CN117972584A - Mode identification method and system for optical fiber sensing data - Google Patents
Mode identification method and system for optical fiber sensing data Download PDFInfo
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- CN117972584A CN117972584A CN202410045033.1A CN202410045033A CN117972584A CN 117972584 A CN117972584 A CN 117972584A CN 202410045033 A CN202410045033 A CN 202410045033A CN 117972584 A CN117972584 A CN 117972584A
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- 239000013307 optical fiber Substances 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000012549 training Methods 0.000 claims abstract description 35
- 238000003909 pattern recognition Methods 0.000 claims abstract description 28
- 238000000605 extraction Methods 0.000 claims abstract description 15
- 238000012795 verification Methods 0.000 claims description 12
- 230000011218 segmentation Effects 0.000 claims description 8
- 238000001228 spectrum Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 abstract description 4
- 238000003745 diagnosis Methods 0.000 abstract description 3
- 230000008030 elimination Effects 0.000 abstract description 3
- 238000003379 elimination reaction Methods 0.000 abstract description 3
- 230000003287 optical effect Effects 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000003595 spectral effect Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000005236 sound signal Effects 0.000 description 2
- 238000012952 Resampling Methods 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
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- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012567 pattern recognition method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
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Abstract
The invention discloses a mode identification method and a system for optical fiber sensing data, which relate to the technical field of sensors, and the method comprises the following steps: acquiring vibration data of a field environment; performing sectional calibration on the vibration data; extracting characteristics of vibration data subjected to subsection calibration to obtain a vibration sample set; constructing a pattern library, and training the pattern library by using the vibration sample set to update the pattern library; and carrying out pattern recognition and event warning by using the updated pattern library. The invention improves the precision of interference identification and provides powerful support for subsequent fault diagnosis and elimination by combining vibration data, subsection calibration, feature extraction, mode library construction and training and real-time mode identification and event alarming of the field environment.
Description
Technical Field
The invention relates to the technical field of sensors, in particular to a mode identification method and system for optical fiber sensing data.
Background
In the existing optical fiber sensing technology, an optical signal is used as a carrier for transformation and transmission, and a measured parameter is converted into a change in optical characteristics by utilizing the optical transmission characteristics of an optical fiber. However, in practical applications, due to the complexity of the external environment, the optical characteristics of the optical fiber may be disturbed by various factors, resulting in errors in the measurement results.
At present, although some systems based on sound signal acquisition and recognition are used for recognizing the interference type, the systems have poor effect when processing complex background noise interference, the demodulation of audio signals is difficult, and the difficulty of interference pattern recognition is further increased. This limits the application of fiber optic sensing technology in certain environments and therefore a need exists for a method and system that can more accurately identify interference patterns.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a mode identification method of optical fiber sensing data, which aims to solve the problem of low identification precision of an interference mode in the prior art.
In one aspect, the present invention provides a method for identifying a pattern of optical fiber sensing data, including:
Acquiring vibration data of a field environment;
performing sectional calibration on the vibration data;
Extracting characteristics of vibration data subjected to subsection calibration to obtain a vibration sample set;
Constructing a pattern library, and training the pattern library by using the vibration sample set to update the pattern library;
And carrying out pattern recognition and event warning by using the updated pattern library.
Preferably, the step of performing segment calibration on the vibration data includes:
performing sectional calibration according to the laying mode of the optical fiber;
and (5) performing fine segmentation calibration in a region easy to generate large interference.
Preferably, feature extraction is performed on vibration data after segment calibration to obtain a vibration sample set, including:
extracting vibration characteristics from vibration data subjected to sectional calibration;
the extracted vibration features comprise a time domain amplitude feature, a time duration feature, a power spectrum feature and an instantaneous frequency feature.
Preferably, training the pattern library using the vibration sample set includes:
dividing the vibration sample set to obtain a training set and a verification set;
And training the pattern matching model in the pattern library by using the training set, verifying the pattern matching model by using the verification set, and updating the pattern recognition matching model when the recognition precision of the pattern matching model reaches a preset requirement.
Preferably, the mode identification and event warning by using the updated mode library comprises:
Acquiring collected real-time vibration data;
extracting characteristics of the collected real-time vibration data to obtain real-time vibration characteristics;
and inputting the extracted real-time vibration characteristics into an updated mode library, outputting a mode identification result, and recording intrusion alarms and alarm position audios according to the mode identification result.
In another aspect, a pattern recognition system for fiber optic sensing data includes:
The data acquisition module is used for acquiring vibration data of the field environment;
the environment calibration module is used for carrying out sectional calibration on the vibration data;
The feature extraction module is used for extracting features of the vibration data subjected to the sectional calibration to obtain a vibration sample set;
The mode library establishing and updating module is used for establishing a mode library and training the mode library by utilizing the vibration sample set so as to update the mode library;
and the mode identification and alarm module is used for carrying out mode identification and event alarm by using the updated mode library.
Preferably, the environment calibration module is specifically configured to:
performing sectional calibration according to the laying mode of the optical fiber;
and (5) performing fine segmentation calibration in a region easy to generate large interference.
Preferably, the feature extraction module is specifically configured to:
extracting vibration characteristics from vibration data subjected to sectional calibration;
the extracted vibration features comprise a time domain amplitude feature, a time duration feature, a power spectrum feature and an instantaneous frequency feature.
Preferably, the mode library building and updating module is specifically configured to:
dividing the vibration sample set to obtain a training set and a verification set;
And training the pattern matching model in the pattern library by using the training set, verifying the pattern matching model by using the verification set, and updating the pattern recognition matching model when the recognition precision of the pattern matching model reaches a preset requirement.
Preferably, the pattern recognition and alarm module includes:
Acquiring collected real-time vibration data;
extracting characteristics of the collected real-time vibration data to obtain real-time vibration characteristics;
and inputting the extracted real-time vibration characteristics into an updated mode library, outputting a mode identification result, and recording intrusion alarms and alarm position audios according to the mode identification result.
The beneficial effects of the invention are as follows: the invention provides a mode identification method and a system for optical fiber sensing data, wherein the method comprises the following steps: acquiring vibration data of a field environment; performing sectional calibration on the vibration data; extracting characteristics of vibration data subjected to subsection calibration to obtain a vibration sample set; constructing a pattern library, and training the pattern library by using the vibration sample set to update the pattern library; and carrying out pattern recognition and event warning by using the updated pattern library. The invention improves the precision of interference identification and provides powerful support for subsequent fault diagnosis and elimination by combining vibration data, subsection calibration, feature extraction, mode library construction and training and real-time mode identification and event alarming of the field environment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flow chart of a pattern recognition method for optical fiber sensing data provided by the invention;
Fig. 2 is a schematic structural diagram of a pattern recognition system for optical fiber sensing data according to the present invention.
Detailed Description
Embodiments of the technical scheme of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and thus are merely examples, and are not intended to limit the scope of the present invention.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs.
Example 1
As shown in fig. 1, the present invention provides a method for identifying a pattern of optical fiber sensing data, including:
Step1, vibration data of a field environment are obtained;
Vibration data of a field environment is acquired through a DAS system, and particularly, an ultra-narrow linewidth optical pulse signal which is subjected to pulse modulation is emitted into an optical fiber in a fixed period by using the DAS system. The signal is used to detect and collect Rayleigh scattered light on an acoustic cable. The DAS system synchronously detects and collects Rayleigh scattered light caused by sound waves, and acquires phase information of the Rayleigh scattered light detected in each emission period in real time. Resampling Rayleigh scattered light in each scattering period, recovering dynamic strain information at each position on the acoustic wave optical cable at fixed intervals, denoising original phase information at each position to eliminate noise interference, improve signal quality, correspondingly demodulating the real-time dynamic strain information after denoising, and extracting and acquiring vibration data.
Step 2, carrying out sectional calibration on the vibration data;
In the embodiment of the invention, the step of carrying out segment calibration on the vibration data comprises the following steps: performing sectional calibration according to the laying mode of the optical fiber; and (5) performing fine segmentation calibration in a region easy to generate large interference.
Specifically, the erection mode of the optical fiber comprises buried, overhead and pipeline, and sectional calibration is carried out aiming at each mode. This ensures accurate calibration and monitoring of vibration data in different environments. And under each laying mode, collecting an environmental sample and recording relevant weather environment information. For example, recording temperature, humidity, wind speed, etc., which may affect the characteristics of the vibration data, and calibrating these messages in the vibration data, also requires calibrating the corresponding disturbance patterns.
It should be noted that, fine calibration sample collection is performed in areas (such as rails, bridges, etc.) where large interference is likely to occur. These areas may be affected by specific environmental or external disturbances, requiring finer calibration to capture the characteristics of the vibration data. For these specific areas, for example, noise signals without blank noise and with train passing are collected, and information such as noise frequency, duration, amplitude, whether or not there is a fixed intermittent period, etc. is recorded.
The calibration data will help the pattern library to better make intrusion event alert decisions because they provide detailed records and analysis of various vibration events that may occur in the environment, which is beneficial to improving the accuracy and reliability of pattern recognition, ensuring timely and accurate recognition of intrusion events.
Step 3, extracting characteristics of vibration data subjected to sectional calibration to obtain a vibration sample set;
In the embodiment of the invention, feature extraction is performed on vibration data after segmentation calibration to obtain a vibration sample set, and the method comprises the following steps: and extracting vibration characteristics from the vibration data after the segmentation calibration. Vibration features are extracted from each calibrated vibration data segment, which features can be used to describe different aspects of the vibration signal.
The extracted vibration characteristics comprise time domain amplitude characteristics, time duration characteristics, power spectrum characteristics and instantaneous frequency characteristics, wherein the time domain amplitude characteristics are as follows: including Root Mean Square (RMS), peak, kurtosis, etc. Time duration feature: including the duration of the vibration signal, start-stop time, etc. Power spectral characteristics: and (3) performing signal processing on the vibration signal to obtain the power spectral density of the vibration signal, and extracting related spectral characteristics. Instantaneous frequency characteristics: the characteristic of the instantaneous frequency change of the vibration signal can be obtained by a time-frequency analysis method and the like.
By these feature extraction, the raw vibration data can be converted into a set of vibration features with descriptive properties, which facilitates efficient identification and classification of different types of vibration events. The vibration sample set after the feature extraction is used for constructing and training a pattern library so as to improve the recognition and early warning capability of the pattern library on different types of vibration events.
Step 4, constructing a pattern library, and training the pattern library by using the vibration sample set to update the pattern library;
In an embodiment of the present invention, training the pattern library using the vibration sample set includes: dividing the vibration sample set to obtain a training set and a verification set; and training the pattern matching model in the pattern library by using the training set, verifying the pattern matching model by using the verification set, and updating the pattern recognition matching model when the recognition precision of the pattern matching model reaches a preset requirement.
The mode library provided by the embodiment of the invention has the advantage of automatic updating, can be trained and verified according to the latest vibration sample set, and continuously optimizes the model to adapt to the change of vibration characteristics. Therefore, the accuracy and the applicability of the mode library can be improved, and various vibration abnormal conditions can be better identified and pre-warned.
And step 5, performing pattern recognition and event warning by using the updated pattern library.
In the embodiment of the invention, the mode identification and the event warning are carried out by using the updated mode library, which comprises the following steps: acquiring collected real-time vibration data; extracting characteristics of the collected real-time vibration data to obtain real-time vibration characteristics; and inputting the extracted real-time vibration characteristics into an updated mode library, outputting a mode identification result, and recording intrusion alarms and alarm position audios according to the mode identification result.
And after the pattern library training is completed, starting pattern recognition work. The method comprises the steps of collecting disturbance generated according to pedestrians, vehicles, mechanical operation, mechanical excavation, construction, lightning influence and the like through optical fibers, extracting features of real-time vibration data obtained through collection, inputting the feature extraction to a mode recognition library, carrying out mode recognition through the mode recognition library, triggering intrusion alarm if a mode recognition result shows that a damage mode or abnormal vibration exists, and recording audio of an alarm position for subsequent analysis and processing.
In summary, an embodiment of the present invention provides a method for identifying a mode of optical fiber sensing data, where the method includes: acquiring vibration data of a field environment; performing sectional calibration on the vibration data; extracting characteristics of vibration data subjected to subsection calibration to obtain a vibration sample set; constructing a pattern library, and training the pattern library by using the vibration sample set to update the pattern library; and carrying out pattern recognition and event warning by using the updated pattern library. The invention improves the precision of interference identification and provides powerful support for subsequent fault diagnosis and elimination by combining vibration data, subsection calibration, feature extraction, mode library construction and training and real-time mode identification and event alarming of the field environment. In practical application, the method has important significance for guaranteeing the stable operation of the optical fiber sensing network and improving the monitoring performance of the optical fiber sensing network.
Example 2
As shown in fig. 2, an embodiment of the present invention provides a pattern recognition system for optical fiber sensing data, the system including:
The data acquisition module is used for acquiring vibration data of the field environment;
the environment calibration module is used for carrying out sectional calibration on the vibration data;
The feature extraction module is used for extracting features of the vibration data subjected to the sectional calibration to obtain a vibration sample set;
The mode library establishing and updating module is used for establishing a mode library and training the mode library by utilizing the vibration sample set so as to update the mode library;
and the mode identification and alarm module is used for carrying out mode identification and event alarm by using the updated mode library.
In the embodiment of the invention, the environment calibration module is specifically configured to:
performing sectional calibration according to the laying mode of the optical fiber;
and (5) performing fine segmentation calibration in a region easy to generate large interference.
In an embodiment of the present invention, the feature extraction module is specifically configured to:
extracting vibration characteristics from vibration data subjected to sectional calibration;
the extracted vibration features comprise a time domain amplitude feature, a time duration feature, a power spectrum feature and an instantaneous frequency feature.
In the embodiment of the present invention, the mode library creation and update module is specifically configured to:
dividing the vibration sample set to obtain a training set and a verification set;
And training the pattern matching model in the pattern library by using the training set, verifying the pattern matching model by using the verification set, and updating the pattern recognition matching model when the recognition precision of the pattern matching model reaches a preset requirement.
In an embodiment of the present invention, the mode identifying and alarming module includes:
Acquiring collected real-time vibration data;
extracting characteristics of the collected real-time vibration data to obtain real-time vibration characteristics;
and inputting the extracted real-time vibration characteristics into an updated mode library, outputting a mode identification result, and recording intrusion alarms and alarm position audios according to the mode identification result.
It should be understood that, for the same inventive concept, the mode recognition system for optical fiber sensing data provided in the embodiment of the present invention and the mode recognition method for optical fiber sensing data provided in the foregoing embodiment of the present invention, reference may be made to the foregoing embodiment for more specific working principles of each module in the embodiment of the present invention, which is not repeated in the embodiment of the present invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
Claims (10)
1. A method for pattern recognition of optical fiber sensing data, comprising:
Acquiring vibration data of a field environment;
performing sectional calibration on the vibration data;
Extracting characteristics of vibration data subjected to subsection calibration to obtain a vibration sample set;
Constructing a pattern library, and training the pattern library by using the vibration sample set to update the pattern library;
And carrying out pattern recognition and event warning by using the updated pattern library.
2. The method for pattern recognition of optical fiber sensing data according to claim 1, wherein the step of performing the segment calibration on the vibration data comprises:
performing sectional calibration according to the laying mode of the optical fiber;
and (5) performing fine segmentation calibration in a region easy to generate large interference.
3. The method for identifying the mode of the optical fiber sensing data according to claim 2, wherein the feature extraction is performed on the vibration data after the segment calibration to obtain a vibration sample set, comprising:
extracting vibration characteristics from vibration data subjected to sectional calibration;
the extracted vibration features comprise a time domain amplitude feature, a time duration feature, a power spectrum feature and an instantaneous frequency feature.
4. The method of claim 1, wherein training the pattern library using the vibration sample set comprises:
dividing the vibration sample set to obtain a training set and a verification set;
And training the pattern matching model in the pattern library by using the training set, verifying the pattern matching model by using the verification set, and updating the pattern recognition matching model when the recognition precision of the pattern matching model reaches a preset requirement.
5. The method for pattern recognition of optical fiber sensing data according to claim 1, wherein the pattern recognition and event alerting using the updated pattern library comprises:
Acquiring collected real-time vibration data;
extracting characteristics of the collected real-time vibration data to obtain real-time vibration characteristics;
and inputting the extracted real-time vibration characteristics into an updated mode library, outputting a mode identification result, and recording intrusion alarms and alarm position audios according to the mode identification result.
6. A pattern recognition system for optical fiber sensing data, comprising:
The data acquisition module is used for acquiring vibration data of the field environment;
the environment calibration module is used for carrying out sectional calibration on the vibration data;
The feature extraction module is used for extracting features of the vibration data subjected to the sectional calibration to obtain a vibration sample set;
The mode library establishing and updating module is used for establishing a mode library and training the mode library by utilizing the vibration sample set so as to update the mode library;
and the mode identification and alarm module is used for carrying out mode identification and event alarm by using the updated mode library.
7. The pattern recognition system of optical fiber sensing data according to claim 6, wherein the environment calibration module is specifically configured to:
performing sectional calibration according to the laying mode of the optical fiber;
and (5) performing fine segmentation calibration in a region easy to generate large interference.
8. The method for pattern recognition of optical fiber sensing data according to claim 7, wherein the feature extraction module is specifically configured to:
extracting vibration characteristics from vibration data subjected to sectional calibration;
the extracted vibration features comprise a time domain amplitude feature, a time duration feature, a power spectrum feature and an instantaneous frequency feature.
9. The method for identifying patterns of optical fiber sensing data according to claim 7, wherein the pattern library creating and updating module is specifically configured to:
dividing the vibration sample set to obtain a training set and a verification set;
And training the pattern matching model in the pattern library by using the training set, verifying the pattern matching model by using the verification set, and updating the pattern recognition matching model when the recognition precision of the pattern matching model reaches a preset requirement.
10. The method for pattern recognition of optical fiber sensing data according to claim 8, wherein the pattern recognition and alarm module comprises:
Acquiring collected real-time vibration data;
extracting characteristics of the collected real-time vibration data to obtain real-time vibration characteristics;
and inputting the extracted real-time vibration characteristics into an updated mode library, outputting a mode identification result, and recording intrusion alarms and alarm position audios according to the mode identification result.
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