving the spatial resolution and anti-interference ability. The technical scheme of the present application can be applied to high-precision three-dimensional temperature monitoring of complex scenes such as high-temperature electronic equipment heat dissipation monitoring, aero-engine hot end component temperature field dynamic analysis, and biological medical heat treatment area precise temperature control.

[0062] The entire research and development process embodies the technical fusion of multi-dimensional decoupling architecture design and dynamic perception algorithm: through the physical layer decoupling design of wavelength division multiplexing and multi-core optical fiber, independent signal channel allocation is realized by wavelength-core mapping, breaking through the strong coupling limitation of wavelength resources and sensing density of single-core system; combined with three-dimensional path integration and bending attenuation characteristic analysis, the temperature sensitive unit and strain interference are isolated in the spatial domain, and the cross-sensitivity error is eliminated through nonlinear calibration; further through the dynamic solution of wavelength-space double mapping, the precise binding model of fiber core position and temperature gradient is constructed, and the real-time reconstruction of 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 (double mapping solution), and finally realizing the upgrading of three-dimensional temperature field monitoring capability with high resolution and anti-interference in complex curved surface scenes.

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

[0064] Figure 1 A flowchart of a multi-core optical fiber temperature detection method is provided for the embodiments of the present application, as shown in Figure 1 The method comprises:

[0065] 101. Based on the wavelength division multiplexing technology, the broadband light source is divided into multiple wavelength channels, and the wavelength channels are guided to different core input ports of the multi-core optical fiber, so that each core can transmit a single wavelength of optical signal, and a first mapping relationship of wavelength-core is generated;

[0066] A wavelength channel refers to an independent spectral region into which a broadband light source is divided using wavelength division multiplexing (WDM) technology, with each region carrying a unique wavelength optical signal. For example, wavelength channels could be λ1 to λ7. The first mapping relationship refers to a logical table that uses hardware routing configuration to bind different wavelength channels to designated fiber core ports of multi-core optical fibers, thereby decoupling wavelength resources from the physical fiber core channels.

[0067] In this embodiment, the broadband light source is first spectrally segmented using wavelength division multiplexing (WDM) technology to generate multiple equally spaced wavelength channels, such as λ1 to λ7. Then, the wavelength channels are guided to different core input ports of a multi-core optical fiber using fiber couplers, and each wavelength channel is directionally transmitted to an independent core of the multi-core optical fiber, such as cores C1 to C7, ensuring single-core, single-wavelength transmission. Finally, the wavelength λ is recorded. i With fiber core C j The correspondence is used to generate a traceable first mapping matrix of wavelength-fiber core, providing physical channel identifiers for subsequent signal processing.

[0068] In high-temperature electronic equipment heat dissipation monitoring scenarios, a high-precision arrayed waveguide grating is used to divide a broadband light source, for example, 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, spaced 5 nm apart. Using polarization-independent fiber couplers with an insertion loss of <0.5 dB, each wavelength channel is coupled to seven independent fiber cores (C1 to C7) with a core diameter of 50 μm and a cladding of 125 μm. A wavelength-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 basis for subsequent signal processing, ensuring complete isolation of multi-wavelength signals between fiber cores, and avoiding channel crosstalk.

[0069] 102. The multi-core optical fiber loaded with optical signal is integrated onto the surface of the object under test according to a preset three-dimensional spatial path, and each fiber core covers multiple sensing sub-regions.

[0070] The preset three-dimensional spatial path is a fiber core layout trajectory pre-designed based on the surface curvature and thermally sensitive area distribution of the object being measured; the sensing sub-region is an independent detection unit where each fiber core is segmented and marked along the path, used to achieve discretization of spatial coordinates and temperature values.

[0071] In the embodiments of the present application, based on the three-dimensional model of the measured object, 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 attached to the surface of the object by hot melt adhesive or 3D printing clamp, to keep the fiber core bending curvature consistent with the preset path; finally, the OTDR is used to measure the fiber core length, to mark the sub-region coordinates by distance segmentation, and each fiber core covers multiple sensing sub-regions, to complete the spatial deployment of the sensing unit.

[0072] Based on the three-dimensional curved surface model of the heat sink, for example, the CAD modeling accuracy is ±0.1mm, the improved ant colony algorithm is used to plan the fiber core path, for example, the convergence iteration number is ≤50 times, and the optimization target is to minimize the fiber core crossing and curvature mutation. Using high-flexibility polyimide-coated multi-core optical fiber, through customized 3D printing clamp, for example, material: high-temperature-resistant nylon, melting point > 260℃, the optical fiber is attached to the surface of the heat sink according to the spiral path, to ensure that the fiber core bending radius > 5mm to avoid modal leakage. After deployment, the OTDR is used to measure the fiber core length, to mark the sensing sub-regions by 5mm interval, for example, C3-S 12 represents the sub-region of the fiber core C3 at 60mm from the input end, and the path attachment error is verified by the laser displacement sensor to be <0.2mm, to realize full coverage of the heat-sensitive area on the surface of the heat sink.

[0073] 103、After deployment, the light signals output by each fiber core are received, the wavelength drift amount caused by temperature change is extracted based on the attenuation characteristics of the light signals of each wavelength channel at the fiber core bending area, and the wavelength drift amount is converted into the temperature value of each sensing sub-region in combination with the pre-stored nonlinear calibration curve, to establish a temperature gradient model bound with the spatial position of the fiber core;

[0074] The bending area attenuation characteristic is the power loss of the light signal in the fiber core bending area due to modal leakage, and the attenuation amount is related to the bending radius and temperature; the nonlinear calibration curve is a nonlinear relationship function between the wavelength drift amount Δλ and the temperature change amount ΔT calibrated by experiment, which is used to eliminate strain interference and realize temperature inversion.

[0075] In the embodiments of the present application, after deployment, the light power of the light signals output by each fiber core is extracted by using the phase-locked amplification technology, the light signals of each wavelength channel are positioned at the fiber core bending area, and the wavelength drift amount Δλ caused by the attenuation amplitude temperature change is calculated; the wavelength drift amount Δλ is analyzed by using dense Fourier transform (DFT), and the temperature and strain effects are separated by using the bending radius compensation algorithm; the wavelength drift amount Δλ is input into the pre-stored nonlinear calibration curve, for example, a quadratic polynomial model, to be converted into the temperature change amount ΔT value; finally, the temperature change amount ΔT is bound with the spatial coordinates of the fiber core sub-region, to generate a temperature gradient distribution matrix.

[0076] The output optical signal of the receiving fiber core C5 is received, and a phase-locked amplifier with a bandwidth of 10 kHz and a dynamic range of 120 dB is used to detect a 3 dB decrease in the peak amplitude of the optical power attenuation of the S8 sub-region at a distance of 40 mm from the input end. The spectral characteristics are analyzed by using a fast Fourier transform (FFT) with 4096-point sampling, the wavelength shift amount Δλ is calculated to be 0.3 nm with an accuracy of ±0.02 nm, and the nonlinear calibration curve is combined with the pre-stored temperature change amount ΔT of the fitting equation ΔT = 0.5Δλ + 2Δλ, R = 0.998, and the temperature change amount ΔT of the region is inversely calculated to be 0.6°C. The ΔT value is bound to the local coordinates x = 10 mm and y = 5 mm, which are mapped by the OTDR marking data of step 102, a temperature gradient matrix is constructed, and the rationality of the gradient distribution is verified by a thermal-mechanical coupling finite element model. 2 +2Δλ, R 2 = 0.998, and the temperature change amount ΔT of the region is inversely calculated to be 0.6°C. The ΔT value is bound to the local coordinates x = 10 mm and y = 5 mm, which are mapped by the OTDR marking data of step 102, a temperature gradient matrix is constructed, and the rationality of the gradient distribution is verified by a thermal-mechanical coupling finite element model.

[0077] 104. According to the physical spacing between the fiber cores in the multicore optical fiber, the first mapping relationship, and the transmission time delay of the optical signal in different fiber cores, the wavelength space is calculated to generate a second mapping relationship between the spatial coordinates and the temperature values in the sensing sub-region, 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.

[0078] The transmission time 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; and 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), which is used to reconstruct the continuous temperature field.

[0079] In the embodiments of the present application, the transmission time delay τ i of the optical signal in different fiber cores is measured based on the time of flight (ToF) of the optical pulse, and the path length difference ΔL = τ ij *c (c is the speed of light) is calculated in combination with the physical spacing d i between the fiber cores in the multicore optical fiber; the path length difference ΔL is converted into three-dimensional coordinates of the sub-region by using the least square method; the temperature value ΔT of each sub-region obtained in step 103, for example, ΔT = 0.6°C, is bound to the calculated three-dimensional coordinates (x, y, z) in the format of “coordinate-temperature” to form the second mapping relationship; the wavelength-core mapping (first mapping relationship) and the coordinate-temperature mapping (second mapping relationship) are superimposed to generate three-dimensional temperature field point cloud data; and finally, the continuous temperature field distribution surface is reconstructed by interpolating the point cloud through the Delaunay triangulation algorithm.

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

[0081] 105. According to the temperature gradient model and the three-dimensional temperature field distribution topology, the temperature variation range of the measured object surface is determined.

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

[0083] In the embodiments of the present application, the temperature gradient model and the three-dimensional temperature field distribution topology are input into the Kalman filter for dynamic correlation fusion to generate fusion temperature field data; by comparing the preset safety threshold, for example, T_max=85℃, through spatial domain convolution scanning, the coordinate cluster exceeding the threshold is located; the nonlinear calibration curve is called to inversely calculate the wavelength drift amount Δλ deviation, and the temperature fluctuation range of the abnormal area is quantified, for example, ΔT=±2℃.

[0084] The temperature gradient model and the three-dimensional temperature field topology data are input into the extended Kalman filter, for example, the state vector dimension is 6, including temperature, coordinates and gradient change rate, to predict the dynamic temperature field distribution. The safety threshold T_max=85℃ is set, the JEDEC solid state device heat dissipation standard is referred to, and the sliding window convolution kernel, for example, the sliding window convolution kernel is a 3x3x3 cubic scanning point cloud data, is used to locate the temperature anomaly at coordinates x=15.2mm, y=8.1mm, z=1.8mm, which reaches 92.1℃. The calibration curve is called to inversely calculate Δλ=0.52nm, combined with Monte Carlo error analysis, to determine that the temperature exceeds the standard range ΔT=+7.2℃~+9.0℃ in this area with a confidence degree of 95%, triggering the hierarchical alarm system, wherein the first level alarm is fan speed-up, and the second level alarm is load switching, and an abnormal report containing spatial coordinates and temperature fluctuation range is generated.

[0085] The scheme breaks through the sensing density limit of single-core systems by independently allocating wavelength channels to different cores through the physical coupling architecture of wavelength division multiplexing and multi-core optical fiber; realizes high-density coverage of complex curved surfaces and core-level spatial coordinate binding by combining three-dimensional spatial path flexible integration; eliminates temperature-strain cross-sensitivity errors and improves detection accuracy based on bending attenuation characteristics extraction and nonlinear calibration; realizes dynamic reconstruction of three-dimensional temperature field with millimeter-level resolution by using wavelength-space double mapping solution, combining core spacing and time delay parameters; finally, through temperature gradient and global distribution data fusion, accurately locates abnormal areas and quantifies fluctuation range. The technology forms a closed loop from hardware architecture, signal processing to algorithm solving, significantly improves the anti-interference, spatial resolution and dynamic response speed of three-dimensional temperature field monitoring in complex scenes, and provides a high-reliability solution for precise temperature control in industrial thermal management, biomedical and other fields.

[0086] In some embodiments, according to the temperature gradient model and the three-dimensional temperature field distribution topology, the temperature change range of the measured object surface is determined, comprising:

[0087] 201, dynamically associating and fusing the temperature gradient model and the three-dimensional temperature field distribution topology, generating fused temperature field data by complementing the local temperature change characteristics of the temperature gradient model and 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 local temperature change ΔT and its time gradient calculated by the wavelength drift Δλ of each sensing sub-region of the multi-core optical fiber and the nonlinear calibration curve; the three-dimensional temperature field distribution topology refers to the mapping relationship of three-dimensional coordinates and temperature values generated by the core spacing, transmission time delay and spatial solving algorithm; the dynamic association and fusion is the process of complementarily fusing the local transient characteristics, such as ΔT / Δt, and the global steady-state characteristics, such as spatial thermal field distribution, through a weighted algorithm, to generate fused temperature field data with spatio-temporal resolution.

[0089] In the embodiments of the present application, firstly, the local temperature change ΔT value in the temperature gradient model, such as ΔT of core C5-S8 = 0.6℃ / s, is spatio-temporally aligned with the temperature value of the corresponding coordinate in the three-dimensional temperature field distribution topology, such as temperature value of 85.3℃; then the extended Kalman filter (EKF) algorithm is used, taking 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, to generate fused temperature value, such as 85.3℃±0.2℃, by iteratively updating the weight matrix; finally, based on covariance matrix analysis of the uncertainty of the fusion result, a confidence interval, such as ±0.3℃, is added to each temperature value to form a fused temperature field data set, providing a spatio-temporally consistent data basis for anomaly analysis.

[0090] 202、based on the fusion temperature field data and a preset threshold value, a spatial domain joint analysis is performed to locate a temperature abnormal region on the surface of the measured object, and a temperature change range of the temperature abnormal region is calculated through the nonlinear calibration curve.

[0091] The preset threshold value is a temperature safety limit value set according to the material of the measured object or a safety standard, and is used to trigger an alarm. The spatial domain joint analysis is a technology for locating an over-limit region in a three-dimensional coordinate domain through scanning and clustering algorithms. The temperature abnormal region is a coordinate cluster in the fusion data that continuously exceeds the limit and is spatially connected. The inverse calculation is a process of inversely calculating the temperature fluctuation range from the Δλ data of the abnormal region through the nonlinear calibration curve, and the quantitative results are output in combination with an error model.

[0092] In the embodiments of the present application, a 3*3*3 cubic sliding window convolution kernel is first used to traverse the fusion temperature field data, and all coordinate points exceeding the preset threshold value are marked, for example, the temperature at the coordinate (15.2, 8.1, 1.8) is 92.1℃. Then, adjacent over-limit points are merged to form a temperature abnormal region by a density clustering algorithm DBSCAN, for example, a spherical cluster with a diameter of 3mm, and isolated noise points are removed. Finally, the wavelength shift Δλ data of the temperature abnormal region is extracted, for example, the wavelength shift Δλ = 0.52nm, the nonlinear calibration curve is called to inversely calculate the temperature change ΔT = 8.1℃ of the temperature abnormal region, and the fluctuation range of the temperature change is output in combination with a Monte Carlo simulation, for example, +7.2℃ ~ +9.0℃, to generate an abnormal report.

[0093] The following is a specific example:

[0094] In the surface temperature monitoring of an aero-engine turbine blade, the fiber core C3-S9, which is 120mm away from the blade root, detects a wavelength shift Δλ = 0.45nm, and through the nonlinear calibration curve ΔT = 0.5Δλ 2+2Δλ calculated local temperature change ΔT = 1.1℃, time gradient ΔT / Δt = 0.6℃ / s; at the same time, the corresponding coordinate x = 25.3mm, y = 12.1mm, z = 5.2mm in the three-dimensional temperature field topology is calculated to be 950℃, based on the core spacing 0.3mm and the transmission delay τ = 10ns. By extending Kalman filter EKF to fuse the two data, the fusion temperature value 950.5℃ ± 2℃ is generated, with a 95% confidence interval covering 500 coordinate points on the surface of the blade. The scan found that the temperature at coordinates x = 30.2mm, y = 15.5mm, z = 6.1mm reached 1010℃, exceeding the safety threshold T_max = 1000℃; DBSCAN clustering determines an abnormal area with a diameter of 4mm, and the inversion calculation of its Δλ = 0.8nm corresponds to ΔT = +3.2℃, combined with the error output of ±0.8℃, the over-standard range +2.4℃ ~ +4.0℃ is output, triggering the cooling system to pressurize to 120kPa and generating a maintenance work order, marking the abnormal coordinates and fluctuation range.

[0095] The scheme enhances the spatio-temporal consistency of temperature field data through dynamic association fusion, significantly improves the detection sensitivity of micro-abnormal areas; based on spatial domain intelligent analysis, accurately locates any shape temperature anomaly on complex curved surface, breaking through the geometric limitations of traditional threshold rules; combined with nonlinear inversion and error modeling, provides high-precision temperature fluctuation quantization support for operation and maintenance decision-making. In harsh scenes such as high-temperature equipment and aircraft engines, realize the intelligent heat management upgrade from "passive alarm" to "active prediction", support the reliability improvement and life cycle cost optimization of industrial equipment.

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

[0097] 301. 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;

[0098] The initial light intensity value refers to the reference light power value (unit: dBm) of the optical signal passing through the bend of the multi-core optical fiber under the condition of no external interference, which is used as a reference for subsequent attenuation calculation; the real-time light intensity value refers to the instantaneous power value of the optical signal when passing through the bend in the actual monitoring process, which includes the loss change caused by temperature or strain.

[0099] In the system initialization phase, the initial optical intensity values of the optical signals of each wavelength channel at each bend of the multicore optical fiber are measured by a high-precision optical power meter, such as Keysight N7744A, as the reference data under the condition of no interference. For example, the initial optical intensity of bend point 1 of core C3 is -20 dBm, and the initial optical intensity of bend point 2 is -19.5 dBm. In actual operation, a distributed optical detector is used to capture the optical intensity values at each bend in real time at a sampling rate of 1 MHz, for example, the optical intensity of bend point 1 of core C3 is measured to be -23 dBm at time t = 10 s. All data are stored synchronously to a time series database, such as InfluxDB, by a FPGA controller, indexed by core number, bend position and time stamp, to ensure the spatiotemporal consistency of subsequent analysis.

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

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

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

[0103] 303. Identify the abnormal fluctuation interval of the attenuation ratio caused by temperature change by comparing the change trend of the optical intensity attenuation ratio of the optical signal of the same wavelength channel at different bends;

[0104] Abnormal fluctuation interval of attenuation ratio refers to the time period or spatial region in which the attenuation ratio of different bends under the same wavelength channel deviates significantly from the normal range with time.

[0105] In the embodiments of the present application, the trend of the sequence of light intensity attenuation ratios at different bending points of the same wavelength channel, such as the data of 10 bending points of the fiber core C3, is compared by using a dynamic time warping algorithm (DTW), and a common fluctuation mode is extracted. For example, it is found by analysis that the attenuation ratios of bending points 1 to 9 are stable at 8% within 50 to 60 seconds, while the attenuation ratio of bending point 5 suddenly increases to 15%. The abnormal fluctuation interval deviating from the common trend caused by temperature change is detected by using an isolation forest algorithm, for example, the data of bending point 5 within 50 to 60 seconds is marked as abnormal, and the spatiotemporal coordinates of the candidate temperature-sensitive region are generated.

[0106] 304. Within the abnormal fluctuation interval of the attenuation ratio, a characteristic inflection point of the light intensity attenuation ratio changing with temperature is located, and a wavelength shift distance corresponding to the characteristic inflection point is measured;

[0107] The characteristic inflection point refers to an extreme point of the first derivative of the attenuation ratio curve caused by a sudden temperature change, and corresponds to the starting position of wavelength shift. The wavelength shift distance refers to the absolute value of the shift of the center wavelength before and after the characteristic inflection point.

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

[0109] 305. The wavelength shift distance is matched with a pre-stored wavelength-temperature correlation table to obtain the wavelength shift amount caused by temperature change.

[0110] The wavelength-temperature correlation table refers to a table of corresponding relationships between the wavelength shift amount Δλ and the temperature change amount ΔT calibrated by experiment.

[0111] In the embodiments of the present application, the temperature gradient model generated in step 103 is used to obtain the temperature change amount ΔT of each sensing sub-region, for example, the temperature change amount ΔT = 0.6°C. The temperature change amount ΔT is input into the pre-stored temperature-wavelength correlation table to find the corresponding wavelength shift amount Δλ. For example, if the wavelength-temperature correlation table is a linear relationship of wavelength shift amount Δλ = 0.1ΔT, then ΔT = 0.6°C corresponds to Δλ = 0.06 nm; if the wavelength-temperature correlation table is a nonlinear relationship, for example, Δλ = 0.05ΔT + 0.2ΔT, then the wavelength shift amount Δλ = 0.05*(0.6) + 0.2*(0.6) = 0.06 nm is calculated. 2 +0.2ΔT, then the wavelength shift amount Δλ = 0.05*(0.6) + 0.2*(0.6) = 0.06 nm is calculated.2 +0.2*0.6 = 0.138 nm.

[0112] The following is a specific example:

[0113] In the temperature monitoring of the turbine blade surface of an aero-engine, the bending point 3 of the fiber core C5 is calibrated to an initial light intensity of -18 dBm in the system initialization stage; in actual monitoring, the real-time light intensity of this point is measured to be -21 dBm at time 30 seconds, the attenuation ratio is calculated to be 16.7%, and it is corrected to 0.16 after sliding window filtering. By analyzing the attenuation trend of 10 bending points under the same wavelength channel through the dynamic time warping algorithm, it is found that the attenuation ratio of the bending point 3 continuously deviates from the normal range from time 28 seconds to 32 seconds, and the isolation forest algorithm determines the abnormal fluctuation interval. The first derivative of the data in this interval is calculated, and the feature inflection point is located at time 30 seconds. The spectrum before and after the inflection point is intercepted, and the central wavelength is extracted. The wavelength is measured to shift from the initial value 1545 nm to 1545.3 nm, and the wavelength shift distance is 0.3 nm. According to the pre-stored nonlinear calibration curve ΔT = 0.5Δλ 2 +2Δλ, the temperature change ΔT = 0.645℃ is calculated by substitution, and finally output to the turbine blade surface coordinates x = 50 mm, y = 20 mm, triggering the control system to adjust the cooling airflow and generate an abnormal report, realizing the full closed-loop management from data acquisition to operation and maintenance response.

[0114] The scheme cooperatively analyzes the light signal attenuation characteristics and the wavelength-temperature correlation model, significantly improves the sensitivity and anti-interference ability of temperature change detection, and effectively distinguishes temperature and strain effects. Based on dynamic data fusion and intelligent algorithms, the precise positioning of small abnormal areas on complex curved surfaces is realized, breaking through the geometric limitations of traditional threshold rules and supporting the identification of temperature gradients of any shape. 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. In high-temperature equipment monitoring and other applications, it promotes the upgrade of operation and maintenance mode from passive alarm to active prediction, and provides high-precision and high-timeliness decision support for equipment life extension and safety control.

[0115] In some embodiments, based on the wavelength shift amount and the pre-stored nonlinear calibration curve, the wavelength shift amount is converted into the 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 the nonlinear calibration curve calibrated in advance through experiments, the nonlinear calibration curve stores the corresponding relationship between the wavelength shift amount and the temperature change of each fiber core, and the corresponding relationship is defined in sections according to the material properties and bending segment curvature radius of the fiber core;

[0117] The nonlinear calibration curve refers to a function curve pre-calibrated by experiments, describing the corresponding relationship between the wavelength shift and the temperature change. The curve is defined by segments according to the core material characteristics and the bending segment curvature radius, for example, a quadratic polynomial fitting is used for a common quartz core in the curvature radius interval of 5mm to 10mm, while an exponential function fitting is used for an erbium-doped core in the same curvature interval.

[0118] In the embodiments of the present application, a pre-stored nonlinear calibration curve database is first loaded from the non-volatile memory, and the nonlinear calibration curve database stores the corresponding relationship between the wavelength shift and the temperature change of each core according to the core type and the curvature radius interval. For example, the nonlinear calibration curve of core C1 can include three segments: ΔT = 0.2Δλ + 1.8Δλ for the curvature radius interval of 3mm to 5mm, ΔT = 0.15Δλ + 2.1Δλ for the curvature radius interval of 5mm to 8mm, and ΔT = 0.2Δλ + 1.8Δλ for the curvature radius interval of 8mm to 10mm. 2 2 The system automatically matches the corresponding curve group according to the currently detected core number.

[0119] 402. For each bending segment corresponding to a sensing sub-region, the curve segment matched with the bending segment is found from the nonlinear calibration curve according to the identification of the core where the bending segment is located and the curvature radius of the bending segment;

[0120] The bending segment matching refers to determining the curvature radius interval to which the sensing sub-region belongs according to the physical position of the sensing sub-region in the core, so as to select the corresponding calibration curve segment. The curvature radius of each core when laid is pre-recorded by three-dimensional path planning.

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

[0122] 403. The wavelength shift of the bending segment is input into the matched curve segment, and the temperature change of the sensing sub-region where the bending segment is located is calculated by interpolation, and the temperature value of the sensing sub-region is determined in combination with the initial temperature value;

[0123] The temperature change calculation refers to inputting the measured wavelength shift into the matched calibration curve segment, and calculating the temperature change value by function or interpolation. The initial temperature value refers to the reference temperature recorded when the system starts.

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

[0125] 404. Based on the arrangement order of each curved segment on each fiber core in the transmission path, the temperature values ​​of multiple sensing sub-regions covered by the same fiber core are arranged sequentially according to the direction of optical signal transmission, forming a temperature distribution sequence bound to the axial position of the fiber core.

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

[0127] In this embodiment, the sub-regions on the fiber core are first sorted according to the arrangement order of each curved segment on the fiber core in the optical transmission path, for example, C1-S1 to C1-S20. Then, the temperature values ​​calculated for each sensing sub-region are sequentially filled in according to the direction of optical signal transmission, forming a sequence such as [25.1, 25.3, ..., 26.8]℃. For missing data points, the mean of the adjacent points is used for interpolation.

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

[0129] A temperature gradient model refers to mapping the temperature distribution sequence of all cores in a multi-core optical fiber onto the surface of an object, based on the core spacing and three-dimensional path coordinates, to form a three-dimensional temperature field. This model includes both spatial coordinates and temperature values.

[0130] In this embodiment, the physical spacing parameters of each fiber 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, based on the three-dimensional spatial path coordinates, the temperature values ​​in the temperature distribution sequence of each fiber core are assigned to the corresponding three-dimensional spatial coordinate points. For example, the temperature of C1-S5, 25.6℃, corresponds to coordinates (10.2, 5.1, 2.3) mm. Finally, a continuous three-dimensional temperature gradient model is generated by combining the current temperature values ​​of all fiber cores through Delaunay triangulation.

[0131] Here is a specific example:

[0132] In the scenario of turbine blade surface temperature monitoring of an aero-engine, the system first calls the pre-stored 7-core fiber calibration curve database. Taking core C1 as an example, the calibration coefficient of this quartz fiber in the curvature radius interval of 5.0-8.0 mm is the quadratic coefficient 0.15 and the linear coefficient 2.1. When the sensing subsystem detects a wavelength shift of 0.4 nm in the 8th sensing sub-region of core C1, the system automatically matches the calibration curve segment corresponding to the 7.5 mm curvature radius where the region is located. By substituting into the nonlinear calculation formula, the system calculates the temperature change of this region as 0.8992°C. Combined with the initial reference temperature of this region, 950°C, the current temperature value 950.8992°C is generated, and this value is bound with the three-dimensional space coordinate point X axis 120.5 mm, Y axis 32.7 mm, and Z axis 8.2 mm. The system arranges the temperature data of all sensing sub-regions on core C1 in sequence according to the direction of optical signal transmission, and constructs a complete axial temperature distribution sequence. This sequence clearly records the temperature gradient change from 950.1°C to 950.9°C. 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 in the leading edge of the blade, with a maximum temperature of 952°C, which is 12°C higher than the average temperature of the surrounding area. The system immediately starts the automatic response mechanism to improve the cooling system pressure level, and generates a detailed maintenance work order, which clearly marks the three-dimensional coordinates and specific temperature exceeding value of the abnormal area. The entire data processing and response process only takes 8 milliseconds, realizing a complete closed-loop control from data acquisition to decision execution.

[0133] This scheme realizes high-precision dynamic monitoring of complex curved surface temperature distribution through innovative multi-core fiber sensing architecture and intelligent temperature field reconstruction algorithm. Based on the wavelength-temperature nonlinear calibration model, the system effectively eliminates the measurement errors caused by material property differences and bending deformation, significantly improving the consistency and reliability of temperature detection. Through three-dimensional space path solving and multi-physical field data fusion, a temperature gradient model with millimeter-level spatial resolution is constructed, which can accurately capture the subtle changes of the surface temperature field. The dynamic correlation analysis technology realizes early identification and accurate positioning of temperature anomalies, providing a reliable basis for predictive maintenance of key equipment. The entire system exhibits excellent anti-interference ability and real-time response characteristics, and maintains 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 aero-engines and power equipment.

[0134] In some embodiments, the optical signals are wavelength-space resolved according to the physical spacing between the cores in the multicore optical fiber, the first mapping relationship, and the transmission time delay of the optical signals in different cores, to generate a second mapping relationship between the spatial coordinates and temperature values in the sensing sub-region, and to reconstruct the three-dimensional temperature field distribution topology of the surface of the object to be measured according to the second mapping relationship, including:

[0135] 501. The input port of the multicore optical fiber is taken 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, the initial position of the core corresponding to each wavelength channel in the core spatial distribution model is determined;

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

[0137] In the embodiments of the present application, the physical spacing parameters of the cores of the multicore optical fiber are first measured with the input port of the multicore optical fiber as a reference point, for example, the spacing between the center core and the peripheral core is 0.3 mm. Then a two-dimensional coordinate system is established according to the core arrangement mode, for example, the center core C0 is set as the coordinate origin (0, 0), and the peripheral cores C1 to C6 are distributed at an equal angle of 60 degrees on the circumference of 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 spatial distribution model, for example, λ1 corresponds to C1, and its initial coordinates are (0.3 mm, 0).

[0138] 502. The actual propagation distance of the optical signals in each core is calculated by measuring the transmission time delay of the optical signals from the input port to each sensing sub-region, in combination with the core refractive index parameters;

[0139] The transmission time delay refers to the propagation time difference of the optical signals from the input port to the sensing sub-region, which is usually measured in nanoseconds. The actual propagation distance refers to the geometric path length of the optical signals in the core after considering the core refractive index, which is calculated by the product of the time delay and the group velocity.

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

[0141] 503. According to the actual propagation distance and the core spatial distribution model, the specific position coordinates of each sensing sub-region on the three-dimensional space path are determined; the temperature value detected by each sensing sub-region is bound with the position coordinates corresponding to the sensing sub-region to form a second mapping relationship containing spatial coordinates and temperature values;

[0142] The spatial coordinates refer to the specific position of the sensing sub-region in the three-dimensional space, which is obtained by solving along the preset path according to the actual propagation distance. The second mapping relationship refers to the data structure of binding the temperature value of each sub-region with its spatial coordinates, which is used for temperature field reconstruction.

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

[0144] 504. Based on the position coordinates and temperature values of all sensing sub-regions 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 arrangement rule of the three-dimensional space path of the multicore optical fiber.

[0145] The three-dimensional temperature field distribution topology refers to the temperature distribution model formed after the discrete sub-region temperature values are continuous on the surface of the object, which can directly display the temperature gradient change and abnormal area.

[0146] In the embodiment of the present application, first, the position coordinates and temperature values of all sensing sub-regions in the second mapping relationship are read. Then, according to the layout path of the three-dimensional space path arrangement rule of the multicore optical fiber, a modified radial basis function interpolation algorithm is used for spatial interpolation. For example, the Gaussian kernel function φ(r) = exp(-(r / 0.5) 2) and then a weighted average is performed. Finally, isothermal surfaces are generated by the Marching Cubes algorithm, and the temperature gradient is rendered with different colors to form a visual temperature field, and then a continuous three-dimensional temperature field distribution topology of the surface of the object to be measured is constructed.

[0147] The following is a specific example:

[0148] In the monitoring scene of an aero-engine combustion chamber, the system first establishes a spatial distribution model based on the physical structure of a 7-core optical fiber, with the central core C0 as the coordinate origin and the 6 peripheral cores evenly distributed at a 0.3mm interval. The signal transmission time delay of the 12th sensing sub-area of core C3 is measured by optical time domain reflection technology as 12ns, and the actual propagation distance of 2.4m is calculated by combining the refractive index parameters of the core material. The system calculates the spatial coordinates corresponding to the distance along the preset three-dimensional spiral path as X-axis 25mm, Y-axis 10mm, and Z-axis 5mm, and binds and stores the coordinates with the 950.8 degrees Celsius detected by the temperature sensor. After repeating the above process for 500 sensing sub-areas on the surface of the combustion chamber, the system generates a continuous temperature field using an improved radial basis function interpolation algorithm, clearly showing that there is a local high-temperature area with a diameter of about 3mm at the head of the combustion chamber, and the temperature is significantly higher than that of the surrounding area. Based on the three-dimensional temperature field, the system automatically marks the abnormal area and generates a maintenance warning.

[0149] The scheme realizes the three-dimensional visualization reconstruction of the temperature field of a complex curved surface through the accurate calculation of the spatial coordinates of the multi-core optical fiber. 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 zones. In harsh industrial environments such as high temperature and strong vibration, the system exhibits excellent stability and reliability, providing a new technical means for temperature monitoring and fault warning of key equipment, significantly improving the safety and maintenance efficiency of equipment operation. The scheme 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, and by complementing the local temperature variation characteristics of the temperature gradient model and the spatial thermal field distribution characteristics of the three-dimensional temperature field distribution topology, a fused temperature field data is generated, including;

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

[0152] The first weight refers to the contribution coefficient of the temperature value of the temperature gradient model in the superimposed calculation, reflecting the reliability of the local temperature change. The second weight refers to the contribution coefficient of the temperature value of the three-dimensional temperature field topology, reflecting the integrity of the spatial distribution. The fixed value constraint ensures the energy conservation of the fusion result, and the dynamic adjustment mechanism optimizes the weight distribution according to the measurement confidence.

[0153] In the embodiment of the application, first, a three-dimensional grid node coordinate system is established, the temperature value of the temperature gradient model is given a first weight, and the temperature value of the three-dimensional temperature field distribution topology is given a second weight; the temperature value of each core corresponding three-dimensional grid node in the temperature gradient model (such as 25.6℃ of the 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℃). Then the Euclidean distance of each node to the core is calculated, for example, the first weight is 0.7 and the second weight is 0.3 when the distance is 0.2mm. Finally, weighted superimposition is performed: 25.6x0.7+25.3x0.3=25.51℃, forming the preliminary fusion node temperature, and the sum of the first weight and the second weight is a fixed value and dynamically adjusted according to the distance between the core and the spatial coordinate point.

[0154] 602、In the superimposed temperature value, identify the temperature change steep feature between adjacent sensing sub-regions in the temperature gradient model, and extract the spatial smoothing feature of the temperature value in the same region in the three-dimensional temperature field distribution topology, and complementarily superimpose the steep feature and the smoothing feature according to a preset proportion to form the fused intermediate temperature field data;

[0155] The temperature change steep feature refers to the rapid temperature jump between adjacent sub-regions in the temperature gradient model, reflecting the local heat flow mutation. The spatial smoothing feature refers to the gradual change trend of the temperature value in the three-dimensional temperature field topology, embodying the heat conduction law. The preset proportion is set according to the thermal conductivity of the material, for example, the steep feature weight of metal parts is 60% and the non-metal is 40%.

[0156] In this embodiment, the steep temperature change characteristics between adjacent sensing sub-regions within the temperature gradient model are first detected and identified, for example, the temperature difference between adjacent sub-regions on fiber core C1 reaches 10℃ / mm. Simultaneously, spatial smoothing features of the temperature values ​​within the corresponding region in the three-dimensional temperature field distribution topology are extracted, such as a gradual gradient of 5℃ / mm. Then, the steep and smooth features are fused according to a preset ratio based on material properties: for metal regions, a composite gradient of 0.6×10 + 0.4×5 = 8℃ / mm is used to form the fused intermediate temperature field data.

[0157] 603. For the temperature distribution gap area caused by the fiber core spacing in the intermediate temperature field data, according to the temperature value change trend of the sensing sub-regions around the gap area, the temperature value is diffused and filled along the three-dimensional spatial path extension direction of the multi-core optical fiber so that the temperature values ​​of the adjacent fiber core coverage areas are continuously connected in space.

[0158] Temperature distribution gaps refer to areas not directly measured due to fiber core spacing; their temperature values ​​need to be inferred from surrounding data using diffusion algorithms. The three-dimensional spatial path extension direction refers to the orientation of the multi-core fiber layout, determining the main direction of temperature diffusion.

[0159] In this embodiment, the temperature distribution gap region caused by the fiber core spacing in the intermediate temperature field data is first identified, such as the 0.3mm unmeasured band between fiber cores C2 and C3. Then, the temperature change trend of the sensing sub-regions surrounding the gap region is extracted, such as C2-S5 being 20℃ and C3-S5 being 22℃. Anisotropic diffusion is performed along the three-dimensional spatial path extension direction of the multi-core optical fiber to generate a fill value of 21℃, ensuring spatial continuity of the temperature values ​​in the adjacent fiber core coverage areas. The finite element method is used to ensure heat flow continuity.

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

[0161] The region to be corrected refers to the area where the temperature values ​​of the two models differ by more than a reliability threshold, reflecting potential measurement anomalies. The replacement mechanism prioritizes preserving the overall distribution characteristics of the three-dimensional temperature field to ensure physical plausibility.

[0162] In the embodiments of the present application, the difference threshold is first set to 3℃. Based on the filled intermediate temperature field data detection, it is found that the temperature gradient model at the coordinates (15mm, 8mm) reports 35℃, and the temperature value of the three-dimensional temperature field distribution topology shows 30℃. The difference between the temperature gradient model and the temperature value of the three-dimensional temperature field distribution topology exceeds the preset threshold, and the area is marked as a to-be-corrected area and triggers replacement. The temperature value of the temperature gradient model in the 3x3 grid of the area is uniformly replaced by the temperature value of the three-dimensional temperature field distribution topology, and the final fused temperature field data is generated.

[0163] The following is a specific example:

[0164] In the turbine blade monitoring scene of an aero-engine, the system first performs temperature data fusion on the leading edge region of the blade. The temperature gradient model shows that there is a sharp temperature rise in the third core at the X-axis 15mm sensing sub-region, and the measured temperature is 1025 degrees Celsius, while the corresponding coordinate point temperature of the three-dimensional temperature field topology is 995 degrees Celsius. According to the material characteristics of the nickel-based alloy in this region, 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, and calculates the fused temperature value as 1013 degrees Celsius. Subsequently, the system identifies that there is a steep temperature change feature of 12 degrees Celsius per millimeter in this region, and performs a 7-to-3 weighted fusion with the smooth feature of 6 degrees Celsius per millimeter of the three-dimensional temperature field, finally forming an optimized temperature gradient of 10 degrees Celsius per millimeter. For the inter-core gap region, the system performs temperature diffusion filling along the chord length direction of the blade, so that the temperature transition between adjacent cores remains continuous. Finally, the system detects and corrects three abnormal data differences caused by cooling hole airflow disturbance, and generates a high-precision fused temperature field that clearly shows an 8mm diameter high-temperature area formed by the local peeling of the leading edge thermal barrier coating, providing a key basis for engine maintenance decision-making.

[0165] The scheme realizes the organic unification of local accurate measurement and global temperature distribution through an innovative dynamic fusion algorithm. The intelligent weight allocation mechanism of the system effectively balances the advantages of different data sources, preserving the subtle temperature change characteristics of key parts while ensuring the physical reasonableness of the overall temperature field. Advanced gap filling and anomaly correction techniques significantly improve the integrity and reliability of temperature monitoring on complex curved surfaces. Under extreme conditions such as high temperature and high pressure, the scheme exhibits excellent anti-interference ability and environmental adaptability, and can accurately identify local temperature anomalies that are difficult to detect by 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, making it have wide engineering application value.

[0166] In some embodiments, a spatial domain joint analysis is performed based on the fusion temperature field data and a preset threshold value, a temperature abnormal region on the surface of the measured object is located, and a temperature variation range of the temperature abnormal region is calculated by inversion calculation of the nonlinear calibration curve, including:

[0167] 701. In the fusion temperature field data, the temperature values of each spatial position are compared with the difference from the preset threshold value point by point; the continuous spatial positions with temperature values exceeding the preset threshold value are marked as candidate abnormal regions;

[0168] The candidate abnormal region refers to a continuous spatial region in the fusion temperature field data with a temperature value exceeding a preset safety threshold value. The preset threshold value is determined according to the material characteristics and working environment of the measured object, and reflects the safety operation limit of the equipment.

[0169] In the embodiments of the present application, the system first reads all the temperature values in the fusion temperature field data, and compares the temperature values of each spatial position with the difference from the preset threshold value point by point. For example, in the monitoring of a gas turbine blade, the safety threshold value of nickel-based alloy is set to 980 degrees Celsius. The system uses a three-dimensional connected domain analysis algorithm to cluster the temperature points of the continuous spatial positions with temperature values exceeding the preset threshold value, form a candidate abnormal region contour, and mark it as a candidate abnormal region. Each contour region records a set of spatial coordinate points contained therein, for example, a candidate region containing 15 continuous points is detected.

[0170] 702. For each candidate abnormal region, the number of sensing sub-regions covered by the candidate abnormal region and the corresponding wavelength drift amount are counted; and according to the distribution characteristics of the wavelength drift amount in the candidate abnormal region, a candidate abnormal region conforming to the temperature abnormal propagation rule is selected as a final temperature abnormal region;

[0171] The temperature abnormal propagation rule refers to the physical characteristics that a real abnormal region should present, including the spatial continuity and gradient change rationality of the wavelength drift amount. This step excludes false alarms by analyzing the consistency of the sensing sub-region data.

[0172] In the embodiments of the present application, for each candidate region, the number of sensing sub-regions covered by the candidate abnormal region is counted, for example, a certain candidate region contains 8 sub-regions; the wavelength drift amount of each sensing sub-region is extracted, and whether the spatial distribution conforms to the heat conduction rule is checked according to the distribution characteristics of the wavelength drift amount in the candidate abnormal region; the Mahalanobis distance test is used to exclude isolated abnormal points; and the candidate abnormal regions that pass the verification are retained, for example, 5 final abnormal regions conforming to the requirements are selected.

[0173] 703. The wavelength drift amount of each sensing sub-region in the temperature abnormal region is extracted, and the temperature deviation value of each sensing sub-region is calculated according to the corresponding relationship between the wavelength offset distance and the temperature in the nonlinear calibration curve;

[0174] Temperature deviation value refers to the specific value of the temperature of each point in the abnormal area exceeding the safety threshold, which is obtained by inversion from the wavelength shift through the nonlinear calibration curve. The value reflects the severity of the anomaly.

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

[0176] 704、Taking the maximum value of the temperature deviation value as a reference, and combining the spatial range of the temperature abnormal area, the temperature variation range of the surface of the measured object is determined.

[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 the embodiments of the present application, the system finds the maximum temperature deviation value in the temperature abnormal area, for example, 1.8℃; calculates the equivalent diameter of the temperature abnormal area, for example, 4.2mm; takes the maximum value of the temperature deviation value as a reference, combines the equivalent diameter and the material thermal diffusion coefficient to evaluate the depth of influence; and outputs a complete temperature variation range description, such as a 4.2mm diameter area with a maximum overrun of 1.8℃.

[0179] The following is a specific example:

[0180] In the monitoring of an aero-engine turbine blade, the system detects a temperature anomaly in the leading edge area: fusion temperature field data shows that there are 15 continuous spatial points in the range of X-axis coordinates 120-125mm and Y-axis 50-55mm whose temperature exceeds the safety threshold of 1000℃, with a maximum of 1025℃. The system verifies the candidate abnormal area and confirms that it covers 8 sensing sub-regions, with the wavelength shift Δλ of each sub-region ranging from 0.42-0.48nm, showing a reasonable distribution decreasing from the center to the edge. By matching the calibration curve ΔT = 0.5Δλ 2 + 2Δλ of the nickel-based alloy in this area, the center point temperature deviation is calculated to be 2.1℃, and the edge point is 1.6℃. Combined with the analysis of the geometric characteristics of the area, it is finally determined that the abnormal area is a circular area with a diameter of 6mm, with a maximum overrun temperature of 2.1℃, and the system immediately triggers a secondary alarm and marks the position of the blade leading edge that needs to be checked first.

[0181] The scheme significantly improves the accuracy of temperature anomaly recognition through multi-dimensional data verification and physical law testing. The innovative spatial domain joint analysis method can effectively distinguish between real anomalies and measurement noise, ensuring the reliability of the alarm information. The temperature deviation calculation based on nonlinear calibration provides accurate quantitative basis for anomaly severity assessment. The temperature change range description output by the system contains key parameter indicators and maintains engineering practicability, providing strong support for equipment maintenance decisions. This technology is particularly suitable for high-end equipment monitoring scenarios with strict false alarm rate requirements.

[0182] Figure 2 A structure diagram of a multi-core optical fiber temperature detection system integrating wavelength division multiplexing is provided for the embodiments of the present application, as shown in Figure 2 The system comprises:

[0183] A first generation module 21 is configured to 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 a single wavelength of optical signal, and generate a first mapping relationship between wavelength and core.

[0184] An integration module 22 is configured to integrate the multi-core optical fiber loaded with optical signals into the surface of a measured object along a preset three-dimensional space path, and each core covers multiple sensing sub-areas.

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

[0186] An operation module 24 is configured to perform wavelength space solving on the optical signals according to the physical spacing between the cores in the multi-core optical fiber, the first mapping relationship, and the transmission time delay of the optical signals in different cores, to generate a second mapping relationship between the spatial coordinates and temperature values in the sensing sub-area, and reconstruct the three-dimensional temperature field distribution topology of the surface of the measured object according to the second mapping relationship.

[0187] A second generation module 25 is configured to determine the temperature change range of the surface of the measured object 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 perform Figure 1The implementation principle and technical effects of the fusion wavelength division multiplexing multi-core optical fiber temperature detection method in the embodiment are not described again. The specific manner in which each module and unit in the fusion wavelength division multiplexing multi-core optical fiber temperature detection system in the above embodiment performs operations has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0189] In one possible design, Figure 2 The fusion wavelength division multiplexing multi-core optical fiber temperature detection system in the embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device can 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 configured to perform the above Figure 1 The embodiment of the fusion wavelength division multiplexing multi-core optical fiber temperature detection method.

[0192] The processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be 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 elements, for executing the above method.

[0193] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage 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, the computing device can also include other components, such as an input / output interface, a display component, a communication component, 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 wired or wireless communication between the computing device and other devices, etc.

[0197] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component and the storage component can be basic server resources rented or purchased from the cloud computing platform.

[0198] The application also provides a computer storage medium storing a computer program, and the computer program can implement the above-mentioned method when executed by a computer. Figure 1 The application provides a multi-core optical fiber temperature detection method based on fused wavelength division multiplexing.

[0199] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-mentioned system, device and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described here.

[0200] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0201] Through the foregoing description of the embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and a necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0202] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A multi-core fiber temperature detection method of fused wavelength division multiplexing, characterized by, The method comprises the following steps: dividing a broadband light source into multiple wavelength channels based on wavelength division multiplexing technology, and guiding the wavelength channels to different core input ports of a multicore optical fiber, so that each core can transmit a single wavelength of optical signal, and a first mapping relationship between wavelength and core is generated; integrating the multicore optical fiber loaded with optical signals into the surface of the measured object according to a preset three-dimensional space path, and each core covers multiple sensing sub-regions; after deployment, receiving the optical signals output by each core, extracting the wavelength drift caused by temperature change based on the attenuation characteristics of the optical signals of each wavelength channel at the core bending, and converting the wavelength drift into the temperature value of each sensing sub-region in combination with the pre-stored nonlinear calibration curve, so as to establish a temperature gradient model bound to the spatial position of the core; based on the physical spacing between the cores in the multicore optical fiber, the first mapping relationship and the transmission delay of the optical signals in different cores, performing wavelength space calculation on the optical signals to generate a second mapping relationship between the spatial coordinates and the temperature values in the sensing sub-regions, and reconstructing the three-dimensional temperature field distribution topology of the surface of the measured object according to the second mapping relationship; determining the temperature change range of the surface of the measured object according to the temperature gradient model and the three-dimensional temperature field distribution topology; wherein the extraction of the wavelength drift caused by temperature change based on the attenuation characteristics of the optical signals of each wavelength channel at the core bending comprises: for the optical signal of each wavelength channel, obtaining the initial light intensity value of the optical signal at each bending of the multicore optical fiber, and recording the real-time light intensity value of the optical signal after passing through the bending; according to the difference between the initial light intensity value and the real-time light intensity value, the light intensity attenuation ratio of the optical signal at the bending is calculated; by comparing the light intensity attenuation ratio change trend of the optical signals of the same wavelength channel at different bendings, the abnormal fluctuation range of the attenuation ratio caused by temperature change is identified; in the abnormal fluctuation range of the attenuation ratio, the characteristic inflection point of the light intensity attenuation ratio with temperature change is located, and the wavelength offset distance corresponding to the characteristic inflection point is measured; matching the wavelength offset distance with the pre-stored wavelength-temperature correlation table to obtain the wavelength drift caused by temperature change.

2. The method of claim 1, wherein, determining the temperature change range of the surface of the measured object according to the temperature gradient model and the three-dimensional temperature field distribution topology comprises: dynamically associating and fusing the temperature gradient model and the three-dimensional temperature field distribution topology, generating fusion temperature field data by complementing the local temperature change characteristics of the temperature gradient model and the spatial heat field distribution characteristics of the three-dimensional temperature field distribution topology; based on the fusion temperature field data and the preset threshold, the temperature abnormal area of the measured object surface is located through spatial domain joint analysis, and the temperature change range of the temperature abnormal area is calculated through the nonlinear calibration curve inversion.

3. The method of claim 1, wherein, based on the wavelength drift and the pre-stored nonlinear calibration curve, the wavelength drift is converted into the temperature value of each sensing sub-region to establish a temperature gradient model bound to the spatial position of the core. Accessing a nonlinear calibration curve calibrated in advance by experiments, the nonlinear calibration curve stores a correspondence between a wavelength shift amount and a temperature change of each fiber core, and the correspondence is defined in segments according to material characteristics of the fiber core and a bending segment curvature radius; For each bending segment corresponding to a sensing sub-region, a curve segment matched with the bending segment is searched from the nonlinear calibration curve according to an identifier of a fiber core where the bending segment is located and the bending segment curvature radius; A wavelength shift amount of the bending segment is input into the matched curve segment, and a temperature change amount of the sensing sub-region where the bending segment is located is calculated by interpolation, and a temperature value of the sensing sub-region is determined in combination with an initial temperature value; According to an arrangement order of each bending segment on each fiber core in a transmission path, temperature values of multiple sensing sub-regions covered by the same fiber core are arranged in sequence in an optical signal transmission direction to form a temperature distribution sequence bound with an axial position of the fiber core; Based on physical spacings of the fiber cores in the multicore optical fiber and three-dimensional space path coordinates, temperature values in the temperature distribution sequence of each fiber core are distributed to corresponding three-dimensional space coordinate points to combine current temperature values of all fiber cores to form a temperature gradient model bound with the space coordinates.

4. The method of claim 1, wherein, According to the physical spacings between the fiber cores in the multicore optical fiber, the first mapping relationship, and transmission time delays of the optical signals in different fiber cores, wavelength space solving is performed on the optical signals to generate a second mapping relationship between space coordinates and temperature values in the sensing sub-regions, and a three-dimensional temperature field distribution topology of a surface of a to-be-measured object is reconstructed according to the second mapping relationship, including: An input port of the multicore optical fiber is a reference point, a fiber core space distribution model is established according to the physical spacings of the fiber cores, and based on the first mapping relationship, an initial position of each wavelength channel corresponding to the fiber core in the fiber core space distribution model is determined; Actual propagation distances of the optical signals in each fiber core are calculated by measuring transmission time delays of the optical signals from the input port to each sensing sub-region in combination with a fiber core refractive index parameter; According to the actual propagation distances and the fiber core space distribution model, specific position coordinates of each sensing sub-region on a three-dimensional space path are determined, and temperature values detected by each sensing sub-region are bound with position coordinates corresponding to the sensing sub-region to form a second mapping relationship containing the space coordinates and the temperature values; Based on the position coordinates and the temperature values of all sensing sub-regions in the second mapping relationship, a continuous three-dimensional temperature field distribution topology of a surface of a to-be-measured object is constructed according to a three-dimensional space path arrangement rule of the multicore optical fiber.

5. The method of claim 2, wherein, The temperature gradient model and the three-dimensional temperature field distribution topology are dynamically associated and fused to generate fused temperature field data by complementing local temperature change characteristics of the temperature gradient model and space heat field distribution characteristics of the three-dimensional temperature field distribution topology, including: Superimpose the temperature value of each core corresponding to the three-dimensional grid node in the temperature gradient model with the temperature value of the same spatial coordinate point in the three-dimensional temperature field distribution topology, wherein the temperature value of the temperature gradient model is given a first weight and the temperature value of the three-dimensional temperature field distribution topology is given a second weight when superimposed, 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 core and the spatial coordinate point; In the superimposed temperature value, the steep temperature change characteristics between adjacent sensing sub-regions in the temperature gradient model are identified, and the spatial smoothing characteristics of the temperature values in the same region in the three-dimensional temperature field distribution topology are extracted, and the steep characteristics and the smoothing characteristics are complementarily superimposed according to a preset proportion to form fused intermediate temperature field data; For the temperature distribution gap region caused by the core spacing in the intermediate temperature field data, the temperature value is diffused and filled along the extension direction of the three-dimensional space path of the multicore optical fiber according to the temperature value change trend of the sensing sub-regions around the gap region, so that the temperature values of the adjacent core coverage regions are continuously connected in space; Based on the filled intermediate temperature field data, the region whose 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 region, and the temperature value of the three-dimensional temperature field distribution topology is used to replace the temperature value of the temperature gradient model in the to-be-corrected region to generate fused temperature field data.

6. The method of claim 2, wherein, Based on the spatial domain joint analysis of the fused temperature field data and a preset threshold, the temperature abnormal region on the surface of the measured object is located, and the temperature change range of the temperature abnormal region is calculated by inverse calculation of 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 position whose temperature value exceeds the preset threshold is marked as a candidate abnormal region; For each candidate abnormal region, the number of sensing sub-regions covered by the candidate abnormal region and the corresponding wavelength drift amount are counted; according to the distribution characteristics of the wavelength drift amount in the candidate abnormal region, the candidate abnormal region that meets the temperature abnormal propagation rule is selected as the final temperature abnormal region; The wavelength drift amount of each sensing sub-region in the temperature abnormal region is extracted, and the temperature deviation value of each sensing sub-region is calculated according to the corresponding relationship between the wavelength offset distance and the temperature in the nonlinear calibration curve; Taking the maximum value of the temperature deviation value as a reference, the temperature change range of the surface of the measured object is determined in combination with the spatial range of the temperature abnormal region.

7. A fused WDM multi-core fiber temperature sensing system applied to the fused WDM multi-core fiber temperature sensing method of any one of claims 1-6, characterized in that, The first generation module is configured to divide a wideband 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 multicore optical fiber, so that each core can transmit a single wavelength of light signal, and generate a first mapping relationship between wavelength and core. The integration module is configured to integrate the multicore optical fiber loaded with the light signal into the surface of the measured object along a preset three-dimensional space path, and each core covers a plurality of sensing sub-regions. ​ The processing module is configured to receive the optical signals output by the cores after deployment is completed, extract a wavelength drift amount caused by temperature variation based on an attenuation characteristic of the optical signals of each wavelength channel at the core bend, and convert the wavelength drift amount into a temperature value of each sensing sub-region in combination with a pre-stored nonlinear calibration curve, so as to establish a temperature gradient model bound to the spatial position of the cores; The operation module is configured to perform wavelength space calculation on the optical signals according to a physical spacing between the cores in the multicore optical fiber, the first mapping relationship and a transmission time delay of the optical signals in different cores, to generate a second mapping relationship between a spatial coordinate and a temperature value in the sensing sub-region, and reconstruct a three-dimensional temperature field distribution topology of a surface of the object to be measured according to the second mapping relationship. The second generation module is configured to determine a temperature variation range of the surface of the object to be measured according to the temperature gradient model and the three-dimensional temperature field distribution topology.

8. A computing device, comprising: 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 the multicore optical fiber temperature detection method of the fusion wavelength division multiplexing according to any one of claims 1-6.

9. A computer storage medium, characterized in that The computer program is stored in the computer and is executed by the computer to implement the multicore optical fiber temperature detection method of the fusion wavelength division multiplexing according to any one of claims 1-6.

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