Subway anti-intrusion monitoring method and device based on common communication optical cable

Abstract

The invention discloses a subway anti-intrusion monitoring method and device based on a common communication optical cable, and relates to the technical field of urban rail transit safety monitoring, an intrusion monitoring device based on an optical cable monitoring piece is adopted to monitor subway intrusion behaviors, and the optical cable monitoring piece of the intrusion monitoring device is arranged along a subway tunnel. One end of the optical cable monitoring piece is connected with the optical signal generator, and the other end is in communication connection with the signal processing unit through the optical fiber sensing module and the data acquisition module; according to the invention, the physical state along the optical cable can be monitored in real time by using the phase-sensitive optical time domain reflection technology; the signal processing unit obtains optical fiber length change characteristics by utilizing an electric field expression of a Rayleigh scattering signal and phase change analysis, and then can efficiently and intelligently analyze and output intrusion types and probabilities based on the optical fiber length change characteristics through a deep learning model; and identifying the potential safety hazard of the subway tunnel by analyzing micro vibration, temperature change and stress change along the optical cable.

Description

Technical Field

[0001] The present invention relates to the technical field of urban rail transit safety monitoring, and particularly to a subway intrusion prevention monitoring method and device based on ordinary communication optical cables. Background Art

[0002] With the acceleration of the urbanization process, the subway, as an important part of urban transportation, the importance of its safe operation has become increasingly prominent.

[0003] Existing subway safety monitoring technologies mostly rely on traditional sensors and manual inspections, and there are problems such as limited monitoring range and poor real-time performance.

[0004] Therefore, developing a new type of monitoring technology is of great significance for improving the safety and reliability of subway operation. Summary of the Invention

[0005] In order to solve the above technical problems of subway safety monitoring, the present invention provides a subway intrusion prevention monitoring method and device based on ordinary communication optical cables. The following technical solutions are adopted:

[0006] A subway intrusion prevention monitoring method based on ordinary communication optical cables uses an intrusion monitoring device based on an optical cable monitoring component to monitor subway intrusion behaviors. The detection method includes the following steps:

[0007] Step 1, arrange the optical cable monitoring components of the intrusion monitoring device along the subway tunnel. One end of the optical cable monitoring component is connected to an optical signal generator, and the other end is communicatively connected to a signal processing unit through an optical fiber sensing module and a data acquisition module;

[0008] Step 2, turn on the optical signal generator. The signal processing unit receives the optical signals collected by the data acquisition module, and the signal processing unit analyzes the electric field expression and phase change of the Rayleigh scattering signal to obtain the fiber length change characteristics;

[0009] Step 3, the signal processing unit uses a deep learning model to analyze and output the intrusion type and probability based on the fiber length change characteristics;

[0010] Step 4, if it is determined that the probability of an intrusion appears to be greater than a set probability threshold, the signal processing unit communicates with an alarm unit, and the alarm unit turns on the alarm.

[0011] By adopting the above technical solutions, ordinary communication optical cables are arranged along the subway tunnel as optical cable monitoring components. The optical cable monitoring components are the sensing media for monitoring. The phase-sensitive optical time domain reflectometry (Φ-OTDR) can be used to monitor the physical state along the optical cable in real time. Specifically, the signal processing unit analyzes the electric field expression of the Rayleigh scattering signal and the phase change to obtain the characteristics of the fiber length change. Then, through the deep learning model, the intrusion type and probability can be efficiently and intelligently analyzed and output based on the fiber length change characteristics. By analyzing the micro-vibrations, temperature changes, and stress changes along the optical cable, potential safety hazards in the subway tunnel can be identified, and a warning signal can be issued through the alarm unit.

[0012] It can timely warn of potential safety hazards in the subway tunnel and achieve high-precision and long-distance monitoring.

[0013] Optionally, multiple sections of optical cable monitoring components are used to monitor intrusion in different sections of the subway tunnel. One end of each section of the optical cable monitoring component is connected to an optical signal generator, and the other end is connected to an optical fiber sensing module.

[0014] By adopting the above technical solutions, multiple sections of optical cable monitoring components can be arranged in different sections of the subway tunnel, enabling segmented monitoring of different sections, improving the practicality of the monitoring results, and allowing the staff to quickly handle the situation according to the section where the anomaly occurs.

[0015] Optionally, in step 2, the electric field expression of the Rayleigh scattering signal is as follows:

[0016]

[0017] Where: E R is the electric field strength of the scattered light; E 0 is the initial electric field strength of the scattered light; ω is the angular frequency of the light; t is the time; is the initial phase; is the phase change caused by external disturbances.

[0018] By adopting the above technical solutions, the phase change caused by external disturbances at a certain time point can be calculated through the above expression, providing parameters for subsequent fiber length changes. For subsequent fiber length changes.

[0019] Optionally, the relationship expression between the phase change and the fiber length change is as follows:

[0020]

[0021] Where: is the phase change; n is the refractive index of the optical fiber; λ is the wavelength of the light; ΔL is the fiber length change caused by external disturbances.

[0022] By adopting the above technical solutions, the calculation of the fiber optic length change based on time series can be realized through the above expressions, providing a parameter basis for the subsequent analysis of the fiber optic length change characteristics.

[0023] Optionally, the external perturbation includes vibration or temperature change.

[0024] Optionally, in step 3, a deep learning model for analyzing the fiber optic length change characteristics is established. The deep learning model analyzes the time series data of the fiber optic length change to identify the length change characteristics, and outputs the intrusion behavior analysis result based on the length change characteristics.

[0025] By adopting the above technical solutions, the characteristics of the fiber optic length change can be analyzed based on the deep learning model to identify the intrusion characteristics.

[0026] Collect data on the fiber optic length change under different intrusion scenarios (such as human sabotage, mechanical vibration, temperature change, etc.) and non-intrusion scenarios.

[0027] Ensure that the data set contains sufficient diversity so that the model can learn various possible intrusion patterns. Standardize or normalize the data so that the model can learn better.

[0028] Extract features helpful for classification from the original data, such as statistical features, frequency features, kurtosis, skewness, etc. of the time series. Manual classification can also be used during training, and the model is trained using the labeled data set, where the classification of whether it is an intrusion event is marked.

[0029] Deploy the trained deep learning model to the actual subway intrusion prevention monitoring system.

[0030] Optionally, the intrusion behavior analysis result includes the intrusion type and the intrusion probability. The intrusion types include sabotage, vibration, and temperature change.

[0031] By adopting the above technical solutions, subway intrusion behaviors are usually caused by human sabotage, mechanical failures, or environmental climate. The trained deep learning model can achieve efficient and accurate prediction of the intrusion type and the corresponding probability.

[0032] Optionally, the deep learning model uses the cross-entropy loss function to predict the intrusion type and the intrusion probability. The formula of the cross-entropy loss function is as follows:

[0033]

[0034] where y is the intrusion type; y i is the i-th feature vector of the intrusion type; C is the number of intrusion types, is the probability distribution predicted by the deep learning model.

[0035] By adopting the above technical solution, for multi-classification problems, the cross-entropy loss can invade the type and the predicted probability distribution.

[0036] A subway intrusion prevention monitoring device based on a common communication optical cable, comprising an optical cable monitoring member, an optical signal generator, an optical fiber sensing module, a data acquisition module, a signal processing unit and an alarm unit. The optical cable monitoring member includes a flexible mounting member and a monitoring optical cable. The bottom of the flexible mounting member is mounted at the boundary along the subway tunnel. There is an optical cable mounting groove on the top of the flexible mounting member. The monitoring optical cable is detachably mounted at the optical cable mounting groove. The signal output end of the optical signal generator is connected to one end of the monitoring optical cable. The optical fiber sensing module is connected to the other end of the monitoring optical cable in a passing manner to collect optical signals. The signal processing unit is communicatively connected to the optical fiber sensing module through the data acquisition module. The signal processing unit realizes optical signal analysis based on a computer. The alarm unit is communicatively connected to the signal output end of the signal processing unit.

[0037] By adopting the above technical solution, the optical cable monitoring member adopts a combination of a flexible mounting member and a monitoring optical cable, which can reduce the risk of breakage or fracture of the monitoring optical cable caused by damage. The optical fiber sensing module can collect optical signals. The data acquisition module can collect the optical signals collected by the optical fiber sensing module and send them to the signal processing unit. The signal processing unit uses a computer to realize optical signal analysis. The alarm unit can adopt an audible and visual alarm. In specific applications, multiple audible and visual alarms can be numbered. The alarm of the audible and visual alarm corresponding to the number indicates that an abnormality has occurred at the position of the corresponding monitoring optical cable, which is convenient for the staff to arrive at the scene for disposal in time.

[0038] Optionally, a plurality of heat dissipation holes penetrating the optical cable mounting groove are uniformly arranged on the flexible mounting member. The flexible mounting member is formed of PVC material. The optical cable mounting groove is an arc-shaped groove. After the bottom of the monitoring optical cable is mounted, the part of the top exposed above the optical cable mounting groove surface exceeds three-quarters of the arc.

[0039] By adopting the above technical solution, the flexible mounting member formed of PVC material can provide good support for the monitoring optical cable. The plurality of heat dissipation holes provide good heat dissipation, avoiding errors caused by local heat accumulation of the optical cable. The part of the top of the monitoring optical cable exposed above the optical cable mounting groove surface exceeds three-quarters of the arc. Most of the monitoring optical cable is in a bare state, avoiding the influence of the installation part on the monitoring and enhancing heat dissipation at the same time.

[0040] In summary, the present invention includes at least one of the following beneficial technical effects:

[0041] The present invention can provide a subway intrusion prevention monitoring method and device based on ordinary communication optical cables. An ordinary communication optical cable is arranged along the subway tunnel as an optical cable monitoring component, and the phase-sensitive optical time domain reflectometry (Φ-OTDR) can be used to monitor the physical state along the optical cable in real time; the signal processing unit analyzes the electric field expression and phase change of the Rayleigh scattering signal to obtain the fiber length change characteristics, and then through a deep learning model, it can efficiently and intelligently analyze and output the intrusion type and probability based on the fiber length change characteristics; by analyzing the micro-vibration, temperature change and stress change along the optical cable, the potential safety hazards of the subway tunnel are identified; and a warning signal is sent through the alarm unit. It can timely warn of the potential safety hazards of the subway tunnel and can achieve high-precision and long-distance monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic flow chart of the subway intrusion prevention monitoring method based on ordinary communication optical cables according to the present invention;

[0043] Figure 2 is a schematic diagram of the component connection principle of the subway intrusion prevention monitoring device based on ordinary communication optical cables according to the present invention;

[0044] Figure 3 is a schematic cross-sectional view of the optical cable monitoring component of the subway intrusion prevention monitoring device based on ordinary communication optical cables according to the present invention;

[0045] Figure 4 is a schematic diagram of the flexible mounting component of the subway intrusion prevention monitoring device based on ordinary communication optical cables according to the present invention.

[0046] Description of reference numerals: 1, optical cable monitoring component; 11, flexible mounting component; 111, heat dissipation holes; 12, monitoring optical cable; 2, fiber optic sensing module; 3, data acquisition module; 4, signal processing unit; 5, optical signal generator; 6, alarm unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following further describes the present invention in detail with reference to the accompanying drawings.

[0048] The embodiments of the present invention disclose a subway intrusion prevention monitoring method and device based on ordinary communication optical cables.

[0049] Refer to Figure 1 - Figure 4 , Embodiment 1, a subway intrusion prevention monitoring method based on ordinary communication optical cables, uses an intrusion monitoring device based on the optical cable monitoring component 1 to monitor subway intrusion behaviors. The detection method includes the following steps:

[0050] Step 1: Arrange the optical cable monitoring component 1 of the intrusion monitoring device along the subway tunnel. One end of the optical cable monitoring component 1 is connected to the optical signal generator 5, and the other end is communicatively connected to the signal processing unit 4 through the fiber optic sensing module 2 and the data acquisition module 3;

[0051] Step 2: Turn on the optical signal generator 5. The signal processing unit 4 receives the optical signals collected by the data acquisition module 3, and the signal processing unit 4 analyzes the fiber optic length change characteristics by using the electric field expression of the Rayleigh scattering signal and the phase change;

[0052] Step 3: The signal processing unit 4 uses a deep learning model to analyze and output the intrusion type and probability based on the fiber optic length change characteristics;

[0053] Step 4: If it is determined that the probability of an intrusion appears to be greater than the set probability threshold, the signal processing unit 4 communicates with the alarm unit 6, and the alarm unit 6 activates the alarm.

[0054] Arrange ordinary communication optical cables along the subway tunnel as the optical cable monitoring component 1. The optical cable monitoring component 1 is the sensing medium for monitoring. The phase-sensitive optical time domain reflectometry (Φ-OTDR) can be used to monitor the physical state along the optical cable in real time; specifically, the signal processing unit 4 analyzes the fiber optic length change characteristics by using the electric field expression of the Rayleigh scattering signal and the phase change, and then through the deep learning model, it can efficiently and intelligently analyze and output the intrusion type and probability based on the fiber optic length change characteristics; by analyzing the micro-vibrations, temperature changes, and stress changes along the optical cable, potential safety hazards in the subway tunnel can be identified; and a warning signal is sent through the alarm unit 6.

[0055] It can timely warn of potential safety hazards in the subway tunnel and can achieve high-precision and long-distance monitoring.

[0056] Embodiment 2: Use multiple sections of optical cable monitoring components 1 to monitor intrusion in different sections of the subway tunnel. One end of each section of the optical cable monitoring component 1 is connected to the optical signal generator 5, and the other end is connected to the fiber optic sensing module 2.

[0057] Multiple sections of optical cable monitoring components 1 can be arranged in different sections of the subway tunnel, enabling segmented monitoring of different sections, improving the practicality of the monitoring results, and allowing staff to quickly handle according to the section where the anomaly occurs.

[0058] Embodiment 3: In Step 2, the electric field expression of the Rayleigh scattering signal is as follows:

[0059]

[0060] Where: E R is the electric field strength of the scattered light; E 0 is the initial electric field strength of the scattered light; ω is the angular frequency of the light; t is the time; is the initial phase; is the phase change caused by external disturbances.

[0061] Through the above expression, the phase change caused by external disturbances at a time point can be realized for calculation, providing parameters for subsequent fiber optic length changes.

[0062] Example 4, the relational expression between phase change and fiber optic length change is as follows:

[0063]

[0064] Where: is the phase change; n is the refractive index of the optical fiber; λ is the wavelength of light; ΔL is the fiber optic length change caused by external disturbances.

[0065] Through the above expression, the calculation of fiber optic length change based on time series can be realized, providing a parameter basis for subsequent analysis of fiber optic length change characteristics.

[0066] Example 5, external disturbances include vibration or temperature change.

[0067] Example 6, in step 3, a deep learning model for analyzing fiber optic length change characteristics is established. The deep learning model analyzes the time series data of fiber optic length change to identify length change characteristics, and outputs the intrusion behavior analysis result based on the length change characteristics.

[0068] The fiber optic length change characteristics can be analyzed based on the deep learning model to identify intrusion characteristics.

[0069] Collect data on fiber optic length change in different intrusion scenarios (such as human sabotage, mechanical vibration, temperature change, etc.) and non-intrusion scenarios.

[0070] Ensure that the data set contains sufficient diversity so that the model can learn various possible intrusion patterns. Standardize or normalize the data so that the model can learn better.

[0071] Extract features helpful for classification from the original data, such as statistical features, frequency features, kurtosis, skewness, etc. of the time series. Manual classification can also be used during training, and the model is trained using the labeled data set, where the classification of whether it is an intrusion event is marked.

[0072] Deploy the trained deep learning model to the actual subway anti-intrusion monitoring system.

[0073] Example 7, the intrusion behavior analysis result includes intrusion type and intrusion probability, and the intrusion types include sabotage, vibration and temperature change.

[0074] Generally, subway intrusion behaviors are mainly caused by vandalism, mechanical failures, or environmental climate. A trained deep learning model can efficiently and accurately predict the intrusion type and the corresponding probability.

[0075] Example 8: The deep learning model uses the cross - entropy loss function to predict the intrusion type and the intrusion probability. The formula of the cross - entropy loss function is as follows:

[0076]

[0077] where y is the intrusion type; y i is the i - th feature vector of the intrusion type; C is the number of intrusion types, is the probability distribution predicted by the deep learning model.

[0078] For multi - classification problems, the cross - entropy loss can be used for the intrusion type and the predicted probability distribution.

[0079] Example 9: A subway intrusion monitoring device based on ordinary communication optical cables includes an optical cable monitoring component 1, an optical signal generator 5, an optical fiber sensing module 2, a data acquisition module 3, a signal processing unit 4, and an alarm unit 6. The optical cable monitoring component 1 includes a flexible mounting member 11 and a monitoring optical cable 12. The bottom of the flexible mounting member 11 is installed at the boundary along the subway tunnel. There is an optical cable installation groove on the top of the flexible mounting member 11. The monitoring optical cable 12 is detachably installed at the optical cable installation groove. The signal output end of the optical signal generator 5 is connected to one end of the monitoring optical cable 12. The optical fiber sensing module 2 is connected to the other end of the monitoring optical cable 12 to collect optical signals. The signal processing unit 4 is communicatively connected to the optical fiber sensing module 2 through the data acquisition module 3. The signal processing unit 4 realizes optical signal analysis based on a computer. The alarm unit 6 is communicatively connected to the signal output end of the signal processing unit 4.

[0080] The optical cable monitoring component 1 adopts the form of a combination of a flexible mounting member 11 and a monitoring optical cable 12, which can reduce the risk of breakage or fracture of the monitoring optical cable 12 caused by damage. The optical fiber sensing module 2 can collect optical signals. The data acquisition module 3 can collect the optical signals collected by the optical fiber sensing module 2 and send them to the signal processing unit 4. The signal processing unit 4 uses a computer to realize optical signal analysis. The alarm unit 6 can adopt an audible and visual alarm. In specific applications, multiple audible and visual alarms can be numbered. The alarm of the corresponding numbered audible and visual alarm represents that there is an abnormality at the position of the corresponding monitoring optical cable 12, which is convenient for the staff to arrive at the scene for disposal in time.

[0081] Example 10: A plurality of heat dissipation holes 111 penetrating the optical cable installation groove are uniformly arranged on the flexible mounting member 11. The flexible mounting member 11 is formed of PVC material. The optical cable installation groove is an arc - shaped groove. After the bottom of the monitoring optical cable 12 is installed, the part of the top exposed above the optical cable installation groove surface exceeds three - quarters of the arc.

[0082] The flexible mounting member 11 made of PVC material can provide good support for the monitoring optical cable 12. Multiple heat dissipation holes 111 provide better heat dissipation, avoiding errors caused by local heat accumulation in the optical cable. The part of the monitoring optical cable 12 that protrudes above the optical cable installation groove surface exceeds three - quarters of an arc, and most of the monitoring optical cable 12 is in a bare state, avoiding the influence of the installation part on monitoring and enhancing heat dissipation at the same time.

[0083] The following uses embodiments to illustrate the implementation principles of the subway intrusion prevention monitoring method and device based on ordinary communication optical cables of the present invention:

[0084] A new subway line is under construction in a certain city. To ensure the safety of the subway tunnel, it is decided to adopt a subway intrusion prevention monitoring method based on ordinary communication optical cables. The following are the specific implementation steps:

[0085] Step 1: Arrange the optical cable monitoring member 1 along the subway tunnel. The construction personnel connect one end of the optical cable monitoring member 1 to the optical signal generator 5, and the other end is communicatively connected to the signal processing unit 4 through the optical fiber sensing module 2 and the data acquisition module 3. The optical cable monitoring member 1 uses a flexible mounting member 11 and a monitoring optical cable 12 and is installed along the tunnel boundary;

[0086] Step 2: Turn on the optical signal generator 5, and the signal processing unit 4 starts to receive the optical signals collected by the data acquisition module 3. The signal processing unit 4 calculates the optical fiber length change characteristics by using the electric field expression and phase change analysis of the Rayleigh scattering signal.

[0087] Step 3, the signal processing unit 4 uses a deep learning model to analyze the optical fiber length change characteristics and outputs the intrusion type and probability. During this process, the staff collected a large amount of optical fiber length change data under different scenarios, including human damage, mechanical vibration, temperature change, etc., and standardized the data. The deep learning model has been trained to be able to identify various intrusion patterns;

[0088] Step 4, when the signal processing unit 4 determines that the intrusion probability is greater than the set threshold, it communicates with the alarm unit 6, and the alarm unit 6 immediately starts to alarm. The alarm unit 6 uses an audible and visual alarm, and the staff can quickly locate the abnormal occurrence position according to the alarm number and take measures.

[0089] By adopting the subway intrusion prevention monitoring method based on ordinary communication optical cables, multiple potential safety hazards, including human damage, mechanical vibration, and temperature change, have been successfully warned during the construction of this subway line. The monitoring system has high accuracy and long - distance monitoring capabilities, providing strong guarantee for the safety of the subway tunnel. At the same time, the system also has the following advantages:

[0090] It can monitor the physical state of the subway tunnel in real - time and detect potential safety hazards in a timely manner.

[0091] Adopt a deep learning model to efficiently and accurately analyze the characteristics of the change in the length of the optical fiber and identify intrusion behaviors.

[0092] Through the alarm unit 6, the staff can quickly locate the abnormal position and take measures.

[0093] Adopt the flexible mounting member 11 and the monitoring optical cable 12, which are convenient to install and reduce the construction difficulty.

[0094] The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A subway anti-intrusion monitoring method based on common communication optical cables, characterized in that: An intrusion monitoring device based on an optical cable monitoring element (1) is used to monitor subway intrusion behavior. The detection method comprises the following steps: Step 1, arranging an optical cable monitoring device (1) of an intrusion monitoring device along a subway tunnel, wherein one end of the optical cable monitoring device (1) is connected to an optical signal generator (5), and the other end is connected to a signal processing unit (4) through an optical fiber sensing module (2) and a data acquisition module (3); Step 2, turning on the optical signal generator (5), the signal processing unit (4) receives the optical signal collected by the data acquisition module (3), and the signal processing unit (4) obtains the optical fiber length change characteristics by using the electric field expression and phase change analysis of the Rayleigh scattering signal; Step 3, the signal processing unit (4) uses a deep learning model to analyze and output the intrusion type and probability based on the optical fiber length change characteristics; Step 4: If it is determined that the probability of intrusion is greater than a set probability threshold, the signal processing unit (4) communicates with the alarm unit (6), and the alarm unit (6) turns on the alarm.

2. The subway anti-intrusion monitoring method based on ordinary communication optical cable according to claim 1 is characterized in that: A plurality of optical cable monitoring components (1) are used to carry out intrusion monitoring on different sections of a subway tunnel. One end of each optical cable monitoring component (1) is connected to an optical signal generator (5), and the other end is connected to an optical fiber sensing module (2).

3. The subway anti-intrusion monitoring method based on ordinary communication optical cable according to claim 2 is characterized in that: In step 2, the electric field expression of the Rayleigh scattering signal is as follows: Where: E R is the electric field intensity of the scattered light; E0 is the initial electric field intensity of the scattered light; ω is the angular frequency of the light; t is the time; is the initial phase; is the phase change caused by external disturbance.

4. The subway anti-intrusion monitoring method based on ordinary communication optical cable according to claim 3 is characterized in that: The relationship between phase change and fiber length change is expressed as follows: in: is the phase change; n is the refractive index of the fiber; λ is the wavelength of light; ΔL is the change in fiber length due to external disturbances.

5. The subway anti-intrusion monitoring method based on common communication optical cable according to claim 4 is characterized in that: External disturbances include vibrations or temperature changes.

6. The subway anti-intrusion monitoring method based on common communication optical cable according to claim 5 is characterized in that: In step 3, a deep learning model is established to analyze the characteristics of optical fiber length changes. The deep learning model analyzes the time series data of optical fiber length changes to identify length change characteristics and outputs intrusion behavior analysis results based on the length change characteristics.

7. The subway anti-intrusion monitoring method based on common communication optical cable according to claim 6 is characterized in that: The intrusion behavior analysis results include intrusion type and intrusion probability. Intrusion types include destruction, vibration and temperature change.

8. The subway anti-intrusion monitoring method based on common communication optical cable according to claim 7 is characterized in that: The deep learning model uses the cross entropy loss function to predict the intrusion type and intrusion probability. The formula of the cross entropy loss function is as follows: where y is the invasion type; y i is the ith eigenvector of the intrusion type; C is the number of intrusion types, is the probability distribution predicted by the deep learning model.

9. A subway anti-intrusion monitoring device based on ordinary communication optical cable, characterized in that: The invention comprises an optical cable monitoring component (1), an optical signal generator (5), an optical fiber sensing module (2), a data acquisition module (3), a signal processing unit (4) and an alarm unit (6); the optical cable monitoring component (1) comprises a flexible mounting component (11) and a monitoring optical cable (12); the bottom of the flexible mounting component (11) is mounted at the boundary along the subway tunnel; the top of the flexible mounting component (11) is provided with an optical cable mounting groove; the monitoring optical cable (12) is detachably mounted at the optical cable mounting groove; the signal output end of the optical signal generator (5) is connected to one end of the monitoring optical cable (12); the optical fiber sensing module (2) and the other end of the monitoring optical cable (12) are connected to collect optical signals through a sexual connection; the signal processing unit (4) is communicatively connected to the optical fiber sensing module (2) through the data acquisition module (3); the signal processing unit (4) realizes optical signal analysis based on a computer; and the alarm unit (6) is communicatively connected to the signal output end of the signal processing unit (4).

10. The subway anti-intrusion monitoring device based on ordinary communication optical cable according to claim 9 is characterized in that: a plurality of heat dissipation holes (111) penetrating the optical cable installation groove are evenly arranged on the flexible installation part (11), the flexible installation part (11) is formed of PVC material, the optical cable installation groove is an arc groove, and after the bottom of the monitoring optical cable (12) is installed, the part exposed on the top of the optical cable installation groove surface exceeds three quarters of the arc.

Images