Monitoring Method and System for Underground Smart Pipe Networks Based on Optical Fiber Transmission Detection

Abstract

This application relates to the technical field of underground pipe network monitoring. This application discloses a monitoring method and system for an underground intelligent pipe network based on optical fiber transmission detection. The method includes: obtaining the optical fiber transmission signal of the pipeline; locating the abnormal vibration section of the pipeline according to the optical fiber transmission signal; making a vibration abnormality judgment on the optical fiber transmission signal in the abnormal vibration section to identify the abnormal vibration position; and identifying the abnormal vibration type of the pipeline by using a preset vibration state recognition model according to the optical fiber transmission signal at the abnormal vibration position, so as to monitor the vibration state of the underground intelligent pipe network. This application can accurately locate the specific position of the abnormal vibration of the pipeline, identify the abnormal vibration type based on the deep signal characteristics, improve the accuracy of the recognition result, realize the intelligentization of the vibration monitoring of the underground intelligent pipe network, and improve the accuracy of the monitoring result.

Description

Technical Field

[0001] The present invention relates to the technical field of underground pipe network monitoring, and particularly to a monitoring method and system for an underground intelligent pipe network based on optical fiber transmission detection. Background Art

[0002] With the continuous advancement of urban infrastructure construction, the underground pipeline network is becoming increasingly complex and huge. Ensuring the safe operation of pipelines is crucial for the normal operation of the city. Traditional pipeline vibration state recognition methods identify from the overall pipeline and cannot analyze abnormal states according to the actual environmental conditions of pipelines in different regions, prone to misjudgment; the vibration recognition method is based on low-dimensional features and cannot guarantee the accuracy of the pipeline vibration state recognition result.

[0003] For example, the patent application with the publication number CN116818490A discloses a pipeline strength detection method, system, storage medium and intelligent terminal, including: when the pipeline is placed on the detection platform, controlling the detection platform to rotate and obtaining the external image information of the pipeline; judging the external defect features in the image; if there are external defect features, outputting a signal of insufficient strength; if there are no external defect features, controlling the detection device to detect the inner side wall of the pipeline and obtaining the rotation angle information and vibration numerical information; judging whether the value corresponding to the vibration numerical information is within a reasonable range; if not, outputting a signal of insufficient strength; if so, continuing to rotate and detect and obtaining the forward length information until the length value corresponding to the forward length information is the same as the pipeline length value, then controlling the detection device to stop working.

[0004] The above technical solutions have the problems raised in this background art: identifying from the overall pipeline, unable to analyze abnormal states according to the actual environmental conditions of pipelines in different regions, prone to misjudgment; for the vibration recognition of pipelines, it is based on low-dimensional features and cannot guarantee the accuracy of the pipeline vibration state recognition result; to solve at least one of the above problems, the present application proposes a monitoring method and system for an underground intelligent pipe network based on optical fiber transmission detection. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the main object of the present invention is to provide a monitoring method and system for an underground intelligent pipe network based on optical fiber transmission detection, which can effectively solve the problems in the background art. The specific technical solutions of the present invention are as follows:

[0006] A monitoring method for an underground intelligent pipe network based on optical fiber transmission detection includes:

[0007] Obtaining the optical fiber transmission signal of the pipeline;

[0008] Locating the abnormal vibration section of the pipeline according to the optical fiber transmission signal;

[0009] By analyzing the similarity of the optical fiber transmission signals between adjacent abnormal vibration windows, the consistency of the seismic sources within the abnormal vibration section is judged to obtain multiple different seismic source sections;

[0010] Perform vibration abnormality judgment on the optical fiber transmission signals of each seismic source section to identify the abnormal vibration positions;

[0011] Based on the optical fiber transmission signals at the abnormal vibration positions, use a preset vibration state recognition model to identify the abnormal vibration types of the pipeline, so as to monitor the vibration state of the underground intelligent pipe network.

[0012] Specifically, locating the abnormal vibration section of the pipeline according to the optical fiber transmission signals includes:

[0013] Analyze the vibration state of the optical fiber transmission signals within each preset sliding window to obtain vibration analysis results;

[0014] Perform abnormal vibration judgment on the vibration analysis results to locate the abnormal vibration section of the pipeline.

[0015] Specifically, the analyzing the vibration state of the optical fiber transmission signals within each preset sliding window to obtain vibration analysis results includes:

[0016] According to the historical data of the optical fiber transmission signals, obtain the sliding window and the sliding step size through a preset window optimization model;

[0017] Slide the sliding window on the real-time optical fiber transmission signals according to the sliding step size;

[0018] Within the sliding window, analyze multiple time-domain features of the optical fiber transmission signals to obtain vibration analysis results.

[0019] Specifically, the performing abnormal vibration judgment on the vibration analysis results to locate the abnormal vibration section of the pipeline includes:

[0020] Perform joint calculation on multiple time-domain features in the vibration analysis results to obtain the deviation degree value within each sliding window;

[0021] Identify the sliding windows with the deviation degree value greater than the preset deviation degree threshold as abnormal vibration windows to obtain the abnormal vibration section of the pipeline.

[0022] Specifically, the performing vibration abnormality judgment on the optical fiber transmission signals of each seismic source section to identify the abnormal vibration positions includes:

[0023] Decompose the optical fiber transmission signals of each seismic source section to obtain decomposed signal features;

[0024] Based on the decomposed signal features, use a preset vibration anomaly recognition model to locate the abnormal vibration position.

[0025] Specifically, by analyzing the similarity of the optical fiber transmission signals between adjacent abnormal vibration windows, judge the consistency of the vibration sources within the abnormal vibration section, and obtain multiple different vibration source sections, including:

[0026] Calculate the similarity of the optical fiber transmission signals between adjacent abnormal vibration windows to obtain a similarity value;

[0027] Take the adjacent abnormal vibration windows with the similarity value greater than the preset similarity threshold as the same vibration source windows;

[0028] Fuse the same vibration source windows to obtain multiple fused windows;

[0029] Combine the fused windows and non-same vibration source windows to obtain multiple different vibration source sections.

[0030] Specifically, perform signal decomposition on the optical fiber transmission signals of each vibration source section to obtain decomposed signal features, including:

[0031] Set the decomposition layer according to the frequency distribution range of the optical fiber transmission signals within each vibration source section;

[0032] Decompose the optical fiber transmission signals according to the decomposition layer to obtain sub-band signals of multiple different frequencies;

[0033] Screen out the sub-band signals whose frequencies match the preset vibration source frequency range from the sub-band signals as key sub-band signals;

[0034] Combine the key sub-band signals within each vibration source section respectively to obtain the reconstructed signals within each vibration source section;

[0035] Calculate the frequency features of the reconstructed signals according to the reconstructed signals within each vibration source section to obtain decomposed signal features.

[0036] Specifically, according to the optical fiber transmission signals at the abnormal vibration position, use a preset vibration state recognition model to identify the abnormal vibration type of the pipeline for monitoring the vibration state of the underground intelligent pipe network, including:

[0037] Combine the time-domain features and frequency-domain features of the optical fiber transmission signals corresponding to the abnormal vibration position to obtain a feature set;

[0038] According to the feature set, obtain the abnormal vibration type of the abnormal vibration position through a preset vibration state recognition model.

[0039] Specifically, the obtaining of the optical fiber transmission signals of the pipeline includes:

[0040] Normalize, perform exponential transformation, and denoise the collected optical fiber transmission signals.

[0041] A monitoring system for an underground intelligent pipe network based on optical fiber transmission detection, which is used to implement the monitoring method for the underground intelligent pipe network based on optical fiber transmission detection, includes:

[0042] A signal acquisition module that acquires the optical fiber transmission signals of the pipeline;

[0043] An abnormal section positioning module that locates the abnormal vibration section of the pipeline according to the optical fiber transmission signals;

[0044] An abnormal position identification module that judges the consistency of the seismic sources within the abnormal vibration section by analyzing the similarity of the optical fiber transmission signals between adjacent abnormal vibration windows, obtains multiple different seismic source sections, and performs vibration abnormality judgment on the optical fiber transmission signals of each seismic source section to identify the abnormal vibration positions;

[0045] A vibration state identification module that identifies the abnormal vibration types of the pipeline according to the optical fiber transmission signals of the abnormal vibration positions, using a preset vibration state identification model, so as to monitor the vibration state of the underground intelligent pipe network.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] Based on the optical fiber transmission signals, the present invention locates the abnormal vibration sections of the underground intelligent pipe network, which can narrow the range of identifying the abnormal vibration state of the pipeline. Further positioning the abnormal vibration positions within each abnormal vibration section can accurately locate the specific positions of the abnormal vibrations, improve the positioning accuracy, provide an accurate position basis for quickly taking maintenance measures, identify the vibration types of the vibration positions based on the deep signal characteristics, improve the accuracy of the identification results, realize the intelligent monitoring of the vibration of the underground intelligent pipe network, and improve the accuracy of the monitoring results. Description of the Drawings

[0048] Figure 1 It is the working flow chart of the monitoring method for the underground intelligent pipe network based on optical fiber transmission detection in Embodiment 1 of the present invention;

[0049] Figure 2 It is the schematic diagram of pipeline vibration monitoring in Embodiment 1 of the present invention;

[0050] Figure 3 It is the working schematic diagram of seismic source consistency judgment in Embodiment 1 of the present invention;

[0051] Figure 4 It is the structural schematic diagram of the monitoring system for the underground intelligent pipe network based on optical fiber transmission detection in Embodiment 2 of the present invention. Detailed implementation manners

[0052] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific implementation manners of the present invention will be given in conjunction with the accompanying drawings of the specification.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0054] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other from other embodiments.

[0055] Embodiment 1

[0056] This embodiment provides a monitoring method for an underground intelligent pipe network based on optical fiber transmission detection. As Figure 1 shown, the monitoring method for the underground intelligent pipe network based on optical fiber transmission detection includes:

[0057] S101. Obtain the optical fiber transmission signal of the pipeline;

[0058] S102. Locate the abnormal vibration section of the pipeline according to the optical fiber transmission signal;

[0059] S103. Perform abnormal vibration judgment on the optical fiber transmission signal in the abnormal vibration section to identify the abnormal vibration position;

[0060] S104. According to the optical fiber transmission signal at the abnormal vibration position, use a preset vibration state recognition model to identify the abnormal vibration type of the pipeline, so as to monitor the vibration state of the underground intelligent pipe network.

[0061] The traditional pipeline vibration state recognition method recognizes the pipeline as a whole, and cannot analyze the abnormal state according to the actual environmental conditions of the pipeline in different areas, which is prone to misjudgment. The vibration recognition model recognizes based on low-dimensional vibration features, and cannot guarantee the accuracy of the pipeline vibration state recognition results. This embodiment provides a monitoring method for underground smart pipe networks based on optical fiber transmission detection. First, the collected optical fiber transmission signal is analyzed to locate the abnormal vibration area; secondly, the specific vibration state analysis is performed for the signal characteristics in each abnormal vibration area, and the vibration position in the vibration area is further located to obtain the accurate vibration position; finally, the specific vibration condition of the vibration position is identified, including construction vibration conditions, pipeline failure conditions, etc.

[0062] In this embodiment, first, distributed optical fibers are laid along the pipeline to conduct comprehensive and uninterrupted monitoring of the underground smart pipeline network. When the pipeline vibrates, the stress and strain of the optical fiber will change accordingly, and the corresponding signal characteristics such as the phase and amplitude of the optical fiber transmission signal will fluctuate accordingly. The change in the optical fiber signal can reflect the vibration of the pipeline, and the optical fiber has a high sensitivity to tiny vibrations, providing a data basis for subsequent vibration state identification; under different vibration states, the statistical characteristics of the optical fiber transmission signal will show significant differences. When external factors cause abnormal vibration of the pipeline network, such as construction impact, geological mutation, etc., the energy distribution and fluctuation characteristics of the vibration signal in a short period of time will change, which is reflected in the statistical characteristics of the signal as exceeding the normal threshold range. The signal state is monitored in real time through a sliding window, and a preliminary judgment of abnormal vibration is made to obtain abnormal time periods and abnormal areas. By locating the abnormal vibration section, the recognition range of abnormal vibration state of the pipeline can be narrowed and the efficiency of abnormal state recognition can be improved.

[0063] Specifically, according to the located abnormal vibration section, the time-frequency domain characteristics of the signal at different positions in each abnormal vibration section are analyzed respectively to determine the specific position of the abnormal vibration. By further locating the abnormal vibration position in each abnormal vibration section, the specific position of the abnormal vibration can be accurately located, the positioning accuracy is improved, and an accurate position basis is provided for quickly taking maintenance measures. According to the identified abnormal vibration position, the time domain characteristics and frequency domain characteristics of the signal corresponding to each abnormal vibration position are extracted to obtain a richer feature set. Based on the feature set, the vibration type is classified using a preset vibration state recognition model. By extracting deep signal features, the features include complex textures, trends and other information of the signal in the time-frequency domain, thereby improving the generalization of the model and the accuracy of the classification results, realizing the intelligent vibration monitoring of underground smart pipe networks and improving the accuracy of the monitoring results.

[0064] The present invention is based on optical fiber transmission signals to locate abnormal vibration sections of underground intelligent pipe networks, which can narrow the scope of identification of abnormal vibration states of pipelines, further locate the positions of abnormal vibrations within each abnormal vibration section, accurately locate the specific positions of abnormal vibrations, improve the positioning accuracy, provide an accurate position basis for quickly taking maintenance measures, identify the vibration types of vibration positions based on deep signal characteristics, improve the accuracy of identification results, realize the intelligent monitoring of vibrations of underground intelligent pipe networks, and improve the accuracy of monitoring results.

[0065] Further, as Figure 2 , locating the abnormal vibration section of the pipeline according to the optical fiber transmission signal includes:

[0066] S201. Analyze the vibration state of the optical fiber transmission signal within each preset sliding window to obtain a vibration analysis result;

[0067] S202. Judge abnormal vibrations for the vibration analysis result to locate the abnormal vibration section of the pipeline.

[0068] In this embodiment, the sliding window slides on the collected optical fiber transmission signal, divides the optical fiber transmission signal into signals within each window, performs signal analysis within each window, obtains the vibration time-domain characteristics of the signal in different windows, analyzes the vibration conditions at different times and positions, and obtains the vibration analysis result. By sliding the window at different time positions, the characteristics of different parts of the signal can be obtained, reflecting the local change details of the signal, making the analysis result more accurate. The window size and sliding step can be flexibly adjusted according to the actual situation of the pipe network. For areas with fast vibration changes, reduce the window size and step to capture the fast-changing vibration characteristics. For relatively stable areas, appropriately increase the window and step to improve the data processing efficiency while ensuring the accuracy of monitoring.

[0069] Specifically, there are also slight vibrations during the normal transmission process of the underground pipe network. Under different vibration states, the characteristic analysis results of the optical fiber transmission signal show different patterns. According to the differences in the optical fiber transmission signal between the normal vibration mode and the abnormal vibration mode, it is judged whether there is an abnormal vibration. When the signal characteristics deviate from the normal mode by a certain degree, it is identified as an abnormal vibration. The time and pipeline position corresponding to the window where the signal in the sliding window deviates from the normal state by a certain threshold are used as the abnormal vibration section. Through the sliding window and the identification of abnormal vibration states, the time period and position area of abnormal vibrations can be accurately located, narrowing the scope of abnormal state identification, and providing a clear target range for subsequent in-depth analysis and processing. After determining the abnormal vibration section, each abnormal section can be analyzed and processed separately, without the need to identify the abnormal state based on the entire pipeline, greatly improving the monitoring efficiency.

[0070] Further, analyzing the vibration state of the optical fiber transmission signal within each preset sliding window to obtain a vibration analysis result, including:

[0071] S301. Obtaining a sliding window and a sliding step size through a preset window optimization model based on the historical data of the optical fiber transmission signal;

[0072] S302. Sliding the sliding window on the real-time optical fiber transmission signal according to the sliding step size;

[0073] S303. Analyzing multiple time-domain features of the optical fiber transmission signal within the sliding window to obtain a vibration analysis result.

[0074] In this embodiment, by sliding the sliding window on the optical fiber transmission signal, the settings of the size and sliding step size of the sliding window will affect the accuracy and efficiency of signal analysis. For example, if the window size is too large, some local vibration characteristics will be lost; while if the window size is too small, insufficient signal information can be obtained to accurately judge the vibration state. In this embodiment, by analyzing the historical data of the optical fiber transmission signal, the window optimization model is solved using the historical data. Specifically, the window optimization model is a genetic model. By taking the maximization of the vibration state recognition accuracy as the objective and the sliding window size and sliding step size as variables, the optimal solution is obtained according to the preset number of iterations, and the optimized sliding window size and sliding step size are obtained. By optimizing the window parameters, the vibration characteristics in the signal can be better captured, the accuracy of vibration state analysis can be improved, and reasonable window size and step size settings can reduce unnecessary computational complexity and improve computational efficiency while ensuring analysis accuracy.

[0075] Specifically, set the sliding window according to the optimized window parameters, so that the sliding window moves according to the optimized size and sliding step size. During the movement, analyze the signal according to each window to obtain vibration state information in a timely manner; within each window, extract the time-domain features of the signal, including the mean value, variance, peak value, etc. The time-domain features can directly reflect the changes of the signal in the time dimension, and the vibration state of the optical fiber can be judged by analyzing the time-domain features.

[0076] Further, performing an abnormal vibration judgment on the vibration analysis result to locate the abnormal vibration section of the pipeline, including:

[0077] S401. Performing a joint calculation on multiple time-domain features in the vibration analysis result to obtain a deviation value within each sliding window;

[0078] S402. Identifying the sliding window with the deviation value greater than the preset deviation threshold as an abnormal vibration window to obtain the abnormal vibration section of the pipeline.

[0079] In this embodiment, when analyzing the signal vibration state, a single time-domain feature cannot comprehensively reflect the vibration state of the pipeline. In this embodiment, through the multi-information combination method, multiple extracted time-domain features are jointly processed, and the joint formula is as follows:

[0080] ;

[0081] In the formula, is the deviation value, is the weight coefficient of the i-th time-domain feature, which can be set according to actual calculation requirements and signal characteristics, is the real-time feature value of the i-th time-domain feature, is the i-th time-domain feature value in the normal vibration state, and n is the number of time-domain features. By combining multiple time-domain features, the vibration state of the signal can be evaluated more comprehensively, avoiding misjudgment or missed judgment of abnormal vibrations caused by the limitations of a single feature, and improving the accuracy of judging abnormal states.

[0082] After calculating the deviation value in each window, a deviation threshold , which can be set according to actual calculation requirements, is set. The sliding window with the deviation value greater than the deviation threshold is marked as an abnormal vibration window, and the pipeline position corresponding to the abnormal vibration window is used as the abnormal vibration section of the pipeline. By setting a reasonable deviation threshold, the abnormal vibration window can be accurately identified, and then the abnormal vibration section can be located, which helps to timely detect the abnormal vibration of the pipeline and take corresponding maintenance and repair measures.

[0083] Furthermore, the method for judging vibration abnormality of the optical fiber transmission signal in the abnormal vibration section and identifying the abnormal vibration position includes:

[0084] S501. Judge the source consistency in the abnormal vibration section to obtain multiple different source sections;

[0085] S502. Decompose the optical fiber transmission signal of each source section to obtain the decomposed signal characteristics;

[0086] S503. According to the decomposed signal characteristics, use a preset vibration abnormality recognition model to locate the abnormal vibration position.

[0087] In this embodiment, the scope of the underground pipe network is large, and there will be multiple different seismic sources in the identified abnormal vibration sections. The seismic source consistency judgment method is used to judge the consistency of the seismic sources in different abnormal vibration sections. The vibration signals generated by different seismic sources will have different characteristics. The seismic source consistency judgment method analyzes the characteristics of the vibration signals, classifies the signal parts with similar characteristics into the same seismic source section, fuses the sections belonging to the same seismic source, expands the seismic source identification range, and regards the signal parts with dissimilar characteristics as different seismic source sections. For example, different seismic sources have different frequency components, phases, etc. By analyzing these characteristics, the vibrations generated by different seismic sources can be distinguished, providing a basis for more accurate subsequent abnormal vibration positioning, avoiding confusing the vibrations generated by different seismic sources, improving the analysis accuracy of abnormal vibrations, and helping to more accurately find the root cause of abnormal vibrations.

[0088] Specifically, after identifying multiple seismic source regions, the signals in each seismic source region are decomposed respectively. Using the multi-layer wavelet packet decomposition method, the signals are decomposed into sub-signals of different frequencies. These sub-signals contain the information of the original signal in different frequency ranges. Analyze the sub-signals and extract the characteristics of the sub-signals as the decomposed signal characteristics. By decomposing the signal into multiple sub-signals of different frequencies, a more detailed analysis of the frequency characteristics of the signal can be carried out, which helps to discover the performance of abnormal vibrations at different frequencies, extract various characteristics from different frequency sub-bands, provide rich characteristic information for abnormal vibration identification, and enhance the accuracy of abnormal vibration position positioning.

[0089] Specifically, the characteristics extracted from the decomposed signals are input into a preset vibration anomaly identification model. The vibration anomaly identification model is trained through the decomposed signal characteristics and corresponding abnormal position marks under different seismic sources and different states to obtain a trained vibration anomaly identification model. Input the decomposed signal characteristics of each seismic source section into the trained vibration anomaly identification model. The model outputs the abnormal judgment result and abnormal position of the seismic source section according to the input characteristics. Through the pre-trained vibration anomaly identification model, the position of abnormal vibrations can be accurately located, and the positioning accuracy of abnormal vibrations can be improved.

[0090] Further, as Figure 3 , judging the consistency of the seismic sources in the abnormal vibration section to obtain multiple different seismic source sections includes:

[0091] S601. Calculate the similarity of the optical fiber transmission signals between adjacent abnormal vibration windows to obtain a similarity value;

[0092] S602. Take the adjacent abnormal vibration windows with the similarity value greater than the preset similarity threshold as the same seismic source windows;

[0093] S603. Fuse the same source windows to obtain multiple fused windows;

[0094] S604. Combine the fused windows and the non - same source windows to obtain multiple different source segments.

[0095] In this embodiment, since the regions belonging to the same source are relatively close in position and the signals belonging to the same source have a high similarity, calculate the similarity of the optical fiber transmission signals between each abnormal vibration window and its adjacent abnormal vibration window. Based on the time - domain characteristics of the signals, calculate the similarity of the time - domain characteristics of the signals between adjacent abnormal vibration windows. Construct the signal feature vector of each window according to the time - domain characteristics. By calculating the cosine similarity between the signal feature vectors, measure the signal similarity between the windows. By calculating the signal similarity of adjacent abnormal vibration windows, determine whether the signals of different windows come from the same source.

[0096] Specifically, according to historical data or calculation requirements, set a similarity threshold. When the similarity value between adjacent abnormal vibration windows is greater than the similarity threshold, it is considered that they are caused by the same source, and they are classified as the same source windows. Specifically, a suitable threshold can be selected by observing the signal similarity distribution of different sources. For example, for a certain type of optical fiber transmission system, according to experiments, it is found that the signal similarity of different sources is generally lower than 0.7, then the similarity threshold is set to 0.7. Identify the same source windows through similarity, accurately classify the abnormal vibration windows from the same source, fuse the adjacent abnormal vibration windows belonging to the same source, and concentrate the signal characteristics of the same source together, which can expand the identification range of the abnormal positions of the same source, avoid the signals of the same source being scattered in multiple windows, causing difficulties in analysis, facilitate subsequent feature extraction and analysis, and improve the accuracy and efficiency of source feature analysis.

[0097] Combining the fused same - source windows and the non - fused non - same - source windows together, multiple different source segments can be obtained. Each source segment corresponds to one or more adjacent abnormal vibration windows, representing the influence range of different sources on the optical fiber transmission signal, completing the division of the sources in the abnormal vibration segment, and respectively analyzing the signals and identifying the vibration types according to different sources, improving the accuracy of vibration state analysis.

[0098] Further, perform signal decomposition on the optical fiber transmission signal of each source segment to obtain decomposed signal features, including:

[0099] S701. Set the number of decomposition layers according to the frequency distribution range of the optical fiber transmission signal in each source segment;

[0100] S702. Decompose the optical fiber transmission signal according to the number of decomposition layers to obtain multiple sub-band signals with different frequencies;

[0101] S703. Screen out the sub-band signals whose frequencies match the preset vibration source frequency range from the sub-band signals as key sub-band signals;

[0102] S704. Combine the key sub-band signals in each vibration source section respectively to obtain the reconstructed signals in each vibration source section;

[0103] S705. Calculate the frequency characteristics of the reconstructed signal according to the reconstructed signal in each vibration source section to obtain the decomposed signal characteristics.

[0104] In this embodiment, the signal is decomposed by the multi-layer wavelet packet decomposition method. The number of decomposition layers determines the refinement degree of the frequency resolution. If the frequency distribution range is wide, more decomposition layers are required to more finely separate the information of different frequencies; on the contrary, a narrower frequency range requires fewer decomposition layers. Set the number of decomposition layers according to the frequency distribution range of the optical fiber transmission signal in the vibration source section, and decompose the signal to an appropriate frequency resolution, so as to accurately capture the different frequency components contained in the signal. The calculation formula of the number of decomposition layers is as follows:

[0105] ;

[0106] In the formula, L is the minimum number of decomposition layers, is the maximum frequency of the signal, is the minimum frequency of the signal, is the frequency resolution. Calculate the minimum number of decomposition layers, set the corresponding number of decomposition layers based on the minimum number of decomposition layers, and set the appropriate number of decomposition layers according to the frequency range of the signal, which can avoid the waste of computing resources caused by over-decomposition or the loss of information caused by under-decomposition, and improve the computing efficiency and analysis accuracy.

[0107] Specifically, according to the set number of decomposition layers, use the wavelet basis function to decompose the signal into sub-band signals with different frequencies. Each layer of decomposition will further divide each sub-band signal of the previous layer into two sub-bands, and finally obtain a series of sub-band signals with different frequency ranges. These sub-band signals can more finely represent the information of the original signal at different frequencies. The signal decomposition formula is as follows:

[0108] ;

[0109] In the formula, is the optical fiber transmission signal, It is the wavelet packet decomposition signal of the k-th subband in the j-th layer. Through wavelet packet decomposition, a signal can be decomposed into multiple subband signals with different frequencies, providing a more refined frequency analysis, which helps to deeply explore the frequency information in the signal and discover the signal characteristics hidden in different frequency components.

[0110] Specifically, different vibration sources have specific frequency ranges. The frequency ranges of the decomposed subband signals are matched with the preset vibration source frequency intervals, and the key subband signals directly related to the vibration source are screened out from multiple subband signals, so as to more accurately locate the frequency characteristics of the vibration source. By screening the key subband signals, the interference of irrelevant frequency information is reduced, making the analysis result more accurate; the screened key subband signals are combined together to reconstruct a reconstructed signal that is closer to the original vibration source signal. This reconstructed signal concentrates the information related to the vibration source frequency and can better reflect the characteristics of the vibration source, which helps the subsequent further analysis of the vibration source.

[0111] For each vibration source section, a reconstructed signal is obtained respectively, and the frequency characteristics of each reconstructed signal are calculated, including frequency characteristics such as the central frequency, bandwidth, and energy distribution of the frequency, which reflect the characteristics of the vibration source from different angles. By calculating the frequency characteristics of the reconstructed signal, the characteristics of the vibration source can be understood more deeply, providing more characteristic information for vibration source positioning, classification, and anomaly judgment.

[0112] Furthermore, according to the optical fiber transmission signal at the abnormal vibration position, using a preset vibration state recognition model, the abnormal vibration type of the pipeline is recognized for the vibration state monitoring of the underground intelligent pipe network, including:

[0113] S801: Combine the time-domain characteristics and frequency-domain characteristics of the optical fiber transmission signal corresponding to the abnormal vibration position to obtain a feature set;

[0114] S802: According to the feature set, through a preset vibration state recognition model, obtain the abnormal vibration type at the abnormal vibration position.

[0115] In this embodiment, the time-domain characteristics and frequency-domain characteristics describe the characteristics of the signal from different dimensions. Combining the time-domain characteristics and frequency-domain characteristics together to obtain a feature set can more comprehensively reflect the signal characteristic information at the abnormal vibration position. By combining the two, more abundant and comprehensive information can be provided, which helps to more accurately identify the abnormal vibration type, overcome the limitations of single characteristics, provide a more comprehensive signal description, and improve the accuracy of identifying the abnormal vibration type.

[0116] Specifically, a vibration state recognition model is trained using a large amount of data including a feature set of different abnormal vibration positions and corresponding abnormal vibration type labels. The vibration state recognition model may be a machine learning model, and a specific vibration state recognition model is a neural network model. A pre-trained vibration state recognition model is obtained through training with a large amount of data, and the feature set extracted from the abnormal vibration position is input into the trained vibration state recognition model. The model outputs a predicted abnormal vibration type based on the input feature set. The abnormal vibration types are divided into construction vibration and influence of the pipeline's own state, and the pipeline's own state problems are mainly dealt with. According to the output vibration type results, for problems with the pipeline's own state, the corresponding positions can be quickly repaired to avoid pipeline blockage or leakage, and corresponding countermeasures can be quickly made.

[0117] Furthermore, the acquiring of the optical fiber transmission signal of the pipeline includes: normalizing, exponentially transforming and denoising the acquired optical fiber transmission signal.

[0118] In this embodiment, during the acquisition process of the optical fiber transmission signal, its amplitude will be affected by the sensitivity of the sensor, the transmission distance, the environmental noise, etc. and will be in different magnitude ranges. The signal needs to be normalized and the amplitude of the signal is mapped to the range of [-1,1] to eliminate the influence of different magnitudes and avoid calculation instability or bias towards the signal part with larger amplitude due to excessive amplitude difference. By normalizing signals of different magnitudes to the same range, it is ensured that the subsequent calculation results will not be affected by the amplitude difference, thereby improving the stability and performance of the algorithm. Exponential transformation of the signal can enhance certain features in the signal. For example, for a signal with a smaller amplitude, it can be amplified by exponential transformation, while for a signal with a larger amplitude, its relative change will be compressed. The dynamic range of the signal can be adjusted by exponential transformation to highlight subtle changes in the signal, which helps to improve the detection capability of weak signals.

[0119] Specifically, in the process of collecting optical fiber transmission signals, noise will inevitably be introduced, which will interfere with the analysis and judgment of useful signals. Before the data is used, the signal needs to be denoised to remove or reduce the noise while retaining the useful signal information as much as possible. The noise is separated from the signal through a low-pass filter, high-pass filter or band-pass filter to remove noise interference, improve the signal-to-noise ratio, and make the subsequent analysis of the signal more accurate and reliable.

[0120] Example 2

[0121] In this embodiment, if Figure 4 , providing a monitoring system for an underground smart pipe network based on optical fiber transmission detection, which is used to implement the monitoring method for an underground smart pipe network based on optical fiber transmission detection, including:

[0122] A signal acquisition module that acquires the optical fiber transmission signal of the pipeline;

[0123] An abnormal section location module that locates the abnormal vibration section of the pipeline according to the optical fiber transmission signal;

[0124] An abnormal position identification module that performs vibration abnormality judgment on the optical fiber transmission signal in the abnormal vibration section and identifies the abnormal vibration position;

[0125] A vibration state identification module that, according to the optical fiber transmission signal at the abnormal vibration position, uses a preset vibration state identification model to identify the abnormal vibration type of the pipeline for monitoring the vibration state of the underground intelligent pipe network.

[0126] In this embodiment, the signal acquisition module includes an optical fiber sensor, a signal conditioning circuit, a data acquisition card, and a data storage unit. This module is the basis of the entire vibration monitoring system, responsible for obtaining the vibration information of the pipeline from the physical level and converting it into a digital signal that can be processed. Through the high sensitivity and anti-interference ability of the optical fiber sensor, it can accurately sense the subtle vibrations of the pipeline; the signal conditioning circuit ensures the signal quality, the data acquisition card guarantees the digital conversion of the signal, and the data storage unit provides data support for subsequent offline analysis and long-term monitoring; the abnormal section location module includes a sliding window processor, a time-domain feature extractor, an abnormality judgment unit, and a section location unit. This module is mainly responsible for the preliminary analysis of the acquired signal to find the area with abnormal vibrations. Through sliding window processing, the signal can be segmented and analyzed in the time dimension, and the extracted time-domain features can reflect the local features of the signal. Combining with the abnormality judgment unit, the windows with abnormal vibrations can be effectively screened out, and finally the abnormal vibration section can be located, providing a range basis for subsequent detailed analysis.

[0127] Specifically, the abnormal position recognition module includes a seismic source consistency judgment unit, a signal decomposition unit, a key sub-band filter, and a reconstruction and feature calculation unit. Based on the abnormal section, this module further refines the analysis of the abnormality to identify the specific position of the abnormal vibration. By judging the seismic source consistency, different seismic sources are distinguished. By using signal decomposition and screening key sub-band signals, the position of the abnormal vibration can be more accurately located. Through reconstruction and feature calculation, more accurate information is provided for subsequent vibration state recognition; the vibration state recognition module includes a feature combination unit, a vibration state recognition model, and a result output unit. This module is the core of the entire vibration monitoring system. It uses the information provided by the previous module, integrates more comprehensive features through the feature combination unit, and then, with the help of the vibration state recognition model, judges the type of abnormal vibration, and finally realizes the monitoring and recognition of the real-time vibration state of the underground intelligent pipe network. According to the recognition result, corresponding measures can be taken, such as issuing an alarm, starting a maintenance program, or recording the abnormal situation, providing decision-making support for the maintenance and management of the pipe network.

[0128] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only used to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. Monitoring method for underground intelligent pipe network based on optical fiber transmission detection, characterized in that, Including: Obtain the optical fiber transmission signal of the pipeline; According to the optical fiber transmission signal, locate the abnormal vibration section of the pipeline, wherein the abnormal vibration section is located according to a plurality of abnormal vibration windows identified as having abnormal vibration states by analyzing the time-domain characteristics of the optical fiber transmission signal; Judge the source consistency within the abnormal vibration section by analyzing the similarity of the optical fiber transmission signals between adjacent abnormal vibration windows, and obtain multiple different source sections; Judge the vibration abnormality of the optical fiber transmission signal of each source section to identify the abnormal vibration position; According to the optical fiber transmission signal at the abnormal vibration position, use a preset vibration state recognition model to identify the abnormal vibration type of the pipeline, so as to monitor the vibration state of the underground intelligent pipe network.

2. The monitoring method of the underground intelligent pipe network based on optical fiber transmission detection according to claim 1, wherein According to the optical fiber transmission signal, locating the abnormal vibration section of the pipeline includes: Analyze the vibration state of the optical fiber transmission signal within each preset sliding window to obtain a vibration analysis result, wherein the sliding window slides in the time position; Judge the abnormal vibration of the vibration analysis result to locate the abnormal vibration section of the pipeline.

3. The monitoring method of the underground intelligent pipe network based on optical fiber transmission detection according to claim 2, wherein, The analyzing the vibration state of the optical fiber transmission signal within each preset sliding window to obtain a vibration analysis result includes: According to the historical data of the optical fiber transmission signal, obtain the sliding window and the sliding step length through a preset window optimization model; According to the sliding step length, slide the sliding window on the real-time optical fiber transmission signal; Within the sliding window, analyze multiple time-domain characteristics of the optical fiber transmission signal to obtain a vibration analysis result.

4. The monitoring method of the underground intelligent pipe network based on optical fiber transmission detection according to claim 2, characterized in that, The judging the abnormal vibration of the vibration analysis result to locate the abnormal vibration section of the pipeline includes: Perform a joint calculation on multiple time-domain characteristics in the vibration analysis result to obtain a deviation value within each sliding window; Identify the sliding window with the deviation value greater than the preset deviation threshold as an abnormal vibration window to obtain the abnormal vibration section of the pipeline.

5. The monitoring method of the underground intelligent pipe network based on optical fiber transmission detection according to claim 1, characterized in that, The judging the vibration abnormality of the optical fiber transmission signal of each source section to identify the abnormal vibration position includes: Decompose the optical fiber transmission signal of each source section to obtain decomposed signal characteristics; According to the decomposed signal characteristics, use a preset vibration abnormality recognition model to locate the abnormal vibration position.

6. The monitoring method of the underground intelligent pipe network based on optical fiber transmission detection according to claim 1, characterized in that, The judging the source consistency within the abnormal vibration section by analyzing the similarity of the optical fiber transmission signals between adjacent abnormal vibration windows to obtain multiple different source sections includes: Calculate the similarity of the optical fiber transmission signals between adjacent abnormal vibration windows to obtain a similarity value; Take the adjacent abnormal vibration windows with the similarity value greater than the preset similarity threshold as the same source windows; Fuse the same source windows to obtain multiple fused windows; Combine the fused windows and non-same source windows to obtain multiple different source sections.

7. The monitoring method of the underground intelligent pipe network based on optical fiber transmission detection according to claim 5, characterized in that, The decomposing the optical fiber transmission signal of each source section to obtain decomposed signal characteristics includes: Set the decomposition layer according to the frequency distribution range of the optical fiber transmission signal within each source section; According to the decomposition layer, decompose the optical fiber transmission signal to obtain sub-band signals of multiple different frequencies; Select sub-band signals whose frequencies match the preset vibration source frequency range from the sub-band signals as key sub-band signals; Combine the key sub-band signals in each vibration source section respectively to obtain the reconstructed signals in each vibration source section; Calculate the frequency characteristics of the reconstructed signals based on the reconstructed signals in each vibration source section to obtain the decomposed signal characteristics.

8. The monitoring method of the underground intelligent pipe network based on optical fiber transmission detection according to claim 1, wherein, Based on the optical fiber transmission signal at the abnormal vibration position, use a preset vibration state recognition model to identify the abnormal vibration type of the pipeline for monitoring the vibration state of the underground intelligent pipe network, including: Combine the time domain characteristics and frequency domain characteristics of the optical fiber transmission signal corresponding to the abnormal vibration position to obtain a feature set; Based on the feature set, obtain the abnormal vibration type at the abnormal vibration position through a preset vibration state recognition model.

9. The monitoring method of the underground intelligent pipe network based on optical fiber transmission detection according to claim 1, characterized in that, The obtaining of the optical fiber transmission signal of the pipeline includes: Perform normalization, exponential transformation and denoising processing on the collected optical fiber transmission signal.

10. A monitoring system for an underground intelligent pipe network based on optical fiber transmission detection, characterized in that, For implementing the monitoring method of the underground intelligent pipe network based on optical fiber transmission detection as described in any one of claims 1 to 9, including: A signal acquisition module for acquiring the optical fiber transmission signal of the pipeline; An abnormal section positioning module for positioning the abnormal vibration section of the pipeline according to the optical fiber transmission signal; An abnormal position recognition module for judging the consistency of the vibration sources in the abnormal vibration section by analyzing the similarity of the optical fiber transmission signals between adjacent abnormal vibration windows, obtaining multiple different vibration source sections, and judging the vibration abnormality of the optical fiber transmission signals of each vibration source section to identify the abnormal vibration position; A vibration state recognition module for identifying the abnormal vibration type of the pipeline based on the optical fiber transmission signal at the abnormal vibration position by using a preset vibration state recognition model to monitor the vibration state of the underground intelligent pipe network.

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