Bridge line anomaly detection and alarm method and system

Abstract

The invention relates to the technical field of bridge safety operation, in particular to a bridge line anomaly detection and alarm method and system, and the method comprises the following steps: obtaining bridge line monitoring information through a line anomaly monitoring system; setting a line abnormity positioning judgment mechanism and a back-end processing model, and obtaining a line positioning judgment result based on the line abnormity positioning judgment mechanism, the back-end processing model and the bridge line monitoring information; analyzing the bridge line monitoring information and the line positioning judgment result by using a back-end processing model to obtain a line anomaly detection result, a response level condition and a bridge structure condition; and realizing detection and warning of a bridge line interruption event by combining bridge line monitoring information, a line positioning judgment result, a line anomaly detection result, a response level condition and a bridge structure condition. The bridge safety condition is monitored for a long time, abnormal events are judged, a warning signal is sent out according to the judgment result, and casualties and losses caused by disasters are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge safety operation, and in particular to a bridge line abnormality detection and alarm method and system. Background Art

[0002] At present, bridge safety monitoring technology mainly relies on daily detection, inspection and monitoring to predict potential safety risks. However, in the face of the long and wide-ranging characteristics of highway and railway lines, manual inspections and existing detection methods are inadequate and have poor timeliness, making it difficult to detect emergency events such as line interruptions in the first place. At the same time, although conventional monitoring technology can sense the status of highway and railway infrastructure, it can only be deployed at key structures and sections, and cannot achieve full coverage of monitoring lines.

[0003] Bridges are key nodes in the transportation network, and frequent extreme weather events pose a challenge to bridge safety. In this environment, bridges face a huge risk of collapse. Once an accident occurs, due to the long mileage and high speed of roads and railways, coupled with limited vision at night, vehicles often fail to brake in time, resulting in serious casualties and property losses.

[0004] Therefore, more efficient and intelligent bridge safety monitoring methods are needed to improve the early warning capabilities of bridge safety risks under extreme weather and geological conditions, realize real-time monitoring and intelligent early warning of bridge status, and establish a more complete emergency response mechanism to deal with emergencies such as bridge collapse. Summary of the invention

[0005] In view of the defects of existing methods and the shortcomings of practical applications, in order to achieve long-term and uninterrupted safety status monitoring of widely distributed bridge clusters, warning information is quickly sent to passing vehicles when major accidents such as bridge line interruption occur, thereby effectively reducing casualties and losses caused by disasters. In the first aspect, the present invention provides a bridge line abnormality detection and alarm method, the method includes the following steps: obtaining bridge line monitoring information through a line abnormality monitoring system; setting a line abnormality positioning and discrimination mechanism and a back-end processing model, and obtaining a line positioning and discrimination result based on the line abnormality positioning and discrimination mechanism, the back-end processing model and the bridge line monitoring information; using the back-end processing model to analyze the bridge line monitoring information and the line positioning and discrimination result to obtain the line abnormality detection result, the response level and the bridge structure condition; combining the bridge line monitoring information, the line positioning and discrimination result, the line abnormality detection result, the response level and the bridge structure condition to achieve detection and warning of bridge line interruption events. The present invention combines modern information technology and data analysis methods to improve the intelligent management level of bridge lines. It reduces the burden of manual inspections through automated monitoring and early warning equipment, improves detection and early warning efficiency, and helps relevant departments to quickly formulate and implement emergency plans, thereby reducing the losses and impacts of safety accidents.

[0006] Optionally, the setting of the line abnormality positioning and discrimination mechanism includes: configuring an optical fiber break positioning module in the line abnormality positioning and discrimination mechanism; and establishing a bridge structure and optical fiber line analysis model in the optical fiber break positioning module based on the bridge line monitoring information and the back-end processing model. The optical fiber break positioning module of the present invention can accurately capture breaks or abnormal changes in optical fiber lines, thereby improving the response speed and processing efficiency of abnormal events.

[0007] Optionally, the establishment of a bridge structure and optical fiber line analysis model in the optical fiber break positioning module based on the bridge line monitoring information and the back-end processing model includes: obtaining optical fiber Bragg grating position calibration information and highway mileage matching information according to the bridge line monitoring information and the back-end processing model; setting positioning analysis parameter information according to the optical fiber Bragg grating position calibration information and the highway mileage matching information; and building a bridge structure and optical fiber line analysis model based on the positioning analysis parameter information. The present invention obtains optical fiber Bragg grating position calibration information based on the bridge line monitoring information and the back-end processing model, and can accurately determine the position of the optical fiber Bragg grating in the bridge structure, which helps to quickly locate abnormal optical fiber sections and bridge structure parts when abnormal events occur.

[0008] Optionally, the construction of the bridge structure and optical fiber line analysis model based on the positioning analysis parameter information includes: establishing an initial layout state analysis function, a bridge structure state analysis function and an optical fiber line analysis function based on the positioning analysis parameter information; and obtaining a bridge structure and optical fiber line analysis model by combining the initial layout state analysis function, the bridge structure state analysis function and the optical fiber line analysis function. The analysis model established by the present invention can adapt to different types of bridge structures so that the relevant analysis functions have flexibility and scalability. By adjusting the parameters in the function, it can adapt to different types of bridge structures, thereby improving the applicability of the model of the present method.

[0009] Optionally, the layout state analysis function satisfies the following relationship:

[0010] in, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, The wavelength reflected by the nth grating is The optical signal strength, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength;

[0011] in, Indicates that the wavelength received by optical signal demodulator 2 is The optical signal strength, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength.

[0012] The layout state analysis function of the present invention improves the precision and accuracy of the monitoring results, further enhances the overall performance of the method, and provides a strong guarantee for the safe operation of the bridge.

[0013] Optionally, the bridge structure state analysis function satisfies the following relationship:

[0014] in, represents the intensity of the reflected light signal of the nth grating received by the optical signal demodulator 1, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength;

[0015] in, Indicates that the wavelength received by optical signal demodulator 2 is The optical signal strength, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength.

[0016] The present invention quickly processes and analyzes relevant data through analysis functions, which helps to timely discover abnormal conditions of bridge structures and issue early warning information.

[0017] Optionally, the optical fiber line analysis function satisfies the following relationship:

[0018] in, represents the intensity of the reflected light signal of grating i received by the optical signal demodulator 1, The wavelength of grating i is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength;

[0019] in, represents the intensity of the reflected light signal of grating i+j received by the optical signal demodulator 2, The wavelength of grating i+j is The reflectivity of the arriving optical signal, The wavelength emitted by laser emitter 2 is The optical signal strength.

[0020] The present invention is affected by various interference factors in optical fiber line monitoring. By accurately measuring wavelength deviation and reflectivity and combining advanced signal processing algorithms, the reliability of abnormal detection results can be improved.

[0021] Optionally, the line positioning discrimination result obtained based on the line abnormality positioning discrimination mechanism, the back-end processing model and the bridge line monitoring information includes: obtaining the initial state after layout through the initial layout state analysis function; obtaining the normal service state of the bridge structure using the bridge structure state analysis function; analyzing the fiber break state based on the fiber line analysis function; and obtaining the line positioning discrimination result by combining the initial state, the normal service state, the fiber break state and the bridge line monitoring information. The present invention combines the line abnormality positioning discrimination mechanism and the back-end processing model to realize the automatic monitoring and analysis function, which helps to improve the intelligent level of bridge structure monitoring and reduce manual intervention and error coefficients.

[0022] Optionally, the use of the back-end processing model to analyze the bridge line monitoring information and the line positioning judgment result to obtain the line abnormality detection result, response level and bridge structure condition includes: setting an image overlap comparison function; the back-end processing model uses the image overlap comparison function, the bridge line monitoring information and the line positioning judgment result to analyze the pixel overlap rate of the monitoring image; determines the level judgment result of the fiber optic disconnection event based on the pixel overlap rate; obtains the line abnormality detection result, response level and bridge structure condition based on the level judgment result, the bridge line monitoring information and the line positioning judgment result. The present invention determines the level judgment result of the fiber optic disconnection event based on the image overlap rate, which can more objectively reflect the abnormality of the fiber optic line, thereby optimizing the judgment of the response level.

[0023] In the second aspect, the present invention also provides a bridge line anomaly detection and alarm system, which can efficiently execute a bridge line anomaly detection and alarm method provided by the present invention, the system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are interconnected, the memory includes a computer-readable storage medium as described in the first aspect of the present invention, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call program instructions. The bridge line anomaly detection and alarm system provided by the present invention has a compact structure, strong applicability, and greatly improves the operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of the bridge line abnormality detection and alarm method of the present invention; Figure 2 A schematic diagram of the relationship between the physical spatial position of the grating and the highway mileage in the bridge line anomaly detection and alarm method of the present invention; Figure 3 It is a schematic diagram of image superposition and comparison in the bridge line anomaly detection and alarm method of the present invention; Figure 4 It is a schematic diagram of the operation mechanism of different modules, equipment and devices in the bridge line abnormality detection and alarm method of the present invention; Figure 5 It is a structural schematic diagram of the bridge line abnormality detection and alarm system of the present invention. DETAILED DESCRIPTION

[0025] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0026] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0027] See also Figure 1 In order to achieve long-term, continuous and uninterrupted safety status monitoring of widely distributed bridge clusters, and to ensure that warning information can be quickly conveyed to passing vehicles when a bridge line encounters a major accident such as an interruption, thereby effectively reducing casualties and losses caused by disasters and ensuring traffic safety. The present invention provides a bridge line anomaly detection and alarm method, which includes the following steps: S1. Obtain bridge line monitoring information through the line anomaly monitoring system. The implementation steps and specific contents are as follows: In order to conduct long-term and continuous safety status monitoring of widely distributed bridge clusters, so as to enable rapid response when bridge lines encounter major accidents such as interruptions, in this embodiment, a line abnormality monitoring system is used to obtain bridge line monitoring information.

[0028] The above-mentioned line anomaly monitoring system is mainly constructed by long-distance, large-capacity distributed fiber grating sensors. The relevant sensors have excellent perception capabilities, can cover a distance of more than 10 kilometers, and integrate more than 10,000 fiber gratings on a single optical fiber, thereby realizing high-density monitoring with a measurement point spacing of only 1 meter.

[0029] In terms of equipment installation, customized fixing treatment is mainly carried out according to the material of the bridge. For concrete bridges, expansion bolts are used to stabilize the optical fiber to ensure that it can deform synchronously with the bridge structure; while for steel bridges, welding clamps are used for fixing. Different design methods can ensure that when the bridge structure collapses or breaks, the fiber grating sensor will break accordingly, thereby capturing the state changes of the bridge line in real time and obtaining accurate bridge line monitoring information accordingly.

[0030] Furthermore, the method for obtaining bridge line monitoring information in the present embodiment is only an optional condition of the present invention. In one or some other embodiments, it can be based on the actual situation of the highway bridge and the data monitoring requirements. With the aging of the bridge structure, changes in the environment and the increase in traffic flow, the monitoring requirements of the bridge will also change. The method of the present invention has better flexibility and adaptability, further ensuring the continuity and effectiveness of the monitoring work.

[0031] S2. Set up a line abnormality positioning and discrimination mechanism and a back-end processing model. Based on the above line abnormality positioning and discrimination mechanism, the back-end processing model and the bridge line monitoring information, obtain the line positioning and discrimination result. The specific steps and implementation contents are as follows: In an optional embodiment, a fiber break location module is configured in the above-mentioned line abnormality location judgment mechanism; at the same time, a bridge structure and fiber line analysis model is established in the fiber break location module based on the bridge line monitoring information and the back-end processing model, and its specific contents are as follows: In an optional embodiment, the specific establishment process and implementation steps of the bridge structure and optical fiber line analysis model are as follows: Firstly, the fiber Bragg grating position calibration information and highway mileage matching information are obtained based on the bridge line monitoring information and the back-end processing model.

[0032] In order to achieve efficient monitoring of the safety status of bridge structures and ensure that they can be quickly located and warned when bridges suffer major damage, this embodiment deploys long-distance, large-capacity distributed fiber Bragg grating sensors on highway and railway bridges. The fixing method of the relevant sensors refers to the relevant content of step S1. The expansion bolts are tightened or welded to ensure that the optical fiber is tightly combined with the bridge body, thereby reflecting the deformation of the bridge in real time. When the bridge structure is significantly deformed, broken or collapsed, the above-mentioned tightly fitted optical fiber will break, thereby providing an effective damage data signal. A line abnormality positioning and discrimination mechanism is designed to capture and analyze bridge line monitoring information. The mechanism can continuously transmit optical signals to the long-distance, large-capacity distributed fiber Bragg grating sensors in the line abnormality monitoring system, and receive the reflected bridge line monitoring information and light intensity data.

[0033] Furthermore, the system accurately calibrates and matches the fiber Bragg grating position in the long-distance, large-capacity distributed fiber Bragg grating sensor and the corresponding highway mileage. The above process can convert the fiber Bragg grating break position into the actual highway mileage. Once the optical fiber breaks, the specific location of the damage on the bridge can be quickly determined, so that timely countermeasures can be taken to reduce potential risks and losses.

[0034] In the embodiment, each fiber Bragg grating is assigned a unique number, and the above numbers form a corresponding relationship with their actual physical positions on the bridge, that is, the physical spatial position of the grating numbered i coincides with the highway mileage pile number K(i) on the bridge; similarly, the physical position of the grating numbered i+j corresponds to the highway mileage pile number K(i+j). For details of the above one-to-one correspondence, please refer to Figure 2 ,This information calibration method is beneficial to the monitoring and analysis of the ,bridge structure status in subsequent work, and can locate the problem area quickly and ,accurately.

[0035] based on Figure 2 In the actual operation process, Figure 2 The laser transmitter 1 in the bridge can continuously send optical signals to the fiber Bragg grating sensor on the bridge. When the optical signal passes through the fiber Bragg grating, only the light of a specific wavelength that meets the Bragg condition will be reflected back. The reflected light is then received by the optical signal demodulator 1. Since each fiber Bragg grating has a unique reflection wavelength characteristic, the optical signal demodulator 1 can identify which grating the reflected light source comes from based on different characteristics.

[0036] Once the bridge is damaged and the optical fiber breaks, the back-end processing system will immediately initiate a response mechanism. First, based on the bridge line monitoring information collected by the system, the specific event and location of the break will be quickly locked. Then, with the help of the precise matching relationship between the grating position and the highway mileage established in advance, the back-end processing system can accurately convert the optical fiber break position into geospatial location information. The above conversion process can quickly and accurately locate the damaged area on the bridge, so that effective repair measures can be taken in time to ensure the safe operation of the bridge.

[0037] In addition, by combining bridge line monitoring information and back-end processing models, more valuable information can be obtained, such as further optimization and improvement of fiber grating position calibration information and highway mileage matching information. Based on this, we can have a deeper understanding of the structural status of the bridge and provide strong data support for subsequent bridge maintenance and management.

[0038] The positioning analysis parameter information is set according to the fiber grating position calibration information and the highway mileage matching information.

[0039] In order to conduct positioning analysis, a series of key parameters need to be set based on the position calibration information of the fiber Bragg grating and the highway mileage matching information. The above parameters will be used in the line abnormality positioning and identification mechanism to ensure that potential line problems can be discovered and handled in a timely manner.

[0040] The settings of basic parameters are as follows; Laser transmitter status parameters Indicates the status of laser transmitter 1, 0 means off and 1 means on.

[0041] Indicates the state of laser transmitter 2, 0 means off and 1 means on.

[0042] Optical signal demodulator state parameters Indicates the state of optical signal demodulator 1, 0 means off and 1 means on.

[0043] Indicates the state of the optical signal demodulator 2, 0 means off, 1 means on.

[0044] Laser transmitter optical signal strength Indicates the intensity of the optical signal emitted by the laser transmitter, which is the wavelength of the emitted laser As a function of , for broad-spectrum and strong laser transmitters, the intensity of optical signals at different wavelengths remains consistent.

[0045] Time parameter Indicates that the time information is provided by an independent timing system. In the embodiment, the time format is year (4 digits), month (2 digits), day (2 digits), hour (2 digits), minute (2 digits), second (2 digits), millisecond (3 digits), a total of 15 digits.

[0046] Grating position parameters Indicates the position of the nth grating, where n is a positive integer greater than or equal to 1 and does not exceed the total number N of gratings in the fiber Bragg grating sensor loop.

[0047] Highway mileage parameters Indicates the highway mileage corresponding to the nth raster, in the format of kilometers (4 digits) + meters (3 digits), a total of 7 digits.

[0048] The settings of the fiber and grating characteristic parameters are as follows; In the refractive index of optical fiber, It represents the effective refractive index of optical fiber and is an inherent property of optical fiber.

[0049] In the grating pitch, It represents the pitch of the nth grating, which changes with the deformation of the bridge structure and is an important indicator for monitoring the health of the structure.

[0050] After setting the above basic parameters in the line abnormality positioning and discrimination mechanism, the line abnormality positioning and discrimination mechanism can be further improved. The mechanism will use the status and data of laser transmitters, optical signal demodulators, fiber grating sensors and other equipment, combined with time parameters and highway mileage information, to conduct real-time monitoring and abnormality discrimination of the line. Once an abnormal signal or data deviation is found, the system will immediately trigger an alarm and indicate the abnormal location so that timely measures can be taken to deal with it.

[0051] The relevant contents of the information characteristic parameters are as follows: This parameter represents the specific wavelength of the light signal reflected by the nth grating, which is an inherent and unique property of the grating itself.

[0052] This means that the wavelength of the nth grating pair is exactly The reflectivity is another intrinsic property of the grating, which determines how the grating interacts with light signals of a specific wavelength.

[0053] The parameter measures the wavelength of the light reflected by the nth grating. The intensity of the optical signal. The above intensity value is not only related to the reflectivity of the grating, but also affected by other factors, such as the intensity of the incident light signal and the loss of the optical fiber.

[0054] It describes the optical signal received by the optical signal demodulator 2, with a wavelength of The intensity of the optical signal is measured by the demodulator after the optical signal is transmitted through the optical fiber. It reflects the attenuation of the optical signal during the transmission process and the actual receiving effect of the optical signal reflected by the grating.

[0055] Based on the physical meaning of the above information parameters and the role of different parameters in the transmission and reflection of optical signals, it is helpful to understand and analyze the working principle of fiber grating sensors, as well as to carry out subsequent data processing and anomaly detection.

[0056] The bridge structure and optical fiber line analysis models are constructed based on the positioning analysis parameter information.

[0057] In this embodiment, an initial layout state analysis function, a bridge structure state analysis function and an optical fiber line analysis function are established based on the positioning analysis parameter information; and then, by combining the initial layout state analysis function, the bridge structure state analysis function and the optical fiber line analysis function, the bridge structure and optical fiber line analysis models in the embodiment can be obtained.

[0058] After installing and debugging the relevant equipment in the line abnormality location and judgment mechanism, each key equipment immediately enters the preset working mode. That is, the laser transmitter 1, the optical signal demodulator 1 and the optical signal demodulator 2 are all activated and enter the normal working state. The relevant parameter settings are as follows: and The values ​​of are all set to 1, and at the same time the laser transmitter 2 is in a non-working state, that is, The value of is set to 0.

[0059] In the above-mentioned specific working state, the laser transmitter 1 can continuously emit a broad-spectrum optical signal with constant intensity. When the above-mentioned optical signal passes through the fiber grating, only the specific optical signal with a wavelength matching the specific property of the grating will be reflected back, while the optical signals of the remaining wavelengths can continue to move forward unhindered, passing through the grating, further ensuring the accurate capture of the required optical signal, laying a solid foundation for subsequent data processing and analysis.

[0060] Analysis of the initial state after deployment.

[0061] When the fiber Bragg grating system is in the initial deployment state, in which the crosstalk effect between the gratings and the related changes in the fiber length are ignored, the optical signal demodulator 1 can receive the reflected light signal from the nth grating, and the above wavelength satisfies a specific mathematical expression:

[0062] in, represents the wavelength of the light signal reflected by the nth grating, represents the refractive index of the optical fiber, Indicates the grating pitch of the nth grating.

[0063] A function for analyzing the layout state, i.e., the layout state analysis function in the embodiment, is constructed based on a mathematical formula of the wavelength of the grating reflected light signal. This analysis function can be used to accurately predict the intensity of the nth Bragg grating (FBG) reflected light signal received by the optical signal demodulator 1 and the optical signal demodulator 2.

[0064] The above layout state analysis function includes an analysis function of the intensity of the reflected light signal of the nth grating received by the optical signal demodulator 1 and the optical signal demodulator 2 .

[0065] The analytical relationship of the intensity of the reflected light signal of the nth grating received by the optical signal demodulator 1 is as follows:

[0066] in, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, The wavelength reflected by the nth grating is The optical signal strength, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength; The analytical relationship of the intensity of the reflected light signal of the nth grating received by the optical signal demodulator 2 is as follows:

[0067] in, Indicates that the wavelength received by optical signal demodulator 2 is The optical signal strength, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength.

[0068] Based on the analytical relationship of the intensity of the reflected light signal of the nth Bragg grating (FBG) received by the optical signal demodulator 1 and the optical signal demodulator 2 included in the above layout state analysis function, the intensity of the reflected light signal received by the optical signal demodulator can be accurately predicted. The above function clearly points out the relationship between reflectivity and light signal intensity, where reflectivity determines the proportion of the light signal reflected by the grating, which in turn affects the signal intensity received by the demodulator. On the other hand, the synergy between the laser transmitter and the demodulator, the intensity of the light signal emitted by the laser transmitter is the basis for analyzing the intensity of the reflected light signal, based on which the signal transmission and reflection process between the transmitter and the demodulator can be understood.

[0069] In addition, based on the layout state analysis function, the relationship between reflectivity and signal strength can be understood, which is helpful to optimize the sensor performance in the bridge line anomaly detection and alarm method, and can enhance the signal strength received by the demodulator, thereby improving the sensitivity and accuracy of the bridge line anomaly detection and alarm system.

[0070] Analysis of the service status of bridge structures.

[0071] When a bridge structure is subjected to load and undergoes elastic deformation, the optical fiber that deforms in coordination with it will also undergo corresponding elongation, compression, and axial deformation, which in turn causes the wavelength of the reflected light signal of the nth grating in the optical fiber to change. In the monitoring of the state of the bridge structure, when the optical fiber is deformed due to structural deformation, different optical signal demodulators can detect changes in the wavelengths of the reflected light signals of different gratings. The above changes are closely related to the deformation state of the bridge structure.

[0072] The bridge structure status analysis function established based on this can deeply analyze the changes in the wavelengths of the light signals reflected by each grating captured by different optical signal demodulators, thereby achieving an accurate assessment of the bridge structure status.

[0073] Since the quartz optical fiber has an isotropic property, there is a specific relationship between its radial deformation and axial deformation, which determines the specific relationship between the radial deformation and the axial deformation, and the Poisson's ratio is used to define it in the embodiment.

[0074]

[0075] in, is the radial deformation, represents the cross-sectional diameter of the optical fiber, represents Poisson's ratio, represents the axial deformation, Indicates the length of the optical fiber.

[0076] When the optical fiber is deformed, the wavelength change of the reflected light signal of the nth grating received by the optical signal demodulator 1 satisfies the following formula:

[0077] in, It represents the change of the wavelength of the reflected light signal. represents the grating pitch of the nth grating, represents the elastic-optical effect of optical fiber, represents the axial deformation, represents the waveguide effect, is the radial deformation, represents the refractive index of the optical fiber, is the change in the pitch of the nth grating.

[0078] Furthermore, based on the Poisson's ratio analysis formula and the relationship between the reflected light signal wavelength change, the reflected light signal wavelength change analysis function of the nth grating received by the optical signal demodulator 1 in the bridge structure state analysis function is optimized, and the following relationship is satisfied:

[0079] in, It represents the change of the wavelength of the reflected light signal. represents the grating pitch of the nth grating, represents the elastic-optical effect of optical fiber, Denote the Poisson's ratio as, Indicates the length of the optical fiber. represents the cross-sectional diameter of the optical fiber, represents the axial deformation, represents the refractive index of the optical fiber, is the change in the pitch of the nth grating.

[0080] Then, the intensity of the reflected light signal of the nth grating received by the optical signal demodulator 1 needs to satisfy the following relationship:

[0081] in, represents the intensity of the reflected light signal of the nth grating received by the optical signal demodulator 1, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength.

[0082] The intensity of the optical signal received by the optical signal demodulator 2 in the bridge structure state analysis function needs to satisfy the following relationship:

[0083] in, Indicates that the wavelength received by optical signal demodulator 2 is The optical signal strength, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength.

[0084] Analysis of fiber break status.

[0085] In the optical fiber network, if the optical fiber between grating i and grating i+j is broken, the optical signal demodulator 1 can still normally receive the reflected light signal from the grating i, and the reflected light signal of the optical signal demodulator 1 remains normal without being affected by the break.

[0086] The optical fiber line analysis function in the embodiment mainly analyzes the intensity of the reflected light signal of grating i received by the optical signal demodulator 1 and the intensity of the reflected light signal of grating i received by the optical signal demodulator 2 .

[0087] The intensity of the reflected light signal of grating i received by the optical signal demodulator 1 satisfies the following relationship:

[0088] in, represents the intensity of the reflected light signal of grating i received by the optical signal demodulator 1, The wavelength of grating i is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength; Based on the above analysis function, it can be seen that due to the disconnection of the optical fiber between grating i and grating i+1, the reflected light signal of grating i+1 cannot be transmitted, and its intensity drops sharply to 0 in the whole band, that is, ,in, Indicates the intensity of the reflected light signal returned by grating i+1.

[0089] At the same time, the above-mentioned disconnection also blocks the transmission of the optical signal to the optical signal demodulator 2, so that the intensity of the optical signal received by the demodulator 2 also drops sharply to 0 in the entire band, that is, ,in, Indicates the intensity of the optical signal received by the optical signal demodulator 2.

[0090] In the embodiment, the relevant instruction tasks are issued based on the line abnormality positioning and discrimination mechanism and the back-end processing model. After receiving the relevant information, the back-end processing system, i.e., the back-end processing model of the embodiment, sends an instruction to the laser transmitter 2 to change its state value. Set to 1, so as to start the laser transmitter 2 to transmit the optical signal to the fiber grating sensor. However, due to the break in the optical fiber between grating i and grating i+j, the optical signal cannot be transmitted backwards, so the optical signal demodulator 2 can only receive the reflected light signal of grating i+j (the grating before the break point), and the signal strength is normal.

[0091] Then, the reflected light signal intensity of the grating i received by the optical signal demodulator 2 in the optical fiber line analysis function satisfies the following relationship:

[0092] in, represents the intensity of the reflected light signal of grating i+j received by the optical signal demodulator 2, The wavelength of grating i+j is The reflectivity of the arriving optical signal, The wavelength emitted by laser emitter 2 is The optical signal strength.

[0093] Since the optical fiber is broken between grating i and grating i+j, the reflected light signal of grating i+j-1 cannot be transmitted smoothly, and its intensity drops rapidly to 0 in the whole band, that is, ,in, Indicates the intensity of the reflected light signal returned by grating i+j-1. After the above information is received by the back-end processing system, the system immediately determines the highway mileage pile number of the bridge to There is an anomaly in the interval, and the current absolute time is recorded .

[0094] The bridge structure status analysis function in the line abnormality positioning and discrimination mechanism can accurately locate the highway mileage pile number interval where the abnormality occurs on the bridge through the fiber break between grating i and grating i+j, which helps to quickly identify and repair the fault point and reduce maintenance time and cost. The back-end processing device can receive and process information from the optical signal demodulator in real time. Once the optical signal abnormality caused by the fiber break is detected, it can immediately respond and issue a safety warning, determine the abnormal interval and record the absolute time. This real-time monitoring and response mechanism helps to promptly discover and deal with potential safety hazards.

[0095] Finally, based on the line abnormality positioning and identification mechanism, the back-end processing model and the bridge line monitoring information, the line positioning and identification results are obtained.

[0096] The initial state after layout is obtained through the initial layout state analysis function; the normal service state of the bridge structure is obtained by using the bridge structure state analysis function; the optical fiber break state is analyzed based on the optical fiber line analysis function; the line positioning judgment result is obtained by combining the initial state, normal service state, optical fiber break state and bridge line monitoring information.

[0097] First, the initial layout state analysis function was used to conduct a comprehensive analysis of the initial state after the layout was completed. Then, the bridge structure state analysis function was used to gain an in-depth understanding of the performance of the bridge structure under normal service conditions. At the same time, based on the optical fiber line analysis function, a detailed analysis of the optical fiber disconnection state was conducted. Finally, after combining the initial state, normal service state, optical fiber disconnection state, and real-time monitoring information of the bridge line, the line positioning judgment results were obtained. The above results not only accurately reflect the actual state of the bridge line, provide important reference information for the subsequent maintenance and management of the bridge line, but also provide a strong basis for subsequent maintenance and management.

[0098] S3. Use the back-end processing model to analyze the bridge line monitoring information and line positioning results to obtain line anomaly detection results, response level and bridge structure conditions. The specific steps and implementation contents are as follows: This embodiment also configures a fixed visual device and a flying inspection device in the back-end processing model, which helps to accurately analyze the line abnormality detection results, response level conditions and bridge structure conditions in the subsequent process.

[0099] The specific steps of the fixed visual device and the flying inspection device are as follows: The backend processing model will first determine the two key highway mileage pile numbers on the bridge based on the actual highway route map. and The geospatial coordinates are marked as and The backend processing model then issues shooting instructions to the fixed vision device based on the relevant geospatial location information, taking into account the prevailing weather and lighting conditions.

[0100] The above instructions will require setting the geospatial coordinates and The high-definition infrared camera in the section takes a comprehensive picture of the bridge structure. In order to ensure that clear images can be obtained at night or in low light conditions, the bridge line abnormality detection and alarm system also pre-installs a part of night target lights on the outside of the bridge structure, which can provide necessary lighting for the fixed visual device.

[0101] When the fixed vision device completes the shooting task, it will return the captured image data to the back-end processing model, which will use advanced machine vision technology to automatically analyze the relevant images to identify possible event types. Once an event is identified, the system's back-end processing model will immediately push the relevant information to the personal terminal of the bridge maintenance staff, and determine the specific type of event and the corresponding response level based on the above information.

[0102] In addition, the flying inspection device works in conjunction with the fixed visual device. The flying inspection device uses drones and other aircraft equipped with high-definition cameras and other sensors to conduct more detailed and comprehensive inspections of bridges. The above inspection data will also be returned to the back-end processing model for further analysis and processing.

[0103] After the fixed visual device is deployed, a series of preliminary preparations are required to ensure its monitoring effect, including but not limited to multi-angle shooting of the bridge structure within the visible range in different time periods, different weather conditions and different lighting environments. The images obtained by the above shooting will together constitute a standard state sample set, providing basic data for subsequent monitoring and analysis.

[0104] Next, the standard state sample set is used to accurately calibrate each part of the bridge structure. The above steps can determine the geometric shape and characteristics of each part of the bridge structure under different shooting angles, which helps to more accurately identify changes and abnormalities in the bridge structure in subsequent monitoring.

[0105] In addition, in order to more scientifically evaluate the safety status of the bridge structure, it is also necessary to assign weights to each part according to the actual type of the bridge structure. In the embodiment, a common reinforced concrete continuous beam bridge is taken as an example, and different weights are assigned to each part according to its importance and vulnerability in the overall structure of the bridge. The relevant weight values ​​will serve as an important basis for subsequent analysis and evaluation, which will help to more accurately judge the safety status of the bridge structure. The above weight assignment information can be found in Table 1.

[0106] Table 1 Weight assignment information table

[0107] Based on the information in Table 1 and related monitoring information, it can be seen that for the safety assessment of the bridge structure, in this embodiment, weights are assigned to each key part mainly according to the actual type of the bridge, which not only considers the integrity of the bridge structure, but also goes deep into the importance and vulnerability of each specific part.

[0108] Taking the reinforced concrete continuous beam bridge as an example, the importance of each structural part in the overall safety of the bridge is clarified through the weight assignment information in Table 1. Among them, the main beam and pier, as the main load-bearing structure of the bridge, are assigned the highest weight value of 0.3, which reflects their core position in bridge safety. The pier bottom cap beam, as the key part connecting the pier and the main beam, has a weight value of 0.2, which also reflects its importance that cannot be ignored.

[0109] Auxiliary facilities such as bridge guardrails, street lights, signs and drainage facilities also contribute to the overall safety of the bridge, but their importance is relatively low, so they are given a smaller weight value of 0.1. The above weight distribution results take into account both the main load-bearing parts of the bridge structure and other factors that affect bridge safety.

[0110] This embodiment provides a quantitative method to evaluate the safety status of bridge structures. By comparing actual monitoring data with weight assignments, the safety status of each part of the bridge can be more accurately judged, thereby formulating a more scientific and reasonable maintenance and management strategy, which helps to ensure the long-term safe operation of the bridge and improve the service life of the bridge.

[0111] After the fixed vision device completes the shooting, it will transmit the captured image data to the back-end processing model.

[0112] Furthermore, an image overlap comparison function is set up in the back-end processing model. The back-end processing model uses the image overlap comparison function, bridge line monitoring information and line positioning judgment results to analyze the pixel overlap rate of the monitoring image; and determines the level judgment result of the optical fiber break event based on the pixel overlap rate; finally, the line abnormality detection result, response level and bridge structure condition can be obtained based on the level judgment result, bridge line monitoring information and line positioning judgment results.

[0113] Based on the image overlap contrast function, the degree of pixel overlap can be obtained. In the embodiment, the pixel overlap ratio is used. To evaluate it, the above picture superposition contrast function satisfies the following relationship;

[0114] in, represents the pixel coincidence rate, represents the weighted number of pixels of the captured image, Represents the weighted number of pixels in the standard sample set image.

[0115] The backend processes the image comparison process and then compares the newly acquired image with the image selected from the standard state sample set under similar shooting angles and similar environmental conditions such as light and time. The above comparison process focuses on the degree of pixel overlap in the two images, which is used as a key indicator for evaluating the status of each part of the bridge structure. Through intuitive image comparison analysis, the current status of the bridge structure can be accurately grasped. For a schematic diagram of image overlap comparison, please refer to Figure 3 .

[0116] In order to calculate the average value of the weighted pixel count of the captured images, it is first necessary to count the number of pixels in each captured image that fall within the range of each bridge structure part determined based on the standard sample set image. The range of each bridge structure part is defined based on the geometric shape and number of pixels of each bridge part in the standard sample set. Subsequently, the weighted sum of the relevant pixel counts is performed using the weighted allocation value of each bridge structure part. The specific calculation formula is as follows:

[0117] in, represents the weighted number of pixels of the captured image, Represents the weight value of each part, It is the number of pixels in the captured image that fall within the range of each part of the bridge structure.

[0118] In order to calculate the weighted pixel count of the standard sample set image, we first need to count the number of pixels in each part of the bridge structure in the standard sample set image. The above statistical results will be weighted based on the weight values ​​previously assigned to each part of the bridge structure. That is, the number of pixels in each part will be multiplied by its corresponding weight value, and then the weighted pixel counts of all parts will be summed up to obtain The value of .

[0119] Above Satisfies the following relationship:

[0120] in, represents the weighted pixel number of the standard sample set image, Represents the weight value of each part, is the number of pixels in each part of the bridge structure in the standard sample set image. The above calculation process not only considers the number of pixels in each part of the bridge structure, but also fully considers their importance and vulnerability in the overall structure.

[0121] In this embodiment, when the location of the incident exceeds the visual range of the fixed visual device, or the image quality is poor due to adverse conditions such as bad weather, insufficient light, and the presence of obstructions, the back-end processing model will respond quickly and automatically send instructions to the flight inspection device. The above instructions will instruct the drone equipped with an infrared camera to quickly go to the specific location of the incident and perform close-range shooting operations to capture clear and accurate on-site images. The on-site images taken by the drone will be directly transmitted to relevant personnel for real-time evaluation and judgment. The back-end processing model has the ability to respond quickly under adverse conditions, as well as the flexibility and accuracy of the drone in acquiring on-site images.

[0122] Next, the fiber break event level is determined based on the bridge line monitoring information, line positioning judgment results, and relevant information in the back-end processing model.

[0123] The level determination results of the fiber disconnection event are further analyzed according to the pixel overlap rate. The level determination results are shown in Table 2.

[0124] Table 2 Results of the level determination of fiber disconnection events

[0125] Combined with the information in Table 2, it can be seen that the back-end processing model in the embodiment will further subdivide the bridge line interruption event into three levels: minor, general and severe according to the cause of the event and the relevant professional judgment results quickly analyzed by the relevant model and calculation formula.

[0126] A minor incident refers to a line interruption caused by accidental damage to the optical fiber or a system failure. The bridge structure itself is not significantly affected and vehicle traffic is not blocked. In this case, it is recommended to arrange maintenance personnel to carry out maintenance in a timely manner without affecting the smooth flow of traffic.

[0127] A general event refers to a certain degree of damage to the bridge structure, such as a fiber optic disconnection caused by a vehicle collision, but the damage does not endanger the safe operation of the bridge. The moving vehicle only needs to reduce the speed and pay close attention to continue passing. At this time, the back-end processing model will send a warning signal to the system alarm system and immediately dispatch relevant personnel to the scene to take appropriate measures to ensure the safety of passage.

[0128] A serious incident means that the bridge structure has been severely damaged or even collapsed, resulting in the disconnection of optical fibers and the complete loss of traffic capacity of the line. In this case, the back-end processing model will immediately send a warning signal to the alarm system, which will help the alarm system to activate the sound and light alarm device in time, prompting the moving vehicles to take emergency measures and pull over. At the same time, relevant personnel will be immediately dispatched to the scene to block traffic to reduce the extent of the loss.

[0129] Furthermore, in the present embodiment, the corresponding analysis method of the line abnormality detection results, the response level and the bridge structure condition is only an optional condition of the present invention. In one or some other embodiments, the result analysis method can be optimized according to the bridge abnormality monitoring requirements and the detection and alarm targets. Optimizing the analysis method for specific needs can more accurately identify and handle abnormal events in bridge lines, reduce false alarms and missed alarms, and improve monitoring efficiency to ensure bridge traffic safety.

[0130] S4. Combine the bridge line monitoring information, line positioning and identification results, line anomaly detection results, response level and bridge structure conditions to achieve detection and warning of bridge line interruption events. The specific steps and related contents are as follows: The bridge line abnormality detection and alarm method sets up a line interruption event alarm subsystem.

[0131] In the embodiment, a line interruption event alarm subsystem is designed, which is mainly composed of core components such as an alarm information processing module, a variable information display screen, and an audible and visual warning device.

[0132] The alarm information processing module can receive the line interruption event alarm information from the back-end processing system, the bridge line monitoring information obtained by the line anomaly monitoring system, and the discrimination analysis information in the line anomaly positioning discrimination mechanism, and generate clear alarm text and prompt content accordingly. The above text information describes in detail the specific location of the line interruption, the type of event, and the recommended response measures for the moving vehicle. For example, please note that the bridge 2 kilometers ahead has collapsed seriously. Please slow down immediately and park safely on the roadside. At the same time, the alarm information processing module can also send relevant information to the corresponding smart display screen according to the distance between the interruption location and the variable information display screens along the line.

[0133] Among them, the sound and light warning device plays an important role in prompting and warning. In the event of a serious incident, the alarm subsystem will quickly instruct the sound and light alarm device near the line interruption location to start, and use strong visual and auditory signals to give emergency warnings to passing vehicles, ensuring that drivers can obtain danger information and respond accordingly in the first time.

[0134] In addition, special consideration is given to the situation where fixed visual devices may not be able to capture on-site images immediately. In this case, the line interruption event alarm subsystem will immediately activate the emergency response mechanism, urgently display the line abnormality information on the variable information display screen, and simultaneously activate the sound and light alarm device near the optical fiber disconnection location to ensure the timely transmission and reception of warning information and maximize the safety of passing vehicles.

[0135] In summary, through a series of processes and devices in the embodiments, rapid detection and effective warning of bridge line interruption events can be achieved, providing timely and accurate safety guidance for passing vehicles.

[0136] In this embodiment, a bridge line abnormality detection and alarm method is proposed. The coordinated operation and collaborative working mechanism of different modules, equipment and devices in the method can be found in Figure 4 .

[0137] In order to solve the problem that it is difficult to timely detect and warn of road and railway bridge line interruption incidents, the present application integrates a variety of sensors, control equipment and warning devices to form a bridge line abnormality detection and alarm system. Based on the above method, the system can quickly detect, accurately locate and identify line interruption incidents, and promptly issue warnings to passing vehicles on the line, thereby significantly enhancing the operational safety of roads and railways.

[0138] The bridge line anomaly detection and alarm method uses long-distance, high-capacity fiber Bragg grating sensors as sensing and positioning elements. The above sensors are not only affordable, but also have a long laying distance for a single optical fiber, which is very suitable for safety monitoring of long linear structures such as roads and railways. Since the line interruption event perception requires relatively low sensor acquisition accuracy, this further reduces the deployment cost. In addition, the number of power supply and data transmission equipment required for field deployment is small, and the power supply demand is low, thereby reducing maintenance and operating costs.

[0139] The long-distance, large-capacity fiber Bragg grating sensor used in this method is easy to repair. After a circuit break occurs, the optical fiber can be restored to use by simply welding it near the break location without replacing the entire sensor system, which greatly simplifies the maintenance process. At the same time, the optical fiber circuit break is used to sense the line break, and the circuit break event is imaged through fixed visual devices and flying inspection devices to identify the type of event. The relevant monitoring image information is intuitive and easy to understand, allowing relevant personnel to quickly perform remote manual judgment, thereby reducing the false alarm rate and improving the efficiency and accuracy of road and railway bridge safety monitoring.

[0140] See also Figure 5 In an optional embodiment, the present invention further provides a bridge line abnormality detection and alarm system, the above-mentioned bridge line abnormality detection and alarm system includes a processor, an input device, an output device and a memory, the above-mentioned processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and execute the specific steps of the bridge line abnormality detection and alarm method and related embodiments provided by the present invention. A bridge line abnormality detection and alarm system of the present invention has a complete structure and is objective and stable.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A bridge line abnormality detection and alarm method, characterized in that: The steps include: Obtain bridge line monitoring information through the line anomaly monitoring system; Setting a line abnormality positioning and discrimination mechanism and a back-end processing model, and obtaining a line positioning and discrimination result based on the line abnormality positioning and discrimination mechanism, the back-end processing model and the bridge line monitoring information; Analyzing the bridge line monitoring information and the line positioning identification results using the back-end processing model to obtain line anomaly detection results, response level conditions and bridge structure conditions; By combining the bridge line monitoring information, the line positioning identification result, the line abnormality detection result, the response level situation and the bridge structure condition, the detection and warning of the bridge line interruption event are achieved.

2. The bridge line abnormality detection and alarm method according to claim 1 is characterized in that: The circuit abnormality location determination mechanism is set up as follows: A fiber break location module is configured in the line abnormality location identification mechanism; A bridge structure and optical fiber line analysis model is established in the optical fiber break locating module based on the bridge line monitoring information and the back-end processing model.

3. The bridge line abnormality detection and alarm method according to claim 2 is characterized in that: The establishing of the bridge structure and optical fiber line analysis model in the optical fiber break location module based on the bridge line monitoring information and the back-end processing model includes: Obtaining fiber grating position calibration information and highway mileage matching information based on the bridge line monitoring information and the back-end processing model; Setting positioning analysis parameter information according to the fiber grating position calibration information and the highway mileage matching information; A bridge structure and optical fiber line analysis model is constructed based on the positioning analysis parameter information.

4. The bridge line abnormality detection and alarm method according to claim 3 is characterized in that: The constructing of the bridge structure and optical fiber line analysis model based on the positioning analysis parameter information includes: Establishing an initial layout state analysis function, a bridge structure state analysis function and an optical fiber line analysis function based on the positioning analysis parameter information; The bridge structure and optical fiber line analysis model is obtained by combining the initial layout state analysis function, the bridge structure state analysis function and the optical fiber line analysis function.

5. The bridge line abnormality detection and alarm method according to claim 4 is characterized in that: The layout state analysis function satisfies the following relationship: ， in, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, The wavelength reflected by the nth grating is The optical signal strength, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength; ， in, Indicates that the wavelength received by optical signal demodulator 2 is The optical signal strength, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength.

6. The bridge line abnormality detection and alarm method according to claim 4, characterized in that: The bridge structure state analysis function satisfies the following relationship: ， in, represents the intensity of the reflected light signal of the nth grating received by the optical signal demodulator 1, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength; ， in, Indicates that the wavelength received by optical signal demodulator 2 is The optical signal strength, It means that the wavelength of the nth grating pair is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength.

7. The bridge line abnormality detection and alarm method according to claim 4, characterized in that: The optical fiber line analysis function satisfies the following relationship: ， in, represents the intensity of the reflected light signal of grating i received by the optical signal demodulator 1, The wavelength of grating i is The reflectivity of the arriving optical signal, Indicates that the wavelength emitted by laser transmitter 1 is The optical signal strength; ， in, represents the intensity of the reflected light signal of grating i+j received by the optical signal demodulator 2, The wavelength of grating i+j is The reflectivity of the arriving optical signal, The wavelength emitted by laser emitter 2 is The optical signal strength.

8. The bridge line abnormality detection and alarm method according to claim 4, characterized in that: The obtaining of the line location determination result based on the line abnormality location determination mechanism, the back-end processing model and the bridge line monitoring information includes: The initial state after layout is obtained through the initial layout state analysis function; Using the bridge structure state analysis function to obtain the normal service state of the bridge structure; Analyzing the optical fiber disconnection status based on the optical fiber line analysis function; The line positioning result is obtained by combining the initial state, the normal service state, the optical fiber disconnection state and the bridge line monitoring information.

9. The bridge line abnormality detection and alarm method according to claim 1, characterized in that: The use of the back-end processing model to analyze the bridge line monitoring information and the line positioning identification result to obtain line anomaly detection results, response level conditions and bridge structure conditions includes: Set the image overlay comparison function; The back-end processing model uses the image overlap comparison function, the bridge line monitoring information and the line positioning discrimination result to analyze the pixel overlap rate of the monitoring image; Determining a level judgment result of the optical fiber disconnection event according to the pixel overlap rate; The line abnormality detection result, the response level situation and the bridge structure condition are obtained according to the level judgment result, the bridge line monitoring information and the line positioning judgment result.

10. A bridge line abnormality detection and alarm system, characterized in that: The system includes a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the bridge line abnormality detection and alarm method according to any one of claims 1 to 9.

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