A method for monitoring the fastening state of a dynamic cable based on optical fiber sensing

Abstract

The present invention relates to a method for monitoring the fastening state of a dynamic cable based on optical fiber sensing, belonging to the technical field of submarine dynamic cable monitoring. Among them, the method includes selecting multiple fastening points on the dynamic cable according to the dynamic cable structure and obtaining the DAS monitoring data of the fastening points; constructing a time series of fastening point detection values through principal component analysis according to the DAS monitoring data of the fastening points; obtaining the environmental wind speed value and the occurrence time of the environmental wind speed value, and establishing an environmental time series through sorting according to the environmental wind speed value and the occurrence time of the environmental wind speed value; constructing a real-time detection sequence of the fastening points according to the environmental time series and the time series of the fastening point detection values, and updating the real-time detection sequence of the fastening points according to the occurrence time of the environmental wind speed value; obtaining the anchoring state of the fastening points through abnormal judgment of the fastening points according to the real-time detection sequence of the fastening points, and realizing the judgment of the abnormal state of the fastening points of the submarine cable under complex seabed conditions by analyzing the correlation of data at multiple times and multiple positions.

Description

Technical Field

[0001] The present invention belongs to the technical field of subsea dynamic cable monitoring, and particularly relates to a method for monitoring the fastening state of a dynamic cable based on optical fiber sensing. Background Art

[0002] A typical offshore floating wind power system generally consists of a wind turbine, a floating platform, a mooring device, and a cable. Among them, the dynamic cable is suspended below the floating platform in a certain line type and is connected to subsea equipment to provide power, data, and signals for the subsea equipment. The operation risk of the wind power dynamic cable is much higher than that of the static submarine cable fixed to the pile foundation or laid on the seabed. Especially under the action of environmental loads such as ocean currents and waves, the fastening state of the submarine cable will be affected, which may cause damage to the submarine cable and seriously affect the stable operation of the power grid system. Among them, the top position where the dynamic cable is connected to the floating platform, the connection point with the subsea anchor chain, and the mud entry point are subjected to large tensile loads caused by their own weight, and at the same time are subjected to repeated bending loads caused by harsh working conditions and large-amplitude floating body movements. These are the parts where the change in the fastening state is most obvious before and after the abnormality.

[0003] At present, the safety monitoring of submarine cables is mainly realized by using distributed monitoring technology. Common devices include distributed fiber optic acoustic sensors (referred to as DAS for short), which detect the phase difference between two vibration signals before and after, and further analyze the strain change rate and vibration center frequency when the submarine cable vibrates. The vibration data change of the dynamic cable during normal operation in the seabed is mostly related to environmental factors such as wind speed and ocean current velocity. Once the anchoring state is abnormal, it will change the shape of the submarine cable in the seawater. At this time, the vibration data detected by the device under the same environmental conditions will be different. However, since the marine environment, wind force, suspended objects, wiring methods, etc. in the seawater of the dynamic cable will all affect the vibration of the submarine cable, and each reason may lead to different abnormal states, and at the same time, the vibration data of the submarine cable may change very greatly over time.

[0004] Therefore, there is an urgent need for a method for monitoring the fastening state of a dynamic cable based on optical fiber sensing to solve the above problems. Summary of the Invention

[0005] To solve the above problems existing in the prior art, the present invention proposes a method for monitoring the fastening state of a dynamic cable based on optical fiber sensing to solve the technical problems of multi-factor influence of vibration events and dynamic change of vibration data of the floating wind turbine dynamic cable during operation in the prior art.

[0006] The object of the present invention can be achieved by the following technical solutions:

[0007] A method for monitoring the fastening state of a dynamic cable based on optical fiber sensing includes:

[0008] Select multiple fastening points on the dynamic cable according to the dynamic cable structure, and obtain the DAS monitoring data of the fastening points. The DAS monitoring data of the fastening points includes the strain change rate of the fastening points and the center frequency of the fastening points;

[0009] Construct a time series of fastening point detection values through the principal component analysis method according to the DAS monitoring data of the fastening points;

[0010] Obtain the environmental wind speed value and the occurrence time of the environmental wind speed value, and establish an environmental time series through sorting according to the environmental wind speed value and the occurrence time of the environmental wind speed value;

[0011] Construct a real-time detection sequence of the fastening points according to the environmental time series and the time series of the fastening point detection values, and update the real-time detection sequence of the fastening points according to the occurrence time of the environmental wind speed value;

[0012] Obtain the anchoring state of the fastening points through the fastening point anomaly judgment according to the real-time detection sequence of the fastening points;

[0013] The fastening point anomaly judgment includes obtaining the anchoring state of the same fastening point at different times through the correlation judgment of the front and back sequences according to the real-time detection sequence of the fastening points;

[0014] Obtain the anchoring states of different fastening points at the same time according to the correlation coefficient outlier judgment condition of the real-time detection sequence of the fastening points.

[0015] Preferably, the construction of the time series of the fastening point detection values through the principal component analysis method according to the DAS monitoring data of the fastening points includes:

[0016] Construct an SF matrix according to the DAS monitoring data of the fastening points. The mathematical expression of the SF matrix is:

[0017] ,

[0018] Among them, is the strain change rate of each fastening point per unit hour, is the center frequency of each fastening point per unit hour, and n is the number of detection values of each fastening point;

[0019] Reduce the dimension of the SF matrix through the principal component analysis method and take the mean value to establish a time series of fastening point detection values.

[0020] Preferably, the obtaining of the environmental wind speed value and the occurrence time of the environmental wind speed value, and the establishment of the environmental time series through sorting according to the environmental wind speed value and the occurrence time of the environmental wind speed value includes:

[0021] Obtain the environmental wind speed value and the occurrence time of the environmental wind speed value through the wind speed measurement system. The environmental wind speed value is the environmental wind speed value of the dynamic cable laying environment obtained in hours;

[0022] Starting from the initial time t s and taking hours as the unit, obtain the occurrence time of each level of wind from level M to level N of the environmental wind speed level. The last moment after obtaining N - M + 1 data is regarded as the cut-off time t of this time e ;

[0023] According to the environmental wind speed value, sort the occurrence times of the corresponding environmental wind speed values in ascending order to form an environmental time series

[0024] Preferably, constructing a real-time detection sequence of the fastening point according to the environmental time series and the time series of the fastening point detection value, and updating the real-time detection sequence of the fastening point according to the occurrence time of the environmental wind speed value includes:

[0025] Extract the dynamic cable fastening point detection values at the corresponding moments of the environmental time series according to the time series of the fastening point detection values to form a real-time detection sequence of the fastening point;

[0026] Starting from time t e+1 and counting the environmental wind speed value at the current moment every hour. If the environmental wind speed level W at the current moment is within the range of the set [M, N], then update the corresponding time value of the W-level wind speed in the environmental time series, and further extract the dynamic cable fastening point detection value at the moment corresponding to the W-level wind speed from the time series of the fastening point detection values, and replace the corresponding data in the real-time detection sequence of the fastening point

[0027] Preferably, obtaining the anchoring state of the same fastening point at different moments by judging the correlation between the front and back sequences according to the real-time detection sequence of the fastening point includes:

[0028] Obtain L groups of real-time detection sequences of a certain fastening point under the normal state of the submarine cable;

[0029] Calculate the correlation coefficient between the front and back sequences and the first fastening point state threshold according to the L groups of real-time detection sequences of the fastening point through the Spearman correlation coefficient;

[0030] Obtain the anchoring state of the same fastening point at different moments by comparing and judging according to the correlation coefficient between the front and back sequences and the first fastening point state threshold;

[0031] For any fastening point, when the correlation coefficient between the front and back sequences is less than the first fastening point state threshold, that is, the correlation between the rth group of real-time detection sequences C p,r and the (r - 1)th group of real-time detection sequences C p,(r-1) is poor, then judge that the anchoring state of this point is abnormal

[0032] Preferably, the anchoring states of different fastening points at the same moment obtained by the correlation coefficient outlier judgment condition based on the real-time detection sequence of the fastening points include:

[0033] Obtain the real-time detection sequences of g groups of each fastening point under normal conditions;

[0034] Calculate the Spearman correlation coefficients of pairwise combinations between every two fastening points;

[0035] For each group of correlation coefficient data sets of each combination, obtain the data sets within the clustering cluster and the clustering center through improved clustering. The improved clustering includes: iteratively optimizing to obtain the initial clustering center through the differential evolution algorithm, where the differential evolution algorithm uses the sum of squared errors as the fitness function; calculate the Euclidean distances between each data point in the data set and the initial clustering center; update the clustering center through the probability formula until the preset number of iterations is reached;

[0036] Calculate the second fastening point state threshold according to the maximum distance between each data set and its clustering center. The second fastening point state threshold is the judgment threshold for the abnormal state of each combination of fastening points at the same moment;

[0037] Obtain the anchoring states of different fastening points at the same moment according to the second fastening point state threshold through the correlation coefficient outlier judgment condition.

[0038] Preferably, the correlation coefficient outlier judgment condition includes:

[0039] When the Euclidean distance between the data set within different combination clusters and the corresponding clustering center is greater than the corresponding second fastening point state threshold, the correlation coefficient is an outlier; when the Euclidean distance between the data set within different combination clusters and the corresponding clustering center is less than the corresponding second fastening point state threshold, the correlation coefficient is not an outlier;

[0040] When the correlation coefficients of two combinations are outliers at a certain moment, it indicates that the anchoring at the repeated positions in the two combinations is abnormal; when the correlation coefficients of three combinations are outliers at a certain moment, select the two combinations with the largest ratio of the Euclidean distance between the data set within the cluster and the corresponding clustering center to the corresponding second fastening point state threshold among the three combinations, indicating that the anchoring at the repeated positions in these two combinations is abnormal.

[0041] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned dynamic cable fastening state monitoring method based on fiber optic sensing.

[0042] A storage medium containing computer-executable instructions, where the computer-executable instructions are used to execute the above-mentioned dynamic cable fastening state monitoring method based on fiber optic sensing when executed by a computer processor.

[0043] The beneficial effects of the present invention are as follows:

[0044] (1) By comprehensively applying fiber optic sensing monitoring and data analysis methods, for submarine cables with complex working conditions in the marine environment, the real-time monitoring data of the cable fastening points detected by fiber optic sensing devices are processed. By analyzing the correlation of data at multiple times and multiple positions, the sequence correlation coefficient is used for fixed-point vibration detection, overcoming the multi-factor influence of vibration events and the dynamic change of vibration data during the operation of floating wind turbine dynamic cables. Thus, the fastening state of the submarine cable in the suspended state is judged, and the abnormal state of the cable fastening point under complex seabed conditions is realized, solving the problem of on-line operation fastening state monitoring of dynamic submarine cables.

[0045] (2) The initial clustering center is obtained through iterative optimization by the differential evolution algorithm, solving the blindness of randomly selecting the initial clustering center in the traditional clustering algorithm. The clustering center is updated through the probability formula, which can better iterate the center point to obtain better clustering performance. Thus, better clustering is used to obtain the intra-cluster data set and clustering center of multiple groups of correlation coefficient data sets in each combination. Brief Description of the Drawings

[0046] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0047] Figure 1 It is a schematic flow chart of a method for monitoring the fastening state of a dynamic cable based on fiber optic sensing according to the present invention;

[0048] Figure 2 It is a schematic diagram of the laying of a dynamic cable in an embodiment of the present invention;

[0049] Figure 3 It is 50 groups of C 1 and C 2 A schematic diagram of the correlation coefficient of the combination. Detailed Embodiment

[0050] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific embodiments, structures, features and their effects according to the present invention.

[0051] Please refer to Figure 1 , a method for monitoring the fastening state of a dynamic cable based on fiber optic sensing, including:

[0052] S1: Select multiple fastening points on the dynamic cable according to the dynamic cable structure, and obtain the DAS monitoring data of the fastening points. The DAS monitoring data of the fastening points includes the strain change rate of the fastening points and the center frequency of the fastening points;

[0053] S2: Construct a time series of fastening point detection values through the principal component analysis method based on the DAS monitoring data of the fastening points;

[0054] S3: Obtain the environmental wind speed value and the occurrence time of the environmental wind speed value, and establish an environmental time series through sorting according to the environmental wind speed value and the occurrence time of the environmental wind speed value;

[0055] S4: Construct a real-time detection sequence of the fastening points according to the environmental time series and the time series of the fastening point detection values, and update the real-time detection sequence of the fastening points according to the occurrence time of the environmental wind speed value;

[0056] S5: Obtain the anchoring state of the fastening points through abnormal judgment of the fastening points according to the real-time detection sequence of the fastening points.

[0057] In this embodiment, the selection of multiple fastening points on the dynamic cable according to the dynamic cable structure and the acquisition of the DAS monitoring data of the fastening points are specifically implemented through the following steps:

[0058] S101: Select three fastening points on the dynamic cable according to the dynamic cable structure, and the three fastening points are respectively the dynamic cable suspension point, the dynamic cable anchor chain point, and the dynamic cable mud entry point;

[0059] S102: Obtain the DAS monitoring data of the fastening points through the DAS device, that is, the strain change rate and the center frequency of the three fastening points on the dynamic cable;

[0060] It should be noted that the number of the fastening points can be other quantities.

[0061] In this embodiment, the construction of the time series of the fastening point detection values through the principal component analysis method according to the DAS monitoring data of the fastening points is specifically implemented through the following steps:

[0062] S201: Construct an SF matrix according to the DAS monitoring data of the fastening points, and the mathematical expression of the SF matrix is:

[0063] ,

[0064] where, is the strain change rate of each fastening point per unit hour, is the center frequency of each fastening point per unit hour, and n is the number of detection values of each fastening point.

[0065] S202: Reduce the dimension of the SF matrix through the principal component analysis method and take the mean value to establish a time series of the fastening point detection values;

[0066] S202-1: Establish the original sequence of the detection values of each fastening point ;

[0067] Specifically, the original sequence of the detection values of the suspension points of the dynamic cable is and the original sequence of the detection values of the anchor chain points of the dynamic cable is and the original sequence of the detection values of the mud entry points of the dynamic cable is .

[0068] S202-2: Dimensionally reduce the SF matrix through the principal component analysis method to obtain the dimensionally reduced detection values of the fastening points ;

[0069] S202-3: According to the dimensionally reduced detection values of the fastening points, obtain the corresponding elements of the detection value sequence of a certain fastening point at the current moment by taking the average value, then the original sequence of the detection values of a certain fastening point at the current moment the j-th fastening point detection value element in is , and add a fastening point detection value a pj per hour on the basis of the original sequence of the detection values of a certain fastening point, which is the time series of the fastening point detection values.

[0070] Specifically, in this embodiment, taking 100 data detected by the DAS device at each fastening point within each hour as an example, the strain change rate of the fastening point is , the center frequency of the fastening point is , the j-th element in the original sequence of the detection values of a certain fastening point is , and add a fastening point detection value a pj .

[0071] In this embodiment, the obtaining of the environmental wind speed value and the occurrence time of the environmental wind speed value, and the establishment of the environmental time series according to the environmental wind speed value and the occurrence time of the environmental wind speed value through sorting are specifically implemented through the following steps:

[0072] S301: Obtain the environmental wind speed value and the occurrence time of the environmental wind speed value through the wind speed measurement system, and the environmental wind speed value is the environmental wind speed value of the dynamic cable laying environment obtained in hours.

[0073] S302: Starting from the initial time t s , obtain the occurrence time of each level of wind from level M to level N of the environmental wind speed in hours, and the last time after obtaining N - M + 1 data is regarded as the cut-off time t e of this time;

[0074] S303: According to the environmental wind speed value, sort the occurrence times of the corresponding environmental wind speed values in ascending order to form the environmental time series E.

[0075] Specifically, in this embodiment, starting from the initial moment, the occurrence time of each level of wind from level 1 to level 10 of the environmental wind speed is obtained in hours. The last moment after obtaining 10 data is regarded as the cut-off time of this time, and the occurrence moments of the corresponding environmental wind speed values are sorted in ascending order of the environmental wind speed values to form an environmental time series. 。

[0076] In this embodiment, the construction of the real-time detection sequence of the fastening points according to the environmental time series and the time series of the fastening point detection values, and the update of the real-time detection sequence of the fastening points according to the occurrence moments of the environmental wind speed values are specifically implemented through the following steps:

[0077] S401: Extract the dynamic cable fastening point detection values at the corresponding moments of the environmental time series according to the time series of the fastening point detection values to form a real-time detection sequence of the fastening points. The real-time detection sequence of the fastening points includes the real-time detection sequence C of the suspension points 1 , the real-time detection sequence C of the anchor chain points 2 , and the real-time detection sequence C of the mud entry points 3 。

[0078] S402: Starting from the t e + 1 moment, the environmental wind speed value at the current moment is statistically counted every hour. If the environmental wind speed level W at the current moment is within the range of the set [M, N], then update the corresponding time value of the W-level wind speed in the environmental time series E, and further extract the dynamic cable fastening point detection values at the corresponding moments of the W-level wind speed from the time series of the fastening point detection values, and replace the corresponding data in the real-time detection sequence of the fastening points.

[0079] Specifically, in order to exclude the influence of environmental factors on the measurement data, the dynamic cable fastening point detection values at the occurrence moments of the fixed wind speed levels are selected to form the real-time detection sequence C of the fastening points p , and it is judged whether to update the real-time detection sequence of the fastening points in hours, so as to realize the continuous update of the real-time detection sequence of the fastening points with time change, that is, the sequence after the rth update is C p,r 。

[0080] In this embodiment, the determination of the fastening point anchoring state according to the real-time detection sequence of the fastening points through fastening point abnormality judgment is specifically implemented through the following steps:

[0081] S501: Judge the anchoring state of the same fastening point at different moments through the correlation of the front and back sequences according to the real-time detection sequence of the fastening points;

[0082] S501-1: Obtain the L-group real-time detection sequence of a certain fastening point under the normal state of the submarine cable;

[0083] Specifically, in the normal operation state of the submarine cable, L groups of real-time detection sequences of fastening points are obtained for a certain fastening point according to the above steps S1-S4.

[0084] S501-2: Calculate the correlation coefficient between the front and rear sequences and the first fastening point state threshold through the Spearman correlation coefficient according to the L groups of real-time detection sequences of the fastening point;

[0085] S501-21: According to the r-th group of real-time detection sequence C of the fastening point p,r and the (r-1)-th group of real-time detection sequence C of the fastening point p,(r-1) , calculate the correlation coefficient between the front and rear sequences through the Spearman correlation coefficient;

[0086] Specifically, the mathematical expression of the Spearman correlation coefficient is:

[0087] ,

[0088] where ρ is the Spearman correlation coefficient, n is the sample size, R(x i ) is the actual observed value of the i-th data point of variable x, R(y i ) is the actual observed value of the i-th data point of variable y, is the average observed value of all data points of variable x, is the average observed value of all data points of variable y.

[0089] It should be noted that the variable x is the r-th group of real-time detection sequence C of the fastening point p,r , and the variable y is the (r-1)-th group of real-time detection sequence C of the fastening point p,(r-1) .

[0090] Taking the serial numbers of the m data in the r-th group of real-time detection sequence C p,r and the (r-1)-th group of real-time detection sequence C p,(r-1) sorted in ascending order as the rank values, when the values of two data are equal, calculate the average value of the numerical ranks as the rank numbers of these two data. The simplified mathematical expression of the Spearman correlation coefficient is:

[0091] ,

[0092] where r s is the simplified Spearman correlation coefficient, and d is the difference in the corresponding data ranks between the r-th group of real-time detection sequence C p,r and the (r-1)-th group of real-time detection sequence C p,(r-1) .

[0093] It should be noted that when calculating the r-th group of real-time detection sequence C p,r and the (r-1)-th group of real-time detection sequence C p,(r-1)The simplified Spearman correlation coefficient is used as the before-and-after sequence correlation coefficient.

[0094] S501-22: Obtain the first fastening point state threshold by calculating the mean of L-1 groups of correlation coefficients and the standard deviation of L-1 groups of correlation coefficients. The first fastening point state threshold is the judgment threshold for the abnormal state of the same fastening point at different times. The mathematical expression of the first fastening point state threshold is:

[0095] ,

[0096] where, is the mean of L-1 groups of correlation coefficients, is the standard deviation of L-1 groups of correlation coefficients, is the first fastening point state threshold.

[0097] In this embodiment, the threshold estimation for the anchoring state of the same fastening point at different times is as follows: Under the normal operation state of the submarine cable, for a certain fastening point, 50 groups of real-time detection sequences of the fastening point are taken. The sequence numbers obtained by ascending sorting of 10 data in the r-th real-time detection sequence and the r-1-th real-time detection sequence are used as the rank values and substituted into the mathematical expression of the simplified Spearman correlation coefficient, that is ,

[0098] Calculate the mean of L-1 groups of correlation coefficients and the standard deviation of L-1 groups of correlation coefficients to obtain the first fastening point state threshold.

[0099] S501-3: Determine the anchoring state of the same fastening point at different times by comparing and judging according to the before-and-after sequence correlation coefficient and the first fastening point state threshold.

[0100] Specifically, for any fastening point, when the before-and-after sequence correlation coefficient is less than the first fastening point state threshold, that is, the correlation between the r-th real-time detection sequence C p,r and the r-1-th real-time detection sequence C p,(r-1) is poor, then it is judged that the anchoring state of this point is abnormal.

[0101] S502: Obtain the anchoring state of different fastening points at the same time according to the correlation coefficient outlier judgment condition of the fastening point real-time detection sequence.

[0102] S502-1: Obtain g groups of real-time detection sequences of each fastening point under the normal state;

[0103] Specifically, under the normal operation state of the submarine cable, according to the above steps S1-S4, g groups of real-time detection sequences of each of the 3 fastening points are taken.

[0104] S502-2: Calculate the Spearman correlation coefficient of pairwise combinations between every two fastening points;

[0105] Specifically, calculate three combinations C 1 and C 2 , C 1 and C 3 , C 2 and C 3 of the detection values of the three fastening points respectively, and calculate the Spearman correlation coefficients of C

[0106] S502-3: For each group of correlation coefficient data sets of each combination, respectively obtain the data sets within the clustering cluster and the cluster center through improved clustering;

[0107] The steps of the improved clustering include:

[0108] Iteratively optimize through the differential evolution algorithm to obtain the initial cluster center, and the differential evolution algorithm uses the sum of squared errors as the fitness function;

[0109] Calculate the Euclidean distance between each data point in the data set and the initial cluster center;

[0110] Update the cluster center through the probability formula until the preset number of iterations is reached, and the probability formula is:

[0111] ,

[0112] where Z is the updated cluster center, and d i (x) is the Euclidean distance between the current data point and the initial cluster center.

[0113] The data sets within the clustering clusters formed by the g groups of correlation coefficients of each combination are respectively , , , where x gi (i = 1, 2, 3) is the combination number, that is, the g groups of real-time detection sequences of the fastening points under the i-th combination, and y gi is the corresponding Spearman correlation coefficient value, and the cluster centers are respectively , , .

[0114] In this embodiment, 50 groups of real-time detection sequences are taken for each of the three fastening points, that is, g is 50. In other embodiments, other numbers of groups can also be taken for each of the three fastening points according to actual detection needs.

[0115] S502-4: Calculate the second fastening point state threshold according to the maximum distance between each data set and its cluster center, and the second fastening point state threshold is the judgment threshold for the abnormal state of the fastening points of each combination at the same moment;

[0116] The mathematical expression of the second fastening point state threshold is:

[0117] ,

[0118] ,

[0119] ,

[0120] where D i is the distance between each data set and its clustering center, and D max,i is the maximum value of the distance between each data set and its clustering center. is the second fastening point state threshold, and i represents three fastening points.

[0121] S502-5: Obtain the anchoring states of different fastening points at the same moment according to the second fastening point state threshold through the correlation coefficient outlier judgment condition.

[0122] Select the corresponding K groups of vibration data of the fastening points in the state to be monitored, calculate the correlation coefficient data of the corresponding combination and the Euclidean distance from the clustering center, and obtain the anchoring states of different fastening points at the same moment by judging whether the Euclidean distance between the data set within the cluster and its corresponding clustering center is greater than the corresponding second fastening point state threshold.

[0123] Specifically, calculate the Spearman correlation coefficients of three combinations C 1 and C 2 , C 1 and C 3 , C 2 and C 3 of the detection values of 3 fastening points respectively. The k groups of correlation coefficients of each combination respectively form the data sets within the cluster , , . According to the Euclidean distance between the data set within the cluster and its corresponding clustering center , judge whether there is a correlation coefficient outlier by whether it is greater than the corresponding second fastening point state threshold.

[0124] In this embodiment, as Figure 3 shown, it is a schematic diagram of the correlation coefficients of 50 groups of C 1 and C 2 combinations.

[0125] The correlation coefficient outlier judgment condition includes that when the Euclidean distance between the data set within the cluster of different combinations and the corresponding clustering center is greater than the corresponding second fastening point state threshold, the correlation coefficient is an outlier; when the Euclidean distance between the data set within the cluster of different combinations and the corresponding clustering center is less than the corresponding second fastening point state threshold, the correlation coefficient is not an outlier.

[0126] When the outlier of the correlation coefficients of two combinations appears at a certain moment, it indicates that the anchoring at the repeated positions in the two combinations is abnormal; when the outlier of the correlation coefficients of three combinations appears at a certain moment, the ratios of the Euclidean distances between the intra-cluster data sets and the corresponding clustering centers in the three combinations to the corresponding second fastening point state thresholds are taken The two combinations with the largest indicate that the anchoring at the repeated positions in these two combinations is abnormal.

[0127] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium may be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, apparatus, or device.

[0128] The computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0129] The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the foregoing. The computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0130] As described above, the above are only preferred embodiments of the present invention and do not impose any formal limitations on the present invention. Although the present invention has been disclosed above in preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art, without departing from the scope of the technical solution of the present invention, can make some modifications or refinements to the above-disclosed technical content to obtain equivalent embodiments with equivalent changes. However, as long as the content of the technical solution of the present invention is not departed from, any simple modifications, equivalent changes, and refinements made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for monitoring the dynamic cable tightening state based on optical fiber sensing, characterized in that: include: Select multiple fastening points on the dynamic cable according to the dynamic cable structure, and obtain DAS monitoring data of the fastening points, wherein the DAS monitoring data of the fastening points includes a strain change rate of the fastening points and a center frequency of the fastening points; Constructing a time series of fastening point detection values ​​by principal component analysis based on the fastening point DAS monitoring data; Acquire the ambient wind speed value and the time when the ambient wind speed value occurs, and establish an ambient time series by sorting the ambient wind speed value and the time when the ambient wind speed value occurs; Constructing a fastening point real-time detection sequence according to the environmental time sequence and the fastening point detection value time sequence, and updating the fastening point real-time detection sequence according to the occurrence time of the environmental wind speed value; Obtaining the anchoring state of the fastening point by judging the fastening point abnormality according to the fastening point real-time detection sequence; The abnormal judgment of the fastening point includes obtaining the anchoring state of the same fastening point at different times through the correlation judgment of the previous and next sequences according to the real-time detection sequence of the fastening point; The anchoring states of different fastening points at the same time are obtained according to the fastening point real-time detection sequence and the correlation coefficient outlier judgment condition.

2. The method for monitoring the dynamic cable tightening state based on optical fiber sensing according to claim 1, characterized in that: The constructing of a time series of fastening point detection values ​​by principal component analysis according to the fastening point DAS monitoring data comprises: The SF matrix is ​​constructed according to the DAS monitoring data of the fastening point, and the mathematical expression of the SF matrix is: ， in, is the strain change rate of each fastening point per hour, is the center frequency of each fastening point per hour, and n is the number of detection values ​​of each fastening point; According to the SF matrix, the dimension is reduced by principal component analysis and the mean is taken to establish a time series of fastening point detection values.

3. The method for monitoring the dynamic cable tightening state based on optical fiber sensing according to claim 1, characterized in that: The obtaining of the ambient wind speed value and the time when the ambient wind speed value occurs, and establishing the ambient time series by sorting the ambient wind speed value and the time when the ambient wind speed value occurs comprises: Obtaining the ambient wind speed value and the time when the ambient wind speed value occurs through a wind speed measurement system, wherein the ambient wind speed value is a dynamic cable laying ambient wind speed value obtained in hours; From the initial time t s At the beginning, the occurrence time of each wind speed level from level M to level N is obtained in units of hours. The last moment after obtaining N-M+1 data is regarded as the current deadline t e ; The corresponding environmental wind speed value occurrence time is sorted in ascending order according to the environmental wind speed value Construct environmental time series.

4. The method for monitoring the dynamic cable tightening state based on optical fiber sensing according to claim 1, characterized in that: The step of constructing a fastening point real-time detection sequence according to the environmental time sequence and the fastening point detection value time sequence, and updating the fastening point real-time detection sequence according to the occurrence time of the environmental wind speed value comprises: Extracting the dynamic cable fastening point detection value at the corresponding moment of the environmental time series according to the fastening point detection value time series to form a fastening point real-time detection sequence; From e+1 Starting from the moment, the current environmental wind speed value is counted once every hour. If the current environmental wind speed level W is within the range of the set [M, N], the time value corresponding to the W-level wind speed in the environmental time series is updated, and then the dynamic cable fastening point detection value at the moment corresponding to the W-level wind speed is extracted from the fastening point detection value time series, and the corresponding data of the fastening point real-time detection sequence is replaced.

5. The method for monitoring the dynamic cable tightening state based on optical fiber sensing according to claim 1, characterized in that: The method of determining the anchoring state of the same fastening point at different times by judging the correlation between the preceding and following sequences according to the fastening point real-time detection sequence includes: Obtain the real-time detection sequence of a certain fastening point L group under the normal state of the submarine cable; According to the L group of fastening point real-time detection sequences, the Spearman correlation coefficient is used to calculate the before and after sequence correlation coefficient and the first fastening point state threshold; Obtaining the anchoring state of the same fastening point at different times by comparing the correlation coefficient of the preceding and following sequences with the first fastening point state threshold; For any fastening point, when the correlation coefficient of the preceding and following sequences is less than the first fastening point state threshold, that is, the rth group of real-time detection sequence C p,r and the r-1th group of real-time detection sequence C p,(r-1) If the correlation is poor, it is judged that the anchoring state of this point is abnormal.

6. The method for monitoring the dynamic cable tightening state based on optical fiber sensing according to claim 1, characterized in that: The method of obtaining the anchoring states of different fastening points at the same time according to the fastening point real-time detection sequence by using the correlation coefficient outlier judgment condition includes: Obtain g groups of real-time detection sequences of each fastening point under normal conditions; Calculate the Spearman correlation coefficient between each pair of fastening points; For each combination of multiple sets of correlation coefficient data sets, the clustering cluster data sets and cluster centers are obtained by improved clustering, wherein the improved clustering includes: obtaining the initial cluster center by iterative optimization through a differential evolution algorithm, wherein the differential evolution algorithm uses the sum of squared errors as a fitness function; calculating the Euclidean distance between each data point in the data set and the initial cluster center; and updating the cluster center by a probability formula until a preset number of iterations is reached; A second fastening point state threshold is obtained by calculating the maximum distance between each data set and its cluster center, wherein the second fastening point state threshold is a judgment threshold of abnormal fastening point states of each combination at the same time; The anchoring states of different fastening points at the same time are obtained according to the second fastening point state threshold through the correlation coefficient outlier judgment condition.

7. The method for monitoring the dynamic cable tightening state based on optical fiber sensing according to claim 6, characterized in that: The correlation coefficient outlier judgment condition includes: When the Euclidean distance between the data sets in different combined clusters and the corresponding cluster center is greater than the corresponding second fastening point state threshold, the correlation coefficient is outlier; when the Euclidean distance between the data sets in different combined clusters and the corresponding cluster center is less than the corresponding second fastening point state threshold, the correlation coefficient is not outlier; When the correlation coefficients of two combinations are outliers at a certain moment, it means that the anchoring at the repeated position in the two combinations is abnormal; when the correlation coefficients of three combinations are outliers at a certain moment, the two combinations with the largest ratio of the Euclidean distance between the cluster data set and the corresponding cluster center and the corresponding second fastening point state threshold are taken, indicating that the anchoring at the repeated position in these two combinations is abnormal.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the dynamic cable tightening state monitoring method based on optical fiber sensing as described in any one of claims 1 to 7 is implemented.

9. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the dynamic cable tightening state monitoring method based on optical fiber sensing as described in any one of claims 1 to 7.

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