Optical cable maintenance system and methods based on the characteristics of optical cable intrusion incidents

Abstract

This invention discloses a fiber optic cable operation and maintenance system and method that combines fiber optic cable intrusion event feature analysis, relating to the field of fiber optic cable operation and maintenance. The system includes: setting up corresponding monitoring equipment to collect fiber optic cable feature information of the area to be monitored; acquiring fiber optic cable intrusion event feature information and feature information of the fiber optic cable during normal operation; extracting frequency domain information from the collected fiber optic cable feature information and establishing a frequency domain database of fiber optic cable feature information; classifying and identifying the fiber optic cable features in the frequency domain database; establishing a fiber optic cable intrusion judgment model to judge information of different clusters, determining whether the fiber optic cable in the area to be monitored is subject to intrusion, and determining the type of intrusion; and establishing a ground monitoring platform to monitor, access, and display anomalies of the fiber optic cable in the area to be monitored through Internet of Things (IoT) technology. The advantages of this invention are: effectively identifying and judging whether the fiber optic cable in the area to be monitored is subject to intrusion and the type of intrusion, thus improving the reliability and stability of fiber optic cable operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of optical cable operation and maintenance, specifically to an optical cable operation and maintenance system and method that combines optical cable intrusion event characteristic analysis. Background Technology

[0002] With the development of the information age, the use of optical fiber technology is an important means to ensure the stability and reliability of communication data transmission and to meet the high-speed data transmission demands of the era of big data and cloud computing. Effectively identifying the characteristics of existing optical fiber intrusion events, detecting them in a timely manner, and ensuring the normal operation of optical fibers are crucial for maintaining communication networks. Existing optical fiber intrusion events mainly include: human-caused damage, construction damage, natural factors, and illegal occupation. These events can be identified and judged based on characteristics such as vibration information, traffic data, and natural environmental factors. Therefore, analyzing optical fiber operation information by combining the characteristics of optical fiber intrusion events is of great significance.

[0003] Existing optical cable maintenance technologies mainly focus on real-time monitoring of key parameters such as temperature, humidity, and tension of optical cables, as well as using electronic tag technology to monitor and analyze optical cables. However, these technologies have certain delays in optical cable monitoring and cannot identify optical cable intrusion events. They lack effective targeting for accurately responding to different optical cable intrusion events, which is not conducive to timely detection and response to optical cable intrusion events. This may lead to damage to optical cables or their being dug up, resulting in communication anomalies and huge economic losses for operators. Summary of the Invention

[0004] To address the aforementioned technical problems, this paper provides a fiber optic cable operation and maintenance system and method that combines fiber optic cable intrusion event characteristic analysis. This technical solution solves the problems mentioned in the background technology, but such technologies have certain delays in fiber optic cable monitoring and cannot identify fiber optic cable intrusion events. They lack effective targeting for accurately responding to different fiber optic cable intrusion events, which is not conducive to timely detection and response to fiber optic cable intrusion events. This may lead to fiber optic cable damage or being dug up, resulting in communication anomalies and huge economic losses for operators.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for optical cable operation and maintenance that combines the characteristic analysis of optical cable intrusion events, characterized by comprising:

[0007] At appropriate locations within the area to be monitored, appropriate monitoring equipment is installed to collect optical cable characteristic information of the area to be monitored.

[0008] Based on historical data and big data analysis, we obtain characteristic information of optical cable intrusion events and characteristic information of optical cables during normal operation.

[0009] Based on the Fourier transform formula, frequency domain information is extracted from the acquired optical cable feature information, and a frequency domain database of optical cable feature information is established.

[0010] Based on the K-means clustering algorithm model, optical cable features in the frequency domain database of optical cable feature information are classified and identified.

[0011] Based on the DS evidence theory, a fiber optic cable intrusion determination model is established to determine the information of different clusters, whether the fiber optic cable in the monitored area is intruded, and the type of intrusion.

[0012] Establish a ground-based monitoring platform and use Internet of Things (IoT) technology to monitor, access, and display anomalies in the optical cables in the area to be monitored.

[0013] Preferably, the acquisition of characteristic information of optical cable intrusion events and characteristic information of optical cables during normal operation based on historical data and big data analysis specifically includes:

[0014] Based on historical data and big data analysis, characteristic information of optical cable intrusion events is obtained, and a characteristic sample set of optical cable intrusion events is established.

[0015] Based on the monitoring equipment, acquire the optical cable information of the area to be monitored, extract the optical cable characteristic information during normal operation, and establish a sample set of optical cable normal operation characteristics;

[0016] The optical cable characteristic information includes vibration signals, flow data, and information on natural environmental factors.

[0017] Preferably, the step of extracting frequency domain information from the acquired optical cable feature information based on the Fourier transform formula and establishing an optical cable feature information frequency domain database specifically includes:

[0018] Based on the sample sets of optical cable intrusion events and the sample sets of optical cable normal operation characteristics, the data are standardized and preliminarily filtered.

[0019] The Fourier transform formula is used to perform frequency domain transformation on the processed optical cable feature information data, and the frequency domain information in the collected optical cable feature information is extracted.

[0020] Based on the frequency domain information in the optical cable feature information, the waveform, amplitude, and frequency information of the optical cable feature information are obtained, and then labeled and classified.

[0021] Based on the labeled and classified optical cable feature information, a frequency domain database of optical cable feature information and a frequency domain data matrix of optical cable feature information are established.

[0022] The Fourier transform formula is as follows:

[0023]

[0024] In the formula, F(u,v) is the frequency domain information matrix after Fourier transform, G(x,y) is the frequency domain data matrix of optical cable feature information, n is the number of frequency domain data of optical cable feature information, and (x,y) is the position information of the frequency domain data of optical cable feature information in the coordinate system.

[0025] Preferably, the step of classifying and identifying optical cable features in the frequency domain database of optical cable feature information based on the K-means clustering algorithm model specifically includes:

[0026] Based on the frequency domain information matrix after Fourier transform, determine the value of K according to its data type;

[0027] Based on the K-means clustering algorithm, a loss function for the characteristic frequency domain data of each optical cable is established.

[0028] Based on the loss function of the characteristic frequency domain data of each optical cable, a centroid update function for the characteristic frequency domain data of each optical cable is established.

[0029] Based on the frequency domain database of optical cable feature information, the K-means clustering algorithm model is trained and learned, and the received optical cable feature frequency domain data is classified and identified.

[0030] The loss function expression for the characteristic frequency domain data of each optical cable is as follows:

[0031]

[0032] In the formula, b i (a) represents the distance from the i-th optical cable characteristic frequency domain data point to the cluster center, k is the number of clusters, i.e., the type of optical cable characteristic frequency domain data, and n is the set of each optical cable characteristic frequency domain data. i For the i-th optical cable characteristic frequency domain data, α j The j-th cluster center of the optical cable characteristic frequency domain data;

[0033] The centroid update function expression for the characteristic frequency domain data of each optical cable is as follows:

[0034]

[0035] In the formula, This is the j-th cluster center of the updated optical cable characteristic frequency domain data.

[0036] Preferably, based on the DS evidence theory, a fiber optic cable intrusion determination model is established to determine information of different clusters, whether the fiber optic cable in the monitored area is subject to intrusion, and the specific intrusion type is determined as follows:

[0037] Based on the characteristic sample set data of optical cable intrusion events, we analyze the optical cable intrusion events corresponding to different data combinations and establish a database for determining optical cable intrusion events.

[0038] Based on big data analysis, the threshold for the probability of optical cables in the monitored area being disturbed and the corresponding probability threshold for disturbance events are determined.

[0039] Based on the DS evidence theory and combined with the K-means clustering algorithm model, a fiber optic cable intrusion determination model is established.

[0040] This model analyzes the data classified and identified by the K-means clustering algorithm to calculate the probability of optical cables in the monitored area being disturbed and the probability of the disturbance event type.

[0041] By comparing the probability threshold of optical cable being disturbed with the probability threshold of the type of disturbance event, it can be determined whether the optical cable in the area to be monitored is disturbed and the type of disturbance.

[0042] The fundamental expression of the DS evidence theory is:

[0043]

[0044] In the formula, D(A) represents the confidence level of the Ath fiber optic cable intrusion event type, K is a constant, and D i (A i ) represents the probability of the i-th optical cable characteristic frequency domain data of the A-th optical cable intrusion event type, and m represents the number of optical cable characteristic frequency domain data of the A-th optical cable intrusion event.

[0045] Preferably, the establishment of a ground monitoring platform, which uses Internet of Things (IoT) technology to monitor, access, and display anomalies in the optical cables in the area to be monitored, specifically includes:

[0046] Establish a ground monitoring platform to house the algorithm program for judging whether optical cables are being intruded upon by the system, run it, and output the judgment results of whether optical cables are being intruded upon.

[0047] Using cloud and IoT technologies, a ground-based monitoring platform is used to receive, store, and analyze optical cable characteristic monitoring data, and to display and control the optical cable characteristic data through a human-machine interface.

[0048] Based on the type of fiber optic cable intrusion incident, corresponding indicator lights and alarm signals are set up, and warnings and alerts are issued through the ground monitoring platform.

[0049] Furthermore, a fiber optic cable operation and maintenance system combining fiber optic cable intrusion event characteristic analysis is proposed to implement the fiber optic cable operation and maintenance method combining fiber optic cable intrusion event characteristic analysis as described above, including:

[0050] The hardware acquisition module is used to set up corresponding monitoring equipment at appropriate locations in the area to be monitored, and to acquire optical cable characteristic information of the area to be monitored.

[0051] The data extraction module is used to obtain characteristic information of optical cable intrusion events and characteristic information of optical cables during normal operation based on historical data and big data analysis; and to extract frequency domain information from the collected optical cable characteristic information based on the Fourier transform formula, and to establish a frequency domain database of optical cable characteristic information.

[0052] The intrusion judgment module is used to classify and identify optical cable features in the frequency domain database of optical cable feature information according to the K-means clustering algorithm model; and to establish an optical cable intrusion judgment model based on the DS evidence theory to judge the information of different clusters, determine whether the optical cable in the monitored area is intruded, and determine the type of intrusion.

[0053] The monitoring platform module is used to establish a ground monitoring platform and, through Internet of Things (IoT) technology, monitor, access, and display anomalies in the optical cables in the area to be monitored.

[0054] Preferably, the data extraction module specifically includes:

[0055] The feature extraction unit is used to obtain feature information of optical cable intrusion events and feature information of optical cables during normal operation based on historical data and big data analysis.

[0056] The frequency domain conversion unit is used to extract frequency domain information from the acquired optical cable feature information based on the Fourier transform formula, and to establish an optical cable feature information frequency domain database.

[0057] Preferably, the intrusion detection module specifically includes:

[0058] The feature classification unit is used to classify and identify optical cable features in the frequency domain database of optical cable feature information according to the K-means clustering algorithm model.

[0059] The intrusion judgment unit is used to establish an optical cable intrusion judgment model based on the DS evidence theory, judge the information of different clusters, determine whether the optical cable in the monitored area is intruded, and determine the type of intrusion.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] By analyzing the characteristic information of optical cable intrusion events and the characteristic information of optical cables during normal operation, the acquired characteristic data is converted into frequency domain information using the Fourier transform formula. Waveform, amplitude, and frequency information of the characteristic information are obtained through the frequency domain data of the optical cable, allowing for more detailed data analysis. Secondly, the K-means clustering algorithm is used to classify and identify the frequency domain optical cable characteristic data, resulting in a frequency domain database of optical cable characteristic information. Finally, based on the DS evidence theory, an optical cable intrusion determination model is established. By analyzing the data classified and identified by the K-means clustering algorithm, the probability of optical cable intrusion in the monitored area and the probability of the intrusion event type are calculated. Based on the comparison between the calculated values ​​and the probability thresholds for optical cable intrusion and the probability thresholds for the intrusion event type, it is determined whether the optical cable in the monitored area is intruded and the type of intrusion. This enables effective identification and judgment of whether the optical cable in the monitored area is intruded and the type of intrusion, improving the reliability and stability of optical cable operation and maintenance, and allowing for timely measures to improve response efficiency. Attached Figure Description

[0062] Figure 1 This is a flowchart of an optical cable operation and maintenance method that combines optical cable intrusion event feature analysis according to the present invention;

[0063] Figure 2 The flowchart of the present invention is as follows: Based on the Fourier transform formula, frequency domain information is extracted from the acquired optical cable feature information, and a frequency domain database of optical cable feature information is established.

[0064] Figure 3 This is a flowchart illustrating the classification and identification process of optical cable features in a frequency domain database based on the K-means clustering algorithm model, as described in this invention.

[0065] Figure 4 Based on the DS evidence theory, this invention establishes an optical cable intrusion determination model, judges information of different clusters, determines whether the optical cable in the monitored area is intruded, and determines the intrusion type flowchart. Detailed Implementation

[0066] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0067] Reference Figure 1 As shown, a fiber optic cable operation and maintenance method combining fiber optic cable intrusion event characteristic analysis includes:

[0068] At appropriate locations within the area to be monitored, appropriate monitoring equipment is installed to collect optical cable characteristic information of the area to be monitored.

[0069] Based on historical data and big data analysis, we obtain characteristic information of optical cable intrusion events and characteristic information of optical cables during normal operation.

[0070] Based on the Fourier transform formula, frequency domain information is extracted from the acquired optical cable feature information, and a frequency domain database of optical cable feature information is established.

[0071] Based on the K-means clustering algorithm model, optical cable features in the frequency domain database of optical cable feature information are classified and identified.

[0072] Based on the DS evidence theory, a fiber optic cable intrusion determination model is established to determine the information of different clusters, whether the fiber optic cable in the monitored area is intruded, and the type of intrusion.

[0073] Establish a ground-based monitoring platform and use Internet of Things (IoT) technology to monitor, access, and display anomalies in the optical cables in the area to be monitored.

[0074] Understandably, this solution analyzes the characteristic information of optical cable intrusion events and the characteristic information of optical cables during normal operation. It uses the Fourier transform formula to convert the acquired characteristic data into frequency domain information, obtaining waveform, amplitude, and frequency information of the characteristic information through the frequency domain data of the optical cable. This allows for more detailed data analysis. Secondly, the K-means clustering algorithm is used to classify and identify the frequency domain optical cable characteristic data, resulting in a frequency domain database of optical cable characteristic information. Finally, based on the DS evidence theory, an optical cable intrusion determination model is established. By analyzing the data classified and identified by the K-means clustering algorithm, the probability of optical cable intrusion in the monitored area and the probability of the intrusion event type are calculated. Based on the comparison between the calculated values ​​and the probability thresholds for optical cable intrusion and the probability thresholds for the intrusion event type, it is determined whether the optical cable in the monitored area is intruded and the type of intrusion. This effectively identifies and judges whether the optical cable in the monitored area is intruded and the type of intrusion, improving the reliability and stability of optical cable operation and maintenance, and enabling timely measures to be taken to improve response efficiency.

[0075] Reference Figure 2 As shown, the step of extracting frequency domain information from the acquired optical cable feature information based on the Fourier transform formula and establishing a frequency domain database of optical cable feature information specifically includes:

[0076] Based on the sample sets of optical cable intrusion events and the sample sets of optical cable normal operation characteristics, the data are standardized and preliminarily filtered.

[0077] The Fourier transform formula is used to perform frequency domain transformation on the processed optical cable feature information data, and the frequency domain information in the collected optical cable feature information is extracted.

[0078] Based on the frequency domain information in the optical cable feature information, the waveform, amplitude, and frequency information of the optical cable feature information are obtained, and then labeled and classified.

[0079] Based on the labeled and classified optical cable feature information, a frequency domain database of optical cable feature information and a frequency domain data matrix of optical cable feature information are established.

[0080] The Fourier transform formula is as follows:

[0081]

[0082] In the formula, F(u,v) is the frequency domain information matrix after Fourier transform, G(x,y) is the frequency domain data matrix of optical cable feature information, n is the number of frequency domain data of optical cable feature information, and (x,y) is the position information of the frequency domain data of optical cable feature information in the coordinate system.

[0083] Understandably, incidents of optical cable intrusion, such as human-caused damage, construction damage, natural factors, and illegal occupation, can be identified and judged based on characteristics such as vibration information, flow data, and natural environmental factors. Different incidents have different waveforms, amplitudes, and frequency information. This solution uses the Fourier transform formula to convert optical cable characteristic data into frequency domain data, meticulously classifies and identifies different frequency domain data, and establishes a frequency domain database of optical cable characteristic information to facilitate subsequent analysis and processing of the data.

[0084] Reference Figure 3 As shown, the classification and identification of optical cable features in the frequency domain database based on the K-means clustering algorithm specifically includes:

[0085] Based on the frequency domain information matrix after Fourier transform, determine the value of K according to its data type;

[0086] Based on the K-means clustering algorithm, a loss function for the characteristic frequency domain data of each optical cable is established.

[0087] Based on the loss function of the characteristic frequency domain data of each optical cable, a centroid update function for the characteristic frequency domain data of each optical cable is established.

[0088] Based on the frequency domain database of optical cable feature information, the K-means clustering algorithm model is trained and learned, and the received optical cable feature frequency domain data is classified and identified.

[0089] The loss function expression for the characteristic frequency domain data of each optical cable is as follows:

[0090]

[0091] In the formula, b i(a) represents the distance from the i-th optical cable characteristic frequency domain data point to the cluster center, k is the number of clusters, i.e., the type of optical cable characteristic frequency domain data, and n is the set of each optical cable characteristic frequency domain data. i For the i-th optical cable characteristic frequency domain data, α j The j-th cluster center of the optical cable characteristic frequency domain data;

[0092] The centroid update function expression for the characteristic frequency domain data of each optical cable is as follows:

[0093]

[0094] In the formula, This is the j-th cluster center of the updated optical cable characteristic frequency domain data.

[0095] Understandably, by using the K-means clustering algorithm model to train and learn the data from the frequency domain database of optical cable feature information, it is possible to effectively identify and classify the frequency domain data after Fourier transform formula transformation in real time. By performing correlation analysis on the identified and classified frequency domain data of optical cable feature information, it is possible to effectively determine whether the optical cable in the area to be monitored is subject to interference and the type of interference.

[0096] Reference Figure 4 As shown, based on the DS evidence theory, a fiber optic cable intrusion determination model is established to determine information of different clusters, whether the fiber optic cable in the monitored area is subject to intrusion, and the specific intrusion type is determined, including:

[0097] Based on the characteristic sample set data of optical cable intrusion events, we analyze the optical cable intrusion events corresponding to different data combinations and establish a database for determining optical cable intrusion events.

[0098] Based on big data analysis, the threshold for the probability of optical cables in the monitored area being disturbed and the corresponding probability threshold for disturbance events are determined.

[0099] Based on the DS evidence theory and combined with the K-means clustering algorithm model, a fiber optic cable intrusion determination model is established.

[0100] This model analyzes the data classified and identified by the K-means clustering algorithm to calculate the probability of optical cables in the monitored area being disturbed and the probability of the disturbance event type.

[0101] By comparing the probability threshold of optical cable being disturbed with the probability threshold of the type of disturbance event, it can be determined whether the optical cable in the area to be monitored is disturbed and the type of disturbance.

[0102] The fundamental expression of the DS evidence theory is:

[0103]

[0104] In the formula, D(A) represents the confidence level of the Ath fiber optic cable intrusion event type, K is a constant, and D i (A i ) represents the probability of the i-th optical cable characteristic frequency domain data of the A-th optical cable intrusion event type, and m represents the number of optical cable characteristic frequency domain data of the A-th optical cable intrusion event.

[0105] Understandably, this solution establishes a database for identifying optical cable intrusion events by performing correlation analysis on the frequency domain data of historical optical cable characteristic information. Utilizing the theoretical basis of DS evidence, it establishes an optical cable intrusion judgment model. By analyzing the data classified and identified using the K-means clustering algorithm, it calculates the probability that the optical cable in the monitored area has been intruded upon and the probability of the intrusion event type. Based on the comparison between the calculated values ​​and the probability thresholds for the optical cable intrusion and the probability thresholds for the intrusion event type, it determines whether the optical cable in the monitored area has been intruded upon and the type of intrusion, thereby effectively identifying and judging whether the optical cable in the monitored area has been intruded upon and the type of intrusion.

[0106] 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 to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for optical cable operation and maintenance that combines the characteristic analysis of optical cable intrusion events, characterized in that, include: At appropriate locations within the area to be monitored, appropriate monitoring equipment is installed to collect optical cable characteristic information of the area to be monitored. Based on historical data and big data analysis, we obtain characteristic information of optical cable intrusion events and characteristic information of optical cables during normal operation. Based on the Fourier transform formula, frequency domain information is extracted from the acquired optical cable feature information, and a frequency domain database of optical cable feature information is established. Based on the K-means clustering algorithm model, optical cable features in the frequency domain database of optical cable feature information are classified and identified. Based on the DS evidence theory, a fiber optic cable intrusion determination model is established to determine the information of different clusters, whether the fiber optic cable in the monitored area is intruded, and the type of intrusion. Establish a ground-based monitoring platform and use Internet of Things (IoT) technology to monitor, access, and display anomalies in the optical cables in the area to be monitored. Based on the DS evidence theory, a fiber optic cable intrusion determination model is established to judge information of different clusters, determine whether the fiber optic cable in the monitored area is subject to intrusion, and determine the specific type of intrusion, including: Based on the characteristic sample set data of optical cable intrusion events, we analyze the optical cable intrusion events corresponding to different data combinations and establish a database for determining optical cable intrusion events. Based on big data analysis, the threshold for the probability of optical cables in the monitored area being disturbed and the corresponding probability threshold for disturbance events are determined. Based on the DS evidence theory and combined with the K-means clustering algorithm model, a fiber optic cable intrusion determination model is established. This model analyzes the data classified and identified by the K-means clustering algorithm to calculate the probability of optical cables in the monitored area being disturbed and the probability of the disturbance event type. By comparing the probability threshold of optical cable being disturbed with the probability threshold of the type of disturbance event, it can be determined whether the optical cable in the area to be monitored is disturbed and the type of disturbance. The fundamental expression of the DS evidence theory is: Where D(A) represents the confidence level of the Ath fiber optic cable intrusion event type, K is a constant, and D i (A i ) represents the probability of the i-th optical cable characteristic frequency domain data of the A-th optical cable intrusion event type, and m represents the number of optical cable characteristic frequency domain data of the A-th optical cable intrusion event.

2. The optical cable operation and maintenance method combining optical cable intrusion event characteristic analysis according to claim 1, characterized in that, The acquisition of characteristic information of optical cable intrusion events and characteristic information of optical cables during normal operation, based on historical data and big data analysis, specifically includes: Based on historical data and big data analysis, characteristic information of optical cable intrusion events is obtained, and a characteristic sample set of optical cable intrusion events is established. Based on the monitoring equipment, acquire the optical cable information of the area to be monitored, extract the optical cable characteristic information during normal operation, and establish a sample set of optical cable normal operation characteristics; The optical cable characteristic information includes vibration signals, flow data, and information on natural environmental factors.

3. The optical cable operation and maintenance method combining optical cable intrusion event characteristic analysis according to claim 2, characterized in that, The step of extracting frequency domain information from the acquired optical cable feature information based on the Fourier transform formula and establishing a frequency domain database of optical cable feature information specifically includes: Based on the sample sets of optical cable intrusion events and the sample sets of optical cable normal operation characteristics, the data are standardized and preliminarily filtered. The Fourier transform formula is used to perform frequency domain transformation on the processed optical cable feature information data, and the frequency domain information in the collected optical cable feature information is extracted. Based on the frequency domain information in the optical cable feature information, the waveform, amplitude, and frequency information of the optical cable feature information are obtained, and then labeled and classified. Based on the labeled and classified optical cable feature information, a frequency domain database of optical cable feature information and a frequency domain data matrix of optical cable feature information are established. The Fourier transform formula is as follows: In the formula, F(u,v) is the frequency domain information matrix after Fourier transform, G(x,y) is the frequency domain data matrix of optical cable feature information, n is the number of frequency domain data of optical cable feature information, and (x,y) is the position information of the frequency domain data of optical cable feature information in the coordinate system.

4. The optical cable operation and maintenance method combining optical cable intrusion event characteristic analysis according to claim 3, characterized in that, The classification and identification of optical cable features in the frequency domain database based on the K-means clustering algorithm specifically includes: Based on the frequency domain information matrix after Fourier transform, determine the value of K according to its data type; Based on the K-means clustering algorithm, a loss function for the characteristic frequency domain data of each optical cable is established. Based on the loss function of the characteristic frequency domain data of each optical cable, a centroid update function for the characteristic frequency domain data of each optical cable is established. Based on the frequency domain database of optical cable feature information, the K-means clustering algorithm model is trained and learned, and the received optical cable feature frequency domain data is classified and identified. The loss function expression for the characteristic frequency domain data of each optical cable is as follows: Among them, b i (a) represents the distance from the i-th optical cable characteristic frequency domain data point to the cluster center, k is the number of clusters, i.e., the type of optical cable characteristic frequency domain data, and n is the set of each optical cable characteristic frequency domain data. i For the i-th optical cable characteristic frequency domain data, α j The j-th cluster center of the optical cable characteristic frequency domain data; The centroid update function expression for the characteristic frequency domain data of each optical cable is as follows: in, This is the j-th cluster center of the updated optical cable characteristic frequency domain data.

5. The optical cable operation and maintenance method combining optical cable intrusion event characteristic analysis according to claim 4, characterized in that, The establishment of a ground-based monitoring platform, which uses Internet of Things (IoT) technology to monitor, access, and display anomalies in the optical cables in the area to be monitored, specifically includes: Establish a ground monitoring platform to house the algorithm program for judging whether optical cables are being intruded upon by the system, run it, and output the judgment results of whether optical cables are being intruded upon. Using cloud and IoT technologies, a ground-based monitoring platform is used to receive, store, and analyze optical cable characteristic monitoring data, and to display and control the optical cable characteristic data through a human-machine interface. Based on the type of fiber optic cable intrusion incident, corresponding indicator lights and alarm signals are set up, and warnings and alerts are issued through the ground monitoring platform.

6. A fiber optic cable operation and maintenance system incorporating fiber optic cable intrusion event characteristic analysis, used to implement the fiber optic cable operation and maintenance method incorporating fiber optic cable intrusion event characteristic analysis as described in any one of claims 1-5, characterized in that, include: The hardware acquisition module is used to set up corresponding monitoring equipment at appropriate locations in the area to be monitored, and to acquire optical cable characteristic information of the area to be monitored. The data extraction module is used to obtain characteristic information of optical cable intrusion events and characteristic information of optical cables during normal operation based on historical data and big data analysis. Based on the Fourier transform formula, frequency domain information is extracted from the acquired optical cable feature information, and a frequency domain database of optical cable feature information is established. The intrusion judgment module is used to classify and identify optical cable features in the frequency domain database of optical cable feature information according to the K-means clustering algorithm model; and to establish an optical cable intrusion judgment model based on the DS evidence theory to judge the information of different clusters, determine whether the optical cable in the monitored area is intruded, and determine the type of intrusion. The monitoring platform module is used to establish a ground monitoring platform and, through Internet of Things (IoT) technology, monitor, access, and display anomalies in the optical cables in the area to be monitored.

7. The optical cable operation and maintenance system combining optical cable intrusion event characteristic analysis according to claim 6, characterized in that, The data extraction module specifically includes: The feature extraction unit is used to obtain feature information of optical cable intrusion events and feature information of optical cables during normal operation based on historical data and big data analysis. The frequency domain conversion unit is used to extract frequency domain information from the acquired optical cable feature information based on the Fourier transform formula, and to establish an optical cable feature information frequency domain database.

8. The optical cable operation and maintenance system combining optical cable intrusion event characteristic analysis according to claim 7, characterized in that, The intrusion detection module specifically includes: The feature classification unit is used to classify and identify optical cable features in the frequency domain database of optical cable feature information according to the K-means clustering algorithm model. The intrusion judgment unit is used to establish an optical cable intrusion judgment model based on the DS evidence theory, judge the information of different clusters, determine whether the optical cable in the monitored area is intruded, and determine the type of intrusion.

Images