A high-resolution distributed seawater temperature monitoring method

Abstract

The present invention relates to a high-resolution distributed seawater temperature monitoring method, which includes the following steps: S1, forming a two-dimensional temperature matrix by using multiple groups of distributed Raman signals obtained in consecutive multiple time frames; S2, performing noise reduction on the two-dimensional temperature matrix by using a non-local mean algorithm, retaining the overall temperature trend of the seawater area to be measured temperature in the two-dimensional temperature matrix, and obtaining a two-dimensional temperature matrix after the first noise reduction; S3, performing noise reduction on the two-dimensional temperature matrix after the first noise reduction by using a total variation denoising algorithm, retaining the details of the temperature change area in the two-dimensional temperature matrix after the first noise reduction, and obtaining a two-dimensional temperature matrix after the second noise reduction; S4, adding the collected distributed Raman signal as a new row after the last row of the two-dimensional temperature matrix after the second noise reduction to form a two-dimensional temperature matrix of the next time frame, performing noise reduction by using the non-local mean algorithm, and taking the last row as the current temperature of the seawater area to be measured temperature.

Description

Technical Field

[0001] The present invention relates to the field of seawater temperature monitoring, and specifically refers to a high-resolution distributed seawater temperature monitoring method. Background Art

[0002] Seawater temperature is an important environmental factor for the occurrence of red tides. The suitable temperature range for red tide occurrence is 20 - 30°C, and a sudden increase in water temperature by more than 2°C within a week is a precursor to red tide occurrence.

[0003] Distributed optical fiber temperature measurement system is a sensing system developed in recent years for real-time measurement of the spatial temperature field distribution. This system uses light waves as information carriers. During the propagation of light wave signals in optical fibers, their intensity, polarization state, phase and other characteristic quantities will change due to the influence of external environmental factors such as temperature, electromagnetism, and vibration. By monitoring these changes, environmental monitoring and real-time measurement of the temperature field where the optical fiber is located can be achieved. Using optical time domain reflectometry (OTDR), the measurement point can be accurately located. In the prior art, "Novel transient-state analysis approach for distributed temperature sensing based on spontaneous Raman scattering[J]" (Opto-Electronics Review, 2024) proposed a temperature sensing technology based on Raman anti-Stokes scattering. This technology uses the OTDR algorithm to perform differential analysis on the integrated Raman backscattered anti-Stokes scattering signal, reconstruct the temperature distribution along the measured optical fiber, and achieve a temperature resolution of 2°C and a spatial resolution of 1 meter. In the actual measurement of seawater temperature for red tide early warning, the change of seawater temperature is affected by ocean currents, climate, day and night temperature changes, etc. At the same time, due to the characteristics of slow seawater temperature change and the influence of noise in the optical fiber temperature measurement system on the detection results, if the noise reduction methods provided by the prior art are used, it is difficult to capture the change trend and details of seawater, making the existing distributed seawater temperature measurement methods difficult to achieve high resolution.

[0004] Designing a high-resolution distributed seawater temperature monitoring method to address the problems existing in the above prior art is the purpose of the research of the present invention. Summary of the Invention

[0005] Aiming at the problems existing in the above prior art, the present invention provides a high-resolution distributed seawater temperature monitoring method, which can effectively solve at least one of the problems existing in the above prior art.

[0006] The technical solution of the present invention is as follows:

[0007] A high-resolution distributed seawater temperature monitoring method includes the following steps:

[0008] S1. Conduct distributed Raman signal acquisition on the seawater area to be measured in multiple consecutive time frames, and form a two-dimensional temperature matrix with multiple groups of distributed Raman signals obtained in multiple consecutive time frames. Among them, the rows of the two-dimensional temperature matrix represent the changes of Raman signals at different distance positions, and the columns of the two-dimensional temperature matrix represent the changes of Raman signals in different time frames;

[0009] S2. Denoise the two-dimensional temperature matrix through the non-local mean algorithm, and retain the overall temperature trend of the seawater area to be measured in the two-dimensional temperature matrix to obtain a two-dimensional temperature matrix after the first denoising;

[0010] S3. Denoise the two-dimensional temperature matrix after the first denoising through the total variation denoising algorithm, and retain the details of the temperature change area in the two-dimensional temperature matrix after the first denoising to obtain a two-dimensional temperature matrix after the second denoising;

[0011] S4. Conduct distributed Raman signal acquisition on the seawater area to be measured in the next time frame, add the acquired distributed Raman signal as a new row after the last row of the two-dimensional temperature matrix after the second denoising to form a two-dimensional temperature matrix for the next time frame, denoise the two-dimensional temperature matrix for the next time frame through the non-local mean algorithm, and use the last row in the denoising result as the current temperature of the seawater area to be measured.

[0012] Furthermore, in step S1, each Raman signal is sampled multiple times and averaged to obtain a distributed Raman signal.

[0013] Furthermore, step S2 is specifically as follows:

[0014] Adjust the smoothing parameter of the non-local mean algorithm, denoise the two-dimensional temperature matrix with the non-local mean algorithm corresponding to each smoothing parameter, calculate the signal-to-noise ratio of each denoising result, and obtain the smoothing parameter with the highest signal-to-noise ratio;

[0015] Dynamically adjust the smoothing parameter according to the ocean current position where the seawater area to be measured is located and the temperature fluctuation condition of the seawater area to be measured;

[0016] Denoise the two-dimensional temperature matrix with the non-local mean algorithm corresponding to the dynamically adjusted smoothing parameter.

[0017] Furthermore, dynamically adjusting the smoothing parameter according to the ocean current position where the seawater area to be measured is located and the temperature fluctuation condition of the seawater area to be measured includes:

[0018] If the ocean current position where the seawater area to be temperature - measured is located is in a local turbulent area, then decrease the smoothing parameter; otherwise, increase the smoothing parameter.

[0019] If the temperature fluctuation situation around the seawater area to be temperature - measured is greater than a preset threshold, then decrease the smoothing parameter; otherwise, increase the smoothing parameter.

[0020] Furthermore, adjusting the smoothing parameter of the non - local means algorithm includes:

[0021] Increase the smoothing parameter with a step size of 0.1, an initial value of 0.5 for the smoothing parameter, and a maximum value of 10 for the smoothing parameter.

[0022] Furthermore, define the size of the similarity window of the non - local means algorithm as , where N is a positive integer;

[0023] Before denoising the two - dimensional temperature matrix after the first - stage denoising through the total variation denoising algorithm, it includes: dividing the two - dimensional temperature matrix after the first - stage denoising into blocks with for block division.

[0024] Furthermore, step S3 includes:

[0025] Adjust the regularization parameter of the total variation denoising algorithm, denoise the two - dimensional temperature matrix after the first - stage denoising with the total variation denoising algorithm corresponding to each regularization parameter, calculate the signal - to - noise ratio of each denoising result, and obtain the regularization parameter with the highest signal - to - noise ratio;

[0026] Dynamically adjust the regularization parameter according to the ocean current position where the seawater area to be temperature - measured is located and the temperature fluctuation situation of the seawater area to be temperature - measured;

[0027] Denoise the two - dimensional temperature matrix after the first - stage denoising with the total variation denoising algorithm corresponding to the dynamically adjusted regularization parameter.

[0028] Furthermore, dynamically adjusting the regularization parameter according to the ocean current position where the seawater area to be temperature - measured is located and the temperature fluctuation situation of the seawater area to be temperature - measured includes:

[0029] If the ocean current position where the seawater area to be temperature - measured is located is in a local turbulent area, then decrease the regularization parameter; otherwise, increase the regularization parameter;

[0030] If the temperature fluctuation situation around the seawater area to be temperature - measured is greater than a preset threshold, then decrease the regularization parameter; otherwise, increase the regularization parameter.

[0031] Furthermore, adjusting the regularization parameter of the total variation denoising algorithm includes:

[0032] Increase the regularization parameter in steps of 0.1, with 0.1 as the initial value of the regularization parameter and 10 as the maximum value of the regularization parameter.

[0033] Therefore, the present invention provides the following effects and / or advantages:

[0034] In the present application, the one-dimensional Raman signal acquisition is used to form a two-dimensional temperature matrix. Then, the overall temperature trend of the seawater area to be measured in the two-dimensional temperature matrix is retained by the non-local means algorithm, and the details of the temperature change area in the two-dimensional temperature matrix after the first noise reduction are retained by the total variation denoising algorithm, so as to obtain a temperature field with the historical trend and details of the seawater temperature. By combining the newly acquired Raman signal in the next time frame for new noise reduction, the current temperature of the seawater area to be measured can be obtained.

[0035] The method provided by the present application improves the resolution of distributed seawater temperature detection, raises the resolution to ±0.30 °C, and can achieve high-resolution monitoring of ocean temperature for red tide early warning and the like. At the same time, the method provided by the present application has a short response time and meets the real-time requirements.

[0036] In the method provided by the present application, the parameters in the non-local means algorithm and the total variation denoising algorithm are dynamically adjusted according to the ocean current position where the seawater area to be measured is located, the temperature fluctuation situation of the seawater area to be measured, etc., so as to be able to adapt to the short-term change situation of the seawater temperature, retain the change trend of the seawater temperature, and filter out noise.

[0037] Other features and advantages of the present invention will be described in the subsequent specification, and part of them will become obvious from the specification or be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification and the drawings.

[0038] It should be understood that the above summary and the following detailed description of the present invention are exemplary and explanatory, and are intended to provide further explanation of the present invention as claimed. Brief Description of the Drawings

[0039] Figure 1 It is a schematic flowchart provided for one embodiment of the present invention.

[0040] Figure 2 It is a schematic diagram of the original data of the distributed Raman signal.

[0041] Figure 3 It is a curve after the Raman signal is denoised by the method provided by the present application at 40 °C.

[0042] Figure 4The curve after denoising the Raman signal by the method provided in this application at 60°C.

[0043] Figure 5 The curve after denoising the Raman signal by the method provided in this application at 80°C.

[0044] Figure 6 Schematic diagram of the noise reduction effect results of multiple algorithms. Detailed implementation manners

[0045] For the convenience of those skilled in the art to understand, the embodiments will be further described in detail for the present invention:

[0046] Reference Figure 1 , a high-resolution distributed temperature monitoring method for seawater, comprising the following steps:

[0047] S1, performing distributed Raman signal acquisition on the seawater area to be measured in a continuous plurality of time frames, and forming a two-dimensional temperature matrix with a plurality of groups of distributed Raman signals obtained in the continuous plurality of time frames, wherein the rows of the two-dimensional temperature matrix represent the changes of Raman signals at different distance positions, and the columns of the two-dimensional temperature matrix represent the changes of Raman signals at different time frames;

[0048] Further, in step S1, each Raman signal is sampled multiple times and averaged to obtain a distributed Raman signal.

[0049] In this step, the temperature of the seawater area to be measured is obtained through a Raman distributed optical fiber temperature sensor. A plurality of gratings are arranged on one optical fiber temperature sensor, and each grating can obtain a temperature signal, so that temperature signals corresponding to a plurality of positions are obtained on one optical fiber temperature sensor. In this embodiment, the construction method of the two-dimensional temperature matrix is to continuously obtain Raman signals within 20 sets of time frames at the same test temperature, and each Raman signal is obtained through 5000 times of cumulative averaging. Then these signals are stacked in time series to form a two-dimensional temperature matrix, where the rows of the two-dimensional temperature matrix represent the changes of Raman signals at different distance positions, and the columns correspond to different time frames of the Raman signals. Specifically, the sampling is performed on a sensing optical fiber of about 2.3 km, and the system sampling interval is 0.4 m. The number of sampling points for each Raman signal is 5635, and the total number of signal sequences is 20. The size of the combined two-dimensional temperature matrix is 20×5635. The cross-sectional view of the two-dimensional temperature matrix is as Figure 2 shown, Figure 2 showing the obtained original two-dimensional temperature matrix. These trajectories contain 20 frames of original Raman signals, and 14 m of the length of the optical fiber is placed in a water area environment with a set fixed temperature. Figure 2 The data signals of this 14 m length are enlarged and shown in

[0050] S2, denoise the two-dimensional temperature matrix through the non-local means algorithm, retain the overall temperature trend of the seawater area to be measured in the two-dimensional temperature matrix, and obtain a two-dimensional temperature matrix after the first denoising;

[0051] The non-local means algorithm reduces noise by considering pixels in similar regions of the two-dimensional temperature matrix. It mainly depends on the similarity between pixels rather than the pixel values in the local region, and can effectively remove the noise in the two-dimensional temperature matrix and retain details, thereby retaining the overall temperature trend of the seawater area to be measured in the two-dimensional temperature matrix.

[0052] S3, denoise the two-dimensional temperature matrix after the first denoising through the total variation denoising algorithm, retain the details of the temperature change region in the two-dimensional temperature matrix after the first denoising, and obtain a two-dimensional temperature matrix after the second denoising;

[0053] The total variation denoising algorithm denoises by minimizing the total variation of the image (i.e., the sum of the pixel value gradients). It can effectively reduce the noise in the two-dimensional temperature matrix after the first denoising and is excellent in maintaining the edges or textures corresponding to the seawater temperature change, thereby retaining the details of the temperature change region in the two-dimensional temperature matrix after the first denoising.

[0054] Moreover, in this embodiment, by first using the non-local means algorithm for denoising, the fiber optic noise in the two-dimensional temperature matrix can be initially reduced, thereby improving the quality of the temperature data in the two-dimensional temperature matrix and making the subsequent total variation denoising algorithm more stable and efficient. At the same time, denoising with the non-local means algorithm first and then the total variation denoising algorithm can avoid the total variation denoising algorithm from smoothing out the details in the two-dimensional temperature matrix, especially the high-frequency part. Then, when using the non-local means algorithm for denoising again, since some detail information has been lost in the two-dimensional temperature matrix, the effect of the non-local means algorithm may decline. Therefore, performing total variation denoising first may result in a loss of detail retention in the image, making the subsequent non-local means algorithm denoising effect less than expected.

[0055] S4, collect distributed Raman signals for the seawater area to be measured in the next time frame, add the collected distributed Raman signals as a new row after the last row of the two-dimensional temperature matrix after the second denoising to form a two-dimensional temperature matrix for the next time frame, denoise the two-dimensional temperature matrix for the next time frame through the non-local means algorithm, and use the last row in the denoising result as the current temperature of the seawater area to be measured.

[0056] Specifically, based on the original two-dimensional temperature matrix, a new Raman signal needs to be added to the two-dimensional Raman data matrix denoised by the non-local means algorithm, such that the size of the new data matrix is equal to 21×5635. The first 20 pieces of data are the two-dimensional temperature matrix after secondary denoising obtained in step S3, and the 21st added data is the original Raman signal, which is then denoised by the non-local means algorithm. After the denoising is completed, the 21st piece of data is the current temperature distribution of the sensing optical fiber.

[0057] Since the time span of the matrix expands after adding the newly acquired Raman signal as a new row to the two-dimensional matrix, the non-local means algorithm can more effectively suppress transient noise (such as sensor random errors) by searching for similar patterns across time frames (such as periodic temperature fluctuations), while retaining the overall trend of temperature evolution, thereby maximizing the utilization of the variation information of seawater temperature in the time dimension, preserving the spatio-temporal coherence of the temperature field, inheriting the historical denoising results, and ensuring that the newly added data is consistent with the historical trend.

[0058] Furthermore, step S2 is specifically as follows:

[0059] Adjust the smoothing parameter of the non-local means algorithm, perform denoising on the two-dimensional temperature matrix using the non-local means algorithm corresponding to each smoothing parameter, calculate the signal-to-noise ratio of each denoising result, and obtain the smoothing parameter with the highest signal-to-noise ratio;

[0060] Dynamically adjust the smoothing parameter according to the ocean current position of the seawater area to be measured and the temperature fluctuation condition of the seawater area to be measured;

[0061] Perform denoising on the two-dimensional temperature matrix using the non-local means algorithm corresponding to the dynamically adjusted smoothing parameter.

[0062] In this step, the non-local means algorithm is a prior art. The smoothing parameter is the Gaussian kernel decay parameter in the weight calculation of the non-local means algorithm, which determines the sensitivity of the weights between similar blocks. The larger the smoothing parameter, the stronger the smoothing effect but it may blur details; on the contrary, more details can be retained but the noise reduction ability may be insufficient. Since at the beginning, how to set the smoothing parameter of the non-local means algorithm is uncertain, all the smoothing parameters are traversed within a certain range, so as to adjust the noise reduction effect of the non-local means algorithm on the two-dimensional temperature matrix, and the smoothing parameter corresponding to the best signal-to-noise ratio is used as the basic parameter. Then, since the seawater temperature usually changes relatively gently and is affected by factors such as seasonality, depth, and ocean currents, the seawater temperature changes rapidly during seasonal alternations, the deeper the seawater, the slower the seawater temperature changes, and when the ocean current is in a local turbulent area, the seawater temperature is prone to more short-term changes. All these situations will increase the occurrence of noise and produce a stronger temperature change trend. Therefore, it is necessary to dynamically adjust the smoothing parameter so that the two-dimensional temperature matrix after noise reduction can retain the trends and details of the seawater temperature at multiple levels.

[0063] Further, according to the ocean current position of the seawater area to be measured and the temperature fluctuation situation of the seawater area to be measured, dynamically adjusting the smoothing parameter includes:

[0064] If the ocean current position of the seawater area to be measured is in a local turbulent area, then reduce the smoothing parameter, otherwise increase the smoothing parameter;

[0065] If the temperature fluctuation situation around the seawater area to be measured is greater than a preset threshold, then reduce the smoothing parameter, otherwise increase the smoothing parameter.

[0066] Specifically, when the ocean current position of the seawater area to be measured is in a local turbulent area, the seawater temperature is prone to more short-term changes. Or, when the temperature change around the seawater to be measured is large, such as in the seasons of temperature alternation, during the periods of sunrise or sunset, or when the seawater to be measured is in shallow sea, etc., the temperature change around the seawater to be measured is large, which is likely to cause the temperature of the seawater to be measured to also change in the short term. At this time, a smaller smoothing parameter is used to retain these local short-term changes.

[0067] Specifically, if the ocean current position of the seawater area to be measured is in a local turbulent area, then reduce the smoothing parameter by 20%, otherwise increase the smoothing parameter by 20%;

[0068] If the temperature fluctuation situation around the seawater area to be measured is greater than a preset threshold, then reduce the smoothing parameter by 20%, otherwise increase the smoothing parameter by 20%.

[0069] Further, adjusting the smoothing parameter of the non-local means algorithm includes:

[0070] Increase the smoothing parameter in steps of 0.1, with an initial value of 0.5 for the smoothing parameter and a maximum value of 10 for the smoothing parameter.

[0071] This method can gradually adjust the filtering parameter in a small range of steps to observe the influence of different smoothing parameters on the denoising effect.

[0072] Furthermore, define the similarity window size of the non-local means algorithm as , where N is a positive integer;

[0073] Before denoising the two-dimensional temperature matrix after the first denoising through the total variation denoising algorithm, it includes: dividing the two-dimensional temperature matrix after the first denoising into blocks with blocks.

[0074] In this step, the similarity window size of the non-local means algorithm can be set artificially. In this embodiment, N is 3. Since the scale of the two-dimensional temperature matrix obtained in S1 is large, directly applying TV denoising to the global matrix will result in extremely high computational complexity and memory requirements. After dividing the two-dimensional temperature matrix into smaller sub-blocks, the total variation denoising algorithm can be independently applied to each sub-block for denoising, and parallel computing using a multi-core CPU or GPU can significantly improve the processing speed.

[0075] Furthermore, step S3 includes:

[0076] Adjust the regularization parameter of the total variation denoising algorithm, denoise the two-dimensional temperature matrix after the first denoising with the total variation denoising algorithm corresponding to each regularization parameter, calculate the signal-to-noise ratio of each denoising result, and obtain the regularization parameter with the highest signal-to-noise ratio;

[0077] Dynamically adjust the regularization parameter according to the ocean current position of the seawater area to be measured for temperature and the temperature fluctuation condition of the seawater area to be measured for temperature;

[0078] Denoise the two-dimensional temperature matrix after the first denoising with the total variation denoising algorithm corresponding to the dynamically adjusted regularization parameter.

[0079] Furthermore, dynamically adjusting the regularization parameter according to the ocean current position of the seawater area to be measured for temperature and the temperature fluctuation condition of the seawater area to be measured for temperature includes:

[0080] If the ocean current position of the seawater area to be measured for temperature is in a local turbulent area, then decrease the regularization parameter, otherwise increase the regularization parameter;

[0081] If the surrounding temperature fluctuation condition of the seawater area to be measured for temperature is greater than a preset threshold, then decrease the regularization parameter, otherwise increase the regularization parameter.

[0082] Further, adjusting the regularization parameter of the total variation denoising algorithm includes:

[0083] Increasing the regularization parameter with a step size of 0.1, an initial value of 0.1 for the regularization parameter, and a maximum value of 10 for the regularization parameter.

[0084] In the above steps, the adjustment principle of the specific regularization parameter can refer to the adjustment principle of the smoothing parameter of the non-local mean algorithm.

[0085] Experimental data

[0086] Under the conditions that the pulse width is 40 ns, the temperatures at which the optical fiber is located are set to 40 °C, 60 °C, and 80 °C respectively, and the effective temperature measurement length of the optical fiber is 14 m, as Figures 3 - 5 shown, the temperature curve obtained by denoising the Raman signal using the method provided by the present application is smoother than the unfiltered temperature curve, and the measured temperature is near the temperature at which the optical fiber is located. It can be seen from the figure that after denoising by the method provided by the present application, the temperature accuracy of the Raman signal is improved from ±2.82 °C to ±0.30 °C, effectively removing the noise in the Raman signal. Moreover, it shows that the method provided by the present application still has a certain filtering ability and robustness at different temperatures.

[0087] Reference Figure 6 , by using the method provided by the present application and comparing it with the wavelet threshold denoising algorithm, two-dimensional non-local mean algorithm, and bilateral filtering algorithm in the prior art, when the temperature at which the optical fiber is located is 60 °C, it can be seen that the filtering result of the algorithm provided by the present application is closer to the temperature at which the optical fiber is located. Moreover, as can be seen from Table 1, the temperature accuracy calculated by the algorithm provided by the present application is the highest, and the corresponding time is within an acceptable range, meeting the requirements of high-precision temperature measurement.

[0088] Table 1. Comparison of the denoising performance of different filters on the original Raman signal

[0089]

[0090] In summary, the algorithm provided by the present application achieves a good balance between temperature accuracy and system real-time performance. It can not only provide high temperature measurement accuracy but also maintain a fast response time, basically meeting the real-time requirements of the RDTS system and being more suitable for application scenarios with requirements for both temperature measurement accuracy and response time.

[0091] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0092] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of flows and / or blocks.

[0093] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of flows and / or blocks.

[0094] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0095] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

Claims

1. A high-resolution distributed seawater temperature monitoring method, characterized in that: Including the following steps: S1. Conduct distributed Raman signal acquisition on the seawater area to be measured at consecutive multiple time frames, and form a two-dimensional temperature matrix with multiple groups of distributed Raman signals obtained at consecutive multiple time frames. Among them, the rows of the two-dimensional temperature matrix represent the changes of Raman signals at different distance positions, and the columns of the two-dimensional temperature matrix represent the changes of Raman signals at different time frames; S2. Denoise the two-dimensional temperature matrix through the non-local mean algorithm, and retain the overall temperature trend of the seawater area to be measured in the two-dimensional temperature matrix to obtain a two-dimensional temperature matrix after the first denoising. Step S2 is specifically as follows: Adjust the smoothing parameter of the non-local mean algorithm, denoise the two-dimensional temperature matrix with the non-local mean algorithm corresponding to each smoothing parameter, calculate the signal-to-noise ratio of each denoising result, and obtain the smoothing parameter with the highest signal-to-noise ratio; Dynamically adjust the smoothing parameter according to the ocean current position where the seawater area to be measured is located and the temperature fluctuation condition of the seawater area to be measured; including: If the ocean current position where the seawater area to be measured is located is in a local turbulent area, reduce the smoothing parameter, otherwise increase the smoothing parameter; If the temperature fluctuation condition around the seawater area to be measured is greater than a preset threshold, reduce the smoothing parameter, otherwise increase the smoothing parameter; Denoise the two-dimensional temperature matrix with the non-local mean algorithm corresponding to the dynamically adjusted smoothing parameter; S3. Denoise the two-dimensional temperature matrix after the first denoising through the total variation denoising algorithm, and retain the details of the temperature change area in the two-dimensional temperature matrix after the first denoising to obtain a two-dimensional temperature matrix after the second denoising; S4. Conduct distributed Raman signal acquisition on the seawater area to be measured at the next time frame, add the collected distributed Raman signal as a new row after the last row of the two-dimensional temperature matrix after the second denoising to form a two-dimensional temperature matrix at the next time frame, denoise the two-dimensional temperature matrix at the next time frame through the non-local mean algorithm, and use the last row in the denoising result as the current temperature of the seawater area to be measured.

2. A high-resolution distributed seawater temperature monitoring method according to claim 1, characterized in that: In step S1, each Raman signal is sampled multiple times and averaged to obtain a distributed Raman signal.

3. A high-resolution distributed seawater temperature monitoring method according to claim 1, characterized in that: Adjusting the smoothing parameter of the non-local mean algorithm includes: Raise the smoothing parameter with a step size of 0.1, an initial value of 0.5 for the smoothing parameter, and a maximum value of 10 for the smoothing parameter.

4. A high-resolution seawater distributed temperature monitoring method according to claim 1, characterized in that: Define the similarity window size of the non-local means algorithm as , where N is a positive integer; Before denoising the two-dimensional temperature matrix after the first denoising by the total variation denoising algorithm, it includes: dividing the two-dimensional temperature matrix after the first denoising by into blocks.

5. A high-resolution distributed seawater temperature monitoring method according to claim 4, characterized in that: Step S3 includes: Adjust the regularization parameter of the total variation denoising algorithm, denoise the two-dimensional temperature matrix after the first denoising with the total variation denoising algorithm corresponding to each regularization parameter, calculate the signal-to-noise ratio of each denoising result, and obtain the regularization parameter with the highest signal-to-noise ratio; Dynamically adjust the regularization parameter according to the ocean current position where the seawater area to be measured is located and the temperature fluctuation condition of the seawater area to be measured; Denoise the two-dimensional temperature matrix after the first denoising with the total variation denoising algorithm corresponding to the dynamically adjusted regularization parameter.

6. A high-resolution distributed seawater temperature monitoring method according to claim 5, characterized in that: Dynamically adjusting the regularization parameter according to the ocean current position of the seawater area to be measured temperature and the temperature fluctuation condition of the seawater area to be measured temperature includes: If the ocean current position of the seawater area to be measured temperature is in a local turbulent area, reduce the regularization parameter, otherwise increase the regularization parameter; If the surrounding temperature fluctuation condition of the seawater area to be measured temperature is greater than a preset threshold, reduce the regularization parameter, otherwise increase the regularization parameter.

7. A high-resolution distributed seawater temperature monitoring method according to claim 5, characterized in that: Adjusting the regularization parameter of the total variation denoising algorithm includes: Increase the regularization parameter with a step size of 0.1, an initial value of 0.1 for the regularization parameter, and a maximum value of 10 for the regularization parameter.

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