Temperature Measurement and Noise Reduction Method, System, Device, and Medium for Distributed Optical Fiber

Abstract

To provide a method, a system, a device, and a medium for measuring temperature and for reducing noise of a distributed optical fiber, which relate to the technical field of noise reduction of a distributed sensing system.SOLUTION: A method includes the steps of: obtaining an anti-Stokes signal and a Stokes signal of a photovoltaic assembly collected by temperature measurement of a distributed optical fiber and a noise reduction system, calculating a temperature ratio signal; dividing the temperature ratio signal into a plurality of temperature ratio signal sub-blocks; performing initial iterative noise reduction processing on original sub-matrices corresponding to different temperature ratio signal sub-blocks for different iteration times by using a singular value decomposition method combining fusion functions, to obtain noise-reduced temperature data sub-blocks corresponding to each temperature ratio signal sub-block; determining an iteration time with a good noise reduction processing effect and a reference sub-block, and performing iterative noise reduction processing on a non-reference sub-block based on the iteration time with the good noise reduction processing effect, splicing the non-reference sub-block into noise-reduced temperature fusion data, and calculating a final temperature demodulation result.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] This application relates to the field of noise reduction technologies for distributed sensing systems, and particularly to temperature measurement and noise reduction methods, systems, devices, and media for distributed optical fibers.

Background Art

[0002] With the depletion of non-renewable energy in current society and the rapid development of the new energy industry with the goal of "carbon peak and carbon neutrality in 2060", power generation by photovoltaic power has been widely applied and popularized due to its advantages such as being renewable, pollution-free, and sustainable. However, it also brings many safety risks. In recent years, there have been 45 typical accidents of fires in the field of power generation by photovoltaic power investigated, and the losses amount to hundreds of millions. Therefore, a safe and highly reliable temperature monitoring system is urgently needed. The emergence of a distributed sensing system that realizes temperature sensing by using an optical fiber as a sensing and transmission element well meets this demand. Since the optical fiber is made of glass or plastic, it itself does not become charged, has better safety than conventional sensors, and at the same time is excellent in oxidation resistance, magnetic resistance, corrosion resistance, low in cost, easy to lay, does not require regular maintenance, and has a long service life. Therefore, optical fiber sensing technology is widely applied in fields such as power systems, tunnel mines, and fires.

[0003] At present, in order to meet the demand for long-range and high-precision temperature measurement, it is planned to realize the temperature monitoring of a photovoltaic panel in the field of power generation by photovoltaic effect using a distributed sensing system. There are mainly three types of scattering in the distributed sensing system, namely Rayleigh scattering, Brillouin scattering, and Raman scattering. However, when Rayleigh scattering and Brillouin scattering are applied to the temperature measurement site, there are problems of high manufacturing cost and difficult signal processing. Therefore, at present, the distributed sensing system based on Raman scattering is widely applied. However, the signal of Raman scattering is weak and is easily affected by system noise, so the signal-to-noise ratio of the signal becomes low, affecting the temperature measurement accuracy. Therefore, it is necessary to continuously improve the signal-to-noise ratio of the system. Currently, noise reduction is mainly carried out from two different directions: hardware noise removal and data processing.

[0004] However, although the effect of noise reduction from the hardware perspective is obvious, the equipment cost is high and there are certain limitations in actual applications. On the other hand, the conventional noise reduction method from the data processing perspective has operational complexity. At the same time, it depends on artificial experience and has uncertainty. Eventually, the error of the demodulated temperature becomes large and the effect of noise reduction is poor.

Summary of the Invention

Problems to be Solved by the Invention

[0005] This application provides a method, system, device, and medium for temperature measurement and noise reduction of a distributed optical fiber to solve the technical problem in the prior art that the error of the demodulated temperature is large and the effect of noise reduction is poor.

Means for Solving the Problems

[0006] According to a first aspect of the present application, a method for temperature measurement and noise reduction of a distributed optical fiber is provided. The method includes: obtaining the Raman anti-Stokes signal and Raman Stokes signal of a photovoltaic assembly collected by a temperature measurement and noise reduction system of a distributed optical fiber, and calculating a temperature ratio signal; Dividing the temperature ratio signal into a plurality of temperature ratio signal sub-blocks; For each of the temperature ratio signal sub-blocks, performing a first iterative noise reduction process on the original sub-matrix corresponding to the temperature ratio signal sub-block using a singular value decomposition method for combining fusion functions to obtain a corresponding noise reduction temperature data sub-block, where in the first iterative noise reduction process, different temperature ratio signal sub-blocks correspond to different numbers of iterations; Calculating a temperature demodulation result sub-block corresponding to each of the temperature ratio signal sub-blocks; Calculating an evaluation index value for each of the temperature demodulation result sub-blocks, and determining, as a reference sub-block, the temperature ratio signal sub-block corresponding to the temperature demodulation result sub-block with the maximum evaluation index value; Based on the number of iterations corresponding to the reference sub-block, performing two iterative noise reduction processes on the original sub-matrix corresponding to the non-reference sub-blocks using a singular value decomposition method for combining fusion functions to obtain a noise reduction temperature data sub-block corresponding to each of the non-reference sub-blocks; Splicing the noise reduction temperature data sub-blocks corresponding to each of the non-reference sub-blocks and the noise reduction temperature data sub-block corresponding to the reference sub-block to obtain noise reduction temperature fusion data, and calculating a temperature demodulation result corresponding to the noise reduction temperature fusion data.

[0007] Optionally, performing a first iterative noise reduction process on the original sub-matrix corresponding to the temperature ratio signal sub-block using a singular value decomposition method for combining fusion functions to obtain a corresponding noise reduction temperature data sub-block includes: Reforming the temperature ratio signal sub-block to obtain a corresponding original sub-matrix; Performing noise reduction processing on the original sub-matrix to obtain a first-generation noise reduction temperature data sub-block; Based on the fusion function, perform the first fusion of the first-generation noise-reduced temperature data sub-block and the original sub-matrix to obtain a first-fusion sub-matrix, perform noise reduction processing on the first-fusion sub-matrix, and obtain a second-generation noise-reduced temperature data sub-block, Based on the fusion function, fuse the second-generation noise-reduced temperature data sub-block with the original sub-matrix or the first-generation noise-reduced temperature data sub-block to obtain multiple fusion sub-matrices, perform noise reduction processing on the multiple fusion sub-matrices, and obtain a third-generation noise-reduced temperature data sub-block, Determine the first-generation noise-reduced temperature data sub-block, the second-generation noise-reduced temperature data sub-block, or the third-generation noise-reduced temperature data sub-block as the corresponding noise-reduced temperature data sub-block, including.

[0008] Optionally, performing noise reduction processing on the original sub-matrix to obtain the first-generation noise-reduced temperature data sub-block is Performing SVD decomposition processing on the original sub-matrix to obtain a left singular sub-matrix, an initial singular value diagonal matrix, and a right singular sub-matrix, Retaining the target number of singular values of the singular value diagonal matrix and setting the other singular values to 0 to form a target singular value diagonal sub-matrix, Constructing a first-generation noise-reduced temperature data sub-block based on the left singular sub-matrix, the right singular sub-matrix, and the target singular value diagonal sub-matrix, including.

[0009] Optionally, calculating the temperature demodulation result sub-block corresponding to each of the temperature ratio signal sub-blocks is For each of the temperature ratio signal sub-blocks, calculating the corresponding temperature demodulation result sub-block using a preset dual-mode demodulation formula, including.

[0010] Optionally, calculating the evaluation index value of each of the temperature demodulation result sub-blocks is Calculating a first index value of each of the temperature demodulation result sub-blocks based on each of the temperature demodulation result sub-blocks and a preset root mean square error formula; Calculating a second index value of each of the temperature demodulation result sub-blocks based on each of the temperature demodulation result sub-blocks and a preset deviation formula; Calculating a third index value of each of the temperature demodulation result sub-blocks based on each of the temperature demodulation result sub-blocks and a preset smoothness calculation formula; Using the first index value, the second index value, the third index value or a fusion index value of each of the temperature demodulation result sub-blocks as an evaluation index value of each of the temperature demodulation result sub-blocks; The fusion index value is calculated based on at least two of the first index value, the second index value and the third index value.

[0011] Optionally, the preset dual-mode demodulation formula adopts the following formula

Number

Number

Number

[0012] According to a second aspect of the present application, a temperature measurement and noise reduction system for a distributed optical fiber is provided, and the system includes a sensing optical fiber connected in series, a calibration module, a wavelength division multiplexer, an avalanche photodiode, a data acquisition card and a master computer, and a pulse laser connected to both the wavelength division multiplexer and the data acquisition card, and the master computer executes the temperature measurement and noise reduction method for the distributed optical fiber according to any one of the first aspects.

[0013] Optionally, the sensing optical fiber is wound in a circulation along the longitudinal direction of the photovoltaic assembly and is fixedly laid on the back surface of the photovoltaic assembly.

[0014] According to a third aspect of the present application, an electronic device is provided, and the electronic device includes at least one processor and a memory, the memory stores computer-executable instructions, and the at least one processor executes the computer-executable instructions stored in the memory to cause the at least one processor to execute the temperature measurement and noise reduction method for the distributed optical fiber according to the first aspect.

[0015] According to a fourth aspect of the present application, a computer-readable storage medium is provided, and the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to realize the temperature measurement and noise reduction method for the distributed optical fiber according to the first aspect.

[0016] According to a fifth aspect of the present application, a computer program product is provided, and the computer program product includes a computer program, and when the computer program is executed by a processor, the temperature measurement and noise reduction method for the distributed optical fiber according to the first aspect is realized.

Advantages of the Invention

[0017] The temperature measurement and noise reduction method of the distributed optical fiber provided by the present application is to obtain the Raman anti-Stokes signal and Raman Stokes signal of the photovoltaic assembly collected by the temperature measurement and noise reduction system of the distributed optical fiber, calculate the temperature ratio signal, divide the temperature ratio signal into a plurality of temperature ratio signal sub-blocks, and for each temperature ratio signal sub-block, perform the first iterative noise reduction process on the original sub-matrix corresponding to the temperature ratio signal sub-block using the singular value decomposition method of combining the fusion function to obtain the corresponding noise reduction temperature data sub-block, where different temperature ratio signal sub-blocks correspond to different numbers of iterations in the first iterative noise reduction process, calculate the temperature demodulation result sub-block corresponding to each temperature ratio signal sub-block, calculate the evaluation index value of each temperature demodulation result sub-block, and determine the temperature ratio signal sub-block corresponding to the temperature demodulation result sub-block with the maximum evaluation index value as the reference sub-block, and based on the number of iterations corresponding to the reference sub-block, perform two iterative noise reduction processes on the original sub-matrix corresponding to the non-reference sub-block using the singular value decomposition method of combining the fusion function to obtain the noise reduction temperature data sub-block corresponding to each non-reference sub-block, splice the noise reduction temperature data sub-block corresponding to each non-reference sub-block and the noise reduction temperature data sub-block corresponding to the reference sub-block to obtain the noise reduction temperature fusion data, and calculate the temperature demodulation result corresponding to the noise reduction temperature fusion data.

[0018] This application divides the temperature ratio signal into a plurality of temperature ratio signal sub-blocks, calculates the evaluation index value of the temperature demodulation result sub-block corresponding to each temperature ratio signal sub-block, selects the temperature ratio signal sub-block with the optimal evaluation index value, and selects the optimal number of iterations from among a plurality of different numbers of iterations set for the singular value decomposition method of combining the fusion functions. In this way, the accuracy of noise reduction by the singular value decomposition method of combining the fusion functions is guaranteed, the demodulation error of the temperature demodulation result corresponding to the noise-reduced temperature fusion data is reduced, and the noise reduction effect can be enhanced.

[0019] It should be noted that the content described in this part is not intended to identify the main or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will be easily understood from the following description.

Brief Description of the Drawings

[0020] Here, the drawings are incorporated into the description and form a part of this description. They show embodiments that conform to this application and are used to explain the principles of this application together with the description.

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[0021] Through the above drawings, clear embodiments of the present application are shown, and more detailed descriptions will be given later. These drawings and written descriptions are not intended to limit the scope of the concept of the present application by any means, but are intended to explain the concept of the present application to those skilled in the art by referring to specific embodiments.

Embodiments for Carrying Out the Invention

[0022] Here, exemplary embodiments shown in the drawings will be described in detail. When the following description relates to the drawings, unless otherwise expressed, the same numerals in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application.

[0023] There are mainly three types of scattering in the distributed sensing system, namely Rayleigh scattering, Brillouin scattering, and Raman scattering. However, when Rayleigh scattering and Brillouin scattering are applied to the temperature measurement site, there are problems of high manufacturing cost and difficult signal processing. Therefore, currently, the distributed sensing system based on Raman scattering is widely applied. However, the signal of Raman scattering is weak and is easily affected by system noise, so the signal-to-noise ratio of the signal becomes low, which affects the temperature measurement accuracy. Therefore, it is necessary to continuously improve the signal-to-noise ratio of the system. Currently, noise reduction is mainly carried out from two different directions: hardware noise removal and data processing.

[0024] However, although the effect of noise removal from the perspective of hardware is obvious, the equipment cost is high, and there are certain limitations in actual applications. On the other hand, the conventional noise removal method from the perspective of data processing has operational complexity. At the same time, it depends on artificial experience and has uncertainty. Eventually, the demodulation temperature error becomes large, and the effect of noise reduction is poor.

[0025] In order to solve the above technical problems, the overall inventive concept of this application is how to provide a method for applying in the field of noise reduction of a distributed sensing system to reduce the demodulation temperature error and enhance the effect of noise reduction.

[0026] Hereinafter, specific embodiments will be used to describe in detail the technical solution of this application and how the technical solution of this application solves the above technical problems. Several of the following specific embodiments can be combined with each other. For the same or similar concepts or processes, they may not be described again in some embodiments. Hereinafter, the embodiments of this application will be described with reference to the drawings.

[0027] (Embodiment 1) FIG. 1 is a flowchart of a method for temperature measurement and noise reduction of a distributed optical fiber provided by an embodiment of this application. As shown in FIG. 1, the method of this embodiment includes the following.

[0028] In S10, the Raman anti-Stokes signal and Raman Stokes signal of the optoelectronic power assembly collected by the temperature measurement and noise reduction system of the distributed optical fiber are obtained, and the temperature ratio signal is calculated.

[0029] The temperature measurement and noise reduction system of the optical fiber in the embodiment of this application is a distributed sensing system, or is also called a distributed optical fiber temperature measurement system, an optoelectronic power board temperature measurement system, etc. For a specific description of the system structure of this embodiment, refer to Embodiment 3 below.

[0030] In S20, the temperature ratio signal is divided into a plurality of temperature ratio signal sub-blocks.

[0031] It should be understood that the splitting operation in this embodiment provides technical support for the simultaneous application of the singular value decomposition method that adopts different numbers of iterations later, quickly finds the optimal number of iterations in a short time, and can improve the accuracy and demodulation efficiency of the temperature demodulation result.

[0032] In S30, for each temperature ratio signal sub-block, a first-pass iterative noise reduction process is performed on the original sub-matrix corresponding to the temperature ratio signal sub-block using the singular value decomposition method that combines the fusion function, and the corresponding noise reduction temperature data sub-block is obtained. In the first-pass iterative noise reduction process, different temperature ratio signal sub-blocks correspond to different numbers of iterations.

[0033] For example, there are three temperature ratio signal sub-blocks. The number of iterations of the first temperature ratio signal sub-block is 1, the number of iterations of the second temperature ratio signal sub-block is 2, and the number of iterations of the third temperature ratio signal sub-block is 3.

[0034] In S40, the temperature demodulation result sub-block corresponding to each temperature ratio signal sub-block is calculated.

[0035] It should be understood that T i is the temperature demodulation result sub-block corresponding to the i-th temperature ratio signal sub-block.

[0036] In S50, the evaluation index value of each temperature demodulation result sub-block is calculated, and the temperature ratio signal sub-block corresponding to the temperature demodulation result sub-block with the maximum evaluation index value is determined as the reference sub-block.

[0037] In the embodiment of the present application, the evaluation index value of the temperature demodulation result sub-block T i is L iThat is. Exemplarily, there are three temperature ratio signal sub - blocks. The number of iterations of the first temperature ratio signal sub - block is 1, the number of iterations of the second temperature ratio signal sub - block is 2, and the number of iterations of the third temperature ratio signal sub - block is 3. In the above scenario, there are three temperature demodulation result sub - blocks. When the evaluation index value L1 of the temperature demodulation result sub - block T1 is greater than the evaluation index value L2 of the temperature demodulation result sub - block T2, and the evaluation index value L2 of the temperature demodulation result sub - block T2 is greater than the evaluation index value L3 of the temperature demodulation result sub - block T3, the first temperature ratio signal sub - block is the reference sub - block, and the second and third temperature ratio signal sub - blocks are both non - reference sub - blocks. And the number of iterations corresponding to the reference sub - block is 1.

[0038] In S60, based on the number of iterations corresponding to the reference sub - block, two - time iterative noise reduction processing is performed on the original sub - matrix corresponding to the non - reference sub - block using the singular value decomposition method for combining the fusion function, and the noise - reduced temperature data sub - block corresponding to each non - reference sub - block is obtained.

[0039] Based on the above example, when two - time iterative noise reduction processing is performed on the second and third temperature ratio signal sub - blocks, two - time iterative noise reduction processing is performed according to the number of iterations 1 corresponding to the reference sub - block, and the noise - reduced temperature data sub - block corresponding to the second temperature ratio signal sub - block and the noise - reduced temperature data sub - block corresponding to the third temperature ratio signal sub - block are newly obtained.

[0040] In S70, the noise - reduced temperature data sub - block corresponding to each non - reference sub - block and the noise - reduced temperature data sub - block corresponding to the reference sub - block are spliced to obtain the noise - reduced temperature fusion data, and the temperature demodulation result corresponding to the noise - reduced temperature fusion data is calculated.

[0041] In this embodiment, in step S70, a temperature demodulation result corresponding to the noise-reduced temperature fusion data can be calculated by a preset dual-mode demodulation method. This calculation method is consistent with the calculation method and principle in Embodiment 2, and will not be further described in this embodiment.

[0042] The embodiments of the present application divide the temperature ratio signal into a plurality of temperature ratio signal sub-blocks, and calculate the evaluation index value of the temperature demodulation result sub-block corresponding to each temperature ratio signal sub-block, so as to select the temperature ratio signal sub-block with the optimal evaluation index value, and select the optimal number of iterations from a plurality of different numbers of iterations set in the singular value decomposition method for combining the fusion function, thereby ensuring the noise reduction accuracy of the singular value decomposition method for combining the fusion function, reducing the demodulation error of the temperature demodulation result corresponding to the noise-reduced temperature fusion data, and enhancing the noise reduction effect.

[0043] In one possible implementation, in step S30, performing the first iteration noise reduction process on the original sub-matrix corresponding to the temperature ratio signal sub-block using the singular value decomposition method for combining the fusion function to obtain the corresponding noise-reduced temperature data sub-block includes the following.

[0044] In S31, the temperature ratio signal sub-block is reformed to obtain the corresponding original sub-matrix.

[0045] In S32, noise reduction processing is performed on the original sub-matrix to obtain the first-generation noise-reduced temperature data sub-block.

[0046] In S33, based on the fusion function, the first-generation noise-reduced temperature data sub-block and the original sub-matrix are first fused to obtain the first-fused sub-matrix. Noise reduction processing is performed on the first-fused sub-matrix to obtain the second-generation noise-reduced temperature data sub-block.

[0047] It should be understood that the fusion function may be a weighted addition function or other types of functions, and the embodiments of the present application do not specifically limit this fusion function.

[0048] In S34, based on the fusion function, the second-generation noise-reduced temperature data sub-block is fused with the original sub-matrix or the first-generation noise-reduced temperature data sub-block to obtain a plurality of fused sub-matrices. Noise reduction processing is performed on the plurality of fused sub-matrices to obtain the third-generation noise-reduced temperature data sub-block.

[0049] In S35, the first-generation noise-reduced temperature data sub-block, the second-generation noise-reduced temperature data sub-block, or the third-generation noise-reduced temperature data sub-block is determined as the corresponding noise-reduced temperature data sub-block.

[0050] In the above flow, in this embodiment, for the i-th temperature ratio signal sub-block, the following markings are made for various parameters.

[0051] The first-generation noise-reduced temperature data sub-block is L 1i , the second-generation noise-reduced temperature data sub-block is L 2i , the first fused sub-matrix is

Number

Number

Number

Number

[0052] Exemplarily, there are three temperature ratio signal sub - blocks. The number of iterations of the first temperature ratio signal sub - block is 1, the number of iterations of the second temperature ratio signal sub - block is 2, and the number of iterations of the third temperature ratio signal sub - block is 3. In the above scenario, for the first temperature ratio signal sub - block, the operating principle and effect of performing steps S31 to S32 will not be further described here. Refer to the following Example 2. In this example, we will focus on steps S33 to S34 and analyze as follows.

[0053] In the case of the same temperature ratio signal sub - block, the original sub - matrix is A i , where i = 1, 2, or 3.

[0054] In the case of the temperature ratio signal sub - block with i = 1, the original sub - matrix is A1, and the first - generation noise - reduced temperature data sub - block is L 11 .

[0055] In the case of the temperature ratio signal sub - block with i = 2, the original sub - matrix is A2, and the first - generation noise - reduced temperature data sub - block is L 22 , and the first - time fusion sub - matrix is

Number

[0056] In the case of the temperature ratio signal sub - block with i = 3, the original sub - matrix is A3, and the first - generation noise - reduced temperature data sub - block is L 13 , and the first - generation fusion sub - matrix is

Number

Number

[0057] In the process of performing noise reduction processing using the singular value decomposition method in the embodiments of the present application, various settings such as fusion technology and the number of iterations are introduced. This method can quickly select a method corresponding to the optimal number of iterations from multiple technical solutions, further reduce the demodulation error of the demodulation result corresponding to the noise reduction temperature fusion data, and enhance the effect of noise reduction.

[0058] In one possible implementation form, in S32, performing noise reduction processing on the original submatrix to obtain the first-generation noise reduction temperature data sub-block includes the following.

[0059] In S321, perform SVD decomposition processing on the original submatrix to obtain a left singular submatrix, an initial singular value diagonal matrix, and a right singular submatrix.

[0060] In S322, retain the target number of singular values in the singular value diagonal matrix, set the other singular values to 0, and form a target singular value diagonal submatrix. The target singular value diagonal submatrix is also called a new singular value diagonal submatrix.

[0061] In S323, based on the left singular submatrix, the right singular submatrix, and the target singular value diagonal submatrix, construct the first-generation noise reduction temperature data sub-block.

[0062] The noise reduction processing flow in the embodiments of the present application can be divided into three steps: SVD decomposition processing, singular value diagonal submatrix reconstruction, and construction of the first-generation noise reduction temperature data sub-block. The specific description of this noise reduction processing flow is similar to the principle of the noise reduction processing flow in Embodiment 2, and this embodiment will not elaborate further.

[0063] Similarly, in step S33, for the initial fusion submatrix, the execution flow of performing noise reduction processing to obtain the second-generation noise reduction temperature data sub-block is similar to the flow of steps S321 to S323 described above, and will not be further described here.

[0064] Similarly, in step S34, for the multiple fusion submatrices, the execution flow of performing noise reduction processing to obtain the third-generation noise reduction temperature data sub-block is similar to the flow of steps S321 to S323 described above, and will not be further described here.

[0065] In one possible implementation form, in S40, calculating the temperature demodulation result sub-block corresponding to each temperature ratio signal sub-block includes the following.

[0066] For each temperature ratio signal sub-block, use a preset dual-mode demodulation formula to calculate the corresponding temperature demodulation result sub-block.

[0067] The embodiments of the present application can calculate the temperature demodulation result sub-blocks corresponding to each temperature ratio signal sub-block respectively, and provide data support for comparing the corresponding noise removal effects at different iteration times of different algorithms adopted for different temperature demodulation result sub-blocks.

[0068] In one possible implementation form, the preset dual-mode demodulation formula adopts the following formula.

Equation

[0069] Here, T i is the temperature demodulation result sub-block corresponding to the i-th temperature ratio signal sub-block for representing the actual temperature to be measured after demodulation, T0 is a known temperature, h is the Planck constant, h = 6.626×10 -34 J·s, k is the Boltzmann constant, k = 1.38×10 -23 J / K, [Number] is a noise-reduced temperature data sub-block corresponding to the i-th temperature ratio signal sub-block for representing the ratio between the anti-Stokes signal and the Stokes signal at the actual temperature T to be measured. [Number] is used to represent the ratio between the anti-Stokes signal and the Stokes signal at a known temperature.

[0070] In the embodiments of the present application, by introducing dual-mode demodulation, the calculation efficiency and accuracy of the temperature demodulation result sub-block can be improved.

[0071] In one possible implementation, in step S50, calculating the evaluation index value of each temperature demodulation result sub-block includes the following.

[0072] In S51, based on each temperature demodulation result sub-block and a preset root mean square error formula, the first index value of each temperature demodulation result sub-block is calculated.

[0073] In S52, based on each temperature demodulation result sub-block and a preset deviation formula, the second index value of each temperature demodulation result sub-block is calculated.

[0074] In S53, based on each temperature demodulation result sub-block and a preset smoothness calculation formula, the third index value of each temperature demodulation result sub-block is calculated.

[0075] In S54, the first index value, the second index value, the third index value or the fusion index value of each temperature demodulation result sub-block is used as the evaluation index value of each temperature demodulation result sub-block. Here, the fusion index value is calculated based on at least two of the first index value, the second index value and the third index value.

[0076] The calculation methods of steps S51 to S54 are similar to the realization principle and technical effects of the analysis method based on the index of step 38 in the following Example 2, and will not be described further here. The difference is that in Example 1, the calculation is performed in units of temperature demodulation result sub-blocks when dividing the sub-blocks, while in Example 2, the calculation is performed based on the whole when not dividing.

[0077] According to the description of the above flow, this embodiment can improve the signal-to-noise ratio of the temperature measurement system of the distributed optical fiber based on Raman scattering and improve the temperature measurement performance of the system.

[0078] (Example 2) In the embodiment of the present application, the temperature ratio signal can be processed in the following process without being divided and as a whole. The maximum singular value decomposition (M-SVD) noise reduction algorithm provided by Example 2, compared with the singular value decomposition method that combines the fusion function in Example 1, analyzes the temperature ratio signal as a whole, the number of iterations is 1, there is no need to perform fusion processing, and there is no need to perform the subsequent two iterative noise reduction processes and splicing. The obtained noise reduction effect is better than the noise reduction effects of the hard threshold noise reduction method and the soft threshold noise reduction method. The specific analysis is as follows.

[0079] Figure 3 is a flowchart of applying the M-SVD noise reduction algorithm to the temperature measurement and noise reduction system of the distributed optical fiber. As shown in Figure 3, this embodiment is a temperature measurement and noise reduction method for a photovoltaic panel based on distributed sensing, and includes the following steps.

[0080] In S31, a sensing fiber is laid horizontally on the back surface of the photovoltaic panel to construct a temperature measurement system of a distributed optical fiber based on Raman scattering.

[0081] This method mainly includes two steps. The first step is to construct a distributed optical fiber temperature measurement system (which may also be called a temperature measurement system, a temperature measurement simulation system, etc.). The second step is the operation stage of the distributed optical fiber temperature measurement system. Step S31 is in the first step. The photovoltaic assembly includes a photovoltaic panel (i.e., a photovoltaic plate). In this embodiment, before reducing the temperature measurement noise of the photovoltaic plate, a distributed sensing optical fiber (which may also be called a multimode sensing optical fiber, an optical fiber, etc.) is laid on the photovoltaic plate. Specifically, in this embodiment, the optical fiber is arranged horizontally along the back surface of the photovoltaic plate, and the optical fiber is attached to the photovoltaic assembly using an adhesive or the like. The optical fiber is wound around the photovoltaic assembly in a circulating manner, led out from the bottom of the photovoltaic assembly, and accessed to a digital temperature sensor.

[0082] Furthermore, this embodiment can construct a temperature measurement simulation system based on Raman scattering under laboratory conditions. This system can set different temperatures by means of a constant temperature tank and simulate the heat reception of the photovoltaic plate under different conditions.

[0083] In S32, it is judged whether the calibration module is stable. If so, step S33 is executed. If not, step S32 is continuously executed.

[0084] In this step of this embodiment, the temperature measurement system is started. When the calibration module of the temperature measurement system is stable, it indicates that the temperature collected by the digital temperature sensor is stable.

[0085] In S33, the anti-Stokes signal and Stokes signal of the photovoltaic plate are collected, and the signal ratio is calculated.

[0086] As can be seen from the descriptions of steps S31 to S33, in this embodiment, first, a Raman scattering-based photovoltaic panel temperature measurement system is constructed using related components such as a pulsed laser, a wavelength division multiplexer, a photodetector, a master computer, and a photovoltaic panel. Next, the Raman anti-Stokes signal and the Raman Stokes signal under different conditions are collected using the photovoltaic panel temperature measurement system. Finally, based on the dual-mode demodulation principle, the ratio between the anti-Stokes signal and the Stokes signal is calculated to obtain a temperature ratio signal (which can be simply called a ratio signal).

[0087] In S34, the root mean square error is calculated to select an appropriate matrix value m×n.

[0088] In S35, the ratio signal is reformed into an A matrix of m×n dimensions.

[0089] The purpose of step S35 is to reform the temperature ratio signal, that is, the calculated temperature ratio signal X=(x1, x2, x3…x N ) is used to construct a real matrix A of m×n by selecting an appropriate number of matrices, where m×n = N and m, n ≧ 2, and the above N represents the length of the temperature ratio signal X.

[0090] As can be seen from the descriptions of steps S33 to S35, after the temperature value collected by the temperature sensor of the master computer is stabilized, the anti-Stokes signal and the Stokes signal are collected, the ratio value is obtained, and the obtained N signals are reformed into an A matrix of m×n dimensions. The specific form is as follows.

Equation

[0091] Here, m×n = N and m, n ≧ 2, and this N represents the length of the temperature ratio signal X.

[0092] In this embodiment, taking N = 60 as an example, the following analysis is performed. There are 10 types of combination methods for m×n. Specifically, they are 2×30, 3×20, 4×15, 5×12, 6×10, 10×6, 12×5, 15×4, 20×3, and 30×2. The codes for all combination methods are 1 to 10. By repeating the steps, the optimal values of m and n can be determined based on the root mean square error, and the results of the root mean square error corresponding to each combination method are shown in Figure 4.

[0093] As can be seen from Figure 4, the changing trends of the root mean square error due to the demodulation of different combination methods of m×n at different temperatures are similar. Specifically, the root mean square error of combination methods 1 to 6 decreases as m increases, and the root mean square error of combination methods 6 to 10 gradually increases as m increases. Therefore, the noise reduction effect of the M-SVD algorithm corresponding to combination method 6 (that is, when m×n = 10×6) is the best. Referring to Figure 4, it can be concluded that the selection conditions for m and n can be that m and n meet min|m - n| and m≧n.

[0094] In S36, the M-SVD noise reduction algorithm is adopted to perform noise reduction processing to obtain the temperature data after noise reduction.

[0095] The purpose of step S36 is to perform SVD decomposition on the A matrix of m×n dimensions, reconstruct the SVD signal, reform the reconstructed SVD signal, and obtain the temperature data after noise reduction.

[0096] Here, in the SVD decomposition stage, in this embodiment, the A matrix of m×n dimensions can be decomposed by singular value decomposition to obtain the left singular matrix, the singular value diagonal matrix, and the right singular matrix. In this embodiment, the first singular value in the singular value diagonal matrix can be retained, and the other singular values can be set to 0 to form a new singular value diagonal matrix. After obtaining the new singular value diagonal matrix, this embodiment reconstructs the SVD signal to obtain the temperature signal ratio after noise reduction.

[0097] That is, in this embodiment, a new singular value diagonal matrix is reconstructed with the left singular matrix and the right singular matrix. In this embodiment, the temperature signal ratio after reconstruction is reformed into a matrix, and after the reconstructed SVD signal is reformed in the following step S37, the temperature data after noise reduction is fitted into a dual-mode demodulation formula to obtain a temperature demodulation result.

[0098] Furthermore, in this embodiment, the m×n-dimensional A matrix is subjected to SVD decomposition, and the decomposition formula is as follows.

Equation

[0099] Here, U is an m×m left singular matrix, V is an n×n right singular matrix, Σ is a diagonal matrix with main diagonal elements σ1, σ2, σ3, …, σn, σ1, σ2, σ3, …, σn are the singular values of the real matrix A, and σ1 > σ2 > σ3 > …, σn.

[0100] In step S36, only the first singular value (i.e., the largest singular value) is retained in the singular value diagonal matrix Σ, and all other singular values are set to 0, that is, Σ(2:i,2:i) = 0, where i = min(m,n). In this embodiment, the signal reconstruction after processing is used to obtain a temperature ratio curve after noise reduction, and after reformation, it is fitted into the dual-mode demodulation formula in step S37 to obtain a temperature curve after noise reduction.

[0101] In S37, temperature demodulation is performed based on the temperature data after noise reduction to obtain a temperature demodulation result.

[0102] In this embodiment, the temperature demodulation result after noise reduction can be obtained using the dual-mode demodulation formula in this step.

[0103] In step S37, the dual-mode demodulation calculates the measured target temperature demodulated by a method of collecting optical signals at different positions and temperatures of the optical fiber using a distributed optical fiber temperature measurement system and obtaining a ratio. The specific formula is as follows.

Equation

[0104] Here, T is the temperature to be measured obtained by system demodulation, {T d,1 , T d,i , …, T d,N}, where N is the number of data, T0 is a known reference temperature (i.e., the actual temperature), h is the Planck constant, h = 6.626×10 -34 J·s, k is the Boltzmann constant, k = 1.38×10 -23 J / K, Δv is the Raman shift, Δv = 13.2 THz, P as (T) / P s (T) is the ratio of the anti-Stokes signal to the Stokes signal at the temperature T to be measured, and P as (T0) / P s (T0) is the ratio of the anti-Stokes signal to the Stokes signal at the known reference temperature T0.

[0105] In S38, the temperature demodulation result is analyzed based on the index.

[0106] Furthermore, the indexes in S38 include, but are not limited to, the root mean square error, the maximum deviation, the smoothness, etc. The purpose of this step is to perform a comparative analysis of the demodulation effect, that is, to fit the demodulated temperature demodulation result into the formulas corresponding to the three indexes of the root mean square error, the maximum deviation, and the smoothness, and to analyze the demodulation effect before and after noise reduction based on the original data.

[0107] Specifically, the embodiments of the present application analyze each index as follows.

[0108] (1) The root mean square error represents the error between all the demodulated temperature data and the actual temperature. It can reflect the overall temperature measurement effect of a set of data. The smaller the value, the smaller the overall temperature measurement error, indicating better system performance. [Number]

[0109] Here, RMSE is the root mean square error of the temperature demodulation result, T d,i represents the demodulated temperature result, T0 represents the actual temperature, and N is the number of data points.

[0110] (2) The maximum deviation is the maximum absolute error between the demodulated temperature result and the actual temperature, and can represent the worst temperature measurement situation of the temperature measurement system for a distributed optical fiber based on Raman scattering. The smaller this value, the higher the lower limit of the performance of the temperature measurement system.

Number

[0111] Here, MD is the maximum deviation, T d,i represents the demodulated temperature result, and T represents the actual temperature.

[0112] (3) The formula for smoothness is as follows.

Number

[0113] Here, S is the curve smoothness, T d,i represents the demodulated temperature result, and N is the number of data points.

[0114] In this embodiment, the index values of the temperature demodulation results for the three indicators of root mean square error, maximum deviation, and smoothness can be calculated respectively. This embodiment can also perform weighted addition on the index values for multiple indicators to obtain a final single index value.

[0115] This embodiment provides a method for measuring the temperature of a photovoltaic panel based on distributed sensing and reducing noise, and proposes a noise reduction algorithm based on singular value decomposition for the problem of low signal-to-noise ratio of the sensing system. First, under laboratory conditions, a temperature measurement system for a distributed optical fiber based on Raman scattering is constructed. The anti-Stokes light and Stokes light containing the original temperature information are collected to obtain a ratio and a ratio data matrix is obtained. An appropriate matrix value is selected and reformed into the data matrix. Next, singular value decomposition and reconstruction are performed on the matrix, and a ratio signal containing temperature information after noise reduction is obtained using the reformed matrix. The demodulated temperature information is obtained by fitting into a dual-mode demodulation formula. Finally, the noise removal effect is compared based on indicators such as root mean square error, maximum deviation, and smoothness. This embodiment can effectively reduce the demodulated temperature error by the noise reduction algorithm, improve the temperature measurement performance of the system, and at the same time effectively avoid problems such as uncertain threshold selection compared with the conventional wavelet noise removal.

[0116] As shown in FIG. 5, taking the actual temperature of 40°C as an example, this embodiment obtains the ratio value of the collected anti-Stokes signal and Stokes signal, and reforms the length N of the signal into an A matrix of 10×6 dimensions, where N is 60 points. Further, the A matrix is decomposed into three matrices: the left singular value, the singular value diagonal matrix, and the right singular value. The first singular value of the singular value diagonal matrix is retained, and the other singular values are set to 0 to obtain a new singular value diagonal matrix. Then, the left and right singular matrices are reconstructed with it to obtain a new matrix, and the signal is reconstructed again and recorded as the ratio signal after noise reduction. Finally, the temperature measurement curve is obtained by fitting into a dual-mode demodulation formula.

[0117] Similarly, this embodiment repeats the above operations for the collected temperature curves of 50.0°C, 60.0°C, and 70.0°C respectively, and obtains three sets of comparison diagrams of different measurements in FIGS. 6, 7, and 8. Each figure has four lines of adjusted temperature curves by original data, hard threshold noise removal, soft threshold noise removal, and M-SVD noise removal respectively.

[0118] As shown in FIGS. 9, 10, and 11, it is a comparison diagram of the performance of several noise removal algorithms calculated from the root mean square error, maximum deviation, and smoothness respectively. Under the conditions of 40.0°C, 50.0°C, 60.0°C, and 70.0°C, it was found that the noise reduction algorithm can effectively improve the system temperature measurement performance and is more effective than the traditional noise reduction algorithm.

[0119] This embodiment verifies the feasibility of the noise removal algorithm to improve the temperature measurement performance of the distributed sensing temperature measurement system from the experimental perspective. First, under laboratory conditions, the ratio value of the original data of the anti-Stokes and Stokes light beams collected is obtained, and the appropriate matrix value is selected for the ratio value data to be reformed. The reformed matrix is decomposed into the left singular matrix, singular value diagonal matrix, and right singular matrix using SVD. Retain the first singular value in the singular value matrix to obtain a new singular value matrix, reconstruct the left singular matrix, right singular matrix, and new singular value diagonal matrix to obtain the matrix after noise reduction, reform the matrix after noise reduction and fit it into the demodulation formula to obtain the temperature measurement result. Finally, compare the experimental temperature measurement data demodulated by different noise removal methods, and measure the noise removal effect using three indicators: root mean square error, maximum deviation, and smoothness for the demodulated temperature values.

[0120] Compared with the prior art, the advantages of this embodiment are that this embodiment proposes the M-SVD noise reduction algorithm and applies it to the temperature monitoring of the distributed sensing photovoltaic panel. Without changing the hardware structure conditions of the temperature measurement system, it improves the temperature measurement performance of the system from the perspective of data processing. Compared with the traditional wavelet noise removal and traditional SVD noise removal, it avoids the randomness of the selection of wavelets and singular values based on empirical values, reduces the noise removal process and time. This embodiment also uses the sensor calibration plan to conduct experiments, providing the possibility for subsequent fault warnings of the photovoltaic panel.

[0121] (Embodiment 3) As shown in Fig. 2, the temperature measurement and noise reduction system of the distributed optical fiber in this embodiment includes a sensing optical fiber connected in sequence, a calibration module (i.e., a calibration optical fiber), a wavelength division multiplexer, an avalanche photodiode (APD), a data acquisition card (i.e., a high-speed acquisition card) and a master computer (which may be a device such as a computer), and a pulse laser (which may refer to a high-power pulse laser) also connected to both the wavelength division multiplexer and the data acquisition card. Here, the master computer executes the temperature measurement and noise reduction method of the distributed optical fiber described in Example 1 and / or Example 2.

[0122] The specific principles of the operation of each component are as follows. After receiving a driving signal from the driving circuit, the pulse laser generates optical pulses of a certain frequency. The pulsed light enters the sensing optical fiber through the wavelength division multiplexer, interacts with the optical fiber molecules, and generates scattering. Here, the Stokes light and anti-Stokes light generated by Raman scattering are returned to the wavelength division multiplexer by backscattering, the optical signal is converted into an electrical signal by the APD, and finally the acquisition card collects the two electrical signals and transfers them to the master computer. Here, a digital temperature sensor can be arranged inside the space of the calibration module. The specific value of T0 is obtained by the digital temperature sensor, and data processing and temperature demodulation are completed according to the relevant program using the Stokes light and anti-Stokes light signals collected by the master computer.

[0123] In one possible implementation form, the sensing optical fiber circulates and winds along the longitudinal direction of the photovoltaic assembly and is fixedly laid on the back surface of the photovoltaic assembly.

[0124] The temperature measurement and noise reduction system of the distributed optical fiber provided by this embodiment can be used to execute the temperature measurement and noise reduction method of the distributed optical fiber provided by the embodiments of any of the above methods. Its implementation principle and technical effect are similar and will not be described in detail here.

[0125] Note that all user information and data related to this application (including but not limited to data for analysis, stored data, displayed data, etc.) are information and data obtained with the user's permission or sufficient permission from each party. The collection, use, and processing of related data shall comply with the relevant laws, regulations, and standards of the relevant countries and regions, and it is necessary to provide an appropriate operation portal for the user to select permission or rejection.

[0126] That is, in the technical solution of this application, all processes such as the collection, storage, use, processing, transmission, provision, and disclosure of related user personal information shall comply with the provisions of relevant laws and regulations and shall not violate public order and good customs.

[0127] According to the embodiments of this application, this application further provides an electronic device and a readable storage medium.

[0128] FIG. 12 is a schematic structural diagram of an electronic device provided by an embodiment of this application. This electronic device includes a receiver 60, a transmitter 61, at least one processor 62, and a memory 63. This electronic device composed of the above members can be used to implement some specific embodiments of this application above, and will not be described further here.

[0129] The embodiments of this application also provide a computer-readable storage medium in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, each step of the method in the above embodiments is realized.

[0130] The embodiments of this application further provide a computer program product that includes a computer program, and when the computer program is executed by a processor, each step of the method in the above embodiments is realized.

[0131] The systems and various technical embodiments described above in the present application can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can be implemented in one or more computer programs, and these one or more computer programs can be executed and / or interpreted in a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, and can receive data and instructions from a memory system, at least one input device, and at least one output device, and can transmit data and instructions to this memory system, this at least one input device, and this at least one output device.

[0132] The program code for implementing the method of the present application can be described using any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a dedicated computer, or other programmable data processing devices so that when executed by the processor or controller, the functions / operations defined in the flowchart and / or block diagram are implemented. The program code can be executed entirely by a machine, partially by a machine, partially by a machine as an independent software package and partially by a remote machine, or entirely by a remote machine or electronic device.

[0133] In the context of the present application, a computer-readable storage medium may be a tangible medium that includes or stores a program for use by or in combination with an instruction execution system, apparatus, or device. The computer-readable storage medium may be a machine-readable signal medium or a machine-readable storage medium. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0134] To provide interaction with a user, the systems and techniques described in the present application can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user, a keyboard, and a pointing device (such as a mouse or trackball), and the user can provide input to the computer using the keyboard and the pointing device. Other types of devices can also provide interaction with the user. For example, the feedback provided to the user can be any form of sensing feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and the input received from the user can be in any form (including voice input, speech input, or tactile input).

[0135] The systems and techniques described herein can be implemented in a computing system that includes background components (e.g., as a data electronics device), or a computing system that includes middleware components (e.g., an application electronics device), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser, and the user can interact with embodiments of the systems and techniques described in this application through the graphical user interface or the web browser), or a computing system that includes any combination of such background components, middleware components, or front-end components. The components of the system can be interconnected with each other by digital data communication (e.g., a communication network) in any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0136] It should be understood that the above various forms of processes can be used to rearrange, add, or delete steps. For example, each step described in the present disclosure can be executed in parallel, sequentially, or in a different order, and is not limited herein as long as the desired results can be achieved with the technical solution of the present disclosure.

[0137] The above specific embodiments do not constitute a limitation to the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and alternatives are possible according to design requirements and other factors. Modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included within the protection scope of the present application.

Claims

1. A method for temperature measurement and noise reduction of a distributed optical fiber, comprising: obtaining a Raman anti-Stokes signal and a Raman Stokes signal of a photovoltaic assembly collected by a temperature measurement and noise reduction system of a distributed optical fiber, and calculating a temperature ratio signal; dividing the temperature ratio signal into a plurality of temperature ratio signal sub-blocks; for each of the temperature ratio signal sub-blocks, performing a first iterative noise reduction process on the original sub-matrix corresponding to the temperature ratio signal sub-block using a singular value decomposition method for combining a fusion function, and obtaining a corresponding noise reduction temperature data sub-block, wherein in the first iterative noise reduction process, different temperature ratio signal sub-blocks correspond to different numbers of iterations; calculating a temperature demodulation result sub-block corresponding to each of the temperature ratio signal sub-blocks; calculating an evaluation index value of each of the temperature demodulation result sub-blocks, and determining a temperature ratio signal sub-block corresponding to the temperature demodulation result sub-block with the maximum evaluation index value as a reference sub-block; based on the number of iterations corresponding to the reference sub-block, performing a second iterative noise reduction process on the original sub-matrix corresponding to the non-reference sub-block using a singular value decomposition method for combining a fusion function, and obtaining a noise reduction temperature data sub-block corresponding to each of the non-reference sub-blocks; splicing the noise reduction temperature data sub-blocks corresponding to each of the non-reference sub-blocks and the noise reduction temperature data sub-block corresponding to the reference sub-block to obtain noise reduction temperature fusion data, and calculating a temperature demodulation result corresponding to the noise reduction temperature fusion data. A method for temperature measurement and noise reduction of a distributed optical fiber, characterized by the above.

2. Performing a first iterative noise reduction process on the original sub-matrix corresponding to the temperature ratio signal sub-block using a singular value decomposition method for combining a fusion function to obtain a corresponding noise reduction temperature data sub-block includes: reforming the temperature ratio signal sub-block to obtain a corresponding original sub-matrix; performing noise reduction processing on the original sub-matrix to obtain a first-generation noise reduction temperature data sub-block; Based on the fusion function, perform the first fusion of the first-generation noise-reduced temperature data sub-block and the original sub-matrix to obtain a first-fusion sub-matrix, perform noise reduction processing on the first-fusion sub-matrix, and obtain a second-generation noise-reduced temperature data sub-block. Based on the fusion function, fuse the second-generation noise-reduced temperature data sub-block and the original sub-matrix or the first-generation noise-reduced temperature data sub-block to obtain a plurality of fusion sub-matrices, perform noise reduction processing on the plurality of fusion sub-matrices, and obtain a third-generation noise-reduced temperature data sub-block. Determine the first-generation noise-reduced temperature data sub-block, the second-generation noise-reduced temperature data sub-block, or the third-generation noise-reduced temperature data sub-block as the corresponding noise-reduced temperature data sub-block. The method according to claim 1, characterized by the above.

3. Performing noise reduction processing on the original sub-matrix to obtain the first-generation noise-reduced temperature data sub-block includes: Performing SVD decomposition processing on the original sub-matrix to obtain a left singular sub-matrix, an initial singular value diagonal matrix, and a right singular sub-matrix. Retaining the target number of singular values of the initial singular value diagonal matrix, setting the other singular values to 0, and forming a target singular value diagonal sub-matrix. Constructing a first-generation noise-reduced temperature data sub-block based on the left singular sub-matrix, the right singular sub-matrix, and the target singular value diagonal sub-matrix. The method according to claim 2, characterized by the above.

4. Calculating the temperature demodulation result sub-block corresponding to each temperature ratio signal sub-block includes: Calculating the corresponding temperature demodulation result sub-block for each temperature ratio signal sub-block using a preset dual-mode demodulation formula. The method according to claim 1, characterized by the above.

5. Calculating the evaluation index value of each temperature demodulation result sub-block includes: Calculating the first index value of each temperature demodulation result sub-block based on each temperature demodulation result sub-block and a preset root mean square error formula. Calculating the second index value of each temperature demodulation result sub-block based on each temperature demodulation result sub-block and a preset deviation formula. Calculating a third index value for each of the temperature demodulation result sub - blocks based on each of the temperature demodulation result sub - blocks and a preset smoothness calculation formula; including using the first index value, the second index value, the third index value, or a fusion index value of each of the temperature demodulation result sub - blocks as an evaluation index value of each of the temperature demodulation result sub - blocks; wherein the fusion index value is calculated based on at least two of the first index value, the second index value, and the third index value; The method according to claim 1, characterized in that.

6. The preset dual - mode demodulation formula adopts the following formula: 【Number 1】 Ti is the temperature demodulation result sub - block corresponding to the i - th temperature ratio signal sub - block for representing the actual temperature to be demodulated, T0 is a known temperature, h is the Planck constant, h = 6.626×10−34 J·s, k is the Boltzmann constant, k = 1.38×10−23 J / K; 【Number 2】 is the noise - reduced temperature data sub - block corresponding to the i - th temperature ratio signal sub - block for representing the ratio of the anti - Stokes signal to the Stokes signal at the actual temperature T to be measured; 【Number 3】 is used to represent the ratio of the anti - Stokes signal to the Stokes signal at a known temperature; The method according to claim 4, characterized in that.

7. A temperature measurement and noise reduction system for a distributed optical fiber, comprising: A sensing optical fiber, a calibration module, a wavelength division multiplexer, an avalanche photodiode, a data acquisition card and a master computer connected in sequence, and a pulse laser connected to both the wavelength division multiplexer and the data acquisition card, wherein the master computer executes the method according to any one of claims 1 - 6; A distributed optical fiber temperature measurement noise reduction system, characterized in that.

8. The sensing optical fiber is wound in a circulating manner along the longitudinal direction of the photovoltaic assembly and is fixedly laid on the back surface of the photovoltaic assembly. The system according to claim 7, characterized in that.

9. An electronic device including at least one processor and a memory, wherein the memory stores computer - executable instructions. The at least one processor causes the at least one processor to execute the method according to any one of claims 1 to 6 by executing the computer-executable instructions stored in the memory. An electronic device characterized by the above. **Claim 10** A computer-readable storage medium storing computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when the computer-executable instructions are executed by a processor. An electronic device characterized by the above.

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